Reviewerly/exHarmony huggingface dataset:
A Novel Parameter Adaptive Dual Channel MSPCNN Based Single Image Dehazing for Intelligent Transportation Systems

Visibility issues in intelligent transportation systems are exacerbated by bad weather conditions such as fog and haze. It has been observed from recent studies that major road accidents have occurred in the world due to low visibility and inclement weather conditions. Single image dehazing attempts to restore a haze-free image from an unconstrained hazy image. We proposed a dehazing method by cascading two models utilizing a novel parameter-adaptive dual-channel modified simplified pulse coupled neural network (PA-DC-MSPCNN). The first model uses a new color channel for removing haze from images. The second model is the improved brightness preserving model (I-GIHE), which retains the brightness of the image while improving the gradient strength. To integrate the results from these two models and provide a pleasing haze-free image, a PA-DC-MSPCNN-based fusion is used. Furthermore, the proposed approach is deployed on a Xilinx Zynq SoC by exploiting the recently released PYNQ platform. The dehazing system runs on a PYNQ-Z2 all-programmable SoC platform, where it will input the camera feed through the FPGA unit and carry out the dehazing algorithm in the ARM core. This configuration has allowed reaching real-time processing speed for image dehazing. The results of dehazing are analyzed using both synthetic and real-world hazy images. Synthetic hazy images are acquired from the O-HAZE, I-HAZE, SOTS, and FRIDA datasets, while real-world hazy images are taken from the RailSem19, E-TUVD dataset, and the internet. For evaluation, twelve cutting-edge approaches are chosen. The proposed method is also analyzed on underwater and low-light images. Extensive experiments indicate that the proposed method outperforms state-of-the-art methods of qualitative and quantitative performances.
A Comprehensive Review of Blockchain Technology-Enabled Smart Manufacturing: A Framework, Challenges and Future Research Directions

As a new generation of information technology, blockchain plays an important role in business and industrial innovation. The employment of blockchain technologies in industry has increased transparency, security and traceability, improved efficiency, and reduced costs of production activities. Many studies on blockchain technology-enabled system construction and performance optimization in Industry 4.0 have been carried out. However, blockchain technology and smart manufacturing have been individually researched in academia and industry, according to the literature. This survey aims to summarize the existing research to provide theoretical foundations for applying blockchain technology to smart manufacturing, thus creating a more reliable and authentic smart manufacturing system. In this regard, the literature related to four types of critical issues in smart manufacturing is introduced: data security, data sharing, trust mechanisms and system coordination issues. The corresponding blockchain solutions were reviewed and analyzed. Based on the insights obtained from the above analysis, a reference framework for blockchain technology-enabled smart manufacturing systems is put forward. The challenges and future research directions are also discussed to provide potential guides for achieving better utilization of this technology in smart manufacturing.
Will Climate Change Affect Photovoltaic Performances? A Long-Term Analysis from 1971 to 2100 in Italy

This work aims to assess the impacts of climate change on photovoltaic (PV) electricity in two Italian cities, with different latitudes and Köppen–Geiger climate classifications. This was undertaken using the recent EURO-CORDEX set of high-resolution climate projections and PV power generation models, implemented on TRNSYS software. Data for two variables (surface air temperature and solar radiation) were analysed over a long period from 1971 to 2100. For future periods, two of the Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) used in the Intergovernmental Panel on Climate Change Fifth Assessment Report were considered. In both RCP scenarios and both locations, it is estimated that the yearly PV energy produced in the future period will not undergo significant variations on average given that the rate of decrease is foreseen almost constant; instead, a slight reduction in the PV energy was detected in the past period. It can be concluded that the PV market in Italy will grow in the next years considering that the reduction in the foreseen PV purchase costs will be also supported by the slight positive effect of climate change on PV manufacturability.
Emotional sentiment analysis of social media content for mental health safety

Text sentiment analysis is mostly used for the assessment of the author’s mood depending on the context. The purpose of sentiment analysis is to discover the exactness of the underlying emotion in a given situation. It has been applied to various fields, including stock market predictions, social media data on product evaluations, psychology, the judiciary, forecasting, illness prediction, agriculture, and more. Many researchers have worked on these topics and generated important insights. These outcomes are useful in the field because they (outcomes) help people comprehend the general summary quickly. Additionally, sentiment analysis aids in limiting the harmful effects of some posts on various social media sites such as Facebook and Twitter. For these reasons and more, we are proposing an approach to filter the social media content that could be emotionally harmful to the user, through getting the social networks content; for that, we have used Twitter API to get the user posts, and then, we have used API natural understanding language API tool to extract and classify the emotions of the Twitter content into five basic emotional categories—Joy, sadness, anger, fear, disgust—into an array of emotions; after that, we have defined a perfect emotion array from over 450 words from the English language. The main purpose of this comprehensive research article is to examine the proposed solution that we have conducted to improve the quality of content displayed to users emotionally.
Application of the K-medians Clustering Algorithm for Test Analysis in E-learning

E-education allows significantly automating the learning process. One of the key elements of e-learning is testing. The use of tests allows an objective and unbiased assessment of student knowledge. However, this automated approach requires high-quality tests that are relevant to the theoretical material and practical skills acquired by the student while taking an e-learning course. This paper proposes the use of the K-medians clustering algorithm, which makes it possible to divide the test questions into separate clusters in order to analyze them and further improve the tests, and possibly the entire e-course as a whole. The paper presents fragments of program code that can be used by other researchers in solving clustering problems.
A Privacy-Preserving Internet of Things Smart Healthcare Financial System

Several emerging areas, such as sensor networks, the Internet of Things (IoT), and distributed networks are gaining traction where resource-constrained devices communicate by sharing privacy-preserving information. Due to heavy cryptographic components, standard cryptographic algorithms do not fit these IoT devices. In this article, we propose an efficient zero-knowledge blockchain-based privacy-preserving decentralized healthcare finance system that is suitable for lightweight computer devices. The proposed design mainly focuses on noninteractive zero-knowledge proof, which substantially reduces the cost of communication between two devices. We explain the system framework and its use case for a healthcare financial system at a micro-level. However, it can also be extended easily to more general financial systems. Our system framework is efficient and lightweight, using more efficient zero-knowledge-based proofs; validation of the transactions is done in milliseconds. As an advancement to our work, the proposed healthcare financial system for lightweight computer devices is also auditable without leaking any extra information than required.
Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE)

Classification problems of unbalanced data sets are commonplace in industrial production and medical research fields. Different approaches have been proposed to handle these problems by generating synthetic samples, but most of them implement hyperparameters and tend to generate noise, because they neglect the entropy of the initial data. Recently, oversampling methods based on clustering have been proposed to overcome this problem. Unfortunately, they inherit the sensitivity of hard clustering methods. Moreover, the hyperparameters are manually selected. This paper introduces Optimal Entropy Genetic Fuzzy-C-Means SMOTE (OEGFCM-SMOTE) that balances data with minimum noise based on the original mathematical model, soft clustering, and evolutionary optimization. First, to handle the Kmeans sensitivity, OEGFCM-SMOTE uses a SMOTE to generate samples in safe regions based on Fuzzy-C-Means, known to be consistent with the boundary problem. Fuzzy-C-Means SMOTE processes in three steps (grouping, filtering, and interpolation) and implements 4 parameters, namely the number of clusters, the number of neighboring points of the minority data, the threshold of the unbalanced ratio and the exponent of the distribution of the minority data in the promising clusters. Second, the optimal choice of these parameters is based on a mixed-variable optimization model which minimizes the amount of noise measured by the entropy; the feasible domain is estimated by considering the density of the data set and by studying the boundary cases. Finally, this model is solved using the genetic algorithm by adopting genetic operators with appropriate rates. OEGFCM-SMOTE is evaluated using 5 classifiers, 21 unbalanced datasets (15 ordinary size and 6 Big data), and it is compared to 14 oversampling methods using three performance measures. To overcome the problem of multiple comparisons, considering different data sets, Holm’s test is used. OEGFCM-SMOTE consistently outperforms other popular oversampling methods.
Siamese residual network for efficient visual tracking

The Siamese tracking framework has attracted much attention due to its scalability and efficiency in recent years. However, it is less effective in recognizing arbitrary targets with various variations, especially in complex scenarios with background distractors and illumination variations. To this end, we propose a Siamese Residual Network to formulate the characteristics of a specific given target for visual tracking, consisting of an identity branch and a residual branch. The identity branch consists of a generic offline-trained similarity-matching network, which distinguishes the target from the background at the class level. To complement the identity branch for handling complex scenarios and dramatic target appearance variations, we develop a residual branch learned from the samples of exact target states and online distractors under the guidance of the identity branch. These two branches representing arbitrary targets with both class-level and sample-level features achieve accurate target localizations under complicated scenarios. In addition, we propose an adaptive KL-based scheme for updating the residual branch effectively by avoiding overfitting to a long-retained target appearance. Extensive experimental results on OTB-2013, OTB-2015, VOT2016, VOT-2018, VOT-2019, Temple-Color-128, and LaSOT show that the proposed method performs against state-of-the-art trackers.
Critical Factors Influencing Adoption of Blockchain-Enabled Smart Contracts in Construction Projects

Construction projects are premised upon contractual arrangements, and contracts constitute the basis of their success. A contract enables execution of work and transfer of payments, tracking of key performance indicators, and facilitation of collaboration among project stakeholders. Historically, construction projects have faced critical challenges due to poor alignment between clients’ expectations, contract terms, and contractor performance. The advent of advanced digital technologies under the concept of Industry 4.0 has the potential to benefit construction projects through application of blockchain-enabled smart contracts. However, the adoption of smart contracts in construction projects is in its early stages, and the factors that will influence its adoption remain unclear. Therefore, this study aimed to explore and establish the critical factors influencing adoption of smart contracts in construction contractual arrangements. This study administered an international questionnaire survey among experienced construction practitioners with involvement in smart contract initiatives and activities. The results obtained from descriptive statistics and fuzzy set-based analysis show that trialability, relative advantage, competitive advantage, and compatibility of smart contracts are the important predictors of the adoption of such contracts. The findings suggest that practitioners share a view that technological characteristics of blockchain-enabled smart contracts are critical to their adoption, regarding the technology’s perceived practicality in improving effectiveness and efficiency of construction projects. This study contributes to technology diffusion research in construction and highlights drivers that require practitioners’ and industry leaders’ attention to ensure successful adoption of smart contracts for cost-effective delivery of construction projects.
Breast cancer detection and diagnosis using hybrid deep learning architecture

The detection of cancer regions in the breast is important to save the women patient’s life. In this paper, mammogram image is classified into normal, benign, and malignant using the proposed hybrid Convolutional Neural Networks (CNN) architecture. The proposed system consists of a radon transform, data augmentation module, and hybrid CNN architecture. The radon transform transforms each spatial pixel in the source mammogram image into a time–frequency variation image. This image is data augmented to construct a new dataset from the existing dataset to improve the breast cancer detection rate. The data augmented images are classified into three different cases using the proposed hybrid CNN architecture. Further, a mathematical morphological-based segmentation algorithm is used to segment the cancer pixels. In this article, Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets for mammogram image analysis are used to estimate the performance efficiency of the developed deep learning architecture. The developed CNN architecture provides 97.91% Se, 97.83% Sp, 98.44% Acc, and 98.57% JI on the mammogram images available in the DDSM dataset. The developed CNN architecture provides 98% Se, 98.66% Sp, 99.17% Acc, and 98.07% JI on the mammogram images available in the MIAS dataset. For both open access datasets, the experimental results are compared to recent similar works. From the extensive analysis of experimental results of the proposed method, the methodologies presented in this article clearly segment the boundary of the cancer region in an abnormal mammogram image.
AutoML for Deep Recommender Systems: A Survey

Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep learning, deep recommender systems show superior performance by capturing non-linear information and item-user relationships. However, the design of deep recommender systems heavily relies on human experiences and expert knowledge. To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems. This survey performs a comprehensive review of the literature in this field. Firstly, we propose an abstract concept for AutoML for deep recommender systems (AutoRecSys) that describes its building blocks and distinguishes it from conventional AutoML techniques and recommender systems. Secondly, we present a taxonomy as a classification framework containing feature selection search, embedding dimension search, feature interaction search, model architecture search, and other components search. Furthermore, we put a particular emphasis on the search space and search strategy, as they are the common thread to connect all methods within each category and enable practitioners to analyze and compare various approaches. Finally, we propose four future promising research directions that will lead this line of research.
Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

Image processing is an evolving field that calls for more powerful techniques to extract useful information from images. In particular, image segmentation is a preprocessing step that helps separate objects in a digital image. This article introduces an enhanced manta ray foraging optimizer (MRFO) based on two strategies – oppositional learning (OL) and vertical crossover (VC) search – for color image segmentation. This combination technique focuses on the enhancement of the explorative and exploitative cores, without compromising the computational speed. The proposed algorithm, termed OL-MRFO-VC, is integrated with Kapur entropy to identify the best threshold configuration in each image component (RGB). The technique is tested over three datasets consisting of different scenes. The threshold vector consists of both lower and higher levels in the experiments. In addition, OL-MRFO-VC is compared with fourteen competitive metaheuristics, and eleven measures are used to evaluate their performance quantitatively and qualitatively. According to the computational results, our proposed method outperforms state-of-the-art techniques, especially in the higher threshold levels. Furthermore, the p values in the Wilcoxon signed-rank test confirm a significant improvement brought by our proposed method, suggesting a superior capability of OL-MRFO-VC for solving image segmentation problems.
Prompt-tuned Code Language Model as a Neural Knowledge Base for Type Inference in Statically-Typed Partial Code

Partial code usually involves non-fully-qualified type names (non-FQNs) and undeclared receiving objects. Resolving the FQNs of these non-FQN types and undeclared receiving objects (referred to as type inference) is the prerequisite to effective search and reuse of partial code. Existing dictionary-lookup based methods build a symbolic knowledge base of API names and code contexts, which involve significant compilation overhead and are sensitive to unseen API names and code context variations. In this paper, we formulate type inference as a cloze-style fill-in-blank language task. Built on source code naturalness, our approach fine-tunes a code masked language model (MLM) as a neural knowledge base of code elements with a novel "pre-train, prompt and predict" paradigm from raw source code. Our approach is lightweight and has minimum requirements on code compilation. Unlike existing symbolic name and context matching for type inference, our prompt-tuned code MLM packs FQN syntax and usage in its parameters and supports fuzzy neural type inference. We systematically evaluate our approach on a large amount of source code from GitHub and Stack Overflow. Our results confirm the effectiveness of our approach design and the practicality for partial code type inference. As the first of its kind, our neural type inference method opens the door to many innovative ways of using partial code.
GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search

Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, e.g., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text i.e., feeding the program and the query as a flat sequence of tokens to learn the program semantics while the structural information is not fully considered. Furthermore, the widely adopted Graph Neural Networks (GNNs) have proved their effectiveness in learning program semantics, however, they also suffer the problem of capturing the global dependencies in the constructed graph, which limits the model learning capacity. To address these challenges, in this paper, we design a novel neural network framework, named GraphSearchNet, to enable an effective and accurate source code search by jointly learning the rich semantics of both source code and natural language queries. Specifically, we propose to construct graphs for the source code and queries with bidirectional GGNN (BiGGNN) to capture the local structural information of the source code and queries. Furthermore, we enhance BiGGNN by utilizing the multi-head attention module to supplement the global dependencies that BiGGNN missed to improve the model learning capacity. The extensive experiments on Java and Python programming language from the public benchmark CodeSearchNet confirm that GraphSearchNet outperforms current state-of-the-art works by a significant margin.
Translation of the function of hydrothermal mineralization-related focused fluid flux into a mappable exploration criterion for mineral exploration targeting

Formation of hydrothermal mineralization is related to the late stages of, or immediate following, orogenic compressional regimes that have been superimposed on arc magmatism. For instance, mineralization-related fluid flux mechanisms of porphyry Cu formation deal with how magmatic fluids are transferred from the corresponding source chambers to the environment of stock emplacement related to porphyry systems. In this paper, firstly, we review the imperative physicochemical processes of mineralizing fluid flux mechanisms, e.g., the function of intrusive, metalliferous compounds, and permeable structures, and then assemble them to translate the mechanism of forming focused fluid flux into 2D mappable criteria for mineral exploration targeting. According to the review and the ensuing knowledge and idea, we propose a simplifying approach by focusing on a geological point feature - inferred sites of strongly concentrated ore fluid flux - that represents a critical, if not the most critical, geological element in mineral exploration targeting. We demonstrate that this point feature can be derived as a mappable criterion and that there are stronger spatial and thermal relationships between the location of hydrothermal mineral deposits and this proposed point feature relative to the existing point and line features (e.g., intrusive contacts, fault density, fault intersections, and proximity to faults), which have been widely used in the procedure of prospectivity analysis for mineral exploration targeting. We illustrate the procedures of generating mineral exploration targets, proposed in this paper, using geochemical and geological data sets of porphyry Cu deposits of Iran.