Reviewerly/exHarmony huggingface dataset:
A minimax probability extreme machine framework and its application in pattern recognition

In this work we propose a minimax probability extreme learning machine framework (MPME), which combines the benefits of minimax probability machine (MPM) with extreme learning machine (ELM). For binary classification problems, we illustrate that the proposed MPME can be interpreted geometrically by minimizing the maximum of Mahalanobis distances to the two classes. Then two variants of the MPME are presented based on the l2-norm loss and l1-norm loss functions (called LSEMPME and LADMPME) respectively. Without making specific assumption on the data distribution, the proposed methods can provide explicit upper-bounds for the generalization error, moreover the LSEMPME and LADMPME minimize empirical risk simultaneously. The decision hyperplanes of the proposed methods pass through the origin in ELM feature space with few decision variables. By using the multivariate Chebyshev–Cantelli inequality, all the proposed problems can be reformulated as second-order cone programming (SOCP) with global solutions. Furthermore, numerical experiments have been carried out on two databases that are drawn from UCI benchmark database and a practical application database. First, the proposed methods are evaluated for a practical application consisting on the analysis of licorice seeds using near-infrared spectral (NIR) data. Experiments in six different spectral regions illustrate that the proposed methods can improve generalization in most cases. Then the proposed methods are evaluated on benchmark datasets. In comparison with traditional methods including MPM, ELM and support vector machine (SVM), experiments show that the proposed methods achieve comparable results in generalization. With few decision variables, the proposed methods are easy to implement for nonlinear classification and to estimate a lower-bound on the prediction accuracy.
Investigating Universal Appliance Control through Wearable Augmented Reality

The number of interconnected devices around us is constantly growing. However, it may become challenging to control all these devices when control interfaces are distributed over mechanical elements, apps, and configuration webpages. We investigate interaction methods for smart devices in augmented reality. The physical objects are augmented with interaction widgets, which are generated on demand and represent the connected devices along with their adjustable parameters. For example, a loudspeaker can be overlaid with a controller widget for its volume. We explore three ways of manipulating the virtual widgets: (a) in-air finger pinching and sliding, (b) whole arm gestures rotating and waving, (c) incorporating physical objects in the surrounding and mapping their movements to the interaction primitives. We compare these methods in a user study with 25 participants and find significant differences in the preference of the users, the speed of executing commands, and the granularity of the type of control.
Adaptive weighted fuzzy rule interpolation based on ranking values and similarity measures of rough-fuzzy sets

In real-world applications, the performances of traditional fuzzy interpolation methods are not good enough because they may produce inconsistent fuzzy interpolation results when contradictory fuzzy interpolated results are obtained after fuzzy interpolation processes. In recent years, several adaptive fuzzy rule interpolation (AFRI) methods have been proposed for identification and correction of the contradictory fuzzy interpolated results. In this paper, we propose a new adaptive weighted fuzzy rule interpolation (AWFRI) method based on ranking values and similarity measures of rough-fuzzy sets (RFSs). If the degree of similarity obtained between the fuzzy interpolation results is less than a predefined threshold value, the proposed AWFRI method applies the proposed transformation and modification techniques to solve the contradiction between fuzzy interpolation results. The experimental results show that the proposed AWFRI method can overcome the drawbacks of the existing AFRI methods because it obtains higher consistency degrees between fuzzy interpolation results and it deals with AWFRI based on RFSs rather than type-1 fuzzy sets or interval type-2 fuzzy sets.
A Comparative Study of Software Bugs in Micro-clones and Regular Code Clones

Reusing a code fragment through copy/pasting, also known as code cloning, is a common practice during software development and maintenance. Most of the existing studies on code clones ignore micro-clones where the size of a micro-clone fragment can be 1 to 4 LOC. In this paper we compare the bug-proneness of micro-clones with that of regular code clones. From thousands of revisions of six diverse open-source subject systems written in three languages (C, C#, and Java), we identify and investigate both regular and micro-clones that are associated with reported bugs.Our experiment reveals that percentage of changed code fragments due to bug-fix commits is significantly higher in micro-clones than regular clones. The number of consistent changes due to bug-fix commits is significantly higher in micro-clones than regular clones. We also observe that significantly higher percentage of files get affected by bug-fix commits in micro-clones than regular clones. Finally, we found that percentage of severe bugs is significantly higher in micro-clones than regular clones. We perform Mann-Whitney-Wilcoxon (MWW) test to evaluate the statistical significance level of our experimental results. Our findings imply that micro-clones should be emphasized during clone management and software maintenance.
Research data management in practice: Results from a cross-sectional survey of health and medical researchers from an academic institution in Australia

Building or acquiring research data management (RDM) capacity is a major challenge for health and medical researchers and academic institutes alike. Considering that RDM practices influence the integrity and longevity of data, targeting RDM services and support in recognition of needs is especially valuable in health and medical research.This project sought to examine the current RDM practices of health and medical researchers from an academic institution in Australia.A cross-sectional survey was used to collect information from a convenience sample of 81 members of a research institute (68 academic staff and 13 postgraduate students). A survey was constructed to assess selected data management tasks associated with the earlier stages of the research data life cycle.Our study indicates that RDM tasks associated with creating, processing and analysis of data vary greatly among researchers and are likely influenced by their level of research experience and RDM practices within their immediate teams.Evaluating the data management practices of health and medical researchers, contextualised by tasks associated with the research data life cycle, is an effective way of shaping RDM services and support in this group.This study recognises that institutional strategies targeted at tasks associated with the creation, processing and analysis of data will strengthen researcher capacity, instil good research practice and, over time, improve health informatics and research data quality.
K-means clustering based photo voltaic power forecasting using artificial neural network, particle swarm optimization and support vector regression

With the increasing global warming and enormous pollution, it is very obvious to generate power from renewable energy sources. In fact, photo voltaic (PV) power is a main renewable energy source for sustainable power generation. But PV power generation is uncertain and intermittent in nature. Stable and reliable power supply may not be feasible with PV power. Power supply reliability is important as much as its availability. To handle the inconsistency of PV power generation, power sector is highly dependent on forecasting methods and techniques. In this paper the feed forward neural network (FFNN) of artificial neural networks(ANN) with optimization technique particle swarm optimization (PSO) and support vector regression (SVR) are used in short term PV power generation forecasting and their performance is compared with the error calculation in terms of the mean absolute error (MAE) and root mean squared error (RMSE). The PV power and meteorological data of solar irradiation and temperature of Kolkata region of India was used for forecasting. Similar days of the year sunny days, cloudy days and rainy days are clustered using k-means clustering technique. Thereafter, a feed forward neural network is implemented to forecast a day head PV power and week ahead photo voltaic power. PSO is used to optimize the weights of the neural network. The performance of the proposed forecasting method is compared to a data mining approach, support vector regression (SVR).In this paper, the significance of proper selection of input parameters in PV power forecasting is emphasized. With the application of K-means clustering, the accuracy of ANN-PSO approach is improved significantly in a day ahead PV power forecasting and a week ahead PV power forecasting with a good margin. Even the accuracy of ANN-PSO approach after K-means clustering is better than SVR model in a week ahead forecasting
From person to group re-identification via unsupervised transfer of sparse features

The visual association of a person appearing in the field of view of different cameras is today well known as Person Re-Identification. Current approaches find a solution to such a problem by considering persons as individuals, hence avoiding the fact that frequently they form groups or move in crowds. In such cases, the information acquired by neighboring individuals can provide relevant visual context to boost the performance in re-identifying persons within the group. In light of enriched information, groups re-identification encompasses additional problems to the common person re-identification ones, such as severe occlusions and changes in the relative position of people within the group. In this paper, the single person re-identification knowledge is transferred by means of a sparse dictionary learning to group re-identification. First, patches extracted from single person images are used to learn a dictionary of sparse atoms. This is used to obtain a sparsity-driven residual group representation that is exploited to perform group re-identification. To evaluate the performance of the proposed approach, we considered the i-LIDS groups dataset that is the only group re-identification publicly available dataset. The benchmark datasets for single person re-identification evaluation do not include group information, hence we collected two additional datasets under challenging scenarios and used them to validate our solution.
Face To Face with Next Flu Pandemic with a Wiener-Series-Based Machine Learning: Fast Decisions to Tackle Rapid Spread

It's well known the potential arrival of the AH1N1 flu disease can be realized in any large city, as well as its unknown impact and consequences on the people. Depending upon the strength of the propagation of virus, clearly it might fade away any scheme of preparedness has been designed. The experience of the 2009 worldwide flu pandemic have served to improve and test newest methodologies that target to toughen the resilience of the public health systems. In this paper we focus on the usage of a Machine Learning algorithm as an advantageous computational system aimed to support fast and effective decisions in epochs where a flu virus has initialized its spreading in a large or middle- size city. For this end the algorithm uses the formalism of the Wiener series that allows us to estimate predictions and thus manage decisions through these computational methodologies. In order to test the efficiency of the algorithm we used the 2009 Peruvian data where the flu A(H1N1) was spreading in Lima city with a velocity of 40 cases per week. We present simulations by which the usage of Machine Learning algorithms might be of importance to minimize undesired errors and optimize resources of public health services on those epochs where the velocity of spreading and number of contagious reaches their top values.
Design Issues in Automatically Generated Persona Profiles

Increased access to data and computational techniques enable innovations in the space of automated customer analytics, for example, automatic persona generation. Automatic persona generation is the process of creating data-driven representations from user or customer statistics. Even though automatic persona generation is technically possible and provides advantages compared to manual persona creation regarding the speed and freshness of the personas, it is not clear (a) what information to include in the persona profiles and (b) how to display that information. To query into these aspects relating information design of personas, we conducted a user study with 38 participants. In the findings, we report several challenges relating to the design of automatically generated persona profiles, including usability issues, perceptual issues, and issues relating to information content. Our research has implications for the information design of data-driven personas.
Implementation of cognitive perception functions in fuzzy situational control model

In autonomous transport systems, it is useful to be able to continue interrupted traffic after removing the obstacle that caused deviations from the route. This article describes a control model in which cognitive functions, such as context and attention are embedded. The fuzzy situational control model of an autonomously moving machine along a route with obstacles is expanded by tracking context and switching attention mechanisms. Additionally, it describes a knowledge representation about the route, the surrounding space and how to avoid collisions with obstacles.
The development of an electricity book based on augmented reality technologies

This research aims to produce an electricity book using augmented reality (AR) technology. This book can support observation, experimentation, and stimulation activities because AR technology can display animation, sound, and video. The method used in this research is 4D model research and development (RND) whose stages consist of: define, design, develop, and disseminate. The disseminate stage was done by uploading the application to the Google PlayStore. The AR book has passed the validation test stage with the percentage of achievement of 80.44 % according to the material expert, 91.75 % according to the learning media expert. The test results to 10 students showed the performance of 82.48% with 0.68 of gain value. Based on validation test and filed test, give the conclusion that the development of electricity book based on AR technologies has fulfilled the learning process and requirement as physics teaching materials.
An Improved Identity-Based Multivariate Signature Scheme Based on Rainbow

Multivariate Public Key Cryptography (MPKC) is one of the main candidates for post-quantum cryptography, especially in the area of signature schemes. In this paper, we instantiate a certificate Identity-Based Signature (IBS) scheme based on Rainbow, one of the most efficient and secure multivariate signature schemes. In addition, we revise the previous identity-based signature scheme IBUOV based on the Unbalanced Oil and Vinegar (UOV) scheme on the security and choice of parameters and obtain that our scheme is more efficient than IBUOV in terms of key sizes and signature sizes.
A Smart Methodology for Analyzing Secure E-Banking and E-Commerce Websites

Acquiring sensitive information from the user in some malicious web pages which looks like the legitimate webpage and they do a kind of criminal activity that is known as phishing in the electronic world. An attacker can use this kind of phishing or fraud by using such websites, which is a severe risk to web users for their personal and confidential information. So, in the field of e-banking and e-commerce, this act makes a threat for all webpage users. In this paper mainly discerning the different features of legitimate, suspicious and phishing websites. These features are fed to the machine learning algorithms which are built-in WEKA are used for comparison and to check the accuracy of the algorithm. Algorithms used in this comparison are J48, Naïve Bayes, random forest and Logistic Model Tree (LMT) are used and them accurately to predict the website legitimacy is calculated. Also, the best algorithm among different algorithms can be selected. In this paper, we will compare the results in the two ways. Firstly, we find the best algorithm by using the comparison of the different attributes like Correctly Classified Instances, Incorrectly Classified Instances, Mean absolute error and kappa statistics. Secondly, the accuracy of these algorithms will analyze with different parameters like TP Rate, FP Rate, Precision, Recall, F-Measure, MCC, ROC Area and PRC Area that is visualized in the bar chart. The selected algorithm makes the website analyzing process automated. Before making payment on any e-commerce website, this prediction model can be used for determining the legitimacy of that website.
A Secure AMR Fixed Codebook Steganographic Scheme Based on Pulse Distribution Model

Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDM-AFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps, and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of algebraic code excited linear prediction (ACELP), such as G.723.1 and G.729.
Image-to-video person re-identification with cross-modal embeddings

Despite the great progress achieved, image-to-video person re-identification is still challenging in the cross-modal scenario. Currently, state-of-the-art approaches mainly concentrate on the task-specific data, neglecting the extra information from the different but related tasks. In this paper, we propose an end-to-end neural network framework for image-to-video person re-identification with cross-modal embeddings learned from extra information. Concretely speaking, cross-modal embedding layers from image captioning and video captioning models, are incorporated to learn common latent embeddings for multiple modalities. The learned multimodal embeddings are expected to focus on person’s prominent distinctions, due to textual descriptive information generally paying close attention to person’s explicit characteristics. Apart from that, our proposed framework resorts to CNNs and LSTMs for extracting visual and spatiotemporal features, and combines the strengths of identification and verification model to improve the discriminative ability of the learned features. The experimental results demonstrate the effectiveness of our framework on narrowing down the gap between heterogeneous data and obtaining observable improvement in the image-to-video person re-identification task.
Discriminative regularization of the latent manifold of variational auto-encoders

We present an approach on training classifiers or regressors using the latent embedding of variational auto-encoders (VAE), an unsupervised deep learning method, as features. Usually VAEs are trained using unlabeled data and independently from the classifier, whereas we investigate and analyze the performance of a classifier or regressor that is trained jointly with the variational deep network. We found that models trained this way can improve the embedding s.t. to increase classification performance, and also can be used for semi-supervised learning, building up the information extracting latent representation in an incremental fashion. The model was tested on two widely known computer vision benchmarks, and its generalization power was evaluated on an independent dataset. Additionally, generally applicable statistical methods are presented for evaluating similarly performing classifiers, and used to quantify the performance increase. The general applicability and ease-of-use of deep learning approaches allows for a wide applicability of the method.
Vector total fractional-order variation and its applications for color image denoising and decomposition

• Propose a vector total fractional-order variation for color image regularization. • Propose a VTV- β model for color image denoising and develop an alternating algorithm to solve it. • Propose a G β (Ω)model for color image decomposition and develop a new algorithm to solve it. • Experiments show that our denoising model has the highest average PSNR compared to relevant models in the literatures. In this paper, a vector total fractional-order variation (VTV- β ) is proposed. Then, VTV- β model and G β (Ω) model are proposed for color image denoising and decomposition, respectively. Some properties of the VTV- β are investigated and an alternative algorithm is used to solve the two models. Some experimental results are given to show the effectiveness and advantages of our methods.
A projective chirp based stair representation and detection from monocular images and its application for the visually impaired

The most prominent characteristic of a stair is that it has rigid form with periodic pattern of its steps. In this work, we exploit this periodic characteristic in view of geometrical rules. As a stair consists of equidistant nosing lines, under a perspective projection of camera, the projection of these lines on an image follows a projective chirplet transform. We propose to detect a stair by finding a group of lines that best satisfies a projective chirp model. The most advantage of the proposed techniques is that some missed noising lines and thus whole stair could be recovered. We validate the proposed method on both artificial and real datasets. The experimental results show a higher detection rate on different datasets. Finally, a real application alarming the visually impaired about stairs in indoor environments has been conducted and obtained 88.37% of accuracy. The implementations and datasets are made publicly available.
Automatic Assessment of Spoken Language Proficiency of Non-native Children

This paper describes technology developed to automatically grade Italian students (ages 9-16) on their English and German spoken language proficiency. The students' spoken answers are first transcribed by an automatic speech recognition (ASR) system and then scored using a feedforward neural network (NN) that processes features extracted from the automatic transcriptions. In-domain acoustic models, employing deep neural networks (DNNs), are derived by adapting the parameters of an original out of domain DNN. Automatic scores are computed for low level proficiency indicators - such as: lexical richness, syntax correctness, quality of pronunciation, discourse fluency, semantic relevance to the prompt, etc - defined by human experts in language proficiency. A set of experiments was carried out on a large set of data collected during proficiency evaluation campaigns involving thousands of students, manually scored by human experts. Obtained results are presented and discussed.