Reviewerly/exHarmony huggingface dataset:
Decomposition and transposition model-matching technique in the absence of plane-of-array measurements and the evaluation of tilted solar collectors and their harvested solar resource

Monthly optimum tilt angles of a flat-plate solar collector capable of south or north orientations were modeled for the tropical Caribbean island of Trinidad at 10.6° N latitude, using measured monthly average daily global and diffuse horizontal irradiation data from the period 2005–2010, as input to six transposition models comprising three isotropic (Liu and Jordan, Koronakis, and Badescu) and three anisotropic (Hay and Davies, ‘Hay and Davies, Klucher and Reindl', and Ma and Iqbal) models. The anisotropic models were in good agreement with one another, and an easily implementable technique was devised to determine the best suited decomposition-transposition model matches from six decomposition models due to Liu and Jordan and Klein, Page, Collares-Perreira and Rabl, Iqbal, Erbs et al., and Ibrahim. These matches can be used by territories having a similar climate to Trinidad but lacking measured diffuse horizontal irradiation or plane-of-array measurements, and the technique can be implemented globally by other host territories. The Ma and Iqbal model was chosen to simulate in detail the aforementioned collector as well as the one that was south-oriented only. A south/north-oriented collector required twelve monthly and two seasonal [April: 12.5° (north-oriented), October: 32° (south-oriented)], and annual [11.2° (south-oriented)] adjustments with corresponding gains in the collectable annual global solar irradiation compared to that on a horizontal surface of 9.3%, 7.9%, and 1.5%, respectively. In contrast, a south-oriented collector required eight monthly (September–April) and two seasonal (March: 0° and October: 35.5°) adjustments with lower corresponding gains of 7.6% and 7.1%, respectively.
A Hybrid Approach to Gender Classification using Speech Signal

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.
Ex Paucis Plura: Learning Affordance Segmentation from Very Few Examples

While annotating objects in images is already time-consuming, annotating finer details like object parts or affordances of objects is even more tedious. Given the fact that large datasets with object annotations already exist, we address the question whether we can leverage such information to train a convolutional neural network for segmenting affordances or object parts from very few examples with finer annotations. To achieve this, we use a semantic alignment network to transfer the annotations from the small set of annotated examples to a large set of images with only coarse annotations at object level. We then train a convolutional neural network weakly supervised on the small annotated training set and the additional images with transferred labels. We evaluate our approach on the IIT-AFF and Pascal Parts dataset where our approach outperforms other weakly supervised approaches.
Social Transportation: Social Signal and Technology for Transportation Engineering

Welcome to the first issue of the IEEE Transactions on Computational Social Systems (TCSS) this year, and Happy New Year to everyone. We would like to take this opportunity to express sincere gratitude to our editors, reviewers, authors, and readers for your support and great efforts devoted to TCSS. Also, we want to express the best wishes to you all, and hope you have a happy, healthy, and fruitful 2019.
Hybrid G-PRNU: Optimal parameter selection for scale-invariant asymmetric source smartphone identification

The ease in counterfeiting both origin and content of a video necessitates the search for a reliable method to identify the source of a media file - a crucial part of forensic investigation. One of the most accepted solutions to identify the source of a digital image involves comparison of its photo-response non-uniformity (PRNU) fingerprint. However, for videos, prevalent methods are not as efficient as image source identification techniques. This is due to the fact that the fingerprint is affected by the postprocessing steps done to generate the video. In this paper, we answer affirmatively to the question of whether one can use images to generate the reference fingerprint pattern to identify a video source. We introduce an approach called &#x201C;Hybrid G-PRNU&#x201D; that provides a scale-invariant solution for video source identification by matching its fingerprint with the one extracted from images. Another goal of our work is to find the optimal parameters to reach an optimal identification rate. Experiments performed demonstrate higher identification rate, while doing asymmetric comparison of video PRNU with the reference pattern generated from images, over several test cases. Further the fingerprint extractor used for this paper is being made freely available for scholars and researchers in the domain.
Towards self-adaptive efficient global optimization

In this paper, it is proposed to dynamically control the trade-off between exploration and exploitation for the efficient global optimization algorithm. To achieve this, we use the so-called Moment-Generating Function of Improvement criterion, in which an additional parameter, called “temperature”, is introduced to smoothly control the exploration/exploitation balance. It is proposed to adapt the temperature using two approaches: 1) a success-rate based mechanism 2) a self-adaptive algorithm where multiple different temperatures are used to generate new points. The temperature related to the best point is then selected for the next iteration. The self-adaptive algorithm is validated on two multi-modal functions and the result shows that the adaptive temperature mechanism speeds up the convergence rate of EGO, compared to the fixed temperature setting.
Wavelet transform approach for image processing – A research motivation for engineering graduates

The study of wavelet transform and its application to image processing are being incorporated in undergraduate and postgraduate syllabuses. This article discuss the basic concepts of image processing and its importance in real time application. The content of this work elaborates the importance of the image denoising and what is the necessity of image denoising in transmitted images in image enhancement topic among the final year project students as well as an emerging research topic among the research scholars worldwide. Hence this article is prepared in order to trigger those students’ knowledge in basic steps involved in image processing techniques in detail. This study highly focuses on how wavelet transform is involved in denoising and impact of denoising in an image. In general, the basic transforms like Fourier transform and discrete cosine transforms are used to transform the image from one domain to another domain. Further it does not require a priori knowledge of noisy image over a transmission time. Hence the proposed method can provide a satisfactory improvement in image enhancement, assured reduction in root mean square error and improvement in the peak signal-to-noise ratio. The peak signal-to-noise ratio value of noisy image to denoised images is improved more in different wavelet transform methods. Thus, this paper can help the students who are working with visual perception of images and machine learning algorithm under image processing environment.
A Technological Approach to Support the Care Process of Older in Residential Facilities

Faced with an increasing number of elderly housed in residential facilities, there is a request for greater transparency regarding the state of health of the guests and the level of assistance that these guests are offered. The OPENCARE project described in this article aims to respond to this need to promote communication between residential structures and guest families, by introducing a technological platform able to meet this requirement without the need to increase the workload of the operators. Therefore, this article describes the solution adopted, which are based both on data acquired from sensors and on those entered by the operators through a suitably designed interface.
Imitation Learning of Path-Planned Driving Using Disparity-Depth Images

Sensor data representation in autonomous driving is a defining factor for the final performance and convergence of End-to-End trained driving systems. When theoretically a network, trained in a perfect way, should be able to abstract the most useful information from camera data depending on the task, practically this is a challenge. Therefore, many approaches explore leveraging human designed intermediate representations as segmented images. We continue work in the field of depth-image based steering angle prediction and compare networks trained purely on either RGB-stereo images or depth-from-stereo (disparity) images. Since no dedicated depth sensor is used, we consider this as a pixel grouping method where pixel are labeled by their stereo disparity instead of relying on human segment annotations.
VIRTUAL REALITY SEBAGAI MEDIA PEMBELAJARAN RELATIVITAS KHUSUS BERBASIS GOOGLE CARDBOARD PADA SMARTPHONE ANDROID

The Global Education Census 2018 shows that Indonesian students are the highest technology users in the world. Unfortunately, the level of Indonesian science education is at a low level indicated by the Program of International Student Assessment 2015. The Learning physics science is ideally done through the experiments, but the theory of special relativity is difficult to understand. It is difficult because the theory has not happened in real life and no technology has advanced at the speed of light. To understand the theory, Virtual Reality is able to visualize the real concept of relativity by presenting an artificial world to the students. In its development, many virtual reality have been developed in the field of education by the experts because of the students' interest in this application. Virtual reality is more easily developed because it is supported by Google technology. Based on this, the researchers are interested in designing Virtual Reality applications as a Learning Media for Relativity on Android Smartphones. This Research and Development uses the Lee and Owens Model which consists of analysis, design, development, implementation, and evaluation. This research specializes in the development process which includes pre-production, production, and post-production. The application content explains the journey of human beings traveling at the speed of light and feeling the effects of the special theory of relativity. This application was developed using Unity 5.6.2f1 software and Google VR Cardboard. The Tests on the gyroscope and accelerometer concluded that the sensitivity level of the application is very good so that the rotation of the application is the same as the user's rotation. The development of virtual reality technology is carried out systematically, easily and effectively. The development of Virtual Reality using Unity and Google Cardboard on smartphones can be an effective solution, because of Unity 5.6.2f1 applications are free and unity operation is relatively easy for new developers. Hopefully researchers with the existence of this research, other developers can have a guide article in developing virtual reality.
RETRACTED ARTICLE: Translation analysis of English address image recognition based on image recognition

Abstract In the current English semantic recognition of mail, there are problems such as serious information distortion and unrecognizable, which affects the promotion of machine automation to recognize text in emails. This study combines the actual situation of the mail image to set the corresponding image processing algorithm, adopts the conversion from RGB data to gray data to perform image gray processing, and combines the weighted average gray algorithm to improve the image gray definition and softness. At the same time, this study uses homomorphic filtering to enhance the image, uses homomorphic filtering to enhance the sharpness of the text, and uses median filtering to enhance the image. Image edge recognition is performed with the Sobel color difference operator, and the text result is output. Combining experiments to study the performance of the algorithm, the research shows that the algorithm has certain practical effects, which can provide reference for subsequent related research.
Advanced Natural Language Processing

In this chapter, we are going to cover various advanced NLP techniques and leverage machine learning algorithms to extract information from text data and some of the advanced NLP applications with the solution approach and implementation.
Metric prefixes sought for extreme numbers

"Ronna" and "quecca" would help computer scientists keep pace with big data.
Quantum-Resistant Identity-Based Signature with Message Recovery and Proxy Delegation

Digital signature with proxy delegation, which is a secure ownership enforcement tool, allows an original signer to delegate signature rights to a third party called proxy, so that the proxy can sign messages on behalf of the original signer. Many real-world applications make use of this secure mechanism, e.g., digital property transfer. A traditional digital signature mechanism is required to bind a message and its signature together for verification. This may yield extra cost in bandwidth while the sizes of message and signature are relatively huge. Message recovery signature, enabling to reduce the cost of bandwidth, embeds a message into the corresponding signature; therefore, only the signature will be transmitted to the verifier and the message can further be recovered from the signature. In this paper, we, for the first time, propose a novel digital signature scheme in the identity-based context with proxy delegation and message recovery features and, more importantly, our scheme is quantum resistant, in a particular lattice-based signature. Our scheme achieves delegation information and signature existential unforgeability against adaptive chosen warrant and identity. Compared with the seminal lattice-based message recovery signature, our scheme is independent from public key infrastructure, realizes delegation transfer of signature rights, and compresses signature length ulteriorly. To the best of our knowledge, this paper is the first of its type.
Segmentation by Elastica Energy with L&lt;sup&gt;1&lt;/sup&gt; and L&lt;sup&gt;2&lt;/sup&gt; Curvatures: a Performance Comparison

In this paper, we propose an algorithm based on augmented Lagrangian method and give a performance comparison for two segmentation models that use the L 1 -and L 2 -Euler's elastica energy respectively as the regularization for image segmentation.To capture contour curvature more reliably, we develop novel augmented Lagrangian functionals that ensure the segmentation level set function to be signed distance functions, which avoids the reinitialization of segmentation function during the iterative process.With the proposed algorithm and with the same initial contours, we compare the performance of these two high-order segmentation models and numerically verify the different properties of the two models.
Quantum Query Complexity of Unitary Operator Discrimination

Unitary operator discrimination is a fundamental problem in quantum information theory. The basic version of this problem can be described as follows: Given a black box implementing a unitary operator U∈S:={U1, U2} under some probability distribution over S, the goal is to decide whether U=U1 or U=U2. In this paper, we consider the query complexity of this problem. We show that there exists a quantum algorithm that solves this problem with bounded error probability using $\lceil{\sqrt{6}\theta_{\rm cover}^{-1}}\rceil$ queries to the black box in the worst case, i.e., under any probability distribution over S, where the parameter θcover, which is determined by the eigenvalues of $U_1^\dagger {U_2}$, represents the “closeness” between U1 and U2. We also show that this upper bound is essentially tight: we prove that for every θcover > 0 there exist operators U1 and U2 such that any quantum algorithm solving this problem with bounded error probability requires at least $\lceil{\frac{2}{3 \theta_{\rm cover}}}\rceil$ queries under uniform distribution over S.
Cycle ORAM: A Practical Protection for Access Pattern in Untrusted Storage

Oblivious RAM (ORAM) is a cryptographic primitive to hide memory access pattern in untrusted storage, but the cost remains prohibitively expensive. This paper proposes cycle ORAM, an improvement of Ring ORAM for small client setting. It gains the most efficient bandwidth and client storage simultaneously. In the eviction, Cycle ORAM is the first scheme which does not need to evict all buckets in the target path. Instead, it only evicts the root bucket. Moreover, to preserve the stash size, the root bucket is expected to provide the largest empty storage by cycling shifting buckets in the server. On the other hand, to meet the security requirement, the randomness of blocks is carried out in the process of bucket reshuffle. Comparing to Ring ORAM, the simulation shows that stash size is smaller and the amortized bandwidth is 1.5× better. In addition, the size of buffer required in ORAM refreshment is reduced from O(logN) to almost a constant, where N is the number of distinct data blocks in the server.
A Two-Level Recurrent Neural Network Language Model Based on the Continuous Bag-of-Words Model for Sentence Classification

In this paper, a new two-level recurrent neural network language model (RNNLM) based on the continuous bag-of-words (CBOW) model for application to sentence classification is presented. The vector representations of words learned by a neural network language model have been shown to carry semantic sentiment and are useful in various natural language processing tasks. A disadvantage of CBOW is that it only considers the fixed length of a context because its basic structure is a neural network with a fixed length of input. In contrast, the RNNLM does not have a size limit for a context but only considers the previous context’s words. Therefore, the advantage of RNNLM is complementary to the disadvantage of CBOW. Herein, our proposed model encodes many linguistic patterns and improves upon sentiment analysis and question classification benchmarks compared to previously reported methods.
New quantum codes from Hermitian dual-containing codes

Hermitian dual-containing codes play an important role in the constructing quantum codes. In this paper, we present a new criterion of Hermitian dual-containing code based on the rank of generator matrix for a linear code. Then, using the criterion, we construct a class of new quantum maximum-distance-separable (MDS) codes and some new quantum codes.
Variational autoencoding tissue response to microenvironment perturbation

This work applies deep variational autoencoder learning architecture to study multi-cellular growth characteristics of human mammary epithelial cells in response to diverse microenvironment perturbations. Our approach introduces a novel method of visualizing learned feature spaces of trained variational autoencoding models that enables visualization of principal features in two dimensions. We find that unsupervised learned features more closely associate with expert annotation of cell colony organization than biologically-inspired hand-crafted features, demonstrating the utility of deep learning systems to meaningfully characterize features of multi-cellular growth characteristics in a fully unsupervised and data-driven manner.
Adaptive Pushing of Learning Resources in Fragmented English Reading

With the development of social economy, science and technology, and the popularity of mobile Internet, human society has gradually entered the information age.The rapid growth and disorderly distribution of information have posed challenges to English learners who conduct independent and individualized learning anytime and anywhere.Reading plays an important role in language learning.Traditionally, it is difficult for the English reading method based on chapters to meet the fragmented learning needs of learners who are often in a mobile environment.Through the literature research, this paper designed the five-dimensional feature model of learners and the three-dimensional feature model of English reading resources in the fragmented learning environment.Combined with the machine learning algorithm ID3, the decision tree classification model of English fragmented reading was constructed to push resources, adapt to the individual needs of the learners, and then specifically enhance the different aspects of the learner's reading ability, thereby improving the pass rate of the College English Test-Band Four.
Bandwidth selector for nonparametric recursive density estimation for spatial data defined by stochastic approximation method

In this article we propose an automatic selection of the bandwidth of the recursive kernel density estimators for spatial data defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and the stepsize which minimize the MWISE (Mean Weighted Integrated Squared Error), the recursive estimator will be quite similar to the nonrecursive one in terms of estimation error and much better in terms of computational costs. In addition, we obtain the central limit theorem for the nonparametric recursive density estimator under some mild conditions.
Public “Cloud” Provisioning for Venus Express VMC Image Processing

In this paper, we consider the implementation of the “cloud” computing strategy to study data sets associated to the atmospheric exploration of the planet Venus. More concretely, the Venus Monitoring Camera (VMC) onboard Venus Express orbiter provided the largest and the longest so far set of ultraviolet (UV), visible and near-IR images for investigation of the atmospheric circulation. To our best knowledge, this is the first time where the analysis of data from missions to Venus is integrated in the context of the “cloud” computing. The followed path and protocols can be extended to more general cases of space data analysis, and to the general framework of the big data analysis.