Reviewerly/exHarmony huggingface dataset:
Comparison of BP, PSO-BP and statistical models for predicting daily global solar radiation in arid Northwest China

Accurate prediction of global solar radiation (Rs) is important for understanding meteorological and hydrological processes, as well as the utilization of solar energy and development of clean production. In order to improve the accuracy and universality of daily Rs prediction in arid Northwest China, back-propagation neural network (BP) and BP optimized by the particle swarm optimization algorithm (PSO-BP) along with six statistical models (Ångström-Prescott, Bristow-Campbell, Swartman-Ogunlade, Sebaii, Chen and Abdalla) were adopted and compared with measured Rs data from eight representative meteorological stations across four sub-climatic zones, including the temperate continental arid zone, temperate continental high temperature-arid zone, plateau continental semi-arid zone and temperate monsoon semi-arid zone. The results showed that PSO-BP models (coefficient of determination, R2, 0.7649–0.9678) were more accurate than BP models (R2, 0.7215–0.9632) and statistical models (R2, 0.5630–0.9445) for the daily Rs prediction in the four sub-zones of arid Northwest China. The PSO-BP1 and BP1 models (with sunshine duration, maximum and minimum temperature, relative humidity and extraterrestrial radiation as inputs), PSO-BP2 and BP2 (with sunshine duration, maximum and minimum temperature and extraterrestrial radiation as inputs) performed better than the other models, with R2, mean absolute error, root mean square error, relative root mean square error and Nash-Sutcliffe coefficient ranging 0.9228–0.9678, 1.5546–1.6309 MJ·m−2·d−1, 2.0054–1.7579 MJ·m−2·d−1, 0.1517–0.1329 and 0.9017–0.9604, respectively, among which the PSO-BP1 model provided the most accurate results. Sunshine-based models (R2, 0.7533–0.9678) were generally superior to temperature-based models (R2, 0.5630–0.8492), which indicated that sunshine duration was more influential for daily Rs prediction than temperature in this area. Overall, the PSO-BP model exhibits the best generalization capability and is recommended for more accurate daily Rs prediction in arid Northwest China.
Three-way decisions based feature fusion for Chinese irony detection

Irony detection in social networks has drawn much attention in recent years. In this work, we propose a three-way decisions based feature fusion method for Chinese irony detection in microblog. First, we build a set of discriminating features for Chinese irony detection by considering the characteristics of both Chinese language and social networks, including lexical feature, homophonic pun, consecutive punctuation, length of microblog, passive verbs, and affective imbalance. Second, to tackle the problem of extreme imbalance between different kinds of features, we propose a two-stage classification method for Chinese irony detection by considering feature fusion. In the first stage, a three-way decisions based classifier is trained to predict the ironic tendency based on the lexical feature only. This procedure can reduce the thousands-of-dimensions based lexical feature into a one-dimensional feature. In the second stage, the constructed new feature is merged with other five features into a new feature vector for learning. Experimental results show that our proposed two-stage classification method can get a significant improvement than one-stage classification method.
Color image watermarking based on orientation diversity and color complexity

The Just Noticeable Distortion (JND) can reliably measure the perceptual strength in image watermarking, but, it remains a challenge to computationally model the process of embedding watermark without prior knowledge of the image contents. This paper proposed a novel color image watermarking scheme in discrete cosine transform (DCT) domain based on JND, which takes both orientation diversity and color complexity features into account. Firstly, two indicator was introduced which take into account the differences in the texture types and orientation diversity of the Human Visual System (HVS) in the proposed JND contrast masking (CM) processing. In addition, a novel color complexity weight from Cb-channel is used to guarantee the scheme robustness. Then, a novel JND model combined with the proposed contrast masking and color complexity is applied into quantization watermarking scheme. Compared with the state-of-the-art methods for color image watermarking, experimental results using publicly available images show that our proposed scheme is reliable and effective.
An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network

In view of the failure of GNSS signals, this paper proposes an INS/GNSS integrated navigation method based on the recurrent neural network (RNN). This proposed method utilizes the calculation principle of INS and the memory function of the RNN to estimate the errors of the INS, thereby obtaining a continuous, reliable and high-precision navigation solution. The performance of the proposed method is firstly demonstrated using an INS/GNSS simulation environment. Subsequently, an experimental test on boat is also conducted to validate the performance of the method. The results show a promising application prospect for RNN in the field of positioning for INS/GNSS integrated navigation in the absence of GNSS signal, as it outperforms extreme learning machine (ELM) and EKF by approximately 30% and 60%, respectively.
Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction

Supervised learning methods to infer (hypothesize) depth of a scene from a single image require costly per-pixel ground-truth. We follow a geometric approach that exploits abundant stereo imagery to learn a model to hypothesize scene structure without direct supervision. Although we train a network with stereo pairs, we only require a single image at test time to hypothesize disparity or depth. We propose a novel objective function that exploits the bilateral cyclic relationship between the left and right disparities and we introduce an adaptive regularization scheme that allows the network to handle both the co-visible and occluded regions in a stereo pair. This process ultimately produces a model to generate hypotheses for the 3-dimensional structure of the scene as viewed in a single image. When used to generate a single (most probable) estimate of depth, our method outperforms state-of-the-art unsupervised monocular depth prediction methods on the KITTI benchmarks. We show that our method generalizes well by applying our models trained on KITTI to the Make3d dataset.
A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors

Websites attract millions of visitors due to the convenience of services they offer, which provide for interesting targets for cyber attackers. Most of these websites use JavaScript (JS) to create dynamic content. The exploitation of vulnerabilities in servers, plugins, and other third-party systems enables the insertion of malicious codes into websites. These exploits use methods such as drive-by-downloads, pop up ads, and phishing attacks on news, porn, piracy, torrent or free software websites, among others. Many of the recent cyber-attacks exploit JS vulnerabilities, in some cases employing obfuscation to hide their maliciousness and evade detection. It is, therefore, primal to develop an accurate detection system for malicious JS to protect users from such attacks. This study adopts Abstract Syntax Tree (AST) for code structure representation and a machine learning approach to conduct feature learning called Doc2vec to address this issue. Doc2vec is a neural network model that can learn context information of texts with variable length. This model is a well-suited feature learning method for JS codes, which consist of text content ranging among single line sentences, paragraphs, and full-length documents. Besides, features learned with Doc2Vec are of low dimensions which ensure faster detections. A classifier model judges the maliciousness of a JS code using the learned features. The performance of this approach is evaluated using the D3M dataset (Drive-by-Download Data by Marionette) for malicious JS codes and the JSUNPACK plus Alexa top 100 websites datasets for benign JS codes. We then compare the performance of Doc2Vec on plain JS codes (Plain-JS) and AST form of JS codes (AST-JS) to other feature learning methods. Our experimental results show that the proposed AST features and Doc2Vec for feature learning provide better accuracy and fast classification in malicious JS codes detection compared to conventional approaches and can flag malicious JS codes previously identified as hard-to-detect.
Developing a hands‐on activity using virtual reality to help students learn by doing

Abstract This study combined virtual reality (VR) technology, the 6E (Engage, Explore, Explain, Engineer, Enrich, and Evaluate) model, and STEM (Science, Technology, Engineering, and Mathematics) education to develop a hands‐on activity aimed at helping students to achieve “learning by doing.” The participants were 162 tenth‐grade students, divided into the Experimental Group (hands‐on activity using VR technology) and the Control Group (hands‐on activity via lectures). Using sequential analysis, this study investigated how the hands‐on activity influenced the students' behavioral patterns in learning. The results showed that all of the students' learning performances and hands‐on abilities were enhanced. Moreover, the students who used VR technology achieved both significantly better learning performances and hands‐on abilities, indicating that VR might be able to help the students understand abstract scientific concepts and build mental models, which they used to internalize and organize knowledge structures. Furthermore, this study discovered that the students who learned using VR technology formed a cyclical learning pattern, starting with a group discussion (G), moving on to solving problems (S) and developing a product (D), and then going back to another group discussion. However, the students who learned via lectures produced a linear learning pattern in the order of G→S→D.
Technical trading and cryptocurrencies

Abstract This paper carries out a comprehensive examination of technical trading rules in cryptocurrency markets, using data from two Bitcoin markets and three other popular cryptocurrencies. We employ almost 15,000 technical trading rules from the main five classes of technical trading rules and find significant predictability and profitability for each class of technical trading rule in each cryptocurrency. We find that the breakeven transaction costs are substantially higher than those typically found in cryptocurrency markets. To safeguard against data-snooping, we implement a number of multiple hypothesis procedures which confirms our findings that technical trading rules do offer significant predictive power and profitability to investors. We also show that the technical trading rules offer substantially higher risk-adjusted returns than the simple buy-and-hold strategy, showing protection against lengthy and severe drawdowns associated with cryptocurrency markets. However there is no predictability for Bitcoin in the out-of-sample period, although predictability remains in other cryptocurrency markets.
Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery

The data-driven fault indicator for rotating machinery is designed to reveal the possible fault scenarios from the observed statistical vibration signals. This study develops a novel ensemble extreme learning machine (EELM) network to replace the conventional layout by combining binary classifiers (e.g., binary relevance) for compound-fault diagnosis of rotating machinery. The proposed EELMs consist of two sub-networks, namely, the first extreme learning machine (ELM) for clustering, and the second for multi-label classification. The first network generates the Euclidean distance representations from each point to every centroid with unsupervised clustering, and the second identifies potential output tags through multiple-output-node multi-label learning. Compared to the existing multi-label classifiers (e.g., multi-label radial basis function, rank support vector machine, back-propagation multi-label learning, and binary classifiers with binary relevance), the theoretical verification reveals EELMs perform the best in hamming loss, one-error, training time, and achieves the best overall evaluation for the two real-world databases (e.g., Yeast and Image). Regarding the real test for the compound-fault diagnosis of rotating machinery, this paper applies the particle swarm optimization-based variational mode decomposition to decompose the raw vibration signals into a series of intrinsic modes, and selects ten time-domain indicators and five frequency-domain statistical characteristics for feature extraction. The experimental results illustrate that the EELM-based fault diagnosis method achieves the best overall performance.
A Survey on Digital Forensics in Internet of Things

Internet of Things (IoT) is increasingly permeating peoples' lives, gradually revolutionizing our way of life. Due to the tight connection between people and IoT, now civil and criminal investigations or internal probes must take IoT into account. From the forensic perspective, the IoT environment contains a rich set of artifacts that could benefit investigations, while the forensic investigation in IoT paradigm may have to alter to accommodate characteristics of IoT. Therefore, in this article, we analyze the impact of IoT on digital forensics and systematize the research efforts made by previous researchers from 2010 to 2018. We sketch the landscape of IoT forensics and examine the state of IoT forensics under a 3-D framework. The 3-D framework consists of a temporal dimension, a spatial dimension, and a technical dimension. The temporal dimension walks through the standard digital forensic process while the spatial dimension explores where to identify sources of evidence in IoT environment. These two dimensions attempt to provide principles and guidelines for standardizing digital investigations in the context of IoT. The technical dimension guides a way to the exploration of tools and techniques to ensure the enforcement of digital forensics in the ever-evolving IoT environment. Put together, we present a holistic overview of digital forensics in IoT. We also highlight open issues and outline promising suggestions to inspire future study.
A three-way cluster ensemble approach for large-scale data

Cluster ensemble has emerged as a powerful technique for combining multiple clustering results. To address the problem of clustering on large-scale data, this paper presents an efficient three-way cluster ensemble approach based on Spark, which has the ability to deal with both hard clustering and soft clustering. First, this paper proposes the framework of three-way cluster ensemble based on Spark inspired by the theory of three-way decisions, and develops a distributed three-way k-means clustering algorithm. Then, we introduce the concept of cluster unit, which reflects the minimal granularity distribution structure agreed by all the ensemble members. We also introduce quantitative measures for calculating the relationships between units and between clusters. Finally, we propose a consensus clustering algorithm based on cluster units, and we devise various three-way decision strategies to assign small cluster units and no-unit objects. The experimental results using 19 real-world data sets validate the effectiveness of the proposed approach from different indices such as ARI, ACC, NMI and F1-Measure. The experimental results show that the proposed approach can effectively deal with large-scale data, and the proposed consensus clustering algorithm has a lower time cost and does not sacrifice the clustering quality.
Re-designing the business organization using disruptive innovations based on blockchain-IoT integrated architecture for improving agility in future Industry 4.0

Purpose Blockchain and Internet of Things (IoT) technologies have recently gained much attention for Industry 4.0. With the emergence of disruptive technologies, it has become essential to redesign the business for innovations based on blockchain–IoT integrated architecture that helps organizations to improve agility in their operations. The paper aims to discuss this issue. Design/methodology/approach An industrial pump was Sensorized and IoTized to monitor its operations on real time and take predictive measures for managing these assets with more agility. The developed architecture was further extended for proposing the use of blockchain and how it can benefit the organization. Findings The known features of blockchain such as increasing the capacity of decentralization, trust-less transactions, security and allowing autonomous coordination of the devices along with the boons of IoT will help achieve the motto of improving agility in Industry 4.0. Originality/value This paper gives a new dimension to utilization of blockchain technology. blockchain along with IoT that gives a way forward for industries like manufacturing, oil and gas, engineering and construction, utilities, etc. to re-designing the business organization in a more agile way.
Multi-scale fully convolutional neural networks for histopathology image segmentation: From nuclear aberrations to the global tissue architecture

Histopathologic diagnosis relies on simultaneous integration of information from a broad range of scales, ranging from nuclear aberrations ($\approx \mathcal{O}(0.1{\mu m})$) through cellular structures ($\approx \mathcal{O}(10{\mu m})$) to the global tissue architecture ($\gtrapprox \mathcal{O}(1{mm})$). To explicitly mimic how human pathologists combine multi-scale information, we introduce a family of multi-encoder FCNs with deep fusion. We present a simple block for merging model paths with differing spatial scales in a spatial relationship-preserving fashion, which can readily be included in standard encoder-decoder networks. Additionally, a context classification gate block is proposed as an alternative for the incorporation of global context. Our experiments were performed on three publicly available whole-slide images of recent challenges (PAIP 2019, BACH 2020, CAMELYON 2016). The multi-scale architectures consistently outperformed the baseline single-scale U-Nets by a large margin. They benefit from local as well as global context and particularly a combination of both. If feature maps from different scales are fused, doing so in a manner preserving spatial relationships was found to be beneficial. Deep guidance by a context classification loss appeared to improve model training at low computational costs. All multi-scale models had a reduced GPU memory footprint compared to ensembles of individual U-Nets trained on different image scales. Additional path fusions were shown to be possible at low computational cost, opening up possibilities for further, systematic and task-specific architecture optimization. The findings demonstrate the potential of the presented family of human-inspired, end-to-end trainable, multi-scale multi-encoder FCNs to improve deep histopathologic diagnosis by extensive integration of largely different spatial scales.
Carbon price forecasting with complex network and extreme learning machine

Carbon emission price mechanism is the core issue in carbon emission trading. The carbon emission price fluctuation trend is related to the play of the effectiveness of carbon emission trading market, and directly affects the green and low-carbon behavior of enterprises and residents. Therefore, the prediction of carbon price is of great practical significance. This study presents a new carbon price prediction model by using time series complex network analysis technology and extreme learning machine algorithm (ELM). In our model, we first map the carbon price data into a carbon price network (CPN), and then extract the effective information of carbon price fluctuations by using the network topology, and use the extracted effective information to reconstruct the carbon price sample data. With the reconstructed data and the extreme learning machine algorithm, the carbon price network extreme learning machine model (CPN-ELM) is built. To test the validity of the model, we selected the carbon emission price data of the second, third and transition stages of the European Union Emissions Trading System (EU ETS) for empirical analysis, the results show that CPN-ELM can improve the predictive accuracy of ELM in both level accuracy and directional accuracy. Meanwhile, CPN-ELM prediction model has better robustness when facing the random samples, sample data with different frequencies or sample data with structural changes.
Language-based colorization of scene sketches

Being natural, touchless, and fun-embracing, language-based inputs have been demonstrated effective for various tasks from image generation to literacy education for children. This paper for the first time presents a language-based system for interactive colorization of scene sketches, based on semantic comprehension. The proposed system is built upon deep neural networks trained on a large-scale repository of scene sketches and cartoonstyle color images with text descriptions. Given a scene sketch, our system allows users, via language-based instructions, to interactively localize and colorize specific foreground object instances to meet various colorization requirements in a progressive way. We demonstrate the effectiveness of our approach via comprehensive experimental results including alternative studies, comparison with the state-of-the-art methods, and generalization user studies. Given the unique characteristics of language-based inputs, we envision a combination of our interface with a traditional scribble-based interface for a practical multimodal colorization system, benefiting various applications. The dataset and source code can be found at https://github.com/SketchyScene/SketchySceneColorization.
Dual-Stream Pyramid Registration Network

We propose a Dual-Stream Pyramid Registration Network (referred as Dual-PRNet) for unsupervised 3D medical image registration. Unlike recent CNN-based registration approaches, such as VoxelMorph, which explores a single-stream encoder-decoder network to compute a registration field from a pair of 3D volumes, we design a two-stream architecture able to compute multi-scale registration fields from convolutional feature pyramids. Our contributions are two-fold: (i) we design a two-stream 3D encoder-decoder network which computes two convolutional feature pyramids separately for a pair of input volumes, resulting in strong deep representations that are meaningful for deformation estimation; (ii) we propose a pyramid registration module able to predict multi-scale registration fields directly from the decoding feature pyramids. This allows it to refine the registration fields gradually in a coarse-to-fine manner via sequential warping, and enable the model with the capability for handling significant deformations between two volumes, such as large displacements in spatial domain or slice space. The proposed Dual-PRNet is evaluated on two standard benchmarks for brain MRI registration, where it outperforms the state-of-the-art approaches by a large margin, e.g., having improvements over recent VoxelMorph [2] with 0.683 $$\rightarrow $$ 0.778 on the LPBA40, and 0.511 $$\rightarrow $$ 0.631 on the Mindboggle101, in term of average Dice score.