Reviewerly/exHarmony huggingface dataset:
Age Heterogeneity of STEM Educators

One of the ways to improve the quality of math and natural science education is to develop the pedagogical community of STEM education. On the one hand, according to the forecasts, an increase in the number of students and teachers is expected by an average of 20 % in both Russia and worldwide. On the other hand, there is definitely some specificity in the pedagogical community as compared with any other labor collective, since the pedagogical community develops alongside with the student one. In this perspective, the age heterogeneity of the teaching staff is of particular relevance. Consequently, the key point in the management of math and natural science education is the analysis of the age structure of STEM educators. The following subjects are taken to conduct a further analysis of STEM education: mathematics, handicraft, physics, biology, and chemistry. Accordingly, the purpose of the article is to analyze and forecast the heterogeneous development of the age structure of STEM teachers. The authors of the article justify the necessity for changes in the personnel policy on the basis of the assessment of the capacity of the teaching staff of STEM education in schools of the European part of Russia. The leading research approach is the method of the normal and natural distribution of age groups in the structure of the STEM education pedagogical community. As a result of a study conducted in 569 schools of the European part of Russia in 2016–2018, the authors of the article have found out the following: the average age of STEM school teachers in the European part of Russia is 6.8 years higher than the average age of teachers in Russia; there is a significant shortage of STEM teachers aged under 35; the average rate of the teaching load of a STEM school teacher in an academic subject is 0.72, but the load is distributed very unevenly, math teachers having the highest teaching load. The theoretical significance of the research lies in its contribution to the development of scientific ideas concerning the age heterogeneity of STEM school teachers. The research results can be used in building development trajectories of STEM education teaching staff by implementing a series of managerial and organizational measures to achieve the normal state of the age structure of teachers.
Rain Streak Removal via Multi-scale Mixture Exponential Power Model

Rain streaks severely hamper the visible performance of the outdoor surveillance videos, which becomes an attractive issue in recent computer vision research. Existing methods usually encode rain streaks into Gaussian Mixture Model (GM-M). However, the limited number of Gaussian components in the GMM compromises the ability of the model in fitting real noise, such as sparse noise, which is exactly the characteristic of the rain streaks. In this paper, a novel model named Mixture Exponential Power Model (MEPM) is exploited. It sets multiple Laplace noise components and expands the representation capability for the sparse noise. Moreover, considering that the rain streaks in a video occur in different distances from the camera, we encode rain streaks into Multi-scale Mixture Exponential Power Model. The model is opti-mized by expectation-maximization (EM) algorithm and La-grange multiplier strategy. Experiments are implemented on synthetic and real rain videos and verify the superiority of the proposed method, compared with state-of-the-art methods.
Mobile Medical Question and Answer System with Improved Char-level based Convolution Neural Network and Sparse Auto Encoder

Last few years, China has entered into an open and comprehensive two-child era which cause the demand for medical resources of elderly pregnant women more scarce. And the accuracy will be effected by the accuracy of Chinese word segmentation during the classification of questions. To solve those problem above, we are presenting a Chinese Mother-to-Child Domain Question Answering System. We propose a improved end-to-end convolutional neural networks with Sparse Auto Encoder(SAE) layer to extract information at Char-level. It has achieved a significant effect both in accuracy and avoiding over-fitting.
Characterizing finite fields via minimal ring extensions

Let A be a finite local (commutative unital) ring. If A is not a field, there exists a positive integer N such that |B|≤N for each ring B such that A⊂B is an inert (minimal ring) extension. It follows that A is a (finite) field ⇔ the inert extensions of A form infinitely many (equivalently, denumerably many) A-algebra isomorphism classes ⇔ the minimal ring extensions of A form infinitely many (equivalently, denumerably many) A-algebra isomorphism classes ⇔ there exists a minimal ring extension A⊂B such that B is a flat A-module ⇔ there exists a minimal ring extension A⊂B such that B is a local ring and A⊂B is a Galois ring extension.
Question Answering Based on Ensemble Classifier for University Enrolment Advising

Enrolment Advising (EA) for Vietnamese Universities takes place many sites in Vietnam every year, from the high schools to the universities. Many sessions have been organized, both online and offline. It costs a lot of human resources and money for inviting the experts, printing the leaflets, banner, poster, etc. In this context, we try to reduce the costs for EA by an application of machine learning. So, we propose to develop an automatic university enrolment advising system by using ensemble classifier.
Boosting Neuro Evolutionary Techniques for Speech Recognition

For long many years, various speech signal processing techniques have been experimented and optimized using expectation maximization, gradient descent optimization or their variations across end-to-end speech feature extraction and recognition scheme, but the result was below the satisfactory limit despite multitude of time, cost and effort have been invested. Very recently, huge improvement of computing power of devices made it possible to use complex multi-layered neural network technologies (i.e. deep learning or deep neural network) such as convolutional net, long short term memory, bidirectional recurrent neural network as well as complex statistical or evolutionary strategies and its variations to optimize further the results to reduce the error rates. This paper emphasizes on how to devise an efficient technique that would reduce the time, cost and complexity over the deep learning methods with the guidance of genetic algorithm (GA) through intelligently choosing hyper-parameters of the networks. It has been identified that series of iterations to estimate, tune and re-estimate the hyper-parameters can lead to substantial improvement even with the least computing power, compared to one-go implementation of genetic algorithms done earlier.
Enhanced key generation algorithm of hashing message authentication code

The keyed-HMAC is a security tool primarily used to ensure authentication and data integrity in information systems and computer networks. In this study, the authors proposed a new key generation using HMAC-MD5 using XOR and circular left shifting. Findings reveal that the proposed method could generate a key with only 420 ms. As to avalanche effect, it achieved 55.05% which passed the criteria of encryption. Also, the approximate time to successfully retrieve the encrypted key is 3.2845E+62 years. This means that the hash value cannot be easily cracked or hacked using powerful brute-force attack. Results also show that the performance of the proposed method is better than the existing methods. Thus, improved its security level compared to other methods.
N-learning: An Approach for Learning and Teaching Skills in Multirobot Teams

Summary We propose the N-learning practical approach for teaching and learning behaviors in a multirobot system, which is performed through mandatory behavior acquisition based on interactions between the robots at execution time. The proposed methodology can be used to self-program the robots of a team by programming only a single robot with a set of codes that contain behaviors to be transferred and used by other robots as necessary. These codes are implemented in a modular fashion. An advantage of our approach is that when a team of robots is required to perform a specific mission, the set of behaviors required to accomplish that mission can be implemented only once in a single robot or in a distributed fashion. Then, these distributed behaviors are transferred to each of the other robots in the team according to their demand, without the need to reprogram them by hand since the robots in the team can share them autonomously. As an application example, a human critic can teach (or program) only one or a few robots, and these robots are thus able to exchange knowledge with the other team members since they have been preinstalled to run the N-learning system basics. Simulated and real robot experiments are performed to demonstrate the feasibility and validation of our approach.
Deep Spline Networks with Control of Lipschitz Regularity

The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite- dimensional optimization problem, we resolve the ambiguity by searching for the sparsest and most regular solution in the sense of Lipschitz. To that end, we first introduce a bound that relates the properties of the pointwise nonlinearities to the global Lipschitz constant of the network. By using the proposed bound as regularizer, we then derive a representer theorem that shows that the optimum configuration is achievable by a deep spline network. It is a variant of a conventional deep ReLU network where each activation function is a piecewise-linear spline with adaptive knots. The practical interest is that the underlying spline activations can be expressed as linear combinations of ReLU units and optimized using ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization techniques.
Tail Latency Prediction for Datacenter Applications in Consolidated Environments

Consolidating applications is a practical necessity in today's datacenters to reduce cost and improve resource utilization. However, resource sharing among different applications may result in high latency in responses to user requests. Due to the lack of a performance model for tail latency of Fork-Join structures, which underlay the workflows of lots of datacenter applications, the current practice is to overprovision resource in an attempt to satisfy as many user requests as possible. However, this practice leads to low resource utilization. Therefore, it is of importance to have a performance model that can accurately predict tail latency in such an environment, especially at high load regions, where resource provisioning is desired at most. In this paper, we propose an analytical solution for the prediction of tail latency of a target application in a consolidated environment where it is mixed with other background applications. The proposed model is validated against simulation through extensive case studies. The experimental results show the effectiveness of the proposed model in tail latency prediction at high load region, yielding all the prediction errors well within 10% at the load of 75% or higher, making the model a valuable tool for resource provisioning and supporting scheduling decisions in datacenter clusters to guarantee user satisfactions.
Real-time object tracking via self-adaptive appearance modeling

One of the main factors that limit the accuracy and robustness of visual tracking algorithms is the lack of suitable appearance models. The robustness and effectiveness of object appearance models is severely affected by the changing object appearances during the tracking process and the interference of other similar objects around the truth object. In this paper, a self-adaptive appearance model pool based on multi-sample is constructed to improve the robustness of the object appearance models. In order to deal with variable object states, the initial sample given in the first frame and the samples generated in the subsequent tracking process are combined into a sample set to represent various appearances of the object. In addition, a dynamic selection strategy is explored to update and maintain the sample components that are derived from varieties of sources. In order to distinguish the tracking object from other similar candidate objects, multi-feature response fusion strategy is proposed, which can effectively improve the expression ability of the appearance model. Extensive experiments on the popular benchmark datasets demonstrate that the proposed tracking approach performs favorably against several other state-of-the-art tracking algorithms.
Universal Acoustic Modeling Using Neural Mixture Models

Acoustic models are domain dependent and do not perform well if there is a mismatch between training and test conditions. As an alternative, the Mixture of Experts (MoE) model was introduced for multi-domain modeling. It combines the outputs of several domain specific models (or experts) using a gating network. However, one drawback is that the gating network directly uses raw features and is unaware of the state of the experts. In this work, we propose several alternatives to improve the MoE model. First, to make our MoE model state-aware, we use outputs of experts as inputs to the gating network. Then we show that vector based interpolation of the mixture weights is more effective than scalar interpolation. Second, we show that directly learning the mixture weights without using any complex gating is still effective. Finally, we introduce a hybrid attention model that uses the logits and mixture weights from the previous time step to generate the mixture weights at the current time. Our best proposed model outperforms a baseline model using LSTM based gating achieving about 20.48% relative reduction in word error rate (WER). Moreover, it beats an oracle model which picks the best expert for a given test condition.
Finding the representative in a cluster using correlation clustering

Correlation clustering is a widely used technique in data mining.The clusters contain objects, which are typically similar to each other and different from objects from other groups.It can be an interesting task to find the member, which is the most similar to the others for each group.These objects can be called representatives.In this paper, a possible way to find these representatives are shown and software to test the method is also provided.
Fault Detection and Classification for Induction Motors Using Genetic Programming

Induction motors are the workhorse in various industry sectors, and their accurate fault detection is essential to ensure reliable operation of critical industrial processes. Since various types of mechanical and electrical faults could occur, induction motor fault diagnosis can be interpreted as a multi-label classification problem. The current and vibration input data collected by monitoring a motor often require signal processing to extract features that can better characterize these waveforms. However, some extracted features may not be relevant to the classification, feature selection is thus necessary. Given such challenges, in recent years, machine learning methods, including decision trees and support vector machines, are increasingly applied to detect and classify induction motor faults. Genetic programming (GP), as a powerful automatic learning algorithm with its abilities of embedded feature selection and multi-label classification, has not been explored to solve this problem. In this paper, we propose a linear GP (LGP) algorithm to search predictive models for motor fault detection and classification. Our method is able to evolve multi-label classifiers with high accuracies using experimentally collected data in the lab by monitoring two induction motors. We also compare the results of the LGP algorithm to other commonly used machine learning algorithms, and are able to show its superior performance on both feature selection and classification.
Universal gates on garbled circuit construction

Summary Efficient garbled circuit construction can lead to more practical secure computation protocols. Garbled circuit construction has been considered as a separate goal for optimization as efficiency of the secure computation protocol is directly related to the efficiency of garbled circuit construction. Various optimizations such as, point‐and‐permute technique, free‐XOR, garbled row reduction, and dual‐key cipher are proved to make the garbled circuit construction efficient. In this paper, we propose garbled circuit construction with the universal gates; for demonstration purpose, we have considered NOR gates and shown optimization on circuit construction in two models. By the use of single type of gate, the gate array in the circuit representation is eliminated and a constant value is used in protocols. In addition, we have reduced the number of rows in garbled table to two rows per gate with two or zero encryption calls during garbled circuit construction and two or zero decryption calls during evaluation of garbled circuit.
Alternately Guided Depth Super-resolution Using Weighted Least Squares and Zero-order Reverse Filtering

Due to the structural inconsistency between color and depth, texture copying and edge blurring artifacts appear in color-guided depth super-resolution. In this paper, we propose alternately guided depth super-resolution using weighted least squares (WLS) and zero-order reverse filtering. We adopt WLS for alternating guidance, and alternately use color and depth as guidance in WLS to suppress texture copying artifacts. Since color guidance causes edge blurs in depth due to the mismatch between color and depth, we apply zero-order reverse filtering to depth images to alleviate edge blurring artifacts. Moreover, WLS is a global optimization-based filter and thus it is effective in removing depth noise. Experiments on Middlebury and real scene datasets show that the proposed method outperforms state-of-the-art methods in terms of quantitative and qualitative measurements.
Enhancing MD5 hash algorithm using symmetric key encryption

Message integrity and authenticity is crucial with ever increasing network communication. Cryptographic Hash Functions are main building block of message integrity. Many types of hash functions are being used and developed. In this paper, a new keyed hash function is being proposed and described. This proposed algorithm produces a hash code of 128 bits for an arbitrary length of input. The function uses a key during hashing, so that any intruder who does not know the key cannot forge the hash code, and, thus it satisfies the purpose of security, authentication and integrity for a message in network. The paper discusses the algorithm on the function design, its security aspects and implementation details.
Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging

We introduce an adversarial multi-label classification (ADMLC) framework to improve the robustness and performance of existing algorithms on multi-domain signals. The core contribution of our ADMLC is the innovation of an `adversarial module' that serves as a critic to provide augmenting information to improve supervised learning in multi label classification (MLC) tasks. Our approach is not intended to be regarded as an emerging competitor for many well-established algorithms in the field. In fact, many existing deep and shallow architectures can all be adopted as building blocks integrated in the ADMLC framework. We show the performance and generalization ability of ADMLC on diverse tasks including audio and image tagging.
Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes

We are in a machine learning age where several predictive applications that are life dependent are made by machines and robotic devices that relies on ensemble decision making algorithms. These have attracted many researchers and led to the development of an algorithm that is based on the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. Firstly, EKF is used to optimize the slow training speed and improve the efficiency of the RBF network training parameters. Secondly, AdaBoost is applied to generate and combine RBFN-EKF weak predictors to form a strong predictor. Breast cancer survivability and diabetes diagnostic datasets used were obtained from the UCI repository. Results are presented on the proposed model as applied to Breast cancer survivability and Diabetes diagnostic predictive problems. The model outputs an accuracy of 96% when EKF-RBFN is applied as a base classifier compare to 94% when Decision Stump is applied and AdaBoost as an ensemble technique in both examples. The output accuracy of ensemble AdaBoostM1-Random Forest and standalone Random Forest models is 97% in both cases. The study has gone some way towards enhancing our knowledge and improving the prediction accuracy through the amalgamation of EKF, RBFN and AdaBoost algorithms as an ensemble model.
A new model selection procedure for finite mixture regression models

In this article, we propose a new penalized-likelihood method to conduct model selection for finite mixture of regression models. The penalties are imposed on mixing proportions and regression coefficients, and hence order selection of the mixture and the variable selection in each component can be simultaneously conducted. The consistency of order selection and the consistency of variable selection are investigated. A modified EM algorithm is proposed to maximize the penalized log-likelihood function. Numerical simulations are conducted to demonstrate the finite sample performance of the estimation procedure. The proposed methodology is further illustrated via real data analysis.
ADRCN: A Framework to Detect and Mitigate Malicious Insider Attacks in Cloud-Based Environment on IaaS

Security is a critical factor for any of the computing platforms. Cloud computing is a new computing environment but still, its basic technology is the Internet. Thus, Cloud computing environment not only has the threats of its own but it is also prone to security issues of its underlying technology i.e. Internet. In this paper, the authors are proposing a secure routing framework viz. Authenticated Dynamic Routing in Cloud Networks (ADRCN) to mitigate the malicious insider attacks while maintaining the path integrity in the Clouds. Symmetric cryptography with hashing is used to maintain the integrity of the path between the source and destination. The purpose of ADRCN is to maintain the integrity of the path between the client and data center. If malicious insider tries to perform an attack between the client and the data center then it will be detected. This work aims to give a solution for detection and prevention of malicious insider attacks in Cloud-based environments.
Product Review Credibility Analysis

Product reviews are vital sources of customers’ opinions and have a significant impact on purchasing decisions and product rankings on online shopping sites. Unfortunately, fraudsters (spammers) may write deceptive reviews (spam reviews) appreciating or deprecating a product, which can mislead potential customers and negatively affect revenues of many genuine organizations. Therefore, there is a great need for an effective approach to detect fake reviews and spammers. In this paper, we propose a statistical credibility scoring mechanism to identify spam reviews. It consists of three components: detection of duplicate reviews, detection of anomaly in review count and rating distribution, and detection of incentivized reviews. These three methodologies complement each other to effectively indicate the credibility of product reviews without requiring significant computational resources. It can aid data mining and online spam filtering systems to filter out spam product reviews and refine product rankings.