Reviewerly/exHarmony huggingface dataset:
Italian protocol for massive solar integration: Imbalance mitigation strategies

This article proposes two strategies for the mitigation of power imbalances and related costs resulting from increasing PV penetration onto the Italian grid. New “state of the art” solar and netload day ahead forecast models were developed and applied to real data. These strategies consist of: (1) Improving the accuracy of PV and net load power forecast and enlarging the footprint of the controlled grid area; (2) Transforming unconstrained PV plants into “flexible PV plants”: remotely controlled PV plants that can be proactively curtailed and work with cost-optimized Battery Energy Storage Systems. We demonstrate that the first strategy can effectively limit the imbalance impact when integrating a large share of PV generation, reducing imbalance volumes and costs, both at current and future solar penetration levels. We further demonstrate that the second strategy can entirely eliminate the imbalance impact of PV penetration, hence providing operational certainty to the TSO. Indeed, we show how flexible PV plants can be cost-optimally sized to set the imbalance volume at a desired target value regardless of PV installed capacity, hence allowing massive solar penetration. Finally, we show that the cost of implementing these strategies is less than the current cost of handling such imbalance impacts.
Electronic Regulation of Data Sharing and Processing Using Smart Ledger Technologies for Supply-Chain Security

Traditional centralized data storage and processing solutions manifest limitations with regards to overall operational cost and the security and auditability of data. One of the biggest issues with existing solutions is the difficulty of keeping track of who has had access to the data and how the data may have changed over its lifetime; while providing a secure and easy-to-use mechanism to share the data between different users. The ability to electronically regulate data sharing within and across different organizational entities in the supply chain (SC) is an open issue that is only addressed partially by existing legal and regulatory compliance frameworks. In this article, we present Cydon, a decentralized data management platform that executes bespoke distributed applications utilizing a novel search and retrieve algorithm leveraging metadata attributes. Cydon utilizes a smart distributed ledger to offer an immutable audit trail and transaction history for all different levels of data access and modification within a SC and for all data flows within the environment. Results suggest that Cydon provides authorized and fast access to secure distributed data, avoids single points of failure by securely distributing encrypted data across different nodes while maintains an “always-on” chain of custody.
Faster R-CNN Based Table Detection Combining Corner Locating

Table detection in document images has achieved remarkable improvement. However, there is still a problem of inaccurate table boundary locating. This paper proposes Faster R-CNN based table detection combining corner locating method. Firstly, coarse table detection and corner locating are implemented through Faster R-CNN network. Secondly, those corners belonging to the same tables are grouped by coordinate matching. At the same time, unreliable corners are filtered. Finally, table boundaries are adjusted and refined by corresponding corner group. The proposed method improves the precision of table boundary locating at pixel-level. Experiment results show that our method effectively improves the precision of table detection. It achieves an F-measure of 94.9% on ICDAR2017 POD dataset. Compared with traditional Faster R-CNN method, our method increases by 2.8% in F-measure and 2.1% at pixellevel localization.
FPGA Implementation and Design of a Hybrid Chaos-AES Color Image Encryption Algorithm

In this paper, we propose an image encryption algorithm based on four-dimensional chaotic system to generate key and improve advanced encryption standard. The encryption algorithm is optimized by using the pipeline and parallel computing features of Field Programmable Gate Array (FPGA). First, the chaotic system is used as a key generator for the encryption algorithm. Next, in the improved advanced encryption standard, ShiftRows and SubByres are modified with Spin-Sort and Cubic S-Box, and the round of encryption is reduced. We implement the encryption algorithm and the wired image transmission system to the ARM-based SoC-FPGA. The HPS software runs on Linux and is used to control the FPGA encryption algorithm and image transmission. Finally, the results from the encryption security analysis show that the proposed image encryption algorithm is safe and effective.
OBC306: A Large-Scale Oracle Bone Character Recognition Dataset

The oracle bone script from ancient China is among the world's most famous ancient writing systems. Identifying and deciphering oracle bone scripts is one of the most important topics in oracle bone study and requires a deep familiarity with the culture of ancient China. This task remains very challenging for two reasons. The first is that it is executed mainly by humans and requires a high level of experience, aptitude, and commitment. The second is due to the scarcity of domain-specific data, which hinders the advancement of automatic recognition research. A collection of well-labeled oracle-bone data is necessary to bridge the oracle bone and information processing fields; however, such a dataset has not yet been presented. Hence, in this paper, we construct a new large-scale dataset of oracle bone characters called OBC306. We also present the standard deep convolutional neural network-based evaluation for this dataset to serve as a benchmark. Through statistical and visual analyses, we describe the inherent difficulties of oracle bone recognition and propose future challenges for and extensions of oracle bone study using information processing. This dataset contains more than 300,000 character-level samples cropped from oracle-bone rubbings or images. It covers 306 glyph classes and is the largest existing raw oracle-bone character set, to the best of our knowledge. It is anticipated the publication of this dataset will facilitate the development of oracle bone research and lead to optimal algorithmic solutions.
Maximum Likelihood Path Planning for Fast Aerial Maneuvers and Collision Avoidance

We propose a planning method to enable fast autonomous flight in cluttered environments. Typically, autonomous navigation through a complex environment requires a continuous search on a graph generated by a k-connected grid or a probabilistic scheme. As the vehicle travels, updating the graph with data from onboard sensors is expensive as is the search on the graph especially if the paths must be kinodynamically feasible. We propose to avoid the online search to reduce the computational complexity. Our method models the environment differently in two separate regions. Obstacles are considered to be deterministically known within the sensor range and probabilistically known beyond the sensor range. Instead of searching for the path with the lowest cost (typically the shortest path), the method maximizes the likelihood to reach the goal in determining the immediate next step for navigation. With such a problem formulation, the online method realized by a trajectory library can determine a path within 0.2-0.3ms using a single CPU thread on a modem embedded computer. In experiments, it enables a lightweight UAV to fly at 10m/s in a cluttered forest environment (see Fig. 1 as an example).
Comparison of base classifiers for multi-label learning

Multi-label learning methods can be categorised into algorithm adaptation, problem transformation and ensemble methods. Some of these methods depend on a base classifier and the relationship is not well understood. In this paper the sensitivity of five problem transformation and two ensemble methods to four types of classifiers is studied. Their performance across 11 benchmark datasets is measured using 16 evaluation metrics. The best classifier is shown to depend on the method: Support Vector Machines (SVM) for binary relevance, classifier chains, calibrated label ranking, quick weighted multi-label learning and RAndom k-labELsets; k-Nearest Neighbours (k-NN) and Naïve Bayes (NB) for Hierarchy Of Multilabel classifiERs; and Decision Trees (DT) for ensemble of classifier chains. The statistical performance of a classifier is also found to be generally consistent across the metrics for any given method. Overall, DT and SVM have the best performance–computational time trade-off followed by k-NN and NB.
A Bibliometric Analysis of Digital Image Forensics

We present a bibliometric analysis of the evolving field of digital image forensics (DIF) from 2014 to early 2020. The study analyses and discusses the results obtained from the highly indexed Scopus database for various subject areas, renowned authors, funding agencies, and affiliations using a mind map approach. We adapt the PRISMA methodology formerly applied for systematic reviews to refine our search results. The results of this study indicate an increasing awareness of DIF in countries like China, India, and Italy along with an increase in publications since 2018, specifically, with the application of deep learning techniques for DIF.
A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algorithm. The main challenge of the approach was using (1) publicly available weather reports without solar irradiance values and (2) measured PV power without any technical information about the PV system. Using this input data, the developed model can predict the power output of the investigated PV systems with adequate accuracy. The lowest seasonal mean absolute scaled error of the prediction was reached by maximum size of the training set. Transferability of the developed approach was proven by making predictions of the PV power for warm and cold periods and for two different PV systems located in Oldenburg and Munich, Germany. The PV power prediction made with publicly available weather data was compared to the predictions made with fee-based solar irradiance data. The usage of the solar irradiance data led to more accurate predictions even with a much smaller training set. Although the model with publicly available weather data needed greater training sets, it could still make adequate predictions.
Research Trends in Enterprise Service Bus (ESB) Applications: A Systematic Mapping Study

In recent years, enterprise service bus (ESB) has become a favorable adoption as a technology category in the IT industry as it provides secure and guaranteed delivery of services.The elasticity of Enterprise Service Bus (ESB) enables numerous applications to exchange information makes it a significant middleware layer responsible for transferring information in a Service-Oriented Architecture (SOA).ESB is presently the utmost promising tactic for the integration of business applications in distributed and diverse environments.It also offers essential infrastructure support for transforming messages or data, intelligent routing, and protocol transformation.The idea of ESBs emerged from the requirements to move out from traditional integration patterns, that becomes difficult to manage with the passage of time.Our study aim is to understand and provide ongoing research topics, challenges and future directions concerning ESB applications.A systematic mapping study (SMS) is therefore implemented to categorize the selected papers into the following classification: contribution type, ESB applications, research type and their approaches.We have extracted a total of twenty-two papers for this systematic study and they are classified according to defined criteria.The findings of this SMS are discussed and researchers were provided with suggestions on possible directions for future research.
Collaborative filtering based recommendation of sampling methods for software defect prediction

The performance of software defect prediction have been hindered by the imbalanced nature of software defect data. Fortunately, a variety of sampling methods have been employed to improve defect prediction performance. However, researchers and practitioners are usually burdened with selecting the optimal sampling methods for the defect data at hand. In practice, no sampling method has been found to perform best in theory and practice. Therefore it is necessary and valuable to study how to select applicable sampling methods according to the current data characteristics. This paper presents a collaborative filtering based sampling methods recommendation algorithm (CFSR) for automatically recommending applicable sampling methods for the new defect data. CFSR firstly ranks existing sampling methods with historical defect data, and then mines the data similarity between the new and historical defect data with meta-features. Finally, all the information of ranked sampling methods and data similarity are combined to build a recommendation network, with which the user-based collaborative filtering algorithm is employed to recommend appropriate sampling methods for the new defect data. A thorough experiment with five classification algorithms, two prediction performance, five recommendation performance and 12 popular sampling methods was conducted over 20 imbalanced software defect data. The experimental results firstly demonstrate the importance and necessity of present study, and then show that the proposed CFSR method is feasible and effective.
Moving Object Detection With Deep CNNs

In large field of view for open country, the real-time detection and identification of moving objects with high accuracy is a very challenging work due to the excessive amount of data. This paper proposes a novel framework that consists of a coarse-grained detection as well as a fine-grained detection. To solve the problem of noise-induced object fracture during the coarse-grained detection process, we present a low-complexity connected region detection algorithm to extract moving regions. Furthermore, in the fine-grained detection, Deep Convolution Neural Networks are leveraged to detect more precise coordinates and identify the category of objects. To the best of our knowledge, this is the first work that proposes a coarse-to-fine grained framework to detect moving objects on high-resolution scenes. Experimental results show that the proposed framework can robustly work on the high resolution video frames (1920*1080p) with complex situations more fastly and accurately over existing methods.
A constrained optimization algorithm for learning GloVe embeddings with semantic lexicons

GloVe representations of words as vector embeddings in continuous spaces are learned from matrix factorization of the words’ co-occurrences matrix constructed from large corpora. Due to their high quality as textual features, GloVe embeddings have been extensively utilized for many text mining and natural language processing tasks with considerable success. Further improvements of these word representations can be obtained by also taking into account the valuable information of the semantic properties of the words and the complex relationships between them as provided by semantic lexicons. In this paper we adopt optimization techniques from the domain of machine learning with constrained optimization in order to leverage the relational knowledge between words, and we propose an efficient algorithm that produces word embeddings enhanced by the semantic information. The proposed algorithm outperforms other related approaches that utilize semantic information either during training or as a post-processing step. Our claims are validated by experiments on popular text mining and natural language processing tasks, including word similarities, word analogies, and sentiment analysis, which demonstrate that our proposed model can significantly improve the quality of word vector representations.
Presence, Mixed Reality, and Risk-Taking Behavior: A Study in Safety Interventions

Immersive environments have been successfully applied to a broad range of safety training in high-risk domains. However, very little research has used these systems to evaluate the risk-taking behavior of construction workers. In this study, we investigated the feasibility and usefulness of providing passive haptics in a mixed-reality environment to capture the risk-taking behavior of workers, identify at-risk workers, and propose injury-prevention interventions to counteract excessive risk-taking and risk-compensatory behavior. Within a mixed-reality environment in a CAVE-like display system, our subjects installed shingles on a (physical) sloped roof of a (virtual) two-story residential building on a morning in a suburban area. Through this controlled, within-subject experimental design, we exposed each subject to three experimental conditions by manipulating the level of safety intervention. Workers' subjective reports, physiological signals, psychophysical responses, and reactionary behaviors were then considered as promising measures of Presence. The results showed that our mixed-reality environment was a suitable platform for triggering behavioral changes under different experimental conditions and for evaluating the risk perception and risk-taking behavior of workers in a risk-free setting. These results demonstrated the value of immersive technology to investigate natural human factors.
Efficient Algorithm for Mining Non-Redundant High-Utility Association Rules

In business, managers may use the association information among products to define promotion and competitive strategies. The mining of high-utility association rules (HARs) from high-utility itemsets enables users to select their own weights for rules, based either on the utility or confidence values. This approach also provides more information, which can help managers to make better decisions. Some efficient methods for mining HARs have been developed in recent years. However, in some decision-support systems, users only need to mine a smallest set of HARs for efficient use. Therefore, this paper proposes a method for the efficient mining of non-redundant high-utility association rules (NR-HARs). We first build a semi-lattice of mined high-utility itemsets, and then identify closed and generator itemsets within this. Following this, an efficient algorithm is developed for generating rules from the built lattice. This new approach was verified on different types of datasets to demonstrate that it has a faster runtime and does not require more memory than existing methods. The proposed algorithm can be integrated with a variety of applications and would combine well with external systems, such as the Internet of Things (IoT) and distributed computer systems. Many companies have been applying IoT and such computing systems into their business activities, monitoring data or decision-making. The data can be sent into the system continuously through the IoT or any other information system. Selecting an appropriate and fast approach helps management to visualize customer needs as well as make more timely decisions on business strategy.
Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran

The Takab metallogenic province in the northwestern Iran hosts a number of gold resources including carline-like and epithermal type mineralization. These occurrences are hosted by tectonized country rocks of Precambrian basement metamorphic rocks and Tertiary volcanic and sedimentary units which are intruded by the intrusion of probable age of Eocene and the sub-volcanic domes of Miocene age. Plausible mineralized zones in this high-potential area can be delimited based on mineral prospectivity maps during the regional exploration surveys. However, the success of mineral prospectivity mapping (MPM) is always plagued by different types of uncertainties (stochastic and systemic). The main aim of this study is to reduce those uncertainties resulting from (a) the imprecise/incorrect selection of targeting criteria, (b) adverse effects of using inefficient or least efficient evidence maps, and (c) employing an integration methodology which suffers from user interference. In this regard, the first case was modulated through the precise identification and selection of controls on mineralization as well as ore-forming processes; the second case was declined by the sensitivity analysis of input geochemical, geological and structural parameters to MPM using the area under the receiver operating characteristics (ROC) curve; and the third case was controlled by incorporation of genetic algorithm (GA) and artificial neural networks (here, MLP-ANN) as a suitable data-driven MPM method. Through sensitivity analysis and using MLP-GA-ANN and MLP-ANN algorithms, various prospectivity models were generated and compared. The results of prediction-rate curves demonstrated that the exclusion of poor evidence maps and that employing a self-adapted data-driven approach (e.g., MLP-GA-ANN) can significantly reduce the systematic uncertainty in mapping of mineral prospectivity. Accordingly, the superior model was then selected for delineating efficient exploration targets.
Weakly Supervised Adversarial Learning for 3D Human Pose Estimation from Point Clouds

Point clouds-based 3D human pose estimation that aims to recover the 3D locations of human skeleton joints plays an important role in many AR/VR applications. The success of existing methods is generally built upon large scale data annotated with 3D human joints. However, it is a labor-intensive and error-prone process to annotate 3D human joints from input depth images or point clouds, due to the self-occlusion between body parts as well as the tedious annotation process on 3D point clouds. Meanwhile, it is easier to construct human pose datasets with 2D human joint annotations on depth images. To address this problem, we present a weakly supervised adversarial learning framework for 3D human pose estimation from point clouds. Compared to existing 3D human pose estimation methods from depth images or point clouds, we exploit both the weakly supervised data with only annotations of 2D human joints and fully supervised data with annotations of 3D human joints. In order to relieve the human pose ambiguity due to weak supervision, we adopt adversarial learning to ensure the recovered human pose is valid. Instead of using either 2D or 3D representations of depth images in previous methods, we exploit both point clouds and the input depth image. We adopt 2D CNN to extract 2D human joints from the input depth image, 2D human joints aid us in obtaining the initial 3D human joints and selecting effective sampling points that could reduce the computation cost of 3D human pose regression using point clouds network. The used point clouds network can narrow down the domain gap between the network input i.e. point clouds and 3D joints. Thanks to weakly supervised adversarial learning framework, our method can achieve accurate 3D human pose from point clouds. Experiments on the ITOP dataset and EVAL dataset demonstrate that our method can achieve state-of-the-art performance efficiently.