Reviewerly/exHarmony huggingface dataset:
Quantitative micro‐X‐ray fluorescence scanning spectroscopy of wet sediment based on the X‐ray absorption and emission theories: Its application to freshwater lake sedimentary sequences

Abstract Micro‐X‐ray fluorescence scanning spectroscopy of marine and lake sedimentary sequences can provide detailed palaeoenvironmental records through element intensity proxy data. However, problems with the effects of interstitial pore water on the micro‐X‐ray fluorescence intensities have been pointed out. This is because the X‐ray fluorescence intensities are measured directly at the surfaces of split wet sediment core samples. This study developed a new method for correcting X‐ray fluorescence data to compensate for the effects of pore water using a scanning X‐ray analytical microscope. This involved simultaneous use of micro‐X‐ray fluorescence scanning spectroscopy and an X‐ray transmission detector. To evaluate the interstitial pore water content from the X‐ray transmission intensities, a fine‐grained sediment core retrieved from Lake Baikal ( VER 99‐G12) was used to prepare resin‐embedded samples with smooth surfaces and uniform thickness. Simple linear regression between the linear absorption coefficients of the samples and their porosity, based on the Lambert–Beer law, enabled calculation of the interstitial pore spaces and their resin content with high reproducibility. The X‐ray fluorescence intensities of resin‐embedded samples were reduced compared with those of dry sediment samples because of: (i) the X‐ray fluorescence absorption of resin within sediment; and (ii) the sediment dilution effects by resin. An improved micro‐X‐ray fluorescence correction equation based on X‐ray fluorescence emission theory considers the instrument's sensitivity to each element, which provides a reasonable explanation of these two effects. The resin‐corrected X‐ray fluorescence intensity was then successfully converted to elemental concentrations using simple linear regression between the data from micro‐X‐ray fluorescence scanning spectroscopy and from the conventional analyzer. In particular, the calculated concentration of SiO 2 over the depth of the core, reflecting diatom/biogenic silica concentration, was significantly changed by the calibrations, from a progressively decreasing trend to an increasing trend towards the top of the core.
Compact stereoscopic prism-based optical system with an improved accuracy of 3-D geometrical measurements

We address the problem of non-contact geometrical measurements of hard-to-reach objects. For this purpose, we have developed a compact prism-based system for simultaneous acquisition of stereoscopic pair of images using one high-resolution sensor. For a correct mathematical description of this system, we propose to use a ray-tracing camera model based on a vector form of Snell’s law. The procedures of geometrical calibration, calculation of three-dimensional (3-D) point coordinates and surface shape reconstruction are described. The experiments confirm that the developed optical system has a high image quality and the software provides higher precision of 3-D geometrical measurements. Proposed solution may be useful for machine vision, endoscopy and other applications.
Quadri-Histogram Equalization for infrared images using cut-off limits based on the size of each histogram

In infrared images, the pixels representing the objects are hidden in a large number of background pixels with low contrast. Several effective contrast enhancement techniques exist in the state of the art today, however they cause the noise level added to the images to increase. The improvement of contrast is an indispensable procedure for the analysis of infrared images, due to the scarce temperature difference between the objects and the background, captured by the surveillance systems using infrared sensors. Therefore, a contrast enhancement algorithm for infrared imaging based on histogram equalization using clipping is presented in this article. The proposed algorithm divides the histogram into 4 subhistograms, then each subhistogram is modified with a cut limit based on the size of the subhistogram in order to limit the improvement of the contrast. The experimental results prove that the algorithm improves the contrast of infrared images by 99%, especially the contrast between the objects and the background of the infrared images preserving the mean brightness and decreasing the aggregate noise level of them. With the proposed algorithm, the background of the infrared image is restricted while the objects are visually contrasted.
A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.
Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN

Facial micro-expressions, which usually last only for a fraction of a second, are challenging to detect by the human eye or machine. They are useful for understanding the genuine emotional state of a human face, and have various applications in education, medical, surveillance and legal sectors. Existing works on micro-expressions are focused on binary classification of the micro-expressions. However, detecting the micro-expression intensity changes over the spanning time, i.e., the micro-expression profiling, is not addressed in the literature. In this paper, we present a novel deep Convolutional Neural Network (CNN) based hybrid framework for micro-expression intensity change detection together with an image pre-processing technique. The two components of our hybrid framework, namely a micro-expression stage classifier, and an intensity estimator, are designed using a 3D and 2D shallow deep CNNs respectively. Moreover, we propose a fusion mechanism to improve the micro-expression intensity classification accuracy. Evaluation using the recent benchmark micro-expression datasets; CASME, CASME II and SAMM, demonstrates that our hybrid framework can accurately classify the various intensity levels of each micro-expression. Further, comparison with the state-of-the-art methods reveals the superiority of our hybrid approach in classifying the micro-expressions accurately.
Survey and analysis of human activity recognition in surveillance videos

In computer vision, recognizing human activity or behavior is a core challenging problem. This article provides a crisp study of human activity recognition systems in the area of visual surveillance. These systems are used for analysis and understanding of the human behavior. The study starts with the description of various emerging video processing domains, followed by a general process of human action recognition. Then the article covers human detection techniques from images and video. Finally, article also provides a survey of different features and models used in activity recognition systems and an overview of benchmark dataset of video surveillance. From this state-of-the-art survey, researchers can outline promising directions of research.
A Novel Movie Recommendation System Based on Collaborative Filtering and Neural Networks

Movie recommendation is one of the most common recommendation systems, and the related technologies for recommendation are constantly improving. It has evolved from traditional statistical analysis to collaborative filtering and machine learning today. This paper aims to find a set of criteria that are practical, reasonable, and accurate in using existing recommendation systems. We compare some techniques and tools and conduct experiments for popular tools currently used, especially Scikit-learn and TensorFlow. The experiments focus on the comparison of the advantages, error measure, and process time of these tools. From the experimental results, we further propose a novel recommendation system, based on collaborative filtering and neural network, which maintains lower error measure.
Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency

Cloud multi-tenancy is typically constrained to a single interactive service colocated with one or more batch, low-priority services, whose performance can be sacrificed when deemed necessary. Approximate computing applications offer the opportunity to enable tighter colocation among multiple applications whose performance is important. We present Pliant, a lightweight cloud runtime that leverages the ability of approximate computing applications to tolerate some loss in their output quality to boost the utilization of shared servers. During periods of high resource contention, Pliant employs incremental and interference-aware approximation to reduce contention in shared resources, and prevent QoS violations for co-scheduled interactive, latency-critical services. We evaluate Pliant across different interactive and approximate computing applications, and show that it preserves QoS for all co-scheduled workloads, while incurring a 2.1% loss in output quality, on average.
Recognizing metasedimentary sequences potentially hosting concealed massive sulfide accumulations in the Iberian Pyrite Belt using geochemical fingerprints

Promising advances in the development of new greenfield and brownfield exploration methods and methodologies for massive sulfide ore-systems in the Iberian Pyrite Belt (IPB) have been made over the last two decades. However, the effects of lateral and vertical facies variations in metasedimentary piles forming the two main lithostratigraphic units that potentially host massive sulfide deposits [the Phyllite-Quartzite Group (PQG) and the Volcanic-Sedimentary Complex (VSC)] on mineral exploration remain unknown. Herein we report on results of a multi-element geochemical study of 133 samples representing PQG and VSC metasedimentary rocks from the Albernoa area (≈713 km2); this dataset is complemented by whole-rock Sr, Nd and Pb isotopic data for a subset of 12 VSC metasedimentary rock samples. The prevalent siliciclastic component in the analyzed rocks derives from an evolved "felsic source" (as suggested by the Zr/Sc, Th/Sc, Eu/Eu* relationships) that, according to CIA values, should have been affected by significant weathering before they were deposited in the basin from Middle Devonian to Visean time. The siliciclastic component comprises clay and (fine-grained) sand whose relative abundance can be distinguished on the basis of Al2O3/TiO2, Sc/TiO2, Ga/TiO2, Zr/TiO2 and SiO2/Al2O3 ratios. These terrigenous components are, in places, variably mixed with volcanic-derived fractions, leading to the formation of tuffaceous metapelites typical of the VSC sequences but also present in the upper stratigraphic sections of the PQG. In general, VSC samples display the following parameters 2.5 ≤ SiO2/Al2O3 ≤ 6.5, 0.02 ≤ TiO2/Al2O3 ≤ 0.06 and 0.2 ≤ (CaO + Na2O + K2O)/Al2O3 ≤ 0.25, whereas PQG samples display 2.5 ≤ SiO2/Al2O3 ≤ 12, 0.08 ≤ TiO2/Al2O3 ≤ 0.58 and (CaO + Na2O + K2O)/Al2O3 ≤ 0.3. Fe2O3/TiO2 ratios ≥ 10 and Al2O3/(Al2O3 + Fe2O3 + MnO) ratios ≤ ≈0.6 indicate the strong influence of an early-developed (prior to Variscan metamorphism and deformation) hydrothermal component on the prevalent siliciclastic component sometimes mixed with a variable volcanic-derived fraction. Moreover, values of (As + Sb)/Sc, (Cu + Zn + Pb)/Sc and 5 × [(Fe2O3 + MgO + MnO)/Al2O3] ratios between 1 and 10 indicate the influence of seafloor metasomatism processes, and ratios ≥ 10 indicate close proximity to hydrothermal discharge potentially associated with massive sulfide mineralization. The Sr, Nd, Pb isotopic data show that VSC metasedimentary rocks are mostly derived from old, reworked, upper continental crust. Isotope ratios characterizing the main siliciclastic component are somewhat disturbed by the significant presence of volcanic-derived components and/or mineral changes due to early hydrothermal alteration/mineralization processes, which should have involved isotopically distinct fluids.
Deep Learning for Improved Agricultural Risk Management

Deep learning provides many benefits, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications -from credit card fraud detection to recommender systems and customer segmentation.Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped.In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty.We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate.Our findings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches.We infer that it also has great potential to reduce underwriting inefficiencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure.Future work aims to further evaluate its performance, from municipal area-yield, to finer-scale crop-specific producer-scale yield.
A Pedestrian Detection System Accelerated by Kernelized Proposals

When pedestrian detection (PD) is implemented on a central processing unit (CPU), performing real-time processing using a classical sliding window is difficult. Therefore, an efficient proposal generation method is required. A new generation method, named additive kernel binarized normed gradient (AKBING), is proposed herein, and this method is applied to the PD for real-time implementation on a CPU. The AKBING is based on an additive kernel support vector machine (AKSVM) and is implemented using the binarized normed gradient. The proposed PD can operate in real time because all AKSVM computations are approximated via simple atomic operations. In the suggested kernelized proposal method, the popular features and a classifier are combined, and the method is tested on a Caltech Pedestrian dataset and KITTI dataset. The experimental results show that the detection system with the proposed method improved the speed with minor degradation in detection accuracy.
Teacher-Aware Active Robot Learning

This paper investigates Active Robot Learning strategies that take into account the effort of the user in an interactive learning scenario. Most research claims that Active Learning's sample efficiency can reduce training time and therefore the effort of the human teacher. We argue that the performance driven query selection of standard Active Learning can make the job of the human teacher difficult, resulting in a decrease in training quality due to slowdowns or increased error rates. We investigate this issue by proposing a learning strategy that aims to minimize the user's workload by taking into account the flow of the questions. We compare this strategy against a standard Active Learning strategy based on uncertainty sampling and a third strategy being an hybrid of the two. After studying in simulation the validity and the behavior of these approaches, we conducted a user study where 26 subjects interacted with a NAO robot embodying the presented strategies. We reports results from both the robot's performance and the human teacher's perspectives, observing how the hybrid strategy represents a good compromise between learning performance and user's experienced workload. Based on the results, we provide recommendations on the development of Active Robot Learning strategies going beyond robot's performance.
Gossiping the Videos: An Embedding-Based Generative Adversarial Framework for Time-Sync Comments Generation

Recent years have witnessed the successful rise of the time-sync "gossiping comment", or so-called "Danmu" combined with online videos. Along this line, automatic generation of Danmus may attract users with better interactions. However, this task could be extremely challenging due to the difficulties of informal expressions and "semantic gap" between text and videos, as Danmus are usually not straightforward descriptions for the videos, but subjective and diverse expressions. To that end, in this paper, we propose a novel Embedding-based Generative Adversarial (E-GA) framework to generate time-sync video comments with "gossiping" behavior. Specifically, we first model the informal styles of comments via semantic embedding inspired by variational autoencoders (VAE), and then generate Danmus in a generatively adversarial way to deal with the gap between visual and textual content. Extensive experiments on a large-scale real-world dataset demonstrate the effectiveness of our E-GA framework.
Research on direct anonymous attestation mechanism in enterprise information management

Privacy leakage is becoming more and more serious in enterprise information management. To solve the poor security problems, by introducing short signatures into enterprise information managements, this paper proposes a novel direct anonymous attestation scheme. The novel scheme is based on a novel short signature scheme, which is applied into the member joining and signature phases. It simplifies the signing and verification process of member certificates and reduces its computing costs. Under the security hypothesis of Decision Diffie–Hellman and q-Strong Diffie-Hellman difficult problems, the novel attestation scheme meets the provable security of the trusted platform module specifications.
Term Selection for Query Expansion in Medical Cross-Lingual Information Retrieval

We present a method for automatic query expansion for cross-lingual information retrieval in the medical domain. The method employs machine translation of source-language queries into a document language and linear regression to predict the retrieval performance for each translated query when expanded with a candidate term. Candidate terms (in the document language) come from multiple sources: query translation hypotheses obtained from the machine translation system, Wikipedia articles and PubMed abstracts. Query expansion is applied only when the model predicts a score for a candidate term that exceeds a tuned threshold which allows to expand queries with strongly related terms only. Our experiments are conducted using the CLEF eHealth 2013–2015 test collection and show significant improvements in both cross-lingual and monolingual settings.
FPGA Implementation of the ECC Over GF(2 <sup>m</sup> ) for Small Embedded Applications

In this article, we propose a compact elliptic curve cryptographic core over GF(2 m ). The proposed architecture is based on the Lopez-Dahab projective point arithmetic operations. To achieve efficiency in resources usage, an iterative method that uses a ROM-based state machine is developed for the elliptic curve cryptography (ECC) point doubling and addition operations. The compact ECC core has been implemented using Virtex FPGA devices. The number of the required slices is 2,102 at 321MHz and 6,738 slices at 262MHz for different GF(2 m ). Extensive experiments were conducted to compare our solution to existing methods in the literature. Our compact core consumes less area than all previously proposed methods. It also provides an excellent performance for scalar multiplication. In addition, the ECC core is implemented in ASIC 0.18μm CMOS technology, and the results show excellent performance. Therefore, our proposed ECC core method provides a balance in terms of speed, area, and power consumption. This makes the proposed design the right choice for cryptosystems in limited-resource devices such as cell phones, IP cores of SoCs, and smart cards. Moreover, side-channel attack resistance is implemented to prevent power analysis.
CodeGazer

Navigating source code, an activity common in software development, is time consuming and in need of improvement. We present CodeGazer, a prototype for source code navigation using eye gaze for common navigation functions. These functions include actions such as "Go to Definition'' and "Find All Usages'' of an identifier, navigate to files and methods, move back and forth between visited points in code and scrolling. We present user study results showing that many users liked and even preferred the gaze-based navigation, in particular the "Go to Definition'' function. Gaze-based navigation is also holding up well in completion time when compared to traditional methods. We discuss how eye gaze can be integrated into traditional mouse & keyboard applications in order to make "look up'' tasks more natural.
Sentiment Analysis Using Out of Core Learning

Text sentiment detection for a particular language other than English is one of the challenging tasks presently. The reasons are; it needs a large dataset, language has no specific structure, one word has a different meaning, and it is hard for even human to understand the connotation of particular words. There exists several proposed architecture for detecting emotions in the Bengali language using machine learning and deep learning approaches, but they are not accurate enough to predict the perfect emotion of the sentence. And there is still no standalone architecture is available that can extract the sentiments hidden inside of a sentence in different languages. In this paper, we are proposing an abstract model that can enable sentiment analysis without any restriction of using a fixed language somewhat applicable to any language. With the use of natural language processing, we have extracted the features, and these features are then fed to different machine learning models for classification. As our main concern was to build up a general model, this model is confined to binary classification, i.e., positive and negative. Apart from this, In our system architecture, we have implemented stochastic gradient descent for optimization. So our model can be called out of core learning model where the model can be updated when new user data is inserted without training the whole model. For the evaluation of the performance of our model, we have trained the estimators against Bangla translated IMDB review dataset and calculated different evaluation metrics for our estimators. The dataset is translated into Bangla using google translator.
Shadow Removal Detection and Localization for Forensics Analysis

The recent advancements in image processing and computer vision allow realistic photo manipulations. In order to avoid the distribution of fake imagery, the image forensics community is working towards the development of image authenticity verification tools. Methods based on shadow analysis are particularly reliable since they are part of the physical integrity of the scene, thus detecting forgeries is possible whenever inconsistencies are found (e.g., shadows not coherent with the light direction). An attacker can easily delete inconsistent shadows and replace them with correctly cast shadows in order to fool forensics detectors based on physical analysis. In this paper, we propose a method to detect shadow removal done with state-of-the-art tools. The proposed method is based on a conditional generative adversarial network (cGAN) specifically trained for shadow removal detection.