Reviewerly/exHarmony huggingface dataset:
Handwriting posture prediction based on unsupervised model

Writing is an important basic skill for humans. To acquire such a skill, pupils often have to practice writing for several hours each day. However, different pupils usually possess distinct writing postures. Bad postures not only affect the speed and quality of writing, but also severely harm the healthy development of pupils' spine and eyesight. Therefore, it is of key importance to identify or predict pupils' writing postures and accordingly correct bad ones. In this paper, we formulate the problem of handwriting posture prediction for the first time. Further, we propose a neural network constructed with small convolution kernels to extract features from handwriting, and incorporate unsupervised learning and handwriting data analysis to predict writing postures. Extensive experiments reveal that our approach achieves an accuracy rate of 93.3%, which is significantly higher than the 76.67% accuracy of human experts.
How Hiring Baby Boomers Can Assist with the Global Cybersecurity Employee Shortage

There is a global cyber employee talent shortage that is significant. Despite increases in tech spending, this imbalance between supply and demand of skilled information security professionals continues to leave companies vulnerable security breaches and cybercrime. Unfortunately, the pipeline of cyber and information security employees lacks numbers and innovation on how to address the shortage. This article attempts to provide innovative solutions that can assist in addressing this global shortage.
Poster

Generation of secure random numbers has always been a challenging issue in design and development of secure computer systems. Random numbers have important applications in the field of cryptography where the security of the scheme relies upon the random nature of the keys. It is not practically possible to achieve true randomness in a machine, and hence we rely upon Pseudo Random Number Generators (PRNGs) to produce near-true randomness. PRNGs use a mathematical function that relies upon a seed (a preset value required by the function to generate values) and it generates numbers which satisfy certain tests for randomness and appear to be random for a user having no knowledge of the generator function. These pseudorandom functions have their drawbacks due to them being derived from a mathematical function. To generate random numbers that can never be predicted by any observer, requires a causally non-deterministic process where events are not fully determined by prior states. Due to the physical impossibility of acquiring sufficient information to predict the outcome of such an event, its outcomes are guaranteed to be random to all. Various methods to generate pseudorandomness have been employed over the years which includes using mathematical functions, keyboard typing latency of the user, network latency, memory latency etc. as sources of generating random numbers. In this work, we propose a new way of generating pseudorandom numbers using generative adversarial networks. We demonstrate that a GAN can act as a Cryptographically Secure Pseudorandom Number Generator (CPRNG) passing 97% of National Institute of Standards and Technology (NIST) tests.
An abnormal behavior detection method of video crowds and vehicles based on deep learning

Video monitoring-based exception behavior detection of crowds and vehicles has become a hot research hotspot in image processing, machine vision and other related fields. In view of the difficulty of detecting abnormal targets in complex structured outdoor scenes, an anomaly detection method combining optical flow method and convolutional neural network (CNN) is proposed in this paper, the method can be used to detect and warn abnormal targets in complex structured scenes. Extract video motion characteristics by Lucas-Kanade (LK) optical flow, normalize the extracted optical flow through a simple scaling method, detect and alert the anomalies of video crowds and vehicles adopting CNN, evaluate the abnormal behavior detection method using the accuracy and time. The experiment results show the method can detect the abnormal behaviors of crowds and vehicles in complex scenes in time and effectively.
Readability and types of questions in Chilean EFL high school textbooks

This article evaluates the mandatory English as a foreign language textbooks provided by the government in Chile in terms of readability and types of questions. Three readability formulas are used to analyze the texts: Flesch Reading Ease, Flesch‐Kincaid Grade Level, and Coh‐Metrix L2 reading index (RDL2). To evaluate the cognitive demands that comprehension questions place on students, Day and Park’s (2005) taxonomy of questions is used. The findings of this study reveal that the readability of the texts used in textbooks for 10th and 12th graders is similar, but the texts used in the textbook for 11th graders are more difficult to read. Regarding the types of questions, literal comprehension questions are more used in 12th‐grade texts than in those for 10th and 11th grades. Consequently, there does not seem to be a progression either in readability or in cognitive demand across these grades.
Autonomous Navigation on Modified AOR Iterative Method in Static Indoor Environment

Abstract One of the main issues when dealing with mobile robot navigation is that we have to resolve the obstacle avoidance problem, where when moving from a starting point to the goal point, the mobile robot will have to generate a collision-free path in order ensure that it can move efficiently in the environment. In this study, we attempt to resolve the issue by solving it iteratively via numerical technique. This solution is based on the potential field method that utilizes the Laplace’s equation to constrain the generation of potential functions over the regions in the configuration space where the mobile robot operates in. This paper proposed Modified Accelerated Over-Relaxation (MAOR) iterative method for solving robot path planning problem. Through the application of finite-difference technique in it, the experiment shows that the mobile robot is able to generate a smooth path from starting point to goal point. Furthermore, the results obtained from the simulation has shown that this numerical method was able to perform faster solution and generated smoother path comparing the previous works on the similar problem.
Radix-4 Interleaved Modular Multiplication for Cryptographic Applications

Modular arithmetic based processors are widely used in digital signal processing, image processing, cryptography, etc. All major crypto-systems require the use of modular multipliers in one or more stages of computation. Considerable amount of work has been done to optimize the modular multiplication algorithm and improve the implementation to meet various constraints and requirements. Implementation of a radix-4 based interleaved modular multiplier over 256-bit is provided in this paper. Further, the existing algorithm has been modified and proposed a new algorithm with hardware circuit and both implementation done using Xilinx Virtex-7 (XC7VX485T-FFG1761-2) FPGA. The proposed 256-bit modular multiplication meets around 1 μs of computation time at 122.6 MHz and area is 858 slices.
Leveraging Kubernetes for adaptive and cost-efficient resource management

Software providers face the challenge of minimizing the amount of resources used while still meeting their customer's requirements. Several frameworks to manage resources and applications in a distributed environment are available, but their development is still ongoing and the state of the art is rapidly evolving, making it a challenge to use such frameworks and their features effectively in practice. The goal of this paper is to research how applications can be enhanced with adaptive performance management by relying on the capabilities of Kubernetes, a popular framework for container orchestration. In particular, horizontal as well as vertical scaling concepts of Kubernetes may prove useful to support adaptive resource allocation. Moreover, concepts for oversubscription as a way to simulate vertical scaling without having to reschedule applications, are evaluated. Through a series of experiments involving multiple applications and workloads, the effects of different configurations and combinations of horizontal and vertical scaling in Kubernetes are explored. Both the resource utilization of the nodes and the applications' performance are taken into account. In brief, the resource management concepts of Kubernetes allow to simulate vertical scaling without a negative effect on performance. The effectiveness of the default horizontal autoscaler, however, depends on the type of application and the user workload at hand.
Orogenic gold prospectivity mapping using machine learning

SummaryAs major mineral discoveries have become rarer over the last two decades, the industry has begun to turn to new technologies to assist in the exploration process. One such advancement is the application of machine learning and artificial intelligence (AI) to geoscience data. Mineral prospectivity mapping has been around for decades but with the increase in computer power, recently it has gained traction again as a means for exploration teams to take full advantage of the numerous datasets at their disposal. Although having a team of human experts with a wealth of geoscience knowledge and experience is still fundamental to the exploration process, the ability to robustly integrate and analyse large geoscience datasets over vast spatial regions quickly becomes unwieldy if done manually.In this study, we developed a new algorithm for mineral prospectivity mapping using a VNet deep convolutional neural network and applied it to finding gold at the Committee Bay greenstone belt in the Canadian Arctic. The machine learning network took all the geoscience data available from the area and generated a prospectivity map for targeting economic orogenic gold mineralization. The results were subsequently validated on a separate nearby region where the machine predictions were compared to gold assay values from drilling. The gold assays from this region were not included in the training process, and the method demonstrated good success in predicting where the highest gold mineralization occurred.A subsequent gold prospectivity map was produced for the main area in question, and in addition to many new targets the VNet algorithm predicted many targets that the exploration team had previously generated. This suggests that this process assists the exploration team in vetting old targets while opening their eyes to new targets as well. In this way, the algorithm helps to vector in on prospective new and old areas while maximizing the value of all available geoscience data.
Talking to a pedagogical agent in a smart TV: modality matching effect in human-TV interaction

The current study examined how voice control and a virtual agent in a smart TV interplay in influencing users’ evaluation of the TV. In a 2 (input modality: voice control vs. remote controller) X 2(agent type: realistic vs. cartoonlike agent) between-subjects experiment, participants (N = 64) used and evaluated a smart TV in an educational context. Results revealed negative effects of voice control, especially when it accompanied a cartoonlike agent. Those who used the voice control to the cartoonlike agent rated the agentless humanlike, attractive, intelligent and intimate than did those who used the remote controller to the cartoonlike agent. Also, those who used voice control reported that watching the TV was less involving and enjoyable than those who used the remote controller. The negative effects were mitigated, however, when they interacted with a realistic agent. These results suggest the importance of the matching between input modality and visual interface to reduce potential negative effects.
The Truth About Ground Truth: Label Noise in Human-Generated Reference Data

Due to the increasing amount of remotely sensed data, methods for its automatic interpretation become more and more important. Corresponding supervised learning techniques, however, strongly depend on the availability of training data, i.e. data where measurements and labels are provided simultaneously. The creation of reference data for large data sets is very challenging and approaches addressing this task often introduce a significant amount of label noise. While other works focused on the influence of label noise on the training process, this paper studies the impact on the evaluation and shows that the corresponding effects are even more adverse.
Evaluation of DDOS Attack Degree Based on GRA-TOPSIS Model

In this paper, we consider the deficiencies in the traditional use of the indicator system to evaluation the degree of Distributed Denial of Service (DDoS) attack, and propose an evaluation model GRA-TOPSIS. Based on the idea of integrated learning, this method combines Gray Relational Analysis (GRA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Combined with the establishing of indicator system and Combination Weighing Method, a set of evaluation methods for DDOS is constructed. The experimental results show that the GRA-TOPSIS evaluation model can be well integrated into the DDOS degree evaluation process and obtain good data results.
Denoising Medical Images Using Machine Learning, Deep Learning Approaches: A Survey

Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods.In this paper, A median filter algorithm will be modified and the image denoising method to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn- CNN), Gaussian noise, and Salt and pepper noise used in the medical image is explained.PSNR values of the CNN method are higher and showed better results than different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter).Denoising methods performance with indices SSIM, PSNR, and MSE have been tested, and the results of simulation image denoising are also presented in this article.
Data Collection from the Web for Informetric Purposes

This chapter reviews the development of data collection procedures on the web with an emphasis on current practices, data cleansing and matching, data quality and transparency. There are several issues to be considered when collecting data from the web. Transparency is essential to know what is included in the data source, how recent and comprehensive the data are, what timeframe is covered etc. Data quality relates to reliability and accuracy. Mistakes are inevitable, data providers, aggregators, and researchers all make mistakes, but these mistakes should be reduced to a minimum so that meaningful conclusions may be reached from the data analysis. Extensive data cleansing before starting the analysis is needed to try to correct mistakes in the data. When several data sources are used, data from different sources should be matched, and duplicates should be removed.
A Unified Deep Metric Representation for Mesh Saliency Detection and Non-Rigid Shape Matching

In this paper, we propose a deep metric for unifying the representation of mesh saliency detection and non-rigid shape matching. While saliency detection and shape matching are two closely related and fundamental tasks in shape analysis, previous methods approach them separately and independently, failing to exploit their mutually beneficial underlying relationship. In view of the existing gap between saliency and matching, we propose to solve them together using a unified metric representation of surface meshes. We show that saliency and matching can be rigorously derived from our representation as the principal eigenvector and the smoothed Laplacian eigenvectors respectively. Learning the representation jointly allows matching to improve the deformation-invariance of saliency while allowing saliency to improve the feature localization of matching. To parameterize the representation from a mesh, we also propose a deep recurrent neural network (RNN) for effectively integrating multi-scale shape features and a soft-thresholding operator for adaptively enhancing the sparsity of saliency. Results show that by jointly learning from a pair of saliency and matching datasets, matching improves the accuracy of detected salient regions on meshes, which is especially obvious for small-scale saliency datasets, such as those having one to two meshes. At the same time, saliency improves the accuracy of shape matchings among meshes with reduced matching errors on surfaces.
A Pragmatic Assessment of Approaches and Paradigms in Software Risk Management Frameworks

Software risk management involves the process of prior recognition and the assessment of vulnerabilities with the classification approach so that the risk avoidance mechanism can be implemented. It includes one of the key factors in software project management with the goal to improve quality as well as the avoidance of vulnerabilities. The term defect refers to the imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When there is difference in actual and expected result or meeting the wrong requirement it is called a defect and it forms the basis of risk escalation in the software project, which is obviously not accepted in any type of deployment. To make software reliable, the software should be risk-free from any type of vulnerability factor. Along with reliability, another issue that has arisen is software quality in which the associated factor is with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce effective valued software.
MontoloStats - Ontology Modeling Statistics

Within ontology engineering concepts are modeled as classes and relationships, and restrictions as axioms. Reusing ontologies requires assessing if existing ontologies are suited for an application scenario. Different scenarios not only influence concept modeling, but also the use of different restriction types, such as subclass relationships or disjointness between concepts. However, metadata about the use of such restriction types is currently unavailable, preventing accurate assessments for reuse. We created the RDF Data Cube-based dataset MontoloStats, which contains restriction use statistics for 660 LOV and 565 BioPortal ontologies. We analyze the dataset and discuss the findings and their implications for ontology reuse. The MontoloStats dataset reveals that 94% of LOV and 95% of BioPortal ontologies use RDFS-based restriction types, 49% of LOV and 52% of BioPortal ontologies use at least one OWL-based restriction type, and different literal value-related restriction types are not or barely used. Our dataset provides modeling insights, beneficial for ontology reuse to discover and compare reuse candidates, but can also be the basis of new research that investigates novel ontology engineering methodologies with respect to restrictions definition.
PRIVATA

As the value of digital data increases, the data market is in the spotlight as a means of obtaining a personal information. However, the collection of personal information makes a serious privacy violation and it is a serious problem in the use of personal data. Differential privacy, which is a de-facto standard for privacy protection in statistical databases, can be applied to solve the privacy violation problem. To apply differential privacy to the data market, the amount of noise and corresponding data price should be determined between the provider and consumer. However, this matter has not yet been studied. In this work, we introduce a Privata which is a differentially private data market framework to set the appropriate price and noise parameter in the data market environment. The Privata is based on negotiation technique using Rubinstein bargaining considering social welfare to prevent unfair transactions. We explain the Privata overview and negotiation technique in Privata, and show the Privata implementation.
Gemination prediction using DNN for Arabic text-to-speech synthesis

This paper describes a gemination prediction model for Arabic consonants, based on deep neural networks (DNN). Actually, though the importance of gemination to understand the right meaning of the word, the gemination sign (shadda) is very often omitted in modern standard Arabic printed/typed texts, which would generate errors in automatic text applications, such as text-to-speech synthesis and automatic translation. Therefore, gemination prediction for Arabic consonants has been achieved as a part of automatic diacritization module, for DNN-based arabic text-to-speech synthesis. Different DNN models were trained using feedforward and recurrent architectures. The reported results show the ability of recurrent DNN to detect the consonants which have to be geminated in a non-diacritized arabic text, with a very high accuracy.
A Secure Visual-thermal Fused Face Recognition System Based on Non-Linear Hashing

In this paper, we propose a secure visual-thermal fused face recognition system using non-linear hashing. To extract features from both thermal and visible facial images, a deep neural network model pre-trained by visible images, namely InsightFace, is utilized in extracting deep features from both thermal and visible images. Next, we investigate into the effectiveness of using nonlinear hashing in protecting deep features extracted from both thermal and visible face images. To further boost the accuracy performance of the facial recognition system under unfavorable environment, feature- and score-level fusion of thermal and visible images for face matching are studied. The performance of different application scenarios are tested on the EURECOM VIS-TH face dataset. Experiment results suggest that: 1) feature- and score-level fusion techniques are effective in achieving higher accuracy under unfavorable situation; 2) non-linear hashing offers additional layer of protection, namely, privacy preservation, to face image. We also found that the deep model trained by using visible images is applicable to thermal images for feature extraction, which is particularly useful because there is no large thermal dataset available to train deep neural network.
Moving Object Detection in Night Time: A Survey

The object monitoring performance is greatly depends on the enhanced quality of images. The quality of these images is affected at night video systems due to low contrast of salient objects as well as several atmospheric conditions such as fog, rain, dust etc. As a consequence, the intensity, color, polarization, coherence of scenes alters. The awful appearance of night images under subjective lighting and atmospheric conditions is a general problem for analysis in computer vision. In this paper, we have aimed to provide a survey work to researchers under four categories: (i) review on change detection datasets either thermal or visual-thermal, (ii) review on feature extraction based object detection techniques, (iii) review on background modeling based object detection techniques, (iv) review on convolutional neural network (CNN) based object detection techniques. This paper will benefits for those who require a details review over change detection datasets and detection techniques for moving object detection related applications.