Reviewerly/exHarmony huggingface dataset:
A geostatistical approach to analyzing gold distribution controlled by large-scale fault systems – An example from Côte d’Ivoire

Geostatistical approaches can help to understand the general characteristics of an area, to suggest feasible geological models, and to quantify overall patterns in the occurrence of economic mineralisation. We use such an approach to demonstrate the relationships between large-scale faults of various ages and kinematics, lithologies, and gold mineralisation to facilitate understanding of crustal-scale structural controls of gold deposits. Our example comes from Côte d’Ivoire, which is known to have undergone at least three major phases of deformation, two of which produced transpressional faults resulting from ESE-WNW and, later, NE/ENE-SW/WSW, compression. Using aeromagnetic and surface geological data, we interpret these faults and suggest a multiphase Riedel model for the region, taking fault reactivation into consideration. With the help of this simplified model, the spatial relationships between the faults, lithology, and 909 known gold occurrences, including 554 artisanal mining sites, are analysed within a GIS. Using geostatistical methods, gold mineralisation was found to probably be a two-stage process, with the later D2 stage potentially being the peak mineralization event. Gold occurrences are most abundant within c. 3 km of both newly formed and reactivated NE-SW faults, most importantly P1 and Y2 faults, in areas of low-intermediate fault density, and close to lithological contacts within the greenstone sequences.
Optical measurements based on practical methods for detecting time-wise morphing structures

Nowadays non-contact measurement methods have become widely used systems in several fields especially robotics, aerospace, architecture, and cultural heritage. Practical devices, taken from mass markets, are increasingly being used in scientific and engineering research fields thanks to their ability to combine good accuracy with to the low-cost and ready-to-use experimental setup. In the present paper, digital image analysis (based on digital camera devices) and three-dimensional scanning technique (based on Kinect I and Kinect II sensors) are compared to evaluate their performance in detecting a time-wise shape modification. Digital camera and Kinect sensors are used to the non-contact detection of a morphing blade able to modify its geometry according to airflow temperature variation. The comparison showed the capability of the digital image technique to provide quantitative information when a proper alignment is adopted, while the three-dimensional scanning process allows the continuous blade detection useful to quantify the shape modification. Two-dimensional and three-dimensional blade shape reconstruction processes are also discussed.
Bootstrap clustering approaches for organization of data: Application in improving grade separability in cervical neoplasia

This study introduces a novel technique for the self-organization of large datasets, without prior knowledge on the statistical distribution of data. The particular application of interest concerns the self-organization of diffuse reflectance time-lapse curves, expressing the biomarker uptake-wash out kinetics in cervical epithelium. Dynamic spectral imaging generates one curve per pixel resulting to about 700.000 curves per person examined. It comprises an established technology for the non-invasive diagnosis of precancerous lesions, since various curve profiles represent distinct neoplasia grades. The methodology developed in this study constitutes an effective automatic clustering approach improving the discrimination ability between a number of precancerous and non-precancerous abnormalities. The automatic clustering of such a large number of curves is expected to facilitate early diagnosis and prognosis, as well as to assist the management of cervical neoplasia. The effectiveness of the proposed approach stems from the incorporation of data bootstrapping within the clustering approach and the adoption of appropriate distance metrics for assessing class coherence. Each bootstrap step derives a set of cluster centroids, which are then regrouped into active class centers based on a meta-data clustering step. The proposed methodology searches for hidden characteristics within the processed dataset and reveals additional data structures or subclasses that can be utilized for identifying irregular groups, which are of particular importance in disease modeling and management. More specifically, a hidden class was revealed in cervical neoplasia with significant confidence indicated by the metrics of Silhouette, Calonski Harabasz and Dunn´s indices, standard deviation of minimum distance metrics. The results of this study show that appropriate bootstrap extensions of simple clustering schemes can effectively organize large time-series data, giving rise to exploratory approaches for subclass identification that facilitate accurate and early disease diagnosis.
Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods.
Inpainting of Wide-Baseline Multiple Viewpoint Video

We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MVV) with no temporal look-ahead in near real time speed. A Dictionary of Patches (DoP) is built using multi-resolution texture patches reprojected from geometric proxies available in the alternate views. We dynamically update the DoP over time, and a Markov Random Field optimisation over depth and appearance is used to resolve and align a selection of multiple candidates for a given patch, this ensures the inpainting of large regions in a plausible manner conserving both spatial and temporal coherence. We demonstrate the removal of large objects (e.g., people) on challenging indoor and outdoor MVV exhibiting cluttered, dynamic backgrounds and moving cameras.
New quadratic bent functions in polynomial forms with coefficients in extension fields

In this paper, we first discuss the bentness of a large class of quadratic Boolean functions in polynomial form $$f(x)=\sum _{i=1}^{{n}/{2}-1}\mathrm {Tr}^n_1(c_ix^{1+2^i})+ \mathrm {Tr}_1^{n/2}(c_{n/2}x^{1+2^{n/2}})$$ , where n is even, $$c_i\in \mathrm {GF}(2^n)$$ for $$1\le i \le {n}/{2}-1$$ and $$c_{n/2}\in \mathrm {GF}(2^{n/2})$$ . The bentness of these functions can be connected with linearized permutation polynomials. Hence, methods for constructing quadratic bent functions are given. Further, we consider a subclass of quadratic Boolean functions of the form $$f(x)=\sum _{i=1}^{{m}/{2}-1}\mathrm {Tr}^n_1(c_ix^{1+2^{ei}})+ \mathrm {Tr}_1^{n/2}(c_{m/2}x^{1+2^{n/2}})$$ , where $$n=em$$ , m is even, and $$c_i\in \mathrm {GF}(2^e)$$ . The bentness of these functions is characterized and some methods for deriving new quadratic bent functions are given. Finally, when m and e satisfy some conditions, we determine the number of these quadratic bent functions.
Semi-supervised learning using autodidactic interpolation on sparse representation-based multiple one-dimensional embedding

In this paper, we present a novel semi-supervised classification method based on sparse representation (SR) and multiple one-dimensional embedding-based adaptive interpolation (M1DEI). The main idea of M1DEI is to embed the data into multiple one-dimensional (1D) manifolds satisfying that the connected samples have shortest distance. In this way, the problem of high-dimensional data classification is transformed into a 1D classification problem. By alternating interpolation and averaging on the multiple 1D manifolds, the labeled sample set of the data can enlarge gradually. Obviously, proper metric facilitates more accurate embedding and further helps improve the classification performance. We develop a SR-based metric, which measures the affinity between samples more accurately than the common Euclidean distance. The experimental results on several databases show the effectiveness of the improvement.
Adaptively feature learning for effective power defense

Active defense technology is very important in intelligent systems and video surveillance. In some important fields, active defense system can effectively find intruders. Many intelligent video surveillance systems were proposed in recent years. They achieved good performance to some extent. Since power station is an important field, it is important to develop an intelligent video surveillance. Considering that detecting the whole surveillance video is time consumption and computation. So in this paper, we propose an active defense system to find intruders automatically. First, a key frame selection algorithm based on adaptive features is presented to select key frames. These key frames can cover the main content of videos and detecting these key frames can also reduce time consumption and computation. Then, a probabilistic model is proposed to learn the training data distribution. Finally, our system can achieve active defense based on probabilistic model. Experimental results show that our active defense system can achieve finding intruders effectively.
Personalized Social Query Expansion Using Social Annotations

Query expansion is a query pre-processing technique that adds to a given query, terms that are likely to occur in relevant documents in order to improve information retrieval accuracy. A key problem to solve is “how to identify the terms to be added to a query?” While considering social tagging systems as a data source, we propose an approach that selects terms based on (i) the semantic similarity between tags composing a query, (ii) a social proximity between the query and the user for a personalized expansion, and (iii) a strategy for expanding, on the fly, user queries. We demonstrate the effectiveness of our approach by an intensive evaluation on three large public datasets crawled from delicious, Flickr, and CiteULike. We show that the expanded queries built by our method provide more accurate results as compared to the initial queries, by increasing the MAP in a range of 10 to 16% on the three datasets. We also compare our method to three state of the art baselines, and we show that our query expansion method allows significant improvement in the MAP, with a boost in a range between 5 to 18%.
A novel adaptive image zooming method based on nonlocal Cahn–Hilliard equation

Recently, nonlocal operator has become a very hot topic in image processing. For nonlocal partial differential equations (PDEs) methods, almost existed models are based on second order. In this paper, a novel image zooming method based on nonlocal fourth-order PDEs is proposed. The proposed method comprehensively utilizes the characteristic of the higher order PDEs-Cahn–Hilliard equation and the nonlocal operator. Higher order PDEs can well alleviate blocking artifacts and decrease the influence of the noise, and the nonlocal operator has a good performance on texture preserving and can reduce Gaussian noise. In the proposed model, a control parameter is introduced, which can control the speed of the model diffusion. Experimental results show that the proposed method has a good ability for de-noising and texture preserving.
Research of Ethereum Mining Hash Rate Dependency on GPU Hardware Settings

Cryptocurrency mining with GPUs for profit has a fine edge of finding the best rate for mining power versus energy consumption. In this research we explore different GPU settings, namely memory clock and core voltage influence on mining performance measured as hash rate. Additionally, we look for opportunities to lower power consumption. Experiment using combined power of five commonly available graphics cards showed improvement of hash rate with increased memory clock frequencies, and lower power consumption with decreased core voltages. However, these dependencies are not linear, and some other factors, like different memory chips on otherwise similar graphics card models may give contradicting results.
Hidden Node Detection between Observable Nodes Based on Bayesian Clustering

Structure learning is one of the main concerns in studies of Bayesian networks. In the present paper, we consider networks consisting of both observable and hidden nodes, and propose a method to investigate the existence of a hidden node between observable nodes, where all nodes are discrete. This corresponds to the model selection problem between the networks with and without the middle hidden node. When the network includes a hidden node, it has been known that there are singularities in the parameter space, and the Fisher information matrix is not positive definite. Then, the many conventional criteria for structure learning based on the Laplace approximation do not work. The proposed method is based on Bayesian clustering, and its asymptotic property justifies the result; the redundant labels are eliminated and the simplest structure is detected even if there are singularities.
High-Fidelity Prototyping for Mobile Electronic Data Collection Forms Through Design and User Evaluation

Mobile data collection systems are often difficult to use for nontechnical or novice users. This can be attributed to the fact that developers of such tools do not adequately involve end users in the design and development of product features and functions, which often creates interaction challenges.The main objective of this study was to assess the guidelines for form design using high-fidelity prototypes developed based on end-user preferences. We also sought to investigate the association between the results from the System Usability Scale (SUS) and those from the Study Tailored Evaluation Questionnaire (STEQ) after the evaluation. In addition, we sought to recommend some practical guidelines for the implementation of the group testing approach particularly in low-resource settings during mobile form design.We developed a Web-based high-fidelity prototype using Axure RP 8. A total of 30 research assistants (RAs) evaluated this prototype in March 2018 by completing the given tasks during 1 common session. An STEQ comprising 13 affirmative statements and the commonly used and validated SUS were administered to evaluate the usability and user experience after interaction with the prototype. The STEQ evaluation was summarized using frequencies in an Excel sheet while the SUS scores were calculated based on whether the statement was positive (user selection minus 1) or negative (5 minus user selection). These were summed up and the score contributions multiplied by 2.5 to give the overall form usability from each participant.Of the RAs, 80% (24/30) appreciated the form progress indication, found the form navigation easy, and were satisfied with the error messages. The results gave a SUS average score of 70.4 (SD 11.7), which is above the recommended average SUS score of 68, meaning that the usability of the prototype was above average. The scores from the STEQ, on the other hand, indicated a 70% (21/30) level of agreement with the affirmative evaluation statements. The results from the 2 instruments indicated a fair level of user satisfaction and a strong positive association as shown by the Pearson correlation value of .623 (P<.01).A high-fidelity prototype was used to give the users experience with a product they would likely use in their work. Group testing was done because of scarcity of resources such as costs and time involved especially in low-income countries. If embraced, this approach could help assess user needs of the diverse user groups. With proper preparation and the right infrastructure at an affordable cost, usability testing could lead to the development of highly usable forms. The study thus makes recommendations on the practical guidelines for the implementation of the group testing approach particularly in low-resource settings during mobile form design.
Japanese Legal Term Correction using Random Forest

We propose a method that assists legislation drafters in finding inappropriate use of Japanese legal terms and their corrections from Japanese statutory sentences.In particular, we focus on sets of similar legal terms whose usages are strictly defined in legislation drafting rules that have been established over the years.In this paper, we first define input and output of legal term correction task.We regard it as a special case of sentence completion test with multiple choices.Next, we describe a legal term correction method for Japanese statutory sentences.Our method predicts suitable legal terms using Random Forest classifiers.The classifiers in our method use adjacent words to a target legal term as input features, and are optimized in various parameters including the number of adjacent words to be used for each legal term set.We conduct an experiment using actual statutory sentences from 3,983 existing acts and cabinet orders that consist of approximately 47M words in total.As for legal term sets, we pick 27 sets from legislation drafting manuals.The experimental result shows that our method outperformed existing modern word prediction methods using neural language models and that each Random Forest classifier utilizes characteristics of its corresponding legal term set.
Design of an Image Skeletonization Based Algorithm for Overcrowd Detection in Smart Building

Crowd analysis has found its significance in varied applications from security purposes to commercial use. This proposed algorithm aims at contour extraction from skeleton of the foreground image for identifying and counting people and for providing crowd alert in the given scene. The proposed algorithm is also compared with other conventional algorithms like HoG with SVM classifier, Haar cascade and Morphological Operator. Experimental results show that the proposed method aids better crowd analysis than the other three algorithms on varied datasets with varied illumination and varied concentration of people.
A New Design Guideline for Mobile Learning Application: Transactional Distance Perspective

M-learning is the approach of using mobile device to help users engage with learning content. M-learning software exists, but does not have high uptake. This could be the lack of a model that inherited the design guidelines from learning theories. One such learning theory relevant to mobile learning is Transactional Distance Theory (TDT). TDT identifies a number of key aspects and the concept of transactional distance. The research contributes an analysis of how TDT guides the design of mobile learning application. This research also contributes an evaluation to access proof-of-concept mobile learning prototype on their ability to engage learners.
Texture descriptors for representing feature vectors

Pattern representation affects classification performance. Although discovering "universal" features that work for many classification problems is ideal, most representations are problem specific. In this paper, we improve the classification performance of a classifier system by transforming a one-dimensional input descriptor into a two-dimensional space so that effective texture extractors can be extracted to capture hidden data information. We develop two new methods for matrix representation where features are extracted that are more generalizable. The first method for generating a two-dimensional representation of patterns is based on trees, the objective being to reshape the feature vector into a matrix, and the second method performs mathematical operations to build a matrix representation. The proposed framework is then evaluated for its specific power on three medical problems. To evaluate generalizability, we compare the proposed approaches with several other baseline methods across some well-known benchmark datasets that reflect a diversity of classification problems. Not only does our approach show high robustness, but it also exhibits low sensitivity to parameters. When different approaches for transforming a vector into a matrix are combined with several texture descriptors, the resulting system often works well without requiring any ad-hoc optimization. The performance of the tested systems is compared by Wilcoxon signed rank test and Friedman's test.
Image Complexity Guided Network Compression for Biomedical Image Segmentation

Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we map the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to ≈95% of the full-sized network segmentation accuracy, and at the same time, utilizing ≈32x fewer network trainable weights (average reduction) of the full-sized networks.
Information hiding scheme based on optical moiré-pixel matrix

An information hiding scheme for visual cryptography based on optical moiré-pixel matrix is introduced in this paper. The hiding information is formed by black and white unit square visual moiré-pixels with half of the period of a visual moiré pattern. The moiré-pixels can be accurately determined by designing the parameters in the original gratings. To verify the designed information hiding scheme, we experimentally conduct the sharing gratings to decrypt the information by using ordinary low-cost digital printing. The concealment of the sharing gratings is good to ensure the confidentiality. Both the simulation and experimental results show that the provoked scheme can afford enough security demand, yielding that the moiré-pixel matrix is a flexible platform for cryptographic security.