Reviewerly/exHarmony huggingface dataset:
Orthonormal balances as a means of characterizing dietary exposure

Evaluating the effect of nutritional components on health outcomes is hampered by technical difficulties that are closely related to the compositional nature of dietary data. Compositional data consist of parts of a whole and, on the basis of the assumption that any relevant information they provide is contained in the ratios between them, procedures based on log-ratio transformations have been proposed for their analysis. It has been previously suggested that nutrient compositions can be analyzed using isometric log-ratio transformations which allow the compositions to be represented by a system of orthogonal coordinates corresponding to the log-contrasts between the compositional parts. One particular expression of these contrasts is called balances, and the aim of this note is to provide a step-by-step description of how to construct orthonormal balances and show how they can be applied to dietary data by means of an example based on data coming from an Italian population-based study. Nutrient balances are new variables that can be included as covariates in regression models. After fitting a linear regression model using estimates of relative fat mass as a response variable, scores indicating the obesogenic potential of foods can be defined on the basis of their nutritional contents and the estimated effect of the balances on relative fat mass. This approach to evaluating the relationship between diet and health outcomes provides insights into the roles of specific nutrient ratios and foods within a comprehensive and integrated framework that complies with the essentially compositional nature of dietary data.
Bridging the Digital and the Physical User Experience: Analysis of Academic Library Floor Plans

In library user experience (UX) literature, physical and digital spaces are often studied separately. In academic libraries, different stakeholders often control and operate the logistics of each set of spaces. Additionally, library UX studies seldom consider the physical experiences of users beyond the borders of library websites or buildings. Our survey of 37 U.S. academic library websites found that 97% had some kind of digital representations of physical spaces – predominantly floor plans. A content analysis of those floor plans found that many were poorly designed, and identified two specific themes: non-accessible content and a lack of comprehensive information to facilitate the user journey. This paper offers several recommendations for building more accessible digital representations of physical spaces for the library website. These trends and recommendations are intended to be instructive for managers of library websites and elucidate larger themes about academic libraries.
Autoencoder based Robust Transceivers for Fading Channels using Deep Neural Networks

In this paper, we design transceivers for fading channels using autoencoders and deep neural networks (DNN). Specifically, we consider the problem of finding (n, k) block codes such that the codewords are maximally separated in terms of their Hamming distance using autoencoders. We design an encoder and robust decoder for these block codes using DNNs. Towards this, we propose a novel training methodology for the DNN that attempts to maximize the minimum Hamming distance between codewords. We propose a loss function for this training which has stable weight updates during back propagation compared to other loss functions reported in the literature. The block codes learned using the proposed methodology are found to achieve the maximal Hamming distance separation that is known in theory. We also propose two different receiver architectures based on fully connected deep neural network (FCDNN) and bidirectional recurrent neural network (BRNN) that are suited for complex fading channels. The proposed DNN based receiver is shown to achieve significantly better error performance when compared to their classical counterparts in the presence of channel model mismatches. In the presence of model mismatches such as imperfect channel knowledge and noise correlation, the proposed DNN based transceiver is shown to offer increased reliability and robustness than the conventional transceiver.
Broad Graph-Based Non-Negative Robust Continuous Clustering

Recently, the robust continuous clustering (RCC) has been proposed for unsupervised data classification. The RCC algorithm integrates representation learning and clustering by seeking the balance of the distance of data between intra-cluster and inter-cluster. But the inter-cluster distance in RCC highly depend on the pairwise graph of neighbors, which are constructed by the less-expressive original data. This hampers the performance of RCC on complex epically high-dimensional data. Encouraged by the hybrid feature learning and universal approximation capabilities of the broad learning system (BLS), we first propose a broad graph-based robust continuous clustering algorithm to upgrade RCC. The proposed algorithm measures the distance of the pairwise data with the feature learned from the BLS when constructing the graph. Then to further enhance the clustering performance of the RCC on high-dimensional data, we embed the non-negative matrix factorization (NMF) into the broad graph-based RCC algorithm. By resolving the original data into the basis matrix and coefficient matrix and performing the broad graph-based RCC on the coefficient matrix, the proposed approach takes full advantages of the abundant representation from the BLS and the sparsity and interpretability of NMF coefficients. We verified the proposed algorithms on the synthetic dataset, the UCI dataset and some real-world high dimension datasets. All the empirical results show the proposed algorithms outperform the baselines and improve the clustering performance of RCC effectively.
The Infographic Process of Synthesizing Complex Information About the Individual Legacies of Retired Teachers and Researchers in Art and Design

The work presented in this paper is part of the research project “Wisdom Transfer” (WT). The WT project stems from the evidence that there is insufficient inscription and use of individual knowledge and experience of ageing and retired art and design professors and researchers, in Portugal. In this project there is a great cultural approach from which emerges a high responsibility towards the current artistic education system, in which teachers and students are involved. Through the drawing of an infographics, it is intended that the readers - especially current art and design students - have access to information about the education system of that time. In this article, we will first present the research project “Wisdom Transfer”; we will develop a literature review; and we will present the study of the infographic solution we are developing for this project.
Gestalt descriptions for deep image understanding

Abstract In this work, we present a novel visual perception-inspired local description approach as a preprocessing step for deep learning. With the ongoing growth of visual data, efficient image descriptor methods are becoming more and more important. Several local point-based description methods were defined in the past decades before the highly accurate and popular deep learning methods such as convolutional neural networks (CNNs) emerged. The method presented in this work combines a novel local description approach inspired by the Gestalt laws with deep learning, and thereby, it benefits from both worlds. To test our method, we conducted several experiments on different datasets of various forensic application domains, e.g., makeup-robust face recognition. Our results show that the proposed approach is robust against overfitting and only little image information is necessary to classify the image content with high accuracy. Furthermore, we compared our experimental results to state-of-the-art description methods and found that our method is highly competitive. For example it outperforms a conventional CNN in terms of accuracy in the domain of makeup-robust face recognition.
Modulation Classification using Joint Time and Frequency-domain Data

In this paper we propose a modulation classification scheme based on deep learning and convolutional neural networks. The proposed solution is based on I/Q amplitude samples that are classified into a set of 24 modulations, including analog and digital modulations. We investigate the adoption of different features in the training data. The features are computed using I/Q amplitude samples and include statistical information obtained in the time-domain and frequency-domain. Given the high amount of features considered in the training data, we investigate the possibility of projecting the features' data into a set of uncorrelated variables through the Principal Component Analysis procedure. We evaluate the classification performance for the different modulations and signal-to-noise ratio (SNR) values. Moreover, we also quantity the performance of the classifier when the features' data is projected into a subset of the uncorrelated variables. Finally, we compare the performance of the classifier with other works already available in the literature, assessing the effective performance gains of the proposed solution.
Parallel Processing of Frequent Itemset Based on MapReduce Programming Model

Searching frequent itemset in large size diverse database is one of the most important data mining problem and as existing algorithms are insufficient in mechanism that enables automatic parallelization, fault tolerance and data distribution. Solution to this issue we design algorithm using MapReduce programming model. The overarching aim is to enhance the performance of parallel frequent itemset mining on Hadoop. Incorporating ultra-metric tress to improve more efficiency of mining frequent itemset and comparing Apriori algorithm and FP-Growth algorithm based on some parameters. We implement the algorithm with dataset of Market Basket Analytics
Password‐based encryption approach for securing sensitive data

Abstract The direction of computing is affected and lead by several trends. First, we have the Data Overwhelm from Commercial sources (eg, Amazon), Community sources (eg, Twitter), and Scientific applications (eg, Genomics). Next, we have several light‐weight clients belong to many devices spanning from smartphones, tablets to sensors. Then, clouds are getting popular due to their advantages since they are cheaper, greener, and easy to use compared to traditional systems. Finally, sensitive data stored as a plain text on a cloud system would be vulnerable to unauthorized access and the security become an important aspect. We believe that these advancements steer both research and education, and will put together as we look at data security in the cloud. We introduce a password‐based encryption (PBE) approach to protect sensitive data, investigate the performance metrics of the proposed approach, and present the experimental results for the key generation and the encryption/decryption calculations.
Theory of quantum path computing with Fourier optics and future applications for quantum supremacy, neural networks and nonlinear Schrödinger equations

The scalability, error correction and practical problem solving are important challenges for quantum computing (QC) as more emphasized by quantum supremacy (QS) experiments. Quantum path computing (QPC), recently introduced for linear optic based QCs as an unconventional design, targets to obtain scalability and practical problem solving. It samples the intensity from the interference of exponentially increasing number of propagation paths obtained in multi-plane diffraction (MPD) of classical particle sources. QPC exploits MPD based quantum temporal correlations of the paths and freely entangled projections at different time instants, for the first time, with the classical light source and intensity measurement while not requiring photon interactions or single photon sources and receivers. In this article, photonic QPC is defined, theoretically modeled and numerically analyzed for arbitrary Fourier optical or quadratic phase set-ups while utilizing both Gaussian and Hermite-Gaussian source laser modes. Problem solving capabilities already including partial sum of Riemann theta functions are extended. Important future applications, implementation challenges and open issues such as universal computation and quantum circuit implementations determining the scope of QC capabilities are discussed. The applications include QS experiments reaching more than [Formula: see text] Feynman paths, quantum neuron implementations and solutions of nonlinear Schrödinger equation.
Leakage-Free Dissemination of Authenticated Tree-Structured Data With Multi-Party Control

With the increasing development of cloud computing, a number of users choose to outsource their data to a remote database service provider and enjoy flexible data sharing with multiple parties. However, the integrity and confidentiality of outsourced data in a remote cloud server are the main threats users are concerned about. The tree structure is one of the most widely used data organization structures. It is crucial to guarantee the integrity and privacy not only for the content but also for the structure if sensitive information is organized in this structure. At present, despite some solutions have been put forward, none of these considers the additional redaction attack on tree-structured data from attackers while sharing data with others. In this article, we provide a construction of secure redactable signature scheme for tree-structured data which features a multi-party control on the redaction of vertexes in a tree while the authenticity and privacy are assured. Then, we prove that our scheme is unforgeable, private, and transparent. Furthermore, extensive theoretical and experimental analyses are conducted to assess the efficiency of our scheme. The results demonstrate our scheme achieves multi-party redaction control without sacrificing sensible resources especially when a tree has a larger number of vertexes and siblings per vertex.
A Computationally Efficient Real-Time Vehicle and Speed Detection System for Video Traffic Surveillance

This research article presents a computationally efficient real-time vehicle and speed detection method for video traffic surveillance. It applies training and testing phases in parallel over pre-trained video surveillance dataset by employing YOLO algorithm. In order to enhance the computation time, dynamic background subtraction method has been applied in conjunction with YOLO algorithm. The aim is to reduce the total number of frames required to be considered for further processing. DBS has strong adaptability to changed background and can detect the movement in frame. In addition to vehicle detection, the proposed algorithm can detect the speed of detected vehicles using a centroid feature extraction method. Our proposed method is a dual combination of speed and accuracy. The performance of the proposed algorithm is analyzed over 2018 NVIDIA AI City challenge dataset and a comparative analysis is done with various state-of-the-art vehicle detection methods (CNN, R-CNN, Fast R-CNN, and FastYOLO).
Knowledge-based framework for estimating the relevance of scientific articles

The volume of published papers provided by the scientific community has increased over the last years in a drastic way. This fact has led to having a considerable growth of the topics covered by different publications. Despite topics under discussion on these publications were usually regarded as cutting edge subjects when released in conferences and journals, the restless evolution of science may have faded their relative importance away over the years. This issue undoubtedly poses big challenges to those researchers interested in gathering information to enrich their own background. Consequently, the development of a system able to automatically organize and provide relevance to scientific papers should play a crucial role to address the aforementioned problem. In this paper, the Webelance framework is presented. It makes use of a lexicon and Machine Learning techniques to accomplish these tasks. It has been built by using specific metrics for the scientific domain to measure the relative importance of papers. Several experiments using more than 50,000 articles focused on the medicine domain have been addressed to illustrate the viability of the proposal. The obtained results both confirm the usability of the system and its good performance.
Dual feature extractor generative adversarial network for colorization

The coloring efficiency of most colorization algorithms is suboptimal. Therefore, the dual feature extractor generative adversarial network was designed for colorization. The U-Net-like network was used as the trunk network in the generator. The encoder was used to extract the local features of a grayscale image. The branch extractor used the ResNeXt network that was added to the SE module as a high-level feature extractor to extract the global features of the grayscale image. The two features were fused to predict the chrominance. This strategy prevented color leakage and detail loss in colorization. Moreover, the adversarial loss was added to minimize the tendency toward acquiring an unsaturated tone. The effectiveness of the proposed method was evaluated quantitatively and qualitatively.
Geopriv4j

The breach of users' location privacy can be catastrophic. To prevent privacy breaches, numerous location privacy methods have been developed in the last two decades. However, they have not been widely adopted in location-based applications. As a result, users' true location data is directly shared with untrusted service providers or researchers, raising concerns about location privacy. In this paper, we describe our effort to develop an open source repository, named Geopriv4j, in order to facilitate the adoption of location privacy methods in location-based services and research studies. Geopriv4j emphasizes on the practicality of location privacy, by identifying local, on-the-fly privacy methods under multiple categories. To facilitate adoption, Geopriv4j unifies the implementation of location privacy in Java, and provides usage examples as well as a sample Android app. To validate our implementation, we evaluate the location privacy methods in Geopriv4j with CPU, memory, and run time measures, using synthetically generated location traces.
Efficient Cash Withdrawal from ATM Machine Using QRcode Technology

Nowadays, dependency on banking in the virtual world has been increased to the peak position. To make it consistent advanced technologies should be used. As OTP is currently used worldwide for security purposes, it can be overruled by QR code. Main advantage of QR code over OTP data storage.OTP can only confirm that the user is authorised user and not some third party is involved in this transaction while QR code not only confirms the authorised user but QR code itself can store information such as transaction id, transaction date, time and also amount of transaction. So, there is no need of explicitly keeping track of transaction every transaction. Aim of this paper to enhance the functionality of ATM machine using android application. Proposed system is combining the ATM and mobile banking and minimizes the time of withdrawing cash from ATM. This will increase the speed of transaction almost three times fast; could have excellent impact on customer's satisfaction. With the help of QR code information get encrypted so it also increases security. As the population increasing ATM queues will be longer day by day. By implementing proposed system current system will not hampered, by doing some minor changes in existing system it will be possible to get cash within seconds. According to analyst report, cost of transaction using mobile application is almost ten times less than ATM and about fifty times less, if physical bank branch used.
Becoming Travelers

Materially driven research can often feel like a long series of failed experiments, which ends with us telling only the story of how we succeeded. We propose engaging with the making experience as travelers, losing ourselves in the making while preserving the outcomes of each experiment. In doing so, we might be able to, on one hand document this iterative journey as a research outcome in itself and, on the other, identify the roads not travelled as opportunities and starting points for new projects. We present an open ended exploration that led us to articulating the possibilities of becoming travelers in the design process.
Ghosts in the Smart Home

We are in the midst of a 'post-anthropocentric' turn in design, research and technology. The term refers to a renewed interest in a wide range of concepts, theoretical perspectives, and methodologies. Ghosts in the Smart Home is a post-anthropocentric experiment which manifests as a film whose cast of characters are all internet connected 'smart' devices. The motivation is to prototype and establish new ways to see, to be, and to know, which respond to the 21st century's complex socio-technical systems.
A Classification: using Back Propagation Neural Network Algorithm to Identify Cataract Disease

Artificial Neural Networks are often used in the fields of pattern recognition, speech, and image recognition, where high levels of computation are needed. One method that can be used is the Back-propagation Neural Network. The method can be used to identify several diseases, one of which is a cataract. A cataract is one of the most significant diseases that can cause blindness. Cataracts consist of three levels, namely mature cataracts, immature cataracts, and hyper-mature cataracts. The testing method uses two parameter values, namely epoch with value 1000,5000,10000 and learning rate with value 0.01, 0.05, 0.1. From the test, it was found that the best parameters of the above trial results were epoch with a value of 10000 and learning rate with a value of 0.1 on 80 experimental data. In experiments conducted to identify normal, mature, immature, and hyper mature obtained an accuracy of 100%, 95%, 85%, and 90%. The percentage value of the accuracy of the test results using the BPN method is 92.5%.
A Study on Hammering Test using Deep Learning

Technical infrastructure such as bridges and tunnels are critical in economic activities, but in recent years, aging of them has progressed, and demand for checking the soundness of them is expanding. There are several technologies to inspect the aging process of the structures to find cracks on a wall or flacking inside of concrete. Among the technologies to diagnose a structure, the hammering test is a traditional, easy, simple, and effective method. We developed an AI hammering checker by using the k-mean method. However, accuracy is not good in some tasks. So, we started to develop the new version of the AI hammering checker by using deep learning. We explain the outline of the new system in this paper. As a result, the accuracy was increased up to 93.9%.
Real Time Smart Mirror System Using Internet on Things

Smart device configuration figured with interactive media knowledge to give agreeable, advantageous and secure individual administrations in home turns out to be progressively critical in the period of data correspondence innovation. Advanced mirror gives responsive interface to working framework is smart mirror. The mirror with the glass can be utilized. The graphics on the display come through clearer with two side glasses. Our daily routine life is involved in such a way that optimizing time becomes key factor. On basis of user studies and implementation, the rapid growth of smart devices which gives multiple services of data through a user interface of a mirror. Smart Mirrors will replaces ordinary mirrors used daily provides mirror and PC supported data information to user. This project is collect information from web and can be seen on smart mirror. The result of smart mirror gives the complete information about weather, date, time, mailbox, activity list by using Raspberry Pi 3 card. The face recognition module in implemented for authorized access.