Reviewerly/exHarmony huggingface dataset:
An Empirical Evaluation of AI Deep Explainable Tools

Success in machine learning has led to a wealth of Artificial Intelligence (AI) systems. A great deal of attention is currently being set on the development of advanced Machine Learning (ML)-based solutions for a variety of automated predictions and classification tasks in a wide array of industries. However, such automated applications may introduce bias in results, making it risky to use these ML models in security-and privacy-sensitive domains. The prediction should be accurate and models have to be interpretable/explainable to understand how they work. In this research, we conduct an empirical evaluation of two major explainer/interpretable methods called LIME and SHAP on two datasets using deep learning models, including Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). The results demonstrated that SHAP performs slightly better than LIME in terms of Identity, Stability, and Separability from two different datasets (Breast Cancer Wisconsin (Diagnostic) and NIH Chest X-Ray) that we used.
Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Data exchange between multiple renewable energy power plant owners can lead to an improvement in forecast skill thanks to the spatio-temporal dependencies in time series data.However, owing to business competitive factors, these different owners might be unwilling to share their data.In order to tackle this privacy issue, this paper formulates a novel privacypreserving framework that combines data transformation techniques with the alternating direction method of multipliers.This approach allows not only to estimate the model in a distributed fashion but also to protect data privacy, coefficients and covariance matrix.Besides, asynchronous communication between peers is addressed in the model fitting, and two different collaborative schemes are considered: centralized and peer-topeer.The results for a solar energy dataset show that the proposed method is robust to privacy breaches and communication failures, and delivers a forecast skill comparable to a model without privacy protection.
Learning in digital environments: a model for cross-cultural alignment

This article presents a conceptual model of cross-cultural alignments in education in the digital era. The intention was to explore and respond to urgent questions regarding learners and the learning environments in today's networked society. The model explores the enabling or constraining influences of educational systems, digital environments, learners, and educators on other learners and is based on the concept of social justice. The skills and competencies required for efficient learner development in a digital environment include digital competence, collaboration skills, intercultural competence, and lifelong learning skills. The interrelationship of these components and their influence on learners' skills and competencies are discussed through the lens of cross-cultural alignment by examining three intercultural projects worldwide. The authors recommend that educational systems provide educational institutions with a high-quality infrastructure as well as to support educators and learners in the development of digital skills. Future research may examine the model's components and their interrelationships so that it may serve researchers and practitioners as a basis for the design of future intercultural projects.
Unpaired Image Captioning With semantic-Constrained Self-Learning

Image captioning has been an emerging and fast-developing research topic. Nevertheless, most existing works heavily rely on large amounts of image-sentence pairs and therefore hinder the practical applications of captioning in the wild. In this paper, we present a novel Semantic-Constrained Self-learning (SCS) framework that explores an iterative self-learning strategy to learn an image captioner with only unpaired image and text data. Technically, SCS consists of two stages, i.e., pseudo pair generation and captioner re-training, iteratively producing "pseudo" image-sentence pairs via a pre-trained captioner and re-training the captioner with the pseudo pairs, respectively. Particularly, both stages are guided by the recognized objects in the image, that act as semantic constraint to strengthen the semantic alignment between the input image and the output sentence. We leverage a semantic-constrained beam search for pseudo pair generation to regularize the decoding process with the recognized objects via forcing the inclusion/exclusion of the recognized/irrelevant objects in output sentence. For captioner re-training, a self-supervised triplet loss is utilized to preserve the relative semantic similarity ordering among generated sentences with regard to the input image triplets. Moreover, an object inclusion reward and an adversarial reward are adopted to encourage the inclusion of the predicted objects in the output sentence and pursue the generation of more realistic sentences during self-critical training, respectively. Experiments conducted on both dependent and independent unpaired data validate the superiority of SCS. More remarkably, we obtain the best published CIDEr score to-date of 74.7\% on COCO Karpathy test split for unpaired image captioning.
Comparison of ECC and RSA Algorithm with DNA Encoding for IoT Security

IoT is still an emerging technology without a lot of standards around it, which makes it difficult to integrate it into existing businesses, what's more, with restricted assets and expanding gadgets that essentially work with touchy information. Thus, information safety has become urgent for coders and clients. Thus, painstakingly chosen and essentially tested encryption calculations should be utilized to grow the gadgets productively, to decrease the danger of leaking the delicate information. This investigation looks at the ECC calculation (Elliptic Curve Cryptography) and Rivest-Shamir-Adleman (RSA) calculation. Furthermore, adding the study of DNA encoding operation in DNA computing with ECC to avoid attackers from getting access to the valuable data.
Distributed Task Offloading Optimization With Queueing Dynamics in Multiagent Mobile-Edge Computing Networks

Task offloading decision making plays a key role in enabling mobile-edge computing (MEC) technologies in Internet of Things (IoT). However, it meets the significant challenges arising from the stochastic dynamics of task queueing in the application layer and coupled wireless interference in the physical layer in a distributed multiagent network without any centralized communication and computing coordination. In this article, we investigate the distributed task offloading optimization problem with consideration of the upper layer queueing dynamics and the lower-layer coupled wireless interference. We first propose a new optimization model that aims at maximizing the expected offloading rate of multiple agents by optimizing their offloading thresholds. Then, we transform the problem into a game-theoretic formulation, which further leads to the design of a distributed best-response (DBR) iterative optimization framework. The existence of Nash equilibrium strategies in the game-theoretic model has been analyzed. For the individual optimization of each agent's threshold policy, we further propose a programming scheme by transforming a constrained threshold optimization into an unconstrained Lagrangian optimization (ULO). The individual ULO is integrated into the DBR framework to enable agents to cooperate and converge to a global optimum in a distributed manner. Finally, simulation results are provided to validate the proposed method and demonstrate its significant advantage over other existing distributed methods. The numerical results also show that the proposed method can achieve comparable performance to a centralized optimization method.
Frog-GNN: Multi-perspective aggregation based graph neural network for few-shot text classification

Few-shot text classification aims to learn a classifier from very few labeled text instances per class. Previous few-shot research works in NLP are mainly based on Prototypical Networks, which encode support set samples of each class to prototype representations and compute distance between query and each class prototype. In the prototype aggregation progress, much useful information of support set and discrepancy between samples from different class are ignored. In contrast, our model focuses on all query-support pairs without information loss. In this paper, we propose a multi-perspective aggregation based graph neural network (Frog-GNN) that observes through eyes (support and query instance) and speaks by mouth (pair) for few-shot text classification. We construct a graph by pre-trained pair representations and aggregate information from neighborhoods by instance-level representations for message-passing. The final relational features of pairs imply intra-class similarity and inter-class dissimilarity after iteratively interactions among instances. In addition, our Frog-GNN with meta-learning strategy can well generalize to unseen class. Experimental results demonstrate that the proposed GNN model outperforms existing few-shot approaches in both few-shot text classification and relation classification on three benchmark datasets.
Perspective of uncertainty and risk from the CVaR-LCOE approach: An analysis of the case of PV microgeneration in Minas Gerais, Brazil

This present study proposes to investigate whether the classification based on deterministic LCOE (Levelized Cost of Electricity) for PV microgeneration in twenty cities in Minas Gerais (Brazil) presents differences in relation to risk classification, based on a stochastic approach, namely CVaR-LCOE (Conditional Value at Risk – LCOE). In this stochastic approach, the Conditional Value at Risk (CVaR) is calculated with 99.9% confidence level for 5000 LCOE values for each city, generated by Monte Carlo Simulation. CVaR-LCOE results show a significant difference in ranking in relation to the deterministic approach. Diamantina city presented the best expected LCOE and the best CVaR-LCOE results. However, the cities located in Zona da Mata (Muriaé, Juiz de Fora and Manhuaçu) are the worst in the deterministic ranking, but in CVaR-LCOE approach, Juiz de Fora and Manhuaçu results are better than Ipatinga, Governador Valadares, and Poços de Caldas, this last one which presents the worst CVaR-LCOE. It is also clear that in the CVaR-LCOE ranking, the cities in different regions appear in more shuffle positions in the ranking, the opposite was observed in the deterministic ranking. Cities with the lowest LCOE standard deviation had the best classification changes among the two rankings.
Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data

A rough environment or unexpected accident of data acquisition instrument can introduce some anomalies in monitoring data. Those anomalies reduce data quality and lead to the incorrect recognition of machine health status. However, the research on anomaly detection of machine monitoring data (MMD) is very scarce. Moreover, anomaly detection methods in other fields cannot be directly applied to MMD. Therefore, a robust anomaly detection method called similarity-measured isolation forest (SM-iForest) is proposed to detect abnormal segments and the data therein. The inadaptability and instability of iForest were reduced while processing MMD benefiting from the characteristics of sliding-window processing. Moreover, an anomaly identification stage measuring the relative similarity of possible abnormal segments further improved the robustness of iForest. The effectiveness of the proposed method was verified with a vibration simulation signal and three sets of milling force signals. The results demonstrate that SM-iForest can detect the missing, shifting, and swelling segments robustly. Detection results of comparing seven methods suggest that SM-iForest is a promising method to detect MMD anomaly with a high detection rate and low false alarm rate.
Merging ground-based sunshine duration observations with satellite cloud and aerosol retrievals to produce high-resolution long-term surface solar radiation over China

Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval, and reanalysis, getting best-estimated long-term variations in Rs are sorely needed for climate studies. It has been shown that Rs data derived from sunshine duration (SunDu) can provide reliable long-term variability, but such data are available at sparsely distributed weather stations. Here, we merge SunDu-derived Rs with satellite-derived cloud fraction and aerosol optical depth (AOD) to generate high-spatial-resolution (0.1∘) Rs over China from 2000 to 2017. The geographically weighted regression (GWR) and ordinary least-squares regression (OLS) merging methods are compared, and GWR is found to perform better. Based on the SunDu-derived Rs from 97 meteorological observation stations, which are co-located with those that direct Rs measurement sites, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2=0.97 and standard deviation =11.14 W m−2, while GWR driven by only cloud fraction produces similar results with R2=0.97 and standard deviation =11.41 W m−2. This similarity is because SunDu-derived Rs has included the impact of aerosols. This finding can help to build long-term Rs variations based on cloud data, such as Advanced Very High Resolution Radiometer (AVHRR) cloud retrievals, especially before 2000, when satellite AOD retrievals are not unavailable. The merged Rs product at a spatial resolution of 0.1∘ in this study can be downloaded at https://doi.org/10.1594/PANGAEA.921847 (Feng and Wang, 2020).
Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities

Smart city infrastructure has a significant impact on improving the quality of humans life.However, a substantial increase in the urban population from the last few years poses challenges related to resource management, safety, and security.To ensure the safety and security in the smart city environment, this paper presents a novel approach by empowering the authorities to better visualize the threats, by identifying and predicting the highly-reported crime zones in the smart city.To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crime occurrence.Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) is exploited in each dense crime region to predict the number of crime incidents in the future with spatial and temporal information.The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) for New York City (NYC).The accuracy of the model is measured by considering different time scenarios such as the year-wise, (i.e., for each year), and for the total considered duration of ten years using an 80:20 ratio.The 80% of data was used for training and 20% for testing.The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47 as compared to the highest scoring DBSCAN based method with MAE 27.03.
How to identify early defaults in online lending: A cost-sensitive multi-layer learning framework

Credit scoring tools are frequently used by lenders to identify bad borrowers who cannot fully repay their liabilities. This is a classical problem of classification with imbalanced samples, where bad loans only take a small proportion of all applications. Various machine learning techniques have been applied to the prediction of default in the past few decades. In this paper, we aim to capture those early defaulted borrowers who are likely to be fraudsters on the online lending platform by using a multi-layer structured Gradient Boosted Decision Trees with Light Gradient Boosting Machines (ML-LightGBM). Due to the extremely imbalanced sample distribution and the costs of misclassification, we further apply a cost-sensitive framework to the loss function of classification models, in order to improve predictive accuracy. The empirical results, based on a sample of 1.6 million online loans, show that the proposed cost-sensitive ML-LightGBM algorithm outperforms other predictive models. This suggests that the cost-sensitive based ML-LightGBM is a promising technique for fraud detection and credit scoring.
The neural hype, justified!

One year ago, in the SIGIR Forum issue of December 2018, I ranted about the "neural hype" [9]. One year later, I write again to publicly recant my heretical beliefs. What a difference a year makes! In accelerated "deep learning" time, a year seems like an eternity---so much exciting progress has been made in the previous months!
Deep Bilateral Learning for Stereo Image Super-Resolution

Bilateral filter has demonstrated its effectiveness in many traditional methods for image restoration tasks. In this letter, we incorporate the idea of bilateral grid processing in a CNN framework and propose a bilateral stereo super-resolution network (BSSRnet). Specifically, we use a parallax-attention module to incorporate information from left and right views to learn content-aware bilateral filters. Then, these bilateral filters are used to recover missing details at different spatial locations while preserving stereo consistency. Our network is fully differentiable and is robust to both content and disparity variations. Comparative results show that our BSSRnet achieves state-of-the-art performance on the Flickr1024, Middlebury, KITTI 2012 and KITTI 2015 datasets. Source code is available at.
Low-cost and secure multi-image encryption scheme based on P-tensor product compressive sensing

In order to ensure security, the existing image encryption schemes based on traditional compressive sensing (CS) need to generate different measurement matrices according to different images. Due to the requirement of "dimension matching" in matrix multiplication, the larger these sampled images are, the larger these measurement matrices are. These will seriously consume the storages of the communicating parties. In view of the above problems, this paper proposes a double-layer multi-image encryption scheme with lower storage consumption and higher security based on P-tensor product CS. The scheme makes full play to the advantages of P-tensor product in matrix multiplication, and uses the same basic measurement matrix to process multiple images of different sizes. Some intermediate cipher images of different sizes are then generated. In order to ensure security, the basic measurement matrix is updated to generate the needed measurement matrices for other images. Finally, the intermediate cipher images of different sizes are fused and scrambled to one final cipher image. The experiments and analyses show that our scheme greatly reduces the storage consumption while ensuring security.
Some applications of cloud technologies in mathematical calculations

Abstract This article discusses some use of cloud technology in mathematical calculations using Remote Desktop Ulteo OVD. To use such technologies, it is enough to have access to the Internet through a suitable browser to access an open virtual desktop on a powerful remote computer and then use the resources of the remote computer (server) to solve their problems in processing various information resources – solving mathematical problems, working out texts, translating from one language to another, help on the interpretation of different terms, their origin and more. You can organize access to Ulteo OVD from two servers (Application Server (Windows 2008R2) and Session Manager Server (Linux Ubuntu)), using the Proxmox web-based virtual environment. Gran1, Gran2D, Gran3D software can be installed on the application server. The article also examines in detail some examples of the use of the pedagogical software for educational purposes Gran1. In particular, the calculation of the approximate value of the double integral; graphical two-dimensional problem solving, the so-called linear programming problems; two-dimensional problems, including convex programming – finding the smallest value of a convex downward function (or the highest convexity of a function) on a convex set of inequalities (including linear ones). However, the use in the educational process of any technology, including modern information and communication, as well as the content of training, should be pedagogically balanced, which will allow to avoid any negative effects on the formation of personality of a future member of society, his mental and physical development.
ORFFM: An Ontology-Based Semantic Model of River Flow and Flood Mitigation

The provision of the heterogeneous information acquisition and managing of emerging technologies with IoT, cloud-based storage, and improved communication services have filled the data scarcity gap on one hand but raised the challenge to extract, process, and comprehend relevant data of complex integrated multiple domains involving a large number of participants with diverse spatial terminologies and methodologies.To resolve this challenge various big data and natural language processing techniques were applied.Another widely used approach to resolve the challenges of heterogeneity, interoperability, and complexity of integrated domain is ontology-based semantic modeling.We proposed Ontology for River Flow and Flood Mitigation (ORFFM) for semantic knowledge formalization with semantic understandability of irrigation, disaster management, related administrative and agricultural domain concepts by humans and machines.The semantic modeling of distributed river flow network and associated flood disaster mitigation for effective coordination, collaborative response activities leads to reduce the impact of a disaster and improve information representation among stakeholders.Furthermore, semantic formalization and inference are supported by explicitly annotated information.We populated ORFFM with Pakistan's Indus river system, flood disaster management, and Sindh administrative authorities to develop a knowledgebase for knowledge sharing and representation.The formal semantically enriched knowledgebase would contribute towards streamflow optimization and flood mitigation through effective coordination and common conceptualization during disaster management phases.The semantic model of irrigation networks would also be useful for academic purposes to acquire domain knowledge for new entrants in the irrigation and disaster management field.