Reviewerly/exHarmony huggingface dataset:
Efficiency analysis of photovoltaic systems installed in different geographical locations

Abstract Many forms of energy are used in daily life. The most needed of these different forms is electricity. If this energy continues to be met with limited resources, it is clearly seen that sufficient energy needs will not be met in the future. Therefore, in the generation of electrical energy, existing resources must be used extremely efficiently. With the development of technology, electrical energy production is carried out more efficiently with non-renewable energy sources. These technological developments, which come to a certain point, can meet the demanded energy need up to a certain level. For this reason, many studies are carried out on renewable energy sources in order to respond to the needed energy demands. Therefore, in this study, the effect of geographical conditions on panel efficiency in electricity generation with solar panels, which is one of the renewable energy sources, was analyzed. Analyzes were carried out with power plant models installed in four different geographical regions of Turkey using the design and simulation software for photovoltaic systems program for PV systems. The effects of latitude, altitude and temperature on electrical energy production were investigated using the established power plant models.
Perceptual quality assessment for no-reference image via optimization-based meta-learning

Image quality assessment (IQA) is a critical issue in computer vision, which intends to simulate human visual system (HVS) with a view to evaluating the error degree of distorted images from reference images. The algorithms based on deep learning have been successfully introduced to quality evaluation of no-referenced images in the last years. Unfortunately, they are confronted with the problems of over-fitting and weak generalization ability caused by insufficient labeled data. The emergence of meta-learning has brought new ideas, which have been proved to address the issues regarding few-shot learning. However, the commonly used meta-learning metrics only learn the initialization of weights, which can't guarantee the optimal gradient direction. The manual design mode leads to lower accuracy and speed. In our work, an improved meta-learning framework is applied to no-reference (NR) IQA to meet the above challenges. It can achieve maximum generalization performance through only a few update iterations. Specifically, we collected a great many NR-IQA tasks with different distortions to pre-train meta-model and optimizer to learn a general weight initialization and optimization rule. Then, the meta-model acquired meta-knowledge and learned unique learning rate for each task. Finally, it could be directly adapted to new NR-IQA tasks only by fine-tuning a few images. Experiments on synthetic and authentic datasets proved that our approach has more vital learning ability and better generalization performance for evaluating real distorted images, effectively reducing dependence on manual marking.
Single low-light image brightening using learning-based intensity mapping

Inspired by image-to-curve transformation and multi-exposure fusion, in this paper, we have developed a new method to treat the low light image enhancement tasks as an extended problem with multiple virtual exposures by a non-linear intensity mapping function. Considering that existing image-to-curve methods have difficulty in obtaining the desired detail and brightness recovery in any one iteration without relying on any ground truth, we propose a virtual multi-exposure fusion strategy to merge the outputs from these different iterations. Specifically, a simple CNN is trained to learn a pixel-wise intensity mapping function and accordingly adjust a given image multiple times. Then the results of all iterations are retained together with the original input image for fusion via a WGIF-based Multi-scale pyramid to obtain a final enhanced output. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances.
Automated grammar-based feature selection in symbolic regression

With the growing popularity of machine learning (ML), regression problems in many domains are becoming increasingly high-dimensional. Identifying relevant features from a high-dimensional dataset still remains a significant challenge for building highly accurate machine learning models.
STOWP: A light-weight deep residual network integrated windowing strategy for storage workload prediction in cloud systems

Accurate storage workload forecasting of big data applications is a constructive approach to improve the job scheduling and fine-grained load balancing in real-time cluster systems. However, despite the recent advances of deep learning architectures, demands for more accurate workload time series forecasting algorithm exist. Therefore, we propose a light-weight STOrage Workload time series Prediction method named as ‘STOWP’ integrating Neural Basis Expansion Analysis (N-BEATS) deep model with windowing strategy. The STOWP approach implements a multi-input–multi-output (MIMO) window strategy for capturing the historical storage variation patterns of the workload data. Furthermore, a within window scaling strategy is adopted to effectively estimate the diversity of the workload requests during different time horizons. For experimental evaluation, we used Web-Search dataset containing Search Engine’s I/O real-time workload traces. To improve the performance of STOWP, the hyper-parameters’ sensitivity is well investigated. Through results, we observed that the ’STOWP’ improves the RMSE by 3.33% and MAE by 3.44% atleast in comparison with the existing benchmarks storage workload forecasting techniques.
Label distribution learning with noisy labels via three-way decisions

Label distribution learning (LDL) as a soft-labeling paradigm is allowed to learn single or multi-labeled information distribution. Overwhelmingly, in the open world, the distribution of labels is usually disturbed by the noise (such as the man-made induction bias, shake of hardware devices), which in turn affects the decision of downstream tasks. To address this problem, we propose a novel LDL approach by using the three-way decisions theory to clear the amplified noise in this paper. First, we evaluate the confidence of each training sample and use a three-way decisions-based method to identify the trustworthy samples and the noisy samples. Second, we apply the sample correlation between the trustworthy samples and the noisy samples to correct the noisy labels. Finally, we re-weight every sample based on the learned confidences to train the robust LDL model. Experiments show that our approach has better performance in handling noisy data compared to existing algorithms.
Generating extractive sentiment summaries for natural language user queries on products

Multi-document sentiment analysis is an important natural language processing problem. Summaries generated by these analyzers can greatly reduce the time necessary to read a collection of topically-related documents to locate the desired information needs of a user. With the ever-increasing globalization and technology of the modern day, analysis of online user reviews on different products is an especially pertinent application of the aforementioned problem. At present there are way too many user reviews on popular products for potential buyers to spend adequate time to read and extract the most salient product details and opinions of previous buyers. In solving this problem, we propose a fully-automated summarizer to reduce the workload of online customers. The proposed system takes a user query and extracts the most relevant and essential comments made by individual reviewers. As opposed to existing multi-document summarization approaches, our summarizer compiles comprehensive reviews by extracting important facets and sentiment information based on various sentence features rather than applying complex machine learning algorithms. The design of our summarizer is easy to understand and implement, without the required massive training data and excessive training time. The conducted empirical study shows that the proposed summarization system outperforms current state-of-the-art multi-document sentiment summarization approaches.
An adaptive total generalized variational model for speckle reduction in ultrasound images

In this paper, we propose an adaptive total generalized variation model for removing the speckle noise in ultrasound image. The existence and uniqueness of the minimizers to the proposed model are established. Furthermore, we design an alternating minimization algorithm to solve numerically the model. By incorporating the generalized cross validation technique, the regularization parameter of the model can be updated during the iterations of the designed algorithm. Finally, experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with that of the other testing methods.
IPDCN2: Improvised Patch-based Deep CNN for facial retouching detection

With the advancement of photo editing softwares, nowadays facial retouching becomes a common practice across different social media platforms, Curriculum Vitae (CV) related websites, photo sharing applications, and magazines publishing flawless facial images of celebrities. In this paper, we propose an improvised patch-based deep convolution neural network (IPDCN2) to classify whether a facial image is original or retouched. The proposed network comprises of three stages i.e., pre-processing, high-level features extraction, and classification. Initially, we propose a pre-processing stage to extract only relevant patches from the input image by using 68 facial landmarks. In the second stage, an efficient and robust CNN based on residual learning is employed to extract the high-level hierarchical features from these patches. The proposed network uses the concept of residual learning with the help of max pooling layers to maximize the information flow across the neural network. Lastly, the extracted high-level features are passed to fully-connected layers for classification. The experimental results show that proposed network outperforms the existing state-of-the-art techniques by providing an accuracy of 99.84% on ND-IIITD dataset. Moreover, proposed network provides a classification accuracy of 95.80%, 83.70%, and 97.30% on YMU, VMU, and MIW make-up datasets, respectively.
A Synopsis of Critical Aspects for Darknet Research

Descriptives of the darknet, and in particular of the Tor network, appear inconsistent and implausible in nature. In order to gain insight into how these conflicting results are produced, the goal of this study is to review previous research on the matter with regard to terminology used, methodology of sample collection and the analysis of the data. Our results indicate six critical aspects that in particular pertain to (A) an inconsistent use of terminology, (B) the methodology with which the sample was gathered, as well as the handling of (C) short-lived services, (D) botnet command and control servers, (E) web services with undetermined content and (F) duplicates of onion services. Further, we include a small case study on darknet marketplaces to demonstrate how reports concerning the number of a certain category can easily mislead. Through the implications of these aspects the presented description of Tor does not necessarily reflect the actual nature of Tor.
Experimental monitoring and modeling of fatigue damage for 3D-printed polymeric beams under irregular loading

Studying the fatigue of 3D-printed (3DP) structures usually requires time-consuming and ad hoc material characterizations. This paper experimentally demonstrates a non-intrusive structural health monitoring (SHM) framework capable of monitoring and modeling fatigue damage of 3DP structures. The experiments emulate realistic working conditions of machinery with polyethylene terephthalate beam parts as a subsystem. The pre-notched specimens with 0-, 45-, and 90-degree raster angles are manufactured and tested under highly irregular fatigue loads. Based on the phase space warping algorithm, the smooth orthogonal decomposition identifies the slow-time higher-order damage feature space, whose leading subspace is used as the estimated scalar-damage-time history. The critical inflection points (CIPs), identified in the higher-order damage feature subspace, delineate the three fatigue crack propagation stages with different rates of damage evolution. The lower-order projection of the CIPs in the damage-time histories can serve as early and late damage indicators, with a maximally 50% ahead of any observable signs of structural degradation from the response. The first CIP indicates the damage as early as at 10% of the total life span irrespective of the raster orientation. Based on a normalized temporal damage model, a hypothesis is posed to ascribe the raster angle-induced damage mechanisms to inter-laminar and cross-laminar fractures, validated by an optical-microscopy-based postmortem analysis. This paper provides an ad hoc alternative for studying fatigue damage with minimal assumptions and prior knowledge of the 3DP structure. Furthermore, the identified damage mechanisms suggest aligning the raster orientation with the applied stress to maximize the machinery’s fatigue life.
Boosting Few-shot visual recognition via saliency-guided complementary attention

Despite significant progress in recent deep neural networks, most deep learning algorithms rely heavily on abundant training samples. To address the issue, few-shot learning (FSL) methods are designed to learn models that can generalize to novel classes with limited training data. In this work, we propose an effective and interpretable FSL approach termed Saliency-Guided Complementary Attention (SGCA). Concretely, SGCA aims to boost few-shot visual recognition from two perspectives: learning generalizable feature representations and building a robust classification module in a unified framework. For generalizable representation learning, we propose to explore the intrinsic structure of natural images by training the feature extractor with an auxiliary task to segment foreground regions from background clutter. The guidance signals are provided during training by a saliency detector which highlights object regions in images corresponding to the human visual system. Moreover, for robust classification module building, we introduce a complementary attention mechanism based on the learned segmentation to make the classification module focus on various informative parts of the image. Extensive experiments on 5 popular FSL datasets demonstrate that SGCA can outperform state-of-the-art approaches by a significant margin. In addition, extensions of SGCA to other challenging scenarios, including generalized, transductive and semi-supervised FSL, also verify the effectiveness and flexibility of our proposed approach.
Composite wavelet decomposition algorithm combined with correlation analysis in white-light scanning interferometry

White-light scanning interferometry (WLSI) is a mature and efficient technique that is widely used in the field of high-precision three-dimensional measurements. However, coherence and phase information in the WLSI technique are inconsistent, false height jumps, such as batwings artifact and ghost steps, are typical obstacles in applying WLSI, which distort measurement results and reduce the measurement accuracy. In this study, we proposed a composite wavelet decomposition algorithm combined with a correlation analysis to solve the above two obstacles without having any prior knowledge about the tested sample. Two calibration step plates with heights of 459.8 ± 3.0 nm and 90.5 ± 2.8 nm were used to evaluate the accuracy of the proposed algorithm, and the results were compared with those of Centroid and Fourier transform algorithms. Consequently, the proposed algorithm exhibited a higher correction effect, and the accuracy was improved by more than two times, which proved that our approach could effectively suppress the batwings effect and eliminate the ghost steps.
A texture-based approach for offline writer identification

Offline handwriting has become a very important part in the field of behavioral biometrics. In this paper, we proposed an approach to identify writers from their handwritten documents. The foremost contribution of this study is to suggest the use of co-occurrence features to improve the performance of writer identification. A contour texture-based feature is extracted from preprocessed regions of interest (components or sub-images) and their exterior contours are based primarily on Modified Local Binary Pattern (MLBP) and Ink-trace Width and Shape Letters (IWSL) measurements. Considering the contour as a texture, and using these textural descriptors, the joint probability distributions of MLBP and IWSL on different pixels are calculated in order to determine the similarities between different images of handwriting. Identification is carried out using the nearest neighbor rule and Chi-square distance. The proposed system has been evaluated on eight well-known handwriting databases (Arabic IFN/ENIT and KHATT, English IAM and CVL, Dutch Firemaker, Portuguese BFL, Chinese CERUG-CN, and English/Greek ICDAR2013). Experimental results show that the recommended scheme achieves the highest performance on KHATT, CVL, Firemaker, BFL, CERUG-CN, and ICDAR2013 databases, and that it demonstrates competitive performance on IFN/ENIT and IAM databases as compared to those reported by the state-of-the-art identification systems.
Technical debt prioritization

Background: The prioritization of technical debt is an essential task in managing software projects because, with current analysis tools, it is possible to find thousands of technical debt items in the software that would take months or even years to be fully paid. Aims: In this study, we aim to understand which criteria software developers use to prioritize code technical debt in real software projects. Methods: We performed a survey to collect data from open-source software projects in order to reach a large and diverse set of experiences. We analyzed the data using Straussian Grounded Theory techniques: open coding, axial coding, and selective coding. Results: We grouped the criteria into 15 categories and divided them into 2 super-categories related to paying off the technical debt and 3 related to not paying it. Conclusions: When participants decided to pay off technical debt, they wanted to do it soon. However, when they decided not to pay it, it is often because the debt occurred intentionally due to a project decision. Also, participants using similar criteria for their decisions tended to choose similar priority levels for those decisions. Finally, we observed that each software project needs to tailor the rules used to identify code technical debt to their project context.
An empirical study on the challenges that developers encounter when developing Apache Spark applications

Apache Spark is one of the most popular big data frameworks that abstract the underlying distributed computation details. However, even though Spark provides various abstractions, developers may still encounter challenges related to the peculiarity of distributed computation and environment. To understand the challenges that developers encounter, and provide insight for future studies, in this paper, we conduct an empirical study on the questions that developers encounter. We manually analyze 1,000 randomly selected questions that we collected from Stack Overflow. We find that: 1) questions related to data processing (e.g., transforming data format) are the most common among the 11 types of questions that we uncovered. 2) Even though data processing questions are the most common ones, they require the least amount of time to receive an answer. Questions related to configuration and performance require the most time to receive an answer. 3) Most of the issues are caused by developers' insufficient knowledge in API usages, data conversation across frameworks, and environment-related configurations. We also discuss the implication of our findings for researchers and practitioners. In summary, our work provides insights for future research directions and highlight the need for more software engineering research in this area.
QuickSkill: Novice Skill Estimation in Online Multiplayer Games

Matchmaking systems are vital for creating fair matches in online multiplayer games, which directly affects players' satisfactions and game experience. Most of the matchmaking systems largely rely on precise estimation of players' game skills to construct equitable games. However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player. Using these unreliable skill scores at early stages for matchmaking usually leads to disparities in terms of team performance, which causes negative game experience. This is known as the ''cold-start'' problem for matchmaking rating algorithms. To overcome this conundrum, this paper proposes QuickSKill, a deep learning based novice skill estimation framework to quickly probe abilities of new players in online multiplayer games. QuickSKill extracts sequential performance features from initial few games of a player to predict his/her future skill rating with a dedicated neural network, thus delivering accurate skill estimation at the player's early game stage. By employing QuickSKill for matchmaking, game fairness can be dramatically improved in the initial cold-start period. We conduct experiments in a popular mobile multiplayer game in both offline and online scenarios. Results obtained with two real-world anonymized gaming datasets demonstrate that proposed QuickSKill delivers precise estimation of game skills for novices, leading to significantly lower team skill disparities and better player game experience. To the best of our knowledge, proposed QuickSKill is the first framework that tackles the cold-start problem for traditional skill rating algorithms.
A Privacy Protection Scheme for Permissioned Blockchain Based on Trusted Execution Environment

Blockchain has attracted wide attention because of its characteristics of transparency, anti-interference, traceability, decentralization and verification. The traditional blockchain ensures the reliability of all data by making it public, thus reaching consensus and maintaining the consistency of the global state. This means that everyone's transaction data is phanic to the outside world, resulting in a massive breach of users' personal privacy. Since this reason, we propose a new kind of blockchain privacy protection scheme, which applies the trusted execution environment in the blockchain, designs an encrypted communication and authentication scheme, and realizes the function of providing privacy protection for the blockchain. And most of the computations are moved out of the chain, which greatly reduces the delay and burden of on-chain computations. Experiments show that this architecture has good confidentiality and usability.