Reviewerly/exHarmony huggingface dataset:
An innovative approach based on hyperspectral imaging (HSI) combined with chemometrics for soil phytoremediation monitoring

An innovative approach, based on hyperspectral imaging (HSI) coupled with chemometrics, allowing the detection of arsenic (As) in the hyper-accumulator fern Pteris Vittata L., is presented in this study. The aim of this work was to investigate the possibility of monitoring by HSI the As sequestration capacity of plants grown on As-contaminated soils, in order to perform soil remediation. The proposed approach is based on the acquisition by HSI in the SWIR range (1000-2500 nm) of fern leaves, followed by the implementation of a classification model based on Partial Least Square Discriminant Analysis (PLS-DA). Following this procedure, false color maps, representative of the chemical elements distribution on the leaves were obtained, where As is clearly detected without performing any chemical analysis. The proposed approach is not invasive and not destructive. Comparative evaluations were carried out analyzing Pteris Vittata L. leaves collected from plants grown on natural soils containing different As concentrations. To evaluate reliability, robustness and analytical correctness of the proposed HSI approach, micro X-ray fluorescence (μXRF) analyses were carried out on the same samples in order to quantitatively and topologically assess As presence in the leaves of the plants. The achieved results are very promising for monitoring the phytoremediation process by detecting and controlling the uptake of As plants growing on contaminated soils.
Effect of Image Distortion on Facial Age and Gender Classification Performance of Convolutional Neural Networks

Significant improvement in the task of age group categorization and gender classification of facial images has been achieved using deep convolution neural networks (CNN). In this paper, we study the effect of image distortions such as blur, noise, rotation and occlusion on the performance of a state-of-the-art CNN. We found that the CNN was more sensitive to noise compared to blurring, especially for age estimation. By studying occlusion, we also identified the salient regions of the face. An interesting result is that the upper half of the face is more important for age estimation, while for gender classification it is the lower half. These insights should prove useful for future development of CNN models for facial age and gender classification.
Effect of Reverberation Phenomena on Text- independent Speaker Recognition Based Deep Learning

Speaker recognition is one of many biometric authentications, due to its high importance in many applications of security considerations and telecommunications. The main aspiration of speaker recognition system is to know who is speaking depending on voice characteristics. Many current researches focuses on text-dependent speaker recognition which has a pre-knowledge of what utterance the speaker will say. In this paper text-independent speaker recognition system is used, where no prior knowledge is accessible in the context of the speakers’ utterances for all stages. A Convolutional Neural Network (CNN) based feature extraction is extended to a text-independent Speaker recognition task. Also the effect of reverberation on speaker recognition is addressed. All the speech signals are converted into images by obtaining their spectrograms. A proposed CNN model is presented to enhance the performance of the system in case of a reverberant signal. It depends on image processing concepts, and hence spectrograms of signals are used. The proposed model is compared with a conventional benchmark model. The performance of the recognition system is measured by the recognition rate in the case of clean and reverberant data.
Determining Components of Medical Instructional Design based on Virtual Reality by Research Synthesis

Introduction: The purpose of the present study was to determine the components of medical education design based on virtual reality by research synthesis method.&#x0D; Methods: In the present study a synthesis method was used. In order to study the research background and to collect appropriate data among the databases of Science Direct, Springer, Scopus, ProQuest and Eric (ERIC) search. With the keywords of Educational Design, Virtual Reality, Medical Education, Augmented Reality, Research Synthesis identified from 150 external research cases (English language) between 2006 and 2019 and finally 20 of them with the most coordination and proportion were selected for evaluation.&#x0D; Result: By reviewing articles and themes analysis, medical education design has five main components that are as follows: 1- Analytical component including determining educational needs, defining learner characteristics, defining theoretical framework, defining characteristics of virtual reality technology, forming a corporate team. Designer, conduct feasibility study and determine the type of content suitable for presentation through virtual reality. Design component includes defining general goals, targeting goals that are achievable by virtual reality, defining features of a virtual reality-based learning environment (free routing, first-person, third-person vision), determining the type of interaction (visual, photo, touch). Virtual and immersion, defining educational strategies (small and large), determining the level of reality, determining the content and organizing it (navigation) 3. Component of production and production including: scenario design, user interface design, content design, design Avatar, Training Agent Design, Virtual Reality Modeling, Platform Development (Hardware and Software) 4. Support and Implementation including Training Implementation, System Integration Virtual education in curriculum, support (technical and educational) 5. Evaluation component including formative evaluation, aggregate evaluation, ease of use evaluation, small group evaluation, large group evaluation, virtual reality based learning environment and platform evaluation (software and hardware).&#x0D; Conclusion: With respect to that the components of medical education design based on virtual reality have been identified, it is recommended that the medical education process be organized according to the components of educational design and that educational principles and processes be designed according to the capacity of virtual reality technology.
Balancedness and Alignment are Unlikely in Linear Neural Networks.

We study the properties of alignment, a form of implicit regularization, in linear neural networks under gradient descent. We define alignment for fully connected networks with multidimensional outputs and show that it is a natural extension of alignment in networks with 1-dimensional outputs as defined by Ji and Telgarsky, 2018. While in fully connected networks, there always exists a global minimum corresponding to an aligned solution, we analyze alignment as it relates to the training process. Namely, we characterize when alignment is an invariant of training under gradient descent by providing necessary and sufficient conditions for this invariant to hold. In such settings, the dynamics of gradient descent simplify, thereby allowing us to provide an explicit learning rate under which the network converges linearly to a global minimum. We then analyze networks with layer constraints such as convolutional networks. In this setting, we prove that gradient descent is equivalent to projected gradient descent, and that alignment is impossible with sufficiently large datasets.
Robust Pupil Localization Algorithm under Off-axial Pupil Occlusion

The off-axial pupil is one of several challenges in pupil localization during real-time eye tracking. The off-axial occlusion is a laying condition of a head-mounted eye tracker that is not in a straight line towards the eye. Thus, this occlusion causes the pupil to be recorded in an elliptical shape instead of a perfect circular shape. In the case of off-axial occlusion, pupil localization failure is not only caused by a shifting angle that is too large but also a pupil that is occluded by eyelashes and glints. To solve this problem, this study proposes an ellipse fitting and a fine tuning-based algorithm for robust pupil localization under off-axial condition. The proposed algorithm was tested on 600 labelled images taken from the Swirski dataset. The performance of the proposed algorithm was compared with the modified Circular Hough Transform (CHT) algorithm. The result shows that the proposed algorithm produces a significant increase of 0.83 accuracy with a Z-score of 41. S5(p<0.05). In future, this study may be used as a reference for research in the field of pupil localization and eye tracking.
I like to move it, move it: Package for social scientists who want to use OpenPose

Package with OpenPose for dummies manual, R-script to transform OpenPose output into correctly ordered csv-file, poster presentation, and demo-video that can be used to try OpenPose.
Automated Data-Processing Function Identification Using Deep Neural Network

The number of software vulnerabilities is increasing year by year. In the era of big data, data-processing software with many users is more concerned by hackers. It is essential to improve the efficiency of discovering vulnerabilities in data-processing software. We noticed that in the process of discovering vulnerabilities, some problems of existing technology such as fuzzing, symbolic execution, and taint analysis have more or fewer relationships with data-processing functions. In fuzzing, there are two types of sanity checks toward the target program: NCC (Non-critical check) and CC (critical check). It is usually challenging to bypass such a sanity check, which leads to low code coverage during fuzzing. In symbolic execution, the constraint solver still has the problem of trying to deal with the constraints of complex algorithms. In taint analysis, the problem of over-taint and under-taint is always the key to affect the accuracy of the results. Therefore, to solve the above problems, it is necessary to identify the data-processing function. Based on identifying data-processing functions, we could identify those sanity checks, ease the solution of complex constraints, and understand the way of taints propagation to assist in software vulnerability discovery and analysis. This paper proposed a method called DPFI(data-processing function identification) for identifying data-processing functions with deep neural networks. We collected 37000 functions from GitHub and implemented the method on the data set with several neural networks, among which the performance of CNN achieved best and F1-score was 0.90. We then applied the trained model on CGC(cyber grand challenge) data and real softwares for testing. For CGC, we got 448 functions in 20 programs, in which 35 were identified as data-processing functions. For real softwares, such as FFmpeg, 7zip, jpeg, the precision rate all reached 0.90 and F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score was above 0.87.
Performance analysis of intrinsic embedded evolvable hardware using memetic and genetic algorithms

This paper discusses the performance analysis of memetic and genetic algorithms (GA and MA) as the optimising strategy for the design of embedded evolvable hardware. The optimisation algorithm with the fitness evaluation searches for the best configuration to evolve the hardware model. Here, an experimental setup is carried to intrinsically evolve combinational circuits to test the performance of MA and GA. The complete evaluation and evolution is built on a single Virtex 6 (XC6VLX240T-1FFG1156) ML605 Evaluation Kit FPGA. A virtual reconfigurable architecture (VRA) with the hardware fitness circuit is modelled as a second reconfigurable layer over the field programmable gate array (FPGA) to configure the target combinational logic. A FPGA soft core processor evaluates the search algorithm and the best solutions are utilised for the hardware evolution. The experimentation results showed that convergence and evolution time of MA was faster compared to GA when the search space was large. Thus, proving MA is a better option for large search space evaluations for evolvable hardware architectures.
Depth Estimation Using Optical Flow and CNN for the NAO Robot

In the robotics field, one of the main challenges is autonomous navigation using a single camera where camera images are processed in a frame to frame basis.However, getting clear and noise-free images is still a challenge under erratic motion, typical of moving robots.To solve this problem, several works use optical flow techniques to eliminate blurred reference points in RGB images when the robot moves.The NAO robot is an example of a robotic platform that generates an oscillatory movement when walking, thus producing blurred images that may compromise the image processing task.In this work, we focus on the problem of depth estimation in a single image for the NAO robot, which proves useful for autonomous navigation.For the depth estimation, we argue that the erratic movement exhibited by the walking motion of the robot could be exploited to obtain optical flow vectors, which are strongly related to depth observed by the NAO's camera.Thus, we present a real-time system based on a Convolutional Neural Network (CNN) architecture that uses optical flow as input channels in order to estimate depth.To this aim, we present a new dataset that includes optical flow images associated to depth images for training.Our results indicate that optical flow can be exploited in humanoid robots such as NAO, but we are confident that it could be used in other platforms with erratic motion.
Technology in Education: Learning Opportunities for Teachers and Students

The purpose of using technology in education today is to provide students with an opportunity to learn a given topic at their own pace and convenience. Integrating technology into education is of considerable value because using technology effectively has the potential to make learning meaningful (Kirschner, 2015). Within a decade, technology has transformed education by affecting (a) the method of delivering course content to students, (b) student engagement with course content outside class hours, (c) the capability of a teacher to create different learning opportunities for students of diverse learning styles, (d) and the ability to convert course information into knowledge bytes for students of varied educational backgrounds and abilities. In this short span of time, the purpose of using technology in education has shifted from merely delivering course content to aiding students in learning the content. Using technology in education is more like "the idea that you can learn something without directly learning it" (Henriksen et al., 2019, p. 86). In other words, even though technological competence may not be the objective of a course, learning the technology will certainly help in achieving the objective of the course.
Deep Learning: Exemplar Studies in Natural Language Processing and Computer Vision

Deep learning has become the most popular approach in machine learning in recent years. The reason lies in considerably high accuracies obtained by deep learning methods in many tasks especially with textual and visual data. In fact, natural language processing (NLP) and computer vision are the two research areas that deep learning has demonstrated its impact at utmost level. This chapter will firstly summarize the historical evolution of deep neural networks and their fundamental working principles. After briefly introducing the natural language processing and computer vision research areas, it will explain how exactly deep learning is used to solve the problems in these two areas. Several examples regarding the common tasks of these research areas and some discussion are also provided.
Audio-Lingual Method To Improve Students’s Speaking Skill At SMP Negeri 1 Baranti

The objective of this research was to find out whether or not the use of the Audio-Lingual method to improve students’ speaking skill at the eighth-grade SMP Negeri 1 Baranti.This research used the experimental method. The population of this study was one class of class VIII.1 of SMP Negeri 1 Baranti were 23 students. The sample of this study was 23 students. This sample was taken using the cluster random sampling technique. This research applied the audio-lingual method in the class. This research applied one type of speaking skill testing to determine students' speaking skill.The researcher found that the use of the audio-lingual method in teaching speaking, it could improve the speaking skill of the grade VIII.1 students of SMP Negeri 1 Baranti. The mean score of pre-test (54,30) was lower than mean score of post-test (57,04), showed that the p-value was lower than the α (0,000 &lt; 0,05) on the significant level (α=0,05). So, the researcher stated that H0 was rejected and H1 was accepted. Based on data analysis, the researcher concluded that audio-lingual method was effective in improving students speaking skill of class VIII.1 of SMP Negeri 1 Baranti
Effective implementations of topic modeling algorithms

Topic modeling is an area of natural language processing that has been actively developed in the last 15 years. A probabilistic topic model extracts a set of hidden topics from a collection of text documents. It defines each topic by a probability distribution over words and describes each document with a probability distribution over topics. The exploding volume of text data motivates the community to constantly upgrade topic modeling algorithms for multiprocessor systems. In this paper, we provide an overview of effective EM-like algorithms for learning latent Dirichlet allocation (LDA) and additively regularized topic models (ARTM). Firstly, we review 11 techniques for efficient topic modeling based on synchronous and asynchronous parallel computing, distributed data storage, streaming, batch processing, RAM optimization, and fault tolerance improvements. Secondly, we review 14 effective implementations of topic modeling algorithms proposed in the literature over the past 10 years, which use different combinations of the techniques above. Their comparison shows the lack of a perfect universal solution. All improvements described are applicable to all kinds of topic modeling algorithms: PLSA, LDA, MAP, VB, GS, and ARTM.
Eurachem Week 2019 at Tartu (Estonia)

The Eurachem Week 2019 took place from May 20th to 24th, at Tartu, Estonia. The hanseatic city is home of the nation's oldest and most renowned university, the University of Tartu. It is often considered the intellectual centre of the country. Each year a member state of Eurachem organizes the event. The organizers propose a topic for the two-day workshop from May 20th to May 21st. The Estonian colleagues had chosen 'Validation of targeted and non-targeted methods of analysis' as the topic of the workshop. On May 22nd to May 24th the Eurachem working groups, the Executive Committee and the General Assembly of the delegates of the member countries held their meetings.
Diagnosis of Breast Cancer using Hybrid Transfer Learning

Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.
The Concept of Money in the 4th Industrial Revolution – A Legal and Economic Analysis

This article explores some of the changes that the 4th Industrial Revolution brings to our understanding of money. Our analysis does not suggest that the only valid form of money is that provided or backed by the state. We rather argue that it is unlikely that money-like means of payment will prove sustainable in the long-term if not perceived as being vested with some form of legality. Still, mere legality will not prove to be sufficient for the new payment instruments to qualify as money. They must also prove to be able to serve as means of exchange/payment. A sharp reduction in value will diminish the credibility of the payment promise and thus user confidence/trust. Like acceptance of payment on sight, the use of money as a common measure of value is one of the most important properties of fiat (and metallic) money. Retention of value in times of stress is fundamental as regards the new assets’ ability to act as a measure of value and its ability to fit with common perceptions of money. The requisite enquiry should be based on empirical studies of the intertemporal behaviour of the instrument. We suggest that fiat money aside, instruments that could eventually qualify as “money” ought to pass the dual test of legality and relative retention of value. This approach does not suggest a return to the metallic rule, which would limit free circulation of money. It is rather a pragmatic reformulation of the characteristics that means of payment, which do not enjoy the backing (will) of the state, must exhibit to enjoy quasi-money or money-like status. Assets that display high volatility are, thus, unlikely to fulfil the functions of “money” and should instead be dealt under the law of investments if they qualify as such.
Dynamic Virtual Machine Number Selection for Processing-Capacity Constrained Workflow Scheduling in Cloud Computing Environments

Cloud computing service providers offer their users the flexibility of selecting the processing capacity that is affordable. The selected processing capacity is often used to execute a workflow. The makespan of the user's workflow depends on the structure and size of the workflow and the number of virtual machines (VMs) that share the selected processing capacity. However, determining the number of VMs used for executing the workflow under user's quality of services (QoS) is still a big challenge. In this paper, we proposed an approach with four variants to find the number of VMs required for each workflow, and assist in splitting the selected processing capacity onto the VMs for makespan and utilization optimization. The evaluation results show that the proposed approach can help in finding a good trade off between the makespan, utilization and VM set size.