Reviewerly/exHarmony huggingface dataset:
Review of forecasting methods to support photovoltaic predictive maintenance

Predictive maintenance models are thought to be a reliable alternative to costly on-site maintenance techniques in the solar photovoltaic industry. They provide the owners with a third-party system to objectively diagnose and prevent failures in the system or any of its components. These models depend on data acquisition and analysis to compare the estimated performance with the actual energy production of the system, to conclude on the systems' health status. Ambient temperature, cell temperature, and solar irradiance forecasting are crucial parameters in predictive maintenance models’ formulation. These parameters represent the input variables to the model and define its efficiency to a great extent. Nonetheless, the current approaches to predict weather-related parameters have a high dependency on data availability and, in some cases, sensibility to the location where they are being measured. Forecasting climate parameters using neighboring weather stations seems to be a promising option to tackle the cost of on-site sensing and data availability, however, the scarcity of applications of specialized forecasting methods to predict solar irradiance and temperature (cell and ambient) for such applications represents an obstacle. This document will present a review of the state-of-the-art comparing the techniques to forecast solar irradiance, and ambient and cell temperature, and their relationship with predictive maintenance models, by acquiring data from weather stations. This review evaluates the suitability of the models for solar photovoltaic applications, taking on their forecasting horizon, computational cost, and improvement opportunities to propose a framework for future work in the photovoltaic industry.
Dolos: Language‐agnostic plagiarism detection in source code

Abstract Background Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state‐of‐the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods Dolos is compared with state‐of‐the‐art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID‐19 pandemic. Results and Conclusions Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open‐source license at https://dolos.ugent.be . Implications Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment.
On the performance evaluation of object classification models in low altitude aerial data

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures.
Virtual Reality Observations: Using Virtual Reality to Augment Lab-Based Shoulder Surfing Research

Figure 1: We explore the use of virtual reality (VR) for shoulder surfing research in the authentication research domain.We compare the impact of non-immersive/immersive VR observations on participants' observation performance and behaviour while shoulder surfing authentications.We demonstrate the strengths of VR-based shoulder surfing research by exploring three different authentication scenarios: (➊) automated teller machine (ATM), (➋) smartphone PIN, and (➌) smartphone pattern authentication.
Hierarchical intrinsically motivated agent planning behavior with dreaming in grid environments

Biologically plausible models of learning may provide a crucial insight for building autonomous intelligent agents capable of performing a wide range of tasks. In this work, we propose a hierarchical model of an agent operating in an unfamiliar environment driven by a reinforcement signal. We use temporal memory to learn sparse distributed representation of state-actions and the basal ganglia model to learn effective action policy on different levels of abstraction. The learned model of the environment is utilized to generate an intrinsic motivation signal, which drives the agent in the absence of the extrinsic signal, and through acting in imagination, which we call dreaming. We demonstrate that the proposed architecture enables an agent to effectively reach goals in grid environments.
Survey on Handwritten Signature Biometric Data Analysis for Assessment of Neurological Disorder using Machine Learning Techniques

The handwritten signature is considered one of the most widely accepted personal behavioral traits in Biometric system. Handwriting analysis has wide applications in multiple domains such as psychological disorders, medical diagnosis, and recruitment of staff, career counseling, writer credentials, forensic studies, matrimonial sites, e-security, e-health and many more. In this paper, we recapitulate the state-of-the-art techniques and applications based on the handwriting signature analysis for the Assessment of Neurological Disorder using Machine Learning Techniques, In addition to this, achievements and challenges the scientific community should address. Thus, an integrated discussion of various datasets used, feature extraction techniques and classification schemes regarding Parkinson’s disease (PD) and Alzheimer’s disease (AD) is done and surveyed scientifically. The present research paper aims to provide an extensive review of scientific literature, ascertain vulnerable challenges and offer new research directions in the field.
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements

Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers&#x0027; debugging efforts. This becomes even more important in today&#x0027;s software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity &#x2013; at the method or file level. Thus, developers still need to inspect a significant amount of code to locate the vulnerable statement(s) that need to be fixed. This paper presents Velvet, a novel ensemble learning approach to locate vulnerable statements. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph and effectively understand code semantics and vulnerable patterns. To study Velvet&#x0027;s effectiveness, we use an off-the-shelf synthetic dataset and a recently published real-world dataset. In the static analysis setting, where vulnerable functions are not detected in advance, Velvet achieves 4.5&#x00D7; better performance than the baseline static analyzers on the real-world data. For the isolated vulnerability localization task, where we assume the vulnerability of a function is known while the specific vulnerable statement is unknown, we compare Velvet with several neural networks that also attend to local and global context of code. Velvet achieves 99.6&#x0025; and 43.6&#x0025; top-1 accuracy over synthetic data and real-world data, respectively, outperforming the baseline deep learning models by 5.3-29.0&#x0025;.
OcclusionFusion: Occlusion-aware Motion Estimation for Real-time Dynamic 3D Reconstruction

RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlusions. Based on these observations, we propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network. Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrust-worthy motions and enables robust tracking. Experimental results on public datasets and our own recorded data show that our technique outperforms existing single-view-based real-time methods by a large margin. With the reduction of the motion errors, the proposed technique can handle long and challenging motion sequences. Please check out the project page for sequence results: https://wenbinlin.github.io/OcclusionFusion.
UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection – the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method’s utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.
Modelling and analysis of innovative path of English teaching mode under the background of big data

In order to improve the efficiency of the best way to choose innovative English teaching mode, this paper proposes an innovative path model of English teaching mode based on ant colony algorithm. The innovation strategy of English teaching mode is analysed, then the influence index of English teaching mode is analysed, the weight of index is calculated, and the objective function is constructed according to the influence index of English teaching mode. On this basis, the ant colony algorithm is used to find the best path of English teaching mode innovation and the modelling and analysis of English teaching mode innovation path is completed under the background of big data. The experimental results show that the proposed method has higher efficiency in selecting the best path, and the time for searching for the optimal path is shorter, which has a good convergence speed.
Prediction of Normal &amp; Grades of Cancer on Colon Biopsy Images at Different Magnifications Using Minimal Robust Texture &amp; Morphological Features

Classification of colon biopsy images to normal and various cancer grades is a pivotal task for histopathologists as it involves visual analysis under the microscope at different magnifications and hence may give rise to observational inconsistency. This paper emphasis on categorization of colon biopsy images into normal, well, moderate and poor classes thereby analyzing the best magnification and classifier suited for classification. A hybrid feature set consisting of morphological and texture features are obtained from images followed by class balancing to overcome imbalancing problem and then optimized feature selection. Classifiers such as SVM, Random Forest, Multilayer Perceptron and Naive Bayes are experimented for classification. The proposed model is evaluated with colon biopsy images acquired from Aster Medcity, Kochi, India at different magnifications 10X, 20X and 40X where all the magnifications performed well, but 20X gave an improved accuracy of 94.27% with the Random Forest classifier. Advance measures based on entropy triangle are used to rank classifiers apart from the standard performance measures, where Random Forest classifier is best for the proposed model for all magnifications.
Clustering in the wireless channel with a power weighted statistical mixture model in indoor scenario

Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.
A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation

Movie recommendation system offers a mechanism to allocate the user to attain the famous film by getting an opinion from similar users or past rating by user. This produces recommender systems has a crucial part of website and e-commerce application. The primary objective of the system to prefer a recommender system by data clustering and computational intelligence. We proposed a hybrid model collaborative movie recommendation system that performs with a combination of K-means clustering with ant colony optimisation technique (ACO-KM) that has employed in movie dataset. The proposed system compared with existing works, and its efforts have been analysed. The evaluation process of movie recommendation system that offers improved result from ACO-KM collaborative movie recommender system based on precision, recall, mean square error (MSE), and accuracy. By comparison of speed (in seconds) of various approaches in Movielens dataset, our approach gives best result 42.24 compared with existing one 53.22. The outcome of this experiment from Movielens dataset that offers scalability and efficiency in a recommendation by decreasing cold start issues.
Four Most Famous Cyber Attacks for Financial Gains

Cyber attacks are on the rise every day and pose a major threat to the Internet users. Cyber attackers are constantly capable of gaining hidden exposure at the moment and keeping a low profile. There is a need to carry out analyses on cyber attacks for educational purposes. In this paper we analyze four types of most famous cyber-attacks for financial gains: phishing attack, salami slice attack, ransomware attack, and cryptojacking attack. General Terms: Cyber attacks
GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System

Infrared cameras allow non-invasive and 24 h continuous monitoring. Thus, they are widely used in automatic pig monitoring, which is essential to maintain the profitability and sustainability of intensive pig farms. However, in practice, impurities such as insect secretions continuously pollute camera lenses. This causes problems with IR reflections, which can seriously affect pig detection performance. In this study, we propose a noise-robust, real-time pig detection system that can improve accuracy in pig farms where infrared cameras suffer from the IR reflection problem. The system consists of a data collector to collect infrared images, a preprocessor to transform noisy images into clean images, and a detector to detect pigs. The preprocessor embeds a multi-scale spatial attention module in U-net and generative adversarial network (GAN) models, enabling the model to pay more attention to the noisy area. The GAN model was trained on paired sets of clean data and data with simulated noise. It can operate in a real-time and end-to-end manner. Experimental results show that the proposed preprocessor was able to significantly improve the average precision of pig detection from 0.766 to 0.906, with an additional execution time of only 4.8 ms on a PC environment.
WISEFUSE

We characterize production workloads of serverless DAGs at a major cloud provider. Our analysis highlights two major factors that limit performance: (a) lack of efficient communication methods between the serverless functions in the DAG, and (b) stragglers when a DAG stage invokes a set of parallel functions that must complete before starting the next DAG stage. To address these limitations, we propose WISEFUSE, an automated approach to generate an optimized execution plan for serverless DAGs for a user-specified latency objective or budget. We introduce three optimizations: (1) Fusion combines in-series functions together in a single VM to reduce the communication overhead between cascaded functions. (2) Bundling executes a group of parallel invocations of a function in one VM to improve resource sharing among the parallel workers to reduce skew. (3) Resource Allocation assigns the right VM size to each function or function bundle in the DAG to reduce the E2E latency and cost. We implement WISEFUSE to evaluate it experimentally using three popular serverless applications with different DAG structures, memory footprints, and intermediate data sizes. Compared to competing approaches and other alternatives, WISEFUSE shows significant improvements in E2E latency and cost. Specifically, for a machine learning pipeline, WISEFUSE achieves P95 latency that is 67% lower than Photons, 39% lower than Faastlane, and 90% lower than SONIC without increasing the cost.
Deep learning-based method for non-uniform motion-induced error reduction in dynamic microscopic 3D shape measurement

The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform motion-induced error reduction by taking advantage of the powerful ability of nonlinear fitting. First, a specially designed dataset of motion-induced error reduction is generated for network training by incorporating complex nonlinearity. Then, the corresponding DL-based architecture is proposed and it contains two parts: in the first part, a fringe compensation module is developed as network pre-processing to reduce the phase error caused by fringe discontinuity; in the second part, a deep neural network is employed to extract the high-level features of error distribution and establish a pixel-wise hidden nonlinear mapping between the phase with motion-induced error and the ideal one. Both simulations and real experiments demonstrate the feasibility of the proposed method in dynamic macroscopic measurement.
Review: Single attribute and multi attribute facial gender and age estimation

Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment.
Affective Action and Interaction Recognition by Multi-View Representation Learning from Handcrafted Low-Level Skeleton Features

Human feelings expressed through verbal (e.g. voice) and non-verbal communication channels (e.g. face or body) can influence either human actions or interactions. In the literature, most of the attention was given to facial expressions for the analysis of emotions conveyed through non-verbal behaviors. Despite this, psychology highlights that the body is an important indicator of the human affective state in performing daily life activities. Therefore, this paper presents a novel method for affective action and interaction recognition from videos, exploiting multi-view representation learning and only full-body handcrafted characteristics selected following psychological and proxemic studies. Specifically, 2D skeletal data are extracted from RGB video sequences to derive diverse low-level skeleton features, i.e. multi-views, modeled through the bag-of-visual-words clustering approach generating a condition-related codebook. In this way, each affective action and interaction within a video can be represented as a frequency histogram of codewords. During the learning phase, for each affective class, training samples are used to compute its global histogram of codewords stored in a database and later used for the recognition task. In the recognition phase, the video frequency histogram representation is matched against the database of class histograms and classified as the closest affective class in terms of Euclidean distance. The effectiveness of the proposed system is evaluated on a specifically collected dataset containing 6 emotion for both actions and interactions, on which the proposed system obtains 93.64% and 90.83% accuracy, respectively. In addition, the devised strategy also achieves in line performances with other literature works based on deep learning when tested on a public collection containing 6 emotions plus a neutral state, demonstrating the effectiveness of the presented approach and confirming the findings in psychological and proxemic studies.