Reviewerly/exHarmony huggingface dataset:
Mining of skyline patterns by considering both frequent and utility constraints

Association-rule mining (ARM) or frequent itemset mining (FIM) is the most fundamental task in knowledge discovery, which is used to find the occurrence frequency of the item/sets in transactional database. The other factors such as weight, interestingness or unit profit of the items are not considered whether in ARM or FIM. To reveal more information, high-utility itemset mining (HUIM) was designed to consider both quantity and unit profit of items to discover the high-utility itemsets (HUIs). Several algorithms for FIM or HUIM were extensively studied but fewer works concern both frequency and utility together to provide better solutions in decision-making. In the past, the SKYMINE algorithm was designed to find the skyline frequent-utility patterns (SFUPs). A SFUP is a non-dominated pattern, in which each solution dominates the others by considering the aspects of frequency and utility. The SKYMINE algorithm needs, however, amounts of computation to level-wisely discover the SFUPs. In this paper, an efficient utility-list structure is used instead of the UP-tree structure used in SKYMINE to mine the SFUPs. Two algorithms are respectively designed by using the depth-first search (called SKYFUP-D) and breath-first search (SKYFUP-B) to mine the SFUPs. An efficient structure is also designed to record the maximal utility of the potential itemsets, thus reducing the computations for finding the SFUPs in the search space. Extensive experiments are conducted on several real-world and simulated datasets and the results indicate that the designed two algorithms have better performance than that of the state-of-the-art SKYMINE algorithm in terms of runtime, memory usage, search space size and the scalability.
MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.
Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks

This paper focuses on the issue of recognition of facial emotion expressions in video sequences and proposes an integrated framework of two networks: a local network, and a global network, which are based on local enhanced motion history image (LEMHI) and CNN-LSTM cascaded networks respectively. In the local network, frames from unrecognized video are aggregated into a single frame by a novel method, LEMHI. This approach improves MHI by using detected human facial landmarks as attention areas to boost local value in difference image calculation, so that the action of crucial facial unit can be captured effectively. Then this single frame will be fed into a CNN network for prediction. On the other hand, an improved CNN-LSTM model is used as a global feature extractor and classifier for video facial emotion recognition in the global network. Finally, a random search weighted summation strategy is conducted as late-fusion fashion to final predication. Our work also offers an insight into networks and visible feature maps from each layer of CNN to decipher which portions of the face influence the networks’ predictions. Experiments on the AFEW, CK+ and MMI datasets using subject-independent validation scheme demonstrate that the integrated framework of two networks achieves a better performance than using individual network separately. Compared with state-of-the-arts methods, the proposed framework demonstrates a superior performance.
Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

We consider context-response matching with multiple types of representations for multi-turn response selection in retrieval-based chatbots. The representations encode semantics of contexts and responses on words, n-grams, and sub-sequences of utterances, and capture both short-term and long-term dependencies among words. With such a number of representations in hand, we study how to fuse them in a deep neural architecture for matching and how each of them contributes to matching. To this end, we propose a multi-representation fusion network where the representations can be fused into matching at an early stage, at an intermediate stage, or at the last stage. We empirically compare different representations and fusing strategies on two benchmark data sets. Evaluation results indicate that late fusion is always better than early fusion, and by fusing the representations at the last stage, our model significantly outperforms the existing methods, and achieves new state-of-the-art performance on both data sets. Through a thorough ablation study, we demonstrate the effect of each representation to matching, which sheds light on how to select them in practical systems.
A Novel Key-Frames Selection Framework for Comprehensive Video Summarization

Video summarization (VSUMM) has become a popular method in processing massive video data. The key point of VSUMM is to select the key frames to represent the effective contents of a video sequence. The existing methods can only extract the static images of videos as the content summarization, but they ignore the representation of motion information. To cope with these issues, a novel framework for an efficient video content summarization as well as video motion summarization is proposed. Initially, Capsules Net is trained as a spatiotemporal information extractor, and an inter-frames motion curve is generated based on those spatiotemporal features. Subsequently, a transition effects detection method is proposed to automatically segment the video streams into shots. Finally, a self-attention model is introduced to select key-frames sequences inside the shots; thus, key static images are selected as video content summarization, and optical flows can be calculated as video motion summarization. The ultimate experimental results demonstrate that our method is competitive on VSUMM, TvSum, SumMe, and RAI datasets about shot segmentation and video content summarization, and can also represent a good motion summarization result.
Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number

Prediction and forecast of monthly mean daily global solar energy available at selected locations are carried out using a nonlinear autoregressive, a nonlinear autoregressive (exogenous) and a hybrid time series methods. The methods are implemented with artificial neural networks. The proposed hybrid method is based on a combination of nonlinear autoregressive and structural artificial neural networks where the structural input is the current month number. The aim of the hybridization is to improve prediction accuracy of the nonlinear autoregressive methods and simplify the input layer of the nonlinear autoregressive (exogenous) method. Based on various statistical criteria, the hybrid method is verified to predict and forecast global solar energy with noticeably higher accuracy. For example, while a nonlinear autoregressive predicts the global solar energy availability at Abuja with R2-value of 0.78 and correlation coefficient of 0.89, the hybrid method improves the prediction to 0.96 and 0.98. The hybrid model is verified via one-way analysis of variance to consistently perform better than the nonlinear autoregressive method in months ahead forecasting of solar energy. The proposed hybrid method is capable of long-term forecast of up to two years ahead within a typical mean percent forecast error of 5.67%, therefore, is applicable for designing/planning solar energy integration and simulation of agricultural food security, especially in developing countries. A cases study of power output and sizing of standalone PV options showed that decisions based on forecasted and actual solar energy are the same demonstrating the very useful application of the hybrid method.
Adapted K-Nearest Neighbors for Detecting Anomalies on Spatio–Temporal Traffic Flow

Outlier detection is an extensive research area, which has been intensively studied in several domains such as biological sciences, medical diagnosis, surveillance, and traffic anomaly detection.This paper explores advances in the outlier detection area by finding anomalies in spatio-temporal urban traffic flow.It proposes a new approach by considering the distribution of the flows in a given time interval.The flow distribution probability (FDP) databases are first constructed from the traffic flows by considering both spatial and temporal information.The outlier detection mechanism is then applied to the coming flow distribution probabilities, the inliers are stored to enrich the FDP databases, while the outliers are excluded from the FDP databases.Moreover, a k-nearest neighbor for distance-based outlier detection is investigated and adopted for FDP outlier detection.To validate the proposed framework, real data from Odense traffic flow case are evaluated at ten locations.The results reveal that the proposed framework is able to detect the real distribution of flow outliers.Another experiment has been carried out on Beijing data, the results show that our approach outperforms the baseline algorithms for high-urban traffic flow.
Estimating the number of clusters in a dataset via consensus clustering

In unsupervised learning, the problem of finding the appropriate number of clusters -usually notated as k- is very challenging. Its importance lies in the fact that k is a vital hyperparameter for the most clustering algorithms. One algorithmic approach for tacking this problem is to apply a certain clustering algorithm with various cluster configurations and decide to use the one that maximizes a certain internal validity measure. This is a promising and computationally efficient approach since the independent runs are parallelizable. In this paper, we attempt to improve over this estimation approach by incorporating a consensus clustering approach into k estimating scheme. The weighted consensus clustering scheme employs four different indices namely Silhouette (SH), Calinski–Harabasz (CH), Davies–Bouldin (DB), and Consensus (CI) indices to estimate the correct number of cluster. Computational experiments in a dataset with clusters ranging from 2 to 7 show the profound advantages of weighted consensus clustering for correctly finding k in comparison to individual clustering method (e.g, k-means) and simple consensus clustering.
Consortium Blockchain Based Data Aggregation and Regulation Mechanism for Smart Grid

Flexible regulation of smart grid is vital for grid operation. This paper proposes a smart grid data aggregation and regulation mechanism based on consortium blockchain, and its signcryption algorithm can be applied to multidimensional data acquisition and multiple receivers in the consortium blockchain. In the process of regulation, the control center, the grid operator, and the equipment supplier receive fixed-height blocks from the blockchain and obtain plaintext from the decryption. Each receiver analyzes the multidimensional data and formulates corresponding control policies for individual users. Grid operators implement user power regulation by feedback on smart contracts. The security analysis and performance comparison show that the proposed scheme has advantages in computing and communication costs while meeting security requirements for confidentiality and data integrity.
Defining digital literacy development: An examination of pre-service teachers’ beliefs

We identify three conceptions of digital literacy development populating the literature: digital natives, skill-based, and sociocultural perspectives. We adopt a qualitative approach to examine pre-service teachers' beliefs about digital literacy development as aligned with each of these three perspectives. While pre-service teachers were most commonly found to hold skill-based perspectives on digital literacy development, digital natives aligned and sociocultural perspectives were also well-represented. We further identify perspectives on digital literacy development uniquely appearing in students' responses. These include pre-service teachers’ conception of digital literacy development as autonomously developed, technology driven, or project based. We further examine the contexts within which pre-service teachers situate digital literacy as emerging; these include both formal and informal settings. The article concludes with a discussion of implications for instruction.
Phishing Email Detection Using Improved RCNN Model With Multilevel Vectors and Attention Mechanism

The phishing email is one of the significant threats in the world today and has caused tremendous financial losses. Although the methods of confrontation are continually being updated, the results of those methods are not very satisfactory at present. Moreover, phishing emails are growing at an alarming rate in recent years. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails. In this paper, we first analyzed the email structure. Then, based on an improved recurrent convolutional neural networks (RCNN) model with multilevel vectors and attention mechanism, we proposed a new phishing email detection model named THEMIS, which is used to model emails at the email header, the email body, the character level, and the word level simultaneously. To evaluate the effectiveness of THEMIS, we use an unbalanced dataset that has realistic ratios of phishing and legitimate emails. The experimental results show that the overall accuracy of THEMIS reaches 99.848%. Meanwhile, the false positive rate (FPR) is 0.043%. High accuracy and low FPR ensure that the filter can identify phishing emails with high probability and filter out legitimate emails as little as possible. This promising result is superior to the existing detection methods and verifies the effectiveness of THEMIS in detecting phishing emails.
Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges

Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.
Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world’s fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones.
Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases

An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the "locally-interpretable model-agnostic explanations" methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.
Adopting DevOps in the real world: A theory, a model, and a case study

DevOps is a set of practices and cultural values that aims to reduce the barriers between development and operations teams. Due to its increasing interest and imprecise definitions, existing research works have tried to characterize DevOps. Nevertheless, little is known about the practitioners’ understandingabout successful paths for DevOps adoption. Therefore, our goal is to detail real scenarios of DevOps adoption, presenting a theory, a model, and a case study. We used classic Grounded Theory to build a theory about 15 scenarios of successful DevOps adoption in companies from different domains and countries. We proposed a model (i.e., a workflow for DevOps adoption) and evaluated it through a case study at a Brazilian Government institution. We used a focus group to collect the company perceptions about DevOps adoption. This paper increments the existing view of DevOps by detailing real scenarios and explaining the role of each category during DevOps adoption. We provide evidence that collaboration is the core DevOps concern, contrasting with an existing wisdom that automation and tooling can be enough to achieve DevOps. Altogether, our results contribute to: generating an adequate understanding of DevOps, from the practitioners’ perspective; and assisting other institutions in the path towards DevOps adoption.
Feature Boosting Network For 3D Pose Estimation

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.
HSCS: Hierarchical Sparsity Based Co-saliency Detection for RGBD Images

Co-saliency detection aims to discover common and salient objects in an image group containing more than two relevant images. Moreover, depth information has been demonstrated to be effective for many computer vision tasks. In this paper, we propose a novel co-saliency detection method for RGBD images based on hierarchical sparsity reconstruction and energy function refinement. With the assistance of the intrasaliency map, the inter-image correspondence is formulated as a hierarchical sparsity reconstruction framework. The global sparsity reconstruction model with a ranking scheme focuses on capturing the global characteristics among the whole image group through a common foreground dictionary. The pairwise sparsity reconstruction model aims to explore the corresponding relationship between pairwise images through a set of pairwise dictionaries. In order to improve the intra-image smoothness and inter-image consistency, an energy function refinement model is proposed, which includes the unary data term, spatial smooth term, and holistic consistency term. Experiments on two RGBD co-saliency detection benchmarks demonstrate that the proposed method outperforms the state-of-the-art algorithms both qualitatively and quantitatively.
Self-Supervised 3D Hand Pose Estimation Through Training by Fitting

We present a self-supervision method for 3D hand pose estimation from depth maps. We begin with a neural network initialized with synthesized data and fine-tune it on real but unlabelled depth maps by minimizing a set of data-fitting terms. By approximating the hand surface with a set of spheres, we design a differentiable hand renderer to align estimates by comparing the rendered and input depth maps. In addition, we place a set of priors including a data-driven term to further regulate the estimate's kinematic feasibility. Our method makes highly accurate estimates comparable to current supervised methods which require large amounts of labelled training samples, thereby advancing state-of-the-art in unsupervised learning for hand pose estimation.