Reviewerly/exHarmony huggingface dataset:
Integration and Visualization of Mineralogical and Topographical Information Derived from ASTER and DEM Data

This paper proposes a method of combining and visualizing multiple lithological indices derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and topographical information derived from digital elevation model (DEM) data in a single color image that can be easily interpreted from a geological point of view. For the purposes of mapping silicate rocks, carbonate rocks, and clay minerals in hydrothermal alteration zones, two new indices derived from ASTER thermal infrared emissivity data were developed to identify silicate rocks, and existing indices were adopted to indicate the distribution of carbonate rocks and the species and amounts of clay mineral. In addition, another new method was developed to visualize the topography from DEM data. The lithological indices and topographical information were integrated using the hue–saturation–value (HSV) color model. The resultant integrated image was evaluated by field survey and through comparison with the results of previous studies in the Cuprite and Goldfield areas, Nevada, USA. It was confirmed that the proposed method can be used to visualize geological information and that the resulting images can easily be interpreted from a geological point of view.
Multichannel image contrast enhancement based on linguistic rule-based intensificators

This study follows the direct approach to image contrast enhancement, which changes the image contrast at each its pixel and is more effective than the indirect approach that deals with image histograms. However, there are only few studies following the direct approach because, by its nature, it is very complex. Additionally, it is difficult to develop an effective method since it is required to keep a balance in maintaining local and global image features while changing the contrast at each individual pixel. Moreover, raw images obtained from many sources randomly influenced by many external factors can be considered as fuzzy uncertain data. In this context, we propose a novel method to apply and immediately handle expert fuzzy linguistic knowledge of image contrast enhancement to simulate human capability in using natural language. The formalism developed in the study is based on hedge algebras considered as a theory, which can immediately handle linguistic words of variables. This allows the proposed method to produce an image contrast intensificator from a given expert linguistic rule base. A technique to preserve global as well as local image features is proposed based on a fuzzy clustering method, which is applied for the first time in this field to reveal region image features of raw images. The projections of the obtained clusters on each channel are suitably aggregated to produce a new channel image considered as input of the pixelwise defined operators proposed in this study. Many experiments are performed to demonstrate the effect of the proposed method versus the counterparts considered.
A feature enriching object detection framework with weak segmentation loss

This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is an improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performance. To obtain features more suitable for object detection, a more powerful feature extractor – DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed.
Research on automatic target detection and recognition based on deep learning

With the development of computer technology, the related achievements of image processing have been applied. Among them, the results of automatic target detection and recognition are widely used in the fields of reconnaissance, early warning and traffic control with the application of UAV. But now, the research of automatic target detection and tracking is becoming smaller and smaller. The original automatic target detection and recognition algorithm seems to be inadequate. The bottleneck of low-level feature design and optimization makes the accuracy and efficiency of automatic target detection inefficient. Therefore, based on in-depth learning, this paper establishes a method to automatically learn effective image features from images to achieve automatic target detection. Through the simulation of target detection in VEDAI database. The results show that the recognition rate of the proposed model is more than 95%. The results show that the proposed method can realize the automatic detection and recognition of targets very well.
An error elimination method for high-temperature digital image correlation using color speckle and camera

Digital image correlation method is a kind of displacement and strain measurement technique based on visible light illumination. It can realize non-contact and full-field measurement with lower requirements on the measurement environment, thus it's widely used in testing and research for mechanical properties of materials at high temperature. But there are many factors affecting the measurement accuracy due to the complexity of the high temperature environment, of which the influence of the thermal disturbance on digital image correlation method is the most obvious. In order to reduce the influence of it and improve the measurement accuracy, this paper proposes a measurement method for eliminating thermal disturbance error in high temperature deformation measurement based on color speckle and camera. Two kinds of grayscale field information of different colors were endued to the surface of specimen, and we collected images by color camera with channel separation for each frame of them. Then we processed the grayscale field of two channels separately combined with the digital image correlation method, analyzed the information carried by two different grayscale fields, and a simple mathematical model was performed to separate the thermal disturbance error from the measurement results. Finally, we set up the experimental system, built the mathematical model between the two channels through static measurement experiment, which was used to extract and correct the thermal disturbance error of measurement results of the rigid body motion experiment. The high consistency between corrected result and true displacement value proved the feasibility and accuracy of the proposed method.
Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding

Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.
Improving deep learning performance with missing values via deletion and compensation

Missing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed in the literature to face this problem. In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation. This method improves imputation performance by artificially deleting values in the input features and using them as targets in the training process. Nevertheless, although the deletion of samples is demonstrated to be really efficient, it may cause an imbalance between the distributions of the training and the test sets. In order to solve this issue, a compensation mechanism is proposed based on a slight modification of the error function to be optimized. Experiments over several datasets show that the deletion and compensation not only involve improvements in imputation but also in classification in comparison with other classical techniques.
Revealing the structure of the associations between housing system, facilities, management and welfare of commercial laying hens using Additive Bayesian Networks

After the ban of battery cages in 1988, a welfare control programme for laying hens was developed in Sweden. Its goal was to monitor and ensure that animal welfare was not negatively affected by the new housing systems. The present observational study provides an overview of the current welfare status of commercial layer flocks in Sweden and explores the complexity of welfare aspects by investigating and interpreting the inter-relationships between housing system, production type (i.e. organic or conventional), facilities, management and animal welfare indicators. For this purpose, a machine learning procedure referred to as structure discovery was applied to data collected through the welfare programme during 2010-2014 in 397 flocks housed in 193 different farms. Seventeen variables were fitted to an Additive Bayesian Network model. The optimal model was identified by an exhaustive search of the data iterated across incremental parent limits, accounting for prior knowledge about causality, potential over-dispersion and clustering. The resulting Directed Acyclic Graph shows the inter-relationships among the variables. The animal-based welfare indicators included in this study - flock mortality, feather condition and mite infestation - were indirectly associated with each other. Of these, severe mite infestations were rare (4% of inspected flocks) and mortality was below the acceptable threshold (< 0.6%). Feather condition scored unsatisfactory in 21% of the inspected flocks; however, it seemed to be only associated to the age of the flock, ruling out any direct connection with managerial and housing variables. The environment-based welfare indicators - lighting and air quality - were an issue in 5 and 8% of the flocks, respectively, and showed a complex inter-relationship with several managerial and housing variables leaving room for several options for intervention. Additive Bayesian Network modelling outlined graphically the underlying process that generated the observed data. In contrast to ordinary regression, it aimed at accounting for conditional independency among variables, facilitating causal interpretation.
Identification of geochemical anomalies of the porphyry–Cu deposits using concentration gradient modelling: A case study, Jebal-Barez area, Iran

This paper presents a novel gradient model for exploration of blind mineralization. The concentration gradient coefficient techniques have been proved to be very well-suited for typical mining geochemistry applications. Gradient concentration corresponds to concentration variations. Large concentration changes due to geochemical data distribution correspond to surfacing anomaly, and less concentration variations are more likely to be related to the deeper geochemical anomalies. The introduced technique is capable to distinguish the difference between the blind mineralization and zone dispersed mineralization, computationally and without exploration drilling. In this research, deep geochemical anomalies are investigated using the gradient concentration values for the porphyry–Cu deposits of the Jebal-Barez region in Iran. Based on the results obtained from the proposed gradient model, values >1.0 in G(Vz) reveal the existence of blind mineralization in the study zone. The findings based on this method suggest that SW of Jebal-Barez is a highly favourable zone for exploration of blind mineralization. The value for G(Vz) in this zone is equal to 5.04. The results obtained in this research were consistent well with the findings of the previous research in the area and the detected Kerver as the main blind mineralization in Jebal-Barez. A local exploration was carried out in this zone in four areas of Kerver using the gradient model.
Building a corpus of legal argumentation in Japanese judgement documents: towards structure-based summarisation

We present an annotation scheme describing the argument structure of judgement documents, a central construct in Japanese law. To support the final goal of this work, namely summarisation aimed at the legal professions, we have designed blueprint models of summaries of various granularities, and our annotation model in turn is fitted around the information needed for the summaries. In this paper we report results of a manual annotation study, showing that the annotation is stable. The annotated corpus we created contains 89 documents (37,673 sentences; 2,528,604 characters). We also designed and implemented the first two stages of an algorithm for the automatic extraction of argument structure, and present evaluation results.
ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect

Human detection and tracking is an important task in artificial intelligent robotic systems, which usually require a robust target detector to work in a variety of circumstances. In complex environments where a camera is installed on a wheeled mobile robot, this task becomes much more difficult. In this paper, we present a real-time remote-control system for human detection, tracking, security, and verification in such challenging environments. Such a system is useful for security monitoring, data collection, and experimental purpose. In the proposed system, a Kinect RGB-D camera from Microsoft is used as a visual sensing device for the design of human detection and tracking. We also implemented a remote-control system on a four-wheel mobile platform with a Robot Operating System (ROS). By combining these two designs, a wireless controlled mobile platform having the feature of real-time human monitoring can be realized to handle this task efficiently. Experimental results validate the performance of the proposed system for wirelessly controlling the mobile robot to track humans in a real-world environment.
Fine-grained information flow control using attributes

Information flow control (IFC) mechanisms regulate where information is allowed to travel. To enhance IFC, access control encryption (ACE) was proposed where both the no write-down rule and the no read-up rule are supported. Nevertheless, there are still two issues: (1) how to determine whether a communication request should be permitted or denied was not considered; (2) the commutation cost is linear with the number of receivers. Attribute-based system (ABS) can implement one-to-many communication and fine-grained access policies. In this paper, a new IFC scheme is proposed by combing ACE with ABS. Our scheme provides the following features: (1) IFC policies are defined over a universe set of attributes; (2) the computation cost to determine whether a communication request should be permitted or denied is constant, instead of linear with the number of required attributes or receivers; (3) weak attribute privacy is achieved; (4) fine-grained access policies on encrypted data are supported; (5) the communication cost is linear with the number of required attributes and is independent of the number of receivers. To the best of our knowledge, it is the first IFC scheme enforced by using attributes.
Many objective cooperative bat searching algorithm

In this paper, a many objective cooperative bat searching algorithm (MOCBA) is proposed to solve many-objective optimization problems by using the balanceable fitness estimation method. Similar to the particle swarm optimization (PSO) algorithm and the evolutionary algorithm (EA), the cooperative bat searching algorithm (CBA) is a recently developed swarm intelligence optimization algorithm to efficiently solve single-objective optimization problems. With the balanceable fitness estimation method, the MOCBA balances the diversity ability and convergence ability of the algorithm during searching process. Moreover, the convergence issue for MOCBA is also studied. The results on convergence in mean and convergence in probability of the MCOBA are presented. Experimental results are provided to demonstrate the effectiveness of the proposed MOCBA by comparing with fourteen state-of-the-art many-objective optimization algorithms by solving benchmark functions: DTLZ1–DTLZ5 and WFG1–WFG9. By calculating the means, standard deviations and running the Wilcoxon rank sum tests and the Friedmans tests of 100 algorithm executions, the proposed MOCBA shows superior performance among all the fifteen algorithms.
The effects of symmetric and asymmetric social networks on second language communication

This study investigated the effects of Facebook and Twitter on foreign language (Chinese) learners’ written production in both short- (10 days) and long-term (50 days) pseudo-experimental settings. Adopting two concepts (i.e. symmetric vs. asymmetric) from matrix theory in social network analysis, we categorized Facebook as a symmetric social networking site (SNS) and Twitter as an asymmetric SNS. Results show that Facebook participants were more conservative or not highly engaged in building their social connections. In both settings, Facebook participants posted more sentences than Twitter participants per day, and more posts on Facebook were interactive. The Facebook participants believed more strongly that reading others’ posts improved their reading skills. Facebook also displayed evidence on promoting explicit corrective feedback. More interestingly, Facebook appeared to be a more dynamic system; the quality of writing seemed to change over time. There were more grammatical errors on Facebook than on Twitter in both settings. In the long-term setting (not in the short-term setting), a moderate positive correlation was found between the number of characters and the number of grammar errors for Facebook, but not for Twitter. We conclude that symmetric SNSs facilitate more interactions, potentially providing a more effective platform for peer-to-peer corrective feedback compared to asymmetric SNSs.
Efficient mining of extraordinary patterns by pruning and predicting

Pattern mining is an important data mining technology. The existing pattern mining algorithms mainly focus on discovery of ordinary patterns in databases, for example, frequent pattern mining finds patterns with high frequencies and utility pattern mining discovers patterns with high utilities. However, in many real-world applications, people are more interested in finding extraordinary patterns with low frequencies and high utilities or with high frequencies and low utilities. While mining ordinary patterns is computationally hard, it is even harder to mine extraordinary patterns. In particular, a two-phase approach that first generates and materializes candidates (high-frequency patterns or high-utility patterns) and then finds extraordinary patterns from the candidates, suffers from the scalability and efficiency bottlenecks. This paper proposes an efficient algorithm for mining extraordinary patterns. The novelty of our algorithm lies in newly proposed lower bounds both on frequencies and on utilities of patterns, new pruning strategies and new predicting strategies for dramatically reducing the search space, and a novel data structure for efficient computation. The proposed algorithm employs a single-phase approach without materializing candidates, and also adapts the upper bounds on supports and on utilities for pruning. Extensive experiments show that the new pruning and predicting strategies are effective, and the proposed algorithm is efficient and scalable.
Actionable threat intelligence for digital forensics readiness

Purpose The purpose of this paper is to formulate a novel model for enhancing the effectiveness of existing digital forensic readiness (DFR) schemes by leveraging the capabilities of cyber threat information sharing. Design/methodology/approach This paper uses a quantitative methodology to identify the most popular cyber threat intelligence (CTI) elements and introduces a lightweight approach to correlate those with potential forensic value, resulting in the quick and accurate triaging and identification of patterns of malicious activities. Findings While threat intelligence exchange steadily becomes a common practice for the prevention or detection of security incidents, the proposed approach highlights its usefulness for the digital forensics (DF) domain. Originality/value The proposed model can help organizations to improve their DFR posture, and thus minimize the time and cost of cybercrime incidents.
Intrusion signal classification using stochastic configuration network with variable increments of hidden nodes

In the environmental security monitoring application, an optical fiber prewarning system (OFPS) functions not only to locate the intrusion events but also recognize them. As a nonlinear network for recognition, the stochastic configuration network (SCN) is considered a promising method because it does not require setting the network scale beforehand. However, in the specific requirements of the application of OFPS, due to the small feature distance of different intrusion signals to be classified, it is necessary to set a smaller value of error tolerance. However, the side-effect is that meeting the constraint condition faces a challenge. To overcome this, we improve the configuration method of the hidden layer nodes in the SCN network. In the proceeding of the network process, the increment of the hidden layer nodes in each loop is gradually increased, and the space of the corresponding random parameters generated is enlarged. The SCN with variable increments of hidden nodes can adjust the number of hidden nodes added in each loop for continuous construction and obtaining higher classification accuracy. This study has a great significance for the application of SCN in the classification of intrusion signals in OFPS.