Reviewerly/exHarmony huggingface dataset:
Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.
How AI and biometrics are driving next-generation authentication

We are entering a new era of fast and secure authentication enabled by a perfect digital storm: the convergence of AI and biometrics. As with many innovative breakthroughs, this transformation is not down to the advancement of one single technology – several hardware and software developments are connecting to create a new and powerful paradigm.
A Hybrid Spoken Language Processing System for Smart Device Troubleshooting

The purpose of this work is to develop a spoken language processing system for smart device troubleshooting using human-machine interaction. This system combines a software Bidirectional Long Short Term Memory Cell (BLSTM)-based speech recognizer and a hardware LSTM-based language processor for Natural Language Processing (NLP) using the serial RS232 interface. Mel Frequency Cepstral Coefficient (MFCC)-based feature vectors from the speech signal are directly input into a BLSTM network. A dropout layer is added to the BLSTM layer to reduce over-fitting and improve robustness. The speech recognition component is a combination of an acoustic modeler, pronunciation dictionary, and a BLSTM network for generating query text, and executes in real time with an 81.5% Word Error Rate (WER) and average training time of 45 s. The language processor comprises a vectorizer, lookup dictionary, key encoder, Long Short Term Memory Cell (LSTM)-based training and prediction network, and dialogue manager, and transforms query intent to generate response text with a processing time of 0.59 s, 5% hardware utilization, and an F1 score of 95.2%. The proposed system has a 4.17% decrease in accuracy compared with existing systems. The existing systems use parallel processing and high-speed cache memories to perform additional training, which improves the accuracy. However, the performance of the language processor has a 36.7% decrease in processing time and 50% decrease in hardware utilization, making it suitable for troubleshooting smart devices.
Analyzing Performance-Aware Code Changes in Software Development Process

With the continuous expansion of software market and the updating of the maturity of the software development process, the performance requirements of software users have gradually become prominent. Performance issues are closely related to the source code. Thus, with the increasing complex of the software product, its performance changed during the evolution of the software product. Performance optimization related work has always been an important goal for developers who usually coding at a low-level. However, performance problems are well studied on architecture level. All too often, some developers are ignorant of the way their code modifications affect performance and simply to wait until performance drops to a point that is unacceptable to the business side. As software developers did a lot of daily work at code level, we think code level performance awareness can help developers in sight of the performance of the code that they are working with. To deal with this, we firstly build performance-aware code change model to identify the performance changes and its related code changes at the granularity of function between each two reversions of a program. Then, we analyzed the evolution history of the code performance and mined the frequent code change patterns that used to improve performance. We have build related tool to implement the proposed approach and applied it to 8 open source projects.
Fundamental Relationships for Deriving QoE in Systems

In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to distributions of user scores for test conditions. Such models are commonly exploited by service/network providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question arises as to how to combine a) user rating distributions obtained from subjective studies, and b) system performance condition distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove a fundamental relationship showing that the expected system QoE is equal to the expected Mean Opinion Score (MOS) in the system. While subjective user studies commonly report only QoS-to-MOS mapping functions, we show that to derive additional QoE metrics in the system, it is necessary to use corresponding QoS-to-QoE metric mapping functions (beyond only QoS-to-MOS) as derived from user rating distributions in subjective studies. The results of the paper provide important insights for deriving QoE metrics from a systems perspective.
Accurately Simulating Energy Consumption of I/O-Intensive Scientific Workflows

While distributed computing infrastructures can provide infrastructure-level techniques for managing energy consumption, application-level energy consumption models have also been developed to support energy-efficient scheduling and resource provisioning algorithms. In this work, we analyze the accuracy of a widely-used application-level model that have been developed and used in the context of scientific workflow executions. To this end, we profile two production scientific workflows on a distributed platform instrumented with power meters. We then conduct an analysis of power and energy consumption measurements. This analysis shows that power consumption is not linearly related to CPU utilization and that I/O operations significantly impact power, and thus energy, consumption. We then propose a power consumption model that accounts for I/O operations, including the impact of waiting for these operations to complete, and for concurrent task executions on multi-socket, multi-core compute nodes. We implement our proposed model as part of a simulator that allows us to draw direct comparisons between real-world and modeled power and energy consumption. We find that our model has high accuracy when compared to real-world executions. Furthermore, our model improves accuracy by about two orders of magnitude when compared to the traditional models used in the energy-efficient workflow scheduling literature.
High-Quality and High-Capacity Data Hiding Based on Absolute Moment Block Truncation Coding

Numerous images are transmitted over the Internet every day. However, for some sensitive images, such as personal medical images, public networks constitute an insecure environment. Watermarking methods are therefore essential for ensuring information security. Additionally, most digital transmitted images are compressed first so as to increase transmission efficiency. This paper presents a data hiding method for absolute moment block truncation coding (AMBTC)-compressed images. The goal of this method is to improve the Internet transmission efficiency by compressing the image and to ensure secure communication by embedding extra secret information into the compressed image at the same time. In the proposed method, the cover image is first classified into two types of blocks through a block classification scheme. Several embedding rules are proposed according to the block type, and an iteration-based halftoning method is integrated with AMBTC to optimize the image quality. Moreover, the blind decoding method is proposed wherein the secret data are extracted from the stego image itself. Experimental results showed that this method outperformed other state-of-the-art methods in terms of image quality and embedding capacity.
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning

In this paper we present our submission for SemEval-2019 Task 3: EmoContext.The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others.Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU).On the leader-board, we submitted two systems that obtained a micro F1 score (F1µ) of 0.711 and 0.712.After the competition, we merged our two systems with ensembling, which achieved a F1µ of 0.7324 on the test dataset.
Performance analysis of data fragmentation techniques on a cloud server

The advancements in virtualisation and distributed computing have allowed the cloud paradigm to become very popular among users and resources. It allows companies to save costs on infrastructure and maintenance and to focus on the development of products. However, this fast-growing paradigm has brought along some concerns from users, such as the integrity and security of the data, particularly in environments where users rely entirely on providers to secure their data. This paper explores different techniques to fragment data on the cloud and prevent direct unauthorised access to the data. It explores their performance on a cloud instance, where the total time to perform the operation, including the upload and download of the data, is considered. Results from this experiment indicate that fragmentation algorithms show better performance compared to encryption. Moreover, when combining encryption with fragmentation, there is an increase in the security, with the trade-off of the performance.
Solar Tracking System for a Linear Fresnel Plant with Two Degrees of Freedom Reflectors

The mirrors of a traditional Fresnel system are moved with a tracking system that allows rotation around a single axis.However, this type of movement in the LFR generates high losses at its ends.The paper aims to study a configuration in which the primary reflectors that generate the aforementioned losses are made movable around two axes to increase the efficiency of the plant.The work presents the kinematic system for moving the field of mirrors in which the control of solar tracking is carried out with the open loop technique.From a constructive point of view, the mechanical components necessary for the purpose are identified.To allow a robust solar tracking, not influenced by external forces, the rotation of the mirrors is managed by "stepper" type servomotors.In particular, the use of the aforementioned motors generates a motion by steps, of which it is necessary to know in detail the temporal characteristics and the driving logic of the motors.The behavior of the whole set of "electric motor -mechanical system" will be modeled numerically in order to define the control logic of the servomotor that allows the execution of the steps at the right moment, evaluating the torque and the errors in the motion.In fact, the adoption of too small pitches improves solar tracking but needs the presence of a complex reduction system, with numerous gear wheels, which requires too high torque values.
Email Fraud Attack Detection Using Hybrid Machine Learning Approach

Background: E-mail is an efficient way to communicate. It is one of the most commonly used communication methods, and it can be used for achieving legitimate and illegitimate activities. Many features that can be effective in detecting email fraud attacks, are still under investigation. Method: This paper proposes an improved classification accuracy for fraudulent emails that is implemented through feature extraction and hybrid Machine Learning (ML) classifier that combines Adaboost and Majority Voting. Eleven ML classifiers are evaluated experimentally within the hybrid classifier, and the performance of the email fraud filtering is evaluated by using WEKA and R tool on a data set of 9298 email messages. Results: The performance evaluation shows that the hybrid model of Voting using Adaboost outperforms all other classifiers, with the lowest Error Rate of 0.6991%, highest f1-measure of 99.30%, and highest Area Under the Curve (AUC) of 99.9%. Conclusion: The utilized proposed email features with the combination of Adaboost and Voting algorithms prove the efficiency of fraud email detection.
Flickr: Organizing and Tagging Images Online

Flickr was launched when digital cameras first began to outsell analog cameras, and people were drawn to the site for the opportunities it offered them to store, organize, and share their images, as well as for the connections that could be made with other like-minded people. This article examines the links between Flickr’s success and how images are organized within the site, as well as the types of people and organizations that use Flickr and their motivations for doing so. Factors that have contributed to Flickr’s demise in popularity will be explored, and the article finishes with some suggestions for how Flickr could develop in the future, along with some conclusions for image organization.
Evaluating and Comparing Size, Complexity and Coupling Metrics as Web Applications Vulnerabilities Predictors

Most security and privacy issues in software are related to exploiting code vulnerabilities.Many studies have tried to find the correlation between the software characteristics (complexity, coupling, etc.) quantified by corresponding code metrics and its vulnerabilities and to propose automatic prediction models that help developers locate vulnerable components to minimize maintenance costs.The results obtained by these studies cannot be applied directly to web applications because a web application differs in many ways from a non-web application: development, use, etc. and a lot of evaluation of these conclusions has to be made.The purpose of this study is to evaluate and compare the vulnerabilities prediction power of three types of code metrics in web applications.There are a few similar studies that targeted non-web application and to the best of our knowledge, there are no similar studies that targeted web applications.The results obtained show that unlike non-web applications where complexity metrics have better vulnerability prediction power, in web applications the metrics that give better prediction are the coupling metrics with high recall (> 75%) and fewer costs in terms of inspection (<25%).
GenELM: Generative Extreme Learning Machine feature representation

Extreme Learning Machine (ELM) feature representation has been drawing increasing attention, and most of the previous works devoted to learning discriminative features. However, we argue that such kind of features suffer from “categories bias” in target detection tasks, where the scope of the negatives (i.e., backgrounds) is naturally broader than that of the positives (i.e., targets). Technically, the learning of discriminative features aims to maximize positive-negative difference. In this case, the interclass-difference criteria for feature mapping and feature matching would increase model complexity while degrading detection robustness. To address these issues, we construct a generative feature learning framework in this paper. Unlike the discriminative feature representation, generative features tend to cluster the positives from negatives, and in this way, the unique and separable attributes of the positives can be learnt to facilitate the accurate detection of positives (targets). Practically, by exploiting ELM computational advantages, positives clustering oriented feature learning is implemented with a kernel-label alignment function, which guides random features of positives into clusters, while keeping away from the negatives. On the other hand, the density of positives cluster is estimated using a probabilistic model, and thus the likelihood that test samples belonged to the positive category can be approximately inferred. We experimentally evaluate our framework for target detection on synthetic data and Hyperspectral Imagery (HSI), in which reasonable improvements are observed over conventional ELM-based discriminative methods and backpropagation-based multilayer perceptron (MLP) in terms of detection accuracy or learning efficiency.
Discrete Two-Dimensional Fourier Transform in Polar Coordinates Part I: Theory and Operational Rules

The theory of the continuous two-dimensional (2D) Fourier transform in polar coordinates has been recently developed but no discrete counterpart exists to date. In this paper, we propose and evaluate the theory of the 2D discrete Fourier transform (DFT) in polar coordinates. This discrete theory is shown to arise from discretization schemes that have been previously employed with the 1D DFT and the discrete Hankel transform (DHT). The proposed transform possesses orthogonality properties, which leads to invertibility of the transform. In the first part of this two-part paper, the theory of the actual manipulated quantities is shown, including the standard set of shift, modulation, multiplication, and convolution rules. Parseval and modified Parseval relationships are shown, depending on which choice of kernel is used. Similar to its continuous counterpart, the 2D DFT in polar coordinates is shown to consist of a 1D DFT, DHT and 1D inverse DFT.
Application of Theory to the Development of an Adaptive Training System for a Submarine Electronic Warfare Task

Adaptive training (AT) can be an efficient option for providing individualized instruction tailored to trainees’ needs. Given promising research findings involving AT, we were challenged with developing an AT solution for Submarine Electronic Warfare (EW). Submarine EW is a complex task that involves classifying contacts, recognizing changes in the environment, and interpreting real-time displays. To train this task, we designed and developed the Submarine Electronic Warfare Adaptive Trainer (SEW-AT). We drew from multiple theoretical perspectives to drive our design decisions, including Multiple Resource Theory (MRT) and the Zone of Proximal Development (ZPD). Following the trainer’s development, we conducted a training effectiveness evaluation (TEE) to gauge initial performance improvements from SEW-AT. Using Submariners (n = 34) from 4 different Submarine Learning Centers across the United States, we found a 46% reduction in missed reports and a 49% improvement in report accuracy while using SEW-AT. As a next step, we plan to explore how the frequency of adaptation, or adaptation schedules, affect training performance and efficiency to determine if finer-grained adaptations produce greater learning gains.
Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments.
Two birds with one stone: Transforming and generating facial images with iterative GAN

Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications. Although a number of generative models have been developed to tackle this problem, there is still much room for further improvement. In particular, the current solutions usually ignore the perceptual information of images, which we argue that it benefits the output of a high-quality image while preserving the identity information, especially in facial attributes learning area. To this end, we propose to train GAN iteratively via regularizing the min-max process with an integrated loss, which includes not only the per-pixel loss but also the perceptual loss. In contrast to the existing methods only deal with either image generation or transformation, our proposed iterative architecture can achieve both of them. Experiments on the multi-label facial dataset CelebA demonstrate that the proposed model has excellent performance on recognizing multiple attributes, generating a high-quality image, and transforming image with controllable attributes.
CONTEXT-AWARE ONTOLOGY AND WEB SERVICES DISCOVERY FOR DISTRIBUTED EMBEDDED REAL-TIME SYSTEMS

Distributed Embedded Real-Time Systems (DERTS) consists of hundreds of interconnected devices, typically small and wirelessly-connected, which are designed to work for a long period of time. The massive interconnection of devices and the usage of heterogeneous languages, operating platforms and data standards make DERTS a competitive and complex system. In addition, the DERTS program is setting a trend of moving away from centralized, high-cost products towards lower cost and high volume products. In this regard, there is nothing more natural than considering the use of Service Oriented Architecture (SOA) to assist in the development of DERTS. This is because SOA enables different devices to exchange data regardless of issues of complexity. Moreover, context-awareness, which is widely studied for DERTS, also plays an important role for effective communication among devices. Thus, to build service-based DERTS while managing the complexity, context-aware ontologies are the best solution. In this paper, we developed a context-aware ontology for DERTS which is known as ConOntDERTS. To evaluate ConOntDERTS, we used two methods. In the first method, a criteria-based ontology evaluation was used; while in the second method, a survey was conducted to show that the results produced by ConOntDERTS were almost the same as human perception. Results of the evaluation show the consistency and feasibility of our ontology and the statistical test results show that ConOntDERTS ontology can produce consistent results with human perception.