Reviewerly/exHarmony huggingface dataset:
Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.
Predicting 30-Day Hospital Readmission for Diabetes Patients using Multilayer Perceptron

Hospital readmission is considered a key metric in order to assess health center performances.Indeed, readmissions involve different consequences such as the patient's health condition, hospital operational efficiency but also cost burden from a wider perspective.Prediction of 30-day readmission for diabetes patients is therefore of prime importance.The existing models are characterized by their limited prediction power, generalizability and pre-processing.For instance, the benchmarked LACE (Length of stay, Acuity of admission, Charlson comorbidity index and Emergency visits) index traded prediction performance against ease of use for the end user.As such, this study propose a comprehensive pre-processing framework in order to improve the model's performance while exploring and selecting a prominent feature for 30-day unplanned readmission among diabetes patients.In order to deal with readmission prediction, this study will also propose a Multilayer Perceptron (MLP) model on data collected from 130 US hospitals.More specifically, the pre-processing technique includes comprehensive data cleaning, data reduction, and transformation.Random Forest algorithm for feature selection and SMOTE algorithm for data balancing are some example of methods used in the proposed pre-processing framework.The proposed combination of data engineering and MLP abilities was found to outperform existing research when implemented and tested on health center data.The performance of the designed model was found, in this regard, particularly balanced across different metrics of interest with accuracy and Area under the Curve (AUC) of 95% and close to the optimal recall of 99%.
Lorenz model and chaos masking /addition technique

In the present work, the chaotic signal that produces experimentally was studied by using Lorenz model.Nonlinear behavior (chaotic) in a nonlinear device was created using a Lorenz circuit.The dynamical system can be perceived by using the bifurcation diagram.In order to encode the message to the chaotic carrier signal, Lorenz circuit has been used.The decoding of the message is achieved by the synchronization between the chaotic oscillator of the transmitter and the receiver.This work studied the change of time series of the chaotic signal with changing the initial conditions where the stochastic parameters are = 10, R=30, and b=8/3, while initial conditions are X int =y int =z int =0.01, x 1 =0.03, and y int =z int =0.01.
Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.
Anti-Interference Aircraft-Tracking Method in Infrared Imagery

In this paper, we focus on developing an algorithm for infrared-imaging guidance that enables the aircraft to be reliably tracked in the event of interference. The key challenge is to track the aircraft with occlusion caused by decoys and drastic appearance changes resulting from a diversity of attacking angles. To address this challenge, an aircraft-tracking algorithm was proposed, which provides robustness in tracking the aircraft against the decoys. We reveal the inherent structure and infrared signature of the aircraft, which are used as discriminative features to track the aircraft. The anti-interference method was developed based on simulated images but validate the effectiveness on both real infrared image sequences without decoys and simulated infrared imagery. For frequent occlusion caused by the decoys, the mechanism of occlusion detection is exploited according to the variation of the model distance in tracking process. To have a comprehensive evaluation of tracking performance, infrared-image sequences with different attack angles were simulated, and experiments on benchmark trackers were performed to quantitatively evaluate tracking performance. The experiment results demonstrate that our aircraft-tracking method performs favorably against state-of-the-art trackers.
AutoML @ NeurIPS 2018 Challenge: Design and Results

We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets were arranged in a lifelong learning and evaluation scenario and CodaLab was used as the challenge platform. The challenge attracted more than 300 participants in its two month duration. This chapter describes the design of the challenge and summarizes its main results.
Compression Algorithm Selection for Multispectral Mastcam Images

The two mast cameras (Mastcam) onboard the Mars rover, Curiosity, are multispectral imagers with nine bands in each camera.Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times compression.We present a two-step approach to compressing multispectral Mastcam images.First, we propose to apply principal component analysis (PCA) to compress the nine bands into three or six bands.This step optimally compresses the 9-band images through spectral correlation between the bands.Second, several well-known image compression codecs, such as JPEG, JPEG-2000 (J2K), X264, and X265, in the literature are applied to compress the 3-band or 6-band images coming out of PCA.The performance of different algorithms was assessed using four well-known performance metrics.Extensive experiments using actual Mastcam images have been performed to demonstrate the proposed framework.We observed that perceptually lossless compression can be achieved at a 10:1 compression ratio.In particular, the performance gain of an approach using a combination of PCA and X265 is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at a 10:1 compression ratio over that of JPEG when using our proposed approach.
Reformulation-Based Query Answering for RDF Graphs with RDFS Ontologies

Query answering in RDF knowledge bases has traditionally been performed either through graph saturation, i.e., adding all implicit triples to the graph, or through query reformulation, i.e., modifying the query to look for the explicit triples entailing precisely what the original query asks for. The most expressive fragment of RDF for which Reformulation-based query answering exists is the so-called database fragment [13], in which implicit triples are restricted to those entailed using an RDFS ontology. Within this fragment, query answering was so far limited to the interrogation of data triples (non-RDFS ones); however, a powerful feature specific to RDF is the ability to query data and schema triples together. In this paper, we address the general query answering problem by reducing it, through a pre-query reformulation step, to that solved by the query reformulation technique of [13]. We also report on experiments demonstrating the low cost of our reformulation algorithm.
Web image annotation based on Tri-relational Graph and semantic context analysis

Web image annotation has became a hot research topic owing to massive image data and abundant semantic context. In this paper, we propose a Tri-relational Graph (TG) model for web image annotation, which comprises the image data graph, the region data graph and the label graph as subgraphs, and connects them by an additional tripartite graph induced from image segmentation results and label assignments. Through analyzing the global visual similarity between images, the visual similarity between regions, the semantic correlations between labels and the relationships between the three subgraphs by TG model, we perform multilevel Random Walk with Restart algorithm on TG to produce vertex-to-vertex relevance, including image-to-region, region-to-label and image-to-label relevances. Then semi-supervised learning is used to predict labels for unannotated image regions by inserting unlabeled images and their regions into TG. In addition, we also analyze the text context information of web image and achieve the semantic and proper nouns for the further label expansion through WordNet. Experiments on public web images datasets demonstrate that our proposed TG model and multilevel RWR algorithm can achieve good performance on image region annotation and outperform the similar image annotation methods. Moreover label expansion by web semantic context analysis can achieve more accurate and abundant annotation results.
Learning Tree Structures from Noisy Data

We provide high-probability sample complexity guarantees for exact structure recovery of tree-structured graphical models, when only noisy observations of the respective vertex emissions are available. We assume that the hidden variables follow either an Ising model or a Gaussian graphical model, and the observables are noise-corrupted versions of the hidden variables: We consider multiplicative $\pm 1$ binary noise for Ising models, and additive Gaussian noise for Gaussian models. Such hidden models arise naturally in a variety of applications such as physics, biology, computer science, and finance. We study the impact of measurement noise on the task of learning the underlying tree structure via the well-known \textit{Chow-Liu algorithm} and provide formal sample complexity guarantees for exact recovery. In particular, for a tree with $p$ vertices and probability of failure $\delta>0$, we show that the number of necessary samples for exact structure recovery is of the order of $\mc{O}(\log(p/\delta))$ for Ising models (which remains the \textit{same as in the noiseless case}), and $\mc{O}(\mathrm{polylog}{(p/\delta)})$ for Gaussian models.
Quantum enhancement of sensitivity achieved by photon-number-resolving detection in the dark port of a two-path interferometer operating at high intensities

It is shown that the maximal phase sensitivity of a two-path interferometer with high-intensity coherent light and squeezed vacuum in the input ports can be achieved by photon-number-resolving detection of only a small number of photons in a dark output port. It is then possible to achieve the quantum Cram\'er-Rao bound of the two-path interferometer using only the field displacement dependence of the photon number statistics in the single mode output of the dark port represented by a field-displaced squeezed vacuum state. We find that, at small field displacements, it is not sufficient to use the average photon number as the estimator, indicating that an optimal phase estimation depends critically on measurements of the precise photon number. We therefore analyze the effect of detection efficiency on the Fisher information and show that there is a transition from low robustness against photon losses associated with quantum interference effects at low field displacements to high robustness against photon losses at high field displacements. The transition between the two regimes occurs at field shifts proportional to the third power of the squeezing factor, indicating that squeezing greatly enhances the phase interval in which quantum effects are relevant in optimal phase estimations using photon resolving detectors. The case under study could thus be understood as a ``missing link'' between genuine multiphoton interference and the straightforward suppression of noise usually associated with squeezed light.
Analysis of data pre-processing methods for the sentiment analysis of reviews

The aim of this study is to analyse the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods and their combinations are effective for English and an agglutinative language like Turkish. We also try to answer the research question "is there any difference between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis?" We find that the performance results for the English reviews are generally higher than for the Turkish reviews related to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.
PathMarker: protecting web contents against inside crawlers

Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator. Moreover, armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders. In this paper, based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours, we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers. By adding a marker to each Uniform Resource Locator (URL), we can trace the page that leads to the access of this URL and the user identity who accesses this URL. With this supporting information, we can not only perform more accurate heuristic detection using the path related features, but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings. In addition to effectively detecting crawlers at the earliest stage, PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected. We deploy our approach on an online forum website, and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers, plus two external crawlers (i.e., Googlebots and Yahoo Slurp).
Homogeneous Groups of Actors in an AHP-Local Decision Making Context: A Bayesian Analysis

The two procedures traditionally followed for group decision making with the Analytical Hierarchical Process (AHP) are the Aggregation of Individual Judgments (AIJ) and the Aggregation of Individual Priorities (AIP). In both cases, the geometric mean is used to synthesise judgments and individual priorities into a collective position. Unfortunately, positional measures (means) are only representative if dispersion is reduced. It is therefore necessary to develop decision tools that allow: (i) the identification of groups of actors that present homogeneous and differentiated behaviours; and, (ii) the aggregation of the priorities of the near groups to reach collective positions with the greatest possible consensus. Following a Bayesian approach to AHP in a local context (a single criterion), this work proposes a methodology to solve these problems when the number of actors is not high. The method is based on Bayesian comparison and selection of model tools which identify the number and composition of the groups as well as their priorities. This information can be very useful to identify agreement paths among the decision makers that can culminate in a more representative decision-making process. The proposal is illustrated by a real-life case study.
Mediating Effect of Corporate Image and Students’ Satisfaction on the Relationship Between Service Quality and Students’ Loyalty in TVET HLIs

The main purpose of this study is to investigate the mediation effect of corporate image and students’ satisfaction on the relationship between service quality and students’ loyalty in technical vocational and education and training (TVET) of higher learning institutes (HLIs). The respondents of this study were final year students from TVET HLIs in Malaysia. Using proportionate stratified sampling and followed by convenience sampling method, 431 questionnaires were distributed to nine higher TVET institutions. From that, 398 questionnaires were used for data analysis using SPSS version 22 and SmartPLS 3.0. The measurement model showed that the constructs were reliable and valid. Meanwhile, the structural model showed that there is a direct and significance relationship among all constructs. Two mediators were tested in this research which were service quality and corporate image. Bootstrapping procedure indicated that there was a direct and significant effect between latent constructs. The mediating test statistically showed that there was a partial mediation of student satisfaction and corporate image on the relationship between service quality and student loyalty. This research recommended that TVET HLIs should improve the level of service quality, corporate image, and satisfaction to ensure student satisfaction and loyalty.
Development of Vietnamese Speech Synthesis System using Deep Neural Networks

In this paper, we present our first Vietnamese speech synthesis system based on deep neural networks. To improve the training data collected from the Internet, a cleaning method is proposed. The experimental results indicate that by using deeper architectures we can achieve better performance for the TTS than using shallow architectures such as hidden Markov model. We also present the effect of using different amounts of data to train the TTS systems. In the VLSP TTS challenge 2018, our proposed DNN-based speech synthesis system won the first place in all three subjects including naturalness, intelligibility, and MOS.
Quantum link prediction in complex networks

Predicting new links in physical, biological, social, or technological networks has a significant scientific and societal impact. Path-based link prediction methods utilize the explicit counting of even- and odd-length paths between nodes to quantify a score function and infer new or unobserved links. Here, we propose a quantum algorithm for path-based link prediction using a controlled continuous-time quantum walk to encode even and odd path-based prediction scores. Through classical simulations on a few real networks, we confirm that the quantum walk scoring function performs similarly to other path-based link predictors. In a brief complexity analysis we identify the potential of our approach in uncovering a quantum speedup for path-based link prediction.
Pushing Software-Defined Blockchain Components onto Edge Hosts

With the advent of blockchain technology, some management tasks of IoT networks can be moved from central systems to distributed validation authorities. Cloud-centric blockchain implementations for IoT have shown satisfactory performance. However, some features of blockchain are not necessary for IoT. For instance, a competitive consensus. This research presents the idea of customizing and encapsulating the features of blockchain into software-defined components to host them on edge devices. Thus, blockchain resources can be provisioned by edge devices (e-miners) working together closer to the things layer in a cooperative manner. This research uses Edison SoC as e-miners to test the software-defined blockchain components.
Divisible E-Cash from Constrained Pseudo-Random Functions

Electronic cash (e-cash) is the digital analogue of regular cash which aims at preserving users' privacy. Following Chaum's seminal work, several new features were proposed for e-cash to address the practical issues of the original primitive. Among them, divisibility has proved very useful to enable efficient storage and spendings. Unfortunately, it is also very difficult to achieve and, to date, quite a few constructions exist, all of them relying on complex mechanisms that can only be instantiated in one specific setting. In addition security models are incomplete and proofs sometimes hand-wavy. In this work, we first provide a complete security model for divisible e-cash, and we study the links with constrained pseudo-random functions (PRFs), a primitive recently formalized by Boneh and Waters. We exhibit two frameworks of divisible e-cash systems from constrained PRFs achieving some specific properties: either key homomorphism or delegability. We then formally prove these frameworks, and address two main issues in previous constructions: two essential security notions were either not considered at all or not fully proven. Indeed, we introduce the notion of clearing, which should guarantee that only the recipient of a transaction should be able to do the deposit, and we show the exculpability, that should prevent an honest user to be falsely accused, was wrong in most proofs of the previous constructions. Some can easily be repaired, but this is not the case for most complex settings such as constructions in the standard model. Consequently, we provide the first construction secure in the standard model, as a direct instantiation of our framework.
A 60 Gb/s-Level Coarse-Grained Reconfigurable Cryptographic Processor With Less Than 1-W Power

This brief proposes a coarse-grained reconfigurable cryptographic processor for high-throughput secure network processing and cloud computing, which can implement all the commonly used symmetric and hash cryptographic algorithms. To efficiently utilize the enormous computing resource on reconfigurable computing fabrics, a configuration acceleration system is designed for scheduling. A multi-channel storage network is presented to improve the efficiency of reading/loading data. The proposed reconfigurable processor is fabricated on a 9.91 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> silicon by using 65-nm CMOS technology with 500-MHz frequency. Under 60 Gb/s throughput, the power is still controlled within 1 Watt (e.g., AES 64 Gb/s/0.625 W). The energy efficiency (throughput/power) is 5.2× ~ 149.6× larger than that of the state-of-the-art designs.