Reviewerly/exHarmony huggingface dataset:
ID Preserving Face Super-Resolution Generative Adversarial Networks

We propose an ID Preserving Face Super-Resolution Generative Adversarial Networks (IP-FSRGAN) to reconstruct realistic super-resolution face images from low-resolution ones. Inspired by the success of generative adversarial networks (GAN), we introduce a novel ID preserving module to help the generator learn to infer the facial details and synthesize more realistic super-resolution faces. Our method produces satisfactory visual results and also quantitatively outperforms state-of-the-art super-resolution methods on the face datasets including CASIA-Webface, CelebA, and LFW datasets under the metrics of PSNR, SSIM, and cosine similarity. In addition, we propose a framework to apply IP-FSRGAN model to address the face verification task on low-resolution face images. The synthesized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> super-resolution faces achieve a verification accuracy of 97.6%, improved from 92.8% of low resolution faces. We also prove by experiments that the proposed IP-FSRGAN model demonstrates excellent robustness under different downsample scaling factors and extensibility to various face verification models.
Shadow-Chain: A Decentralized Storage System for Log Data

Better use of personal health and clinical data from different stakeholders is essential and highly significant. However, the sharing of such data could easily raise concerns over preserving personal privacy, particularly when the risk of disclosure is uncertain. To tackle this issue, shadow- chain, a decentralized storage system for log data based on blockchain, is proposed for realizing data sharing between parties that do not trust each other. In the system, a behavior monitor is designed to intercept the data reading activities. Through an asynchronous read-write mechanism, the monitor can minimize operational impact on the source's storage system. Meanwhile, the homomorphic encryption scheme and the distributed log collection system are adopted to guarantee the validity and reliability of the log data. Furthermore, the hybrid storage strategy is developed to reduce the storage overhead of the log data. In the strategy, only the hash of log data is stored in the blockchain of the shadow-chain, while the data itself and the associated index information are placed in common storage media. Experiments have been conducted to estimate the performance of our shadow-chain. The results show that the impact of shadow-chain on the source storage system can be controlled within 5 percent, and 75 percent log storage overhead can be reduced through hybrid storage strategy.
Building Science Gateways for Humanities

Building science gateways for humanities content poses new challenges to the science gateway community. Compared with science gateways devoted to scientific content, humanities-related projects usually require 1) processing data in various formats, such as text, image, video, etc., 2) constant public access from a broad audience, and therefore 3) reliable security, ideally with low maintenance. Many traditional science gateways are monolithic in design, which makes them easier to write, but they can be computationally inefficient when integrated with numerous scientific packages for data capture and pipeline processing. Since these packages tend to be single-threaded or nonmodular, they can create traffic bottlenecks when processing large numbers of requests. Moreover, these science gateways are usually challenging to resume development on due to long gaps between funding periods and the aging of the integrated scientific packages. In this paper, we study the problem of building science gateways for humanities projects by developing a service-based architecture, and present two such science gateways: the Moving Image Research Collections (MIRC) – a science gateway focusing on image analysis for digital surrogates of historical motion picture film, and SnowVision - a science gateway for studying pottery fragments in southeastern North America. For each science gateway, we present an overview of the background of the projects, and some unique challenges in their design and implementation. These two science gateways are deployed on XSEDE’s Jetstream academic clouding computing resource and are accessed through web interfaces. Apache Airavata middleware is used to manage the interactions between the web interface and the deep-learning-based (DL) backend service running on the Bridges graphics processing unit (GPU) cluster.
Discovering dependencies with reliable mutual information

Abstract We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or $$\alpha $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math> -approximate top- k dependencies? We address the first question by adopting information-theoretic notions. Specifically, we consider the mutual information score, for which we propose a reliable estimator that enables robust optimization in high-dimensional data. To address the second question, we then systematically explore the algorithmic implications of using this measure for optimization. We show the problem is NP-hard and justify worst-case exponential-time as well as heuristic search methods. We propose two bounding functions for the estimator, which we use as pruning criteria in branch-and-bound search to efficiently mine dependencies with approximation guarantees. Empirical evaluation shows that the derived estimator has desirable statistical properties, the bounding functions lead to effective exact and greedy search algorithms, and when combined, qualitative experiments show the framework indeed discovers highly informative dependencies.
The Effect of Several Kernel Functions to Financial Transaction Anomaly Detection Performance using One-Class SVM

Anomalies in data become ubiquitous and often unavoidable. Despite it might be caused by various errors during the data collection or transportation, some anomalies potentially give important information such as indication of a new underlying process. The aim of anomaly detection task is to determine all such instances in the input dataset in a data-driven fashion. In the past decade, the proliferation of anomaly data in many domains have raised research interest resulted in a plethora of anomaly detection methods A vast number of previous study reports suggested that there is no single model will achieve the best performance for every dataset. This paper presents empiric results on the effect of several kernel functions to performance of One-Class Support Vector Machine (OC-SVM) as an anomaly detector model. The proposed model is tested using financial transactions of microfinance service dataset from an Indonesian Bank. The empiric results showed that OC-SVM model with Sigmoid and RBF kernels achieve the best statistically significant value of training, validation, and testing accuracies than the OC-SVM model with no-kernel, linear kernel and polynomial (degree 3,4,5,6) kernels.
Measure The Level Of Success In Using Google Drive with the Kuder Richardson (KR) Reliability Method

The Google Sheets is a cloud technology part of Google Drive. Google Drive is a cloud operating system owned by Google's search engine. National Elementary School (SDN) to make a report book or Basic Education Databook (Dapodik) still use Microsoft Excel, which has the effect of delaying the delivery of report results to parents. This happens because to fill in the DAPODIK score must be filled by teachers who teach subjects in class. This study aims to provide knowledge to teachers in collaborating to fill DAPODIK and measure how reliable the delivery of material received by each teacher. Using the method of reliability Kuder Richardson KR-20 and KR-21 produces a value before the training on reliability values KR-20 = 0.6328, and KR-21 = 0.6000, after the training, the reliability values KR-20 = 0.5788 and KR-21 = 0.6231. From these results, it is known that the value after training for KR-21 has increased and according to the conventional correlation coefficient. In future research can be re-developed by making application system recommendations in determining teachers in schools to become instructors in accordance with the capabilities possessed based on the reliability testing results of the KR-20 and KR-21.
Protecting location privacy from untrusted wireless service providers

Access to mobile wireless networks has become critical for day-to-day life. However, it also inherently requires that a user's geographic location is continuously tracked by the service provider. It is challenging to maintain location privacy, especially from the provider itself. To do so, a user can switch through a series of identifiers, and even go offline between each one, though it sacrifices utility. This strategy can make it difficult for an adversary to perform location profiling and trajectory linking attacks that match observed behavior to a known user.
Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

Graph embedding aims to learn the low-dimensional representation of nodes in the network, which has been paid more and more attention in many graph-based tasks recently. Graph Convolution Network (GCN) is a typical deep semisupervised graph embedding model, which can acquire node representation from the complex network. However, GCN usually needs to use a lot of labeled data and additional expressive features in the graph embedding learning process, so the model cannot be effectively applied to undirected graphs with only network structure information. In this paper, we propose a novel unsupervised graph embedding method via hierarchical graph convolution network (HGCN). Firstly, HGCN builds the initial node embedding and pseudo-labels for the undirected graphs, and then further uses GCNs to learn the node embedding and update labels, finally combines HGCN output representation with the initial embedding to get the graph embedding. Furthermore, we improve the model to match the different undirected networks according to the number of network node label types. Comprehensive experiments demonstrate that our proposed HGCN and HGCN<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msup><mml:mrow /><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:math> can significantly enhance the performance of the node classification task.
Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.
Sensing data: Encountering data sonifications, materializations, and interactives as <i>knowledge objects</i>

Digital and networked media are characterized by a fundamental disconnection between the modes of operating of these media, the human sensorium and knowledge. In order for humans to consciously participate in this expanded domain of sensibility, additional mediation is required to ‘presentify’ what is not accessible to human perception. This situation has motivated the creation of manifold ‘data objects’ in the form of visualizations, soundscapes and sonifications, 3D materializations, and data-driven interactives in the neighboring domains of art, design, science, and humanities scholarship. These new knowledge objects, as we propose to call them, both produce and mediate knowledge in the process of making data experienceable. They turn that what essentially does not correlate to human sensory capacities into something that the human sensorium is capable of engaging with. As such, they can play a crucial role in providing access, and thereby modalities of critical and creative relating, to what Hansen (2015) describes as the expanded domain of sensibility. Yet, the effects and implications of the sensory specificities of these knowledge objects remain underacknowledged, as are the potentials of these modes of presentifying data. Here, we explore and compare a diversity of such knowledge objects and look at their different media modalities and different experiential qualities, and how these afford ways of knowing. By approaching several knowledge objects as ‘theoretical objects’ (Bal (2013); Damisch in Bois et al. (1998), we investigate which and how experimental sensory techniques are used for data presentification. With this, we want to draw attention to the onto-epistemological implications of the design of these knowledge objects, the main implications being relationality and performativity. Understanding the potential and implications of this performativity would benefit from what we call a creative humanities approach that combines insights from the critical and digital humanities with those from the fields of creative arts and design.
Automatic Sinusoidal Curves Detection in Borehole Images Using the Iterated Local Search Algorithm

Borehole stability is one of the main factors influencing the success of an oil well drilling process. Fractures and beddings (sedimentary layers) are important stability features commonly identified in borehole imaging tools. Their visual presentation in the 2D Ultrasonic Borehole Imager (UBI) consists of sinusoidal curves, in which automatic detection is not trivial. Several previous works proposed different solutions, most of them based on the Hough transform (HT). Nevertheless, HT based solutions have a substantial computational cost. In this work, we propose an innovative approach to identify the harmonic curves related to fractures and sedimentary layers in borehole images based in the Iterative Local Search heuristic, reducing the computational cost of the task significantly.
OMSCS

Lessons learned from the first five years of Georgia Tech's Online Master of Science in Computer Science program.
Efficient Deep Learning Approach for Multi-label Semantic Scene Classification

Recognition of semantic scene is a tedious task in which a single scene image is correlated with multiple classes. Also, the semantic scene classification is represented as multi instance multi label (MIML) classification, a part of multi label (ML) classification. Identifying the complex semantic relationship between the classes is the major issue in the traditional Machine Learning Technique (MLT) for MIML learning. Therefore, an efficient Deep Learning framework, Convolutional Neural Network (CNN) with a Gaussian blur filter is proposed. The proposed framework supports to identify the complex class correlations between various classes. Experimental results show that the proposed CNN for ML learning scene classification achieves a better predictive performance of 90% and a hamming loss of 0.13 when compared with the existing Machine Learning Techniques (MLT).
TWO LEVEL CREDIBILITY-LIMITED REVISIONS

Abstract In this paper, we propose a new kind of nonprioritized operator which we call two level credibility-limited revision . When revising through a two level credibility-limited revision there are two levels of credibility and one of incredibility. When revising by a sentence at the highest level of credibility, the operator behaves as a standard revision, if the sentence is at the second level of credibility, then the outcome of the revision process coincides with a standard contraction by the negation of that sentence. If the sentence is not credible, then the original belief set remains unchanged. In this article, we axiomatically characterize several classes of two level credibility-limited revision operators.
Comparison of Web Services for Sentiment Analysis in Social Networking Sites

Abstract With various type of web services available, it is hard to identify and compare which of the free access web services work best in analysing sentiment of extremist content in social networking sites. For that purpose, a generic approach by working with API of web service using PHP programming language is used to test each dataset that was extracted based on the keyword ‘extremism’. Data from both Twitter and Facebook has been used as these two are the most powerful platforms for expressing one’s feeling. The comparison for web service is done based on the analysis of its accuracy, precision, recall and f-measures in obtaining the lowest score of mean square error (MSE). Four sentiment analysis web services are used which are Sentiment Analyzer, Aylien, ParallelDots, and MonkeyLearn. From the comparison, MonkeyLearn obtained the best final results among all web services with the lowest MSE score of 14%. For the benefit of other researchers, the finding of this will reveal the suitable web service for analysing sentiment issues as critical as extremism.
Bioproductivity of Dnieper Reservoirs Analysis by Inductive Methods

BIoproDuCtIVIty oF DnIeper reserVoIrs AnAlysIs By InDuCtIVe metHoDsThe task was to determine the factors that have the most significant influence on the state of water in the Dnieper reservoirs by constructing a model of dependence of the concentration of chlorophyll a in phytoplankton according to long-term observations in Kremenchug and Kakhovka reservoirs.The results of observations of the Institute of Sciences in 1976Sciences in -1993.Various inductive methods were used to obtain a satisfactory result.Algorithms: linear regression of (LR), LASSO, combinatorial algorithm of (COMBI) GMDH and correlation-rating algorithm (CRA).
Introducing Modularity and Homology in Grammatical Evolution to Address the Analog Electronic Circuit Design Problem

We present a new approach based on grammatical evolution (GE) aimed at addressing the analog electronic circuit design problem. In the new approach, called multi-grammatical evolution (MGE), a chromosome is a variable-length codon string that is divided into as many partitions as subproblems result from breaking down the original optimization problem: circuit topology and component sizing in our case. This leads to a modular approach where the solution of each subproblem is encoded and evolved in a partition of the chromosome. Additionally, each partition is decoded according to a specific grammar and the final solution to the original problem emerges as an aggregation result associated with the decoding process of the different partitions. Modularity facilitates the encoding and evolution of the solution in each subproblem. On the other way, homology helps to reduce the potentially destructive effect associated with standard crossover operators normally used in GE-based approaches. Seven analog circuit designs are addressed by an MGE-based method and the obtained results are compared to those obtained by different methods based on GE and other evolutionary paradigms. A simple parsimony mechanism was also introduced to ensure compliance with design specifications and reduce the number of components of the circuits obtained. We can conclude that our method obtains competitive results in the seven circuits analyzed.
Social Media Representations of Law Enforcement within Four Diverse Chicago Neighborhoods

The perception of excessive use of force by law enforcement towards minorities has become an increasing focus of attention in the national media and public consciousness. With greater ability to record conflicts using smartphones and dissemination of videos via social media, the public may more readily judge the circumstances of law enforcement interactions. The purpose of this study was (a) to understand the general sentiment about law enforcement on social media among communities of color in Chicago, (b) to see if local or national incidents of police conflicts with people of color were mentioned, and (c) identify key themes within these social media posts. Publicly available social media posts were collected from four ethnically and socioeconomically diverse neighborhoods in Chicago. Using a five-person team and keyword searches, the posts were reviewed for content and abstracted if they pertained to law enforcement. They were then analyzed in stages of open, axial, and selective coding methods. Eight main themes were identified. These included the following: (a) law enforcement participation in the community, (b) law enforcement activity in the neighborhood, (c) posts regarding criminal activity, (d) immigration, (e) political protests against police, (f) African American men and law enforcement, (g) sympathy with Sandra Bland case, and (h) #Blacklivesmatter. There appears to be chasm of trust between law enforcement and communities of color. Our findings may help inform law enforcement, public policy-makers, and social workers in their efforts to better address these issues and to institute policies and interventions that not only bridge this gap but also strengthen and empower these communities.
ivPair

The emergence of advanced in-vehicle infotainment (IVI) systems, such as Apple CarPlay and Android Auto, calls for fast and intuitive device pairing mechanisms to discover newly introduced devices and make or break a secure, high-bandwidth wireless connection. Current pairing schemes are tedious and lengthy as they typically require users to go through pairing and verification procedures by manually entering a predetermined or randomly generated pin on both devices. This inconvenience usually results in prolonged usage of old pins, significantly degrading the security of network connections.
Reliability and Threshold-Region Performance of TOA Estimators in Dense Multipath Channels

This paper investigates the reliability of time-of-arrival (TOA) based ranging using maximum-likelihood (ML) estimation in a dense multipath (DM) channel in terms of both the conventional mean squared error (MSE) as well as confidence bounds. We show that in the presence of DM the ML estimator distorts the signal mainlobe due to its whitening property, resulting in a bandwidth (BW) dependent bias, even before the outlier driven threshold region is reached. Low-complexity metrics for accurately determining the performance in terms of the PDF of the estimation error of both ML estimation and joint ML estimation and detection are provided. These metrics are based on the well known method of interval estimation (MIE) combined with local error prediction using the normalized noise-free likelihood (NNLIKE) and consider the non-Gaussian effects of outliers as well as mainlobe distortion.