Reviewerly/exHarmony huggingface dataset:
A Comparison of Compression Codecs for Maritime and Sonar Images in Bandwidth Constrained Applications

Since lossless compression can only achieve two to four times data compression, it may not be efficient to deploy lossless compression in bandwidth constrained applications. Instead, it would be more economical to adopt perceptually lossless compression, which can attain ten times or more compression without loss of important information. Consequently, one can transmit more images over bandwidth limited channels. In this research, we first aimed to compare and select the best compression algorithm in the literature to achieve a compression ratio of 0.1 and 40 dBs or more in terms of a performance metric known as human visual system model (HVSm) for maritime and sonar images. Our second objective was to demonstrate error concealment algorithms that can handle corrupted pixels due to transmission errors in interference-prone communication channels. Using four state-of-the-art codecs, we demonstrated that perceptually lossless compression can be achieved for realistic maritime and sonar images. At the same time, we also selected the best codec for this purpose using four performance metrics. Finally, error concealment was demonstrated to be useful in recovering lost pixels due to transmission errors.
Comprehensive Study of Biometric Authentication Systems, Challenges and Future Trends

Authentication is the key parameter to speak the truth of an attribute claimed by the real entity.There are several ways to make authentication more robust and biometrics is one among them.From past decade, Biometric technology is widely adopted and accepted everywhere to authenticate an individual's identity.Also the adopted technology overcomes the limitations faced by the traditional authentication process such as knowledge based issues including password and token for the authentication of an individual.This paper makes a comprehensive study of the existing biometric methodologies, their usage and limitations that are employed in real time cases.It also presents the motivation for adapting biometrics in current situations.In addition to this, it also makes an attempt to talk on the technical and security related issues towards biometric systems.
Remote Sensing Image Segmentation using OTSU Algorithm

In recent years, extraction of information from remote sensing images is an active topic of research.Feature extraction from an image is performed by image segmentation by dividing the image into distinct and self-seminar pixel groups.In remote sensing images, large quantity of texture information is present.So, it is difficult and time consuming process to segment objects from the background in remote sensing images.Many algorithms have been proposed for the purpose of segmentation of remote sensing images.Thresholding is a simple technique but effective method to separate objects from the background.A commonly used method, the Otsu method, improves the image segmentation effectively.It is the most referenced thresholding methods, as it directly operates on the gray level histogram.In this project, Otsu thresholding algorithm is used to segment the roads and residential areas from the vegetation areas in remote sensing images.
Consistent and accurate frequency oracles under local differential privacy

Local Differential Privacy (LDP) protects user privacy from the data collector. LDP protocols have been increasingly deployed in the industry. A basic building block is frequency oracle (FO) protocols, which estimate frequencies of values. While several FO protocols have been proposed, their resulting estimations, however, do not satisfy the natural consistency requirement that estimations are non-negative and sum up to 1. In this paper, we study how to add post-processing steps to FO protocols to make them consistent while achieving high accuracy for a wide range of tasks, including frequencies of individual values, frequencies of the most frequent values, and frequencies of subsets of values. We consider 10 different methods, some of them explicitly proposed before in the literature, and others introduced in this paper. We establish theoretical relationships between some of them and conducted extensive experimental evaluations. Among them, we propose to use Norm-Hyb, which keeps estimations above a certain threshold unchanged, and then converts negative estimations to 0 while adding a value (typically negative) to all other estimations to ensure a sum of 1.
Accuracy Performance Degradation in Image Classification Models due to Concept Drift

Big data is playing a significant role in the current computing revolution.Industries and organizations are utilizing their insights for Business Intelligence by using Deep Learning Networks (DLN).However, dynamic characteristics of BD introduce many critical issues for DLN; Concept Drift (CD) is one of them.CD issue appears frequently in Online Supervised Learning environments in which data trends change over time.The problem may even worsen in a BD environment due to the veracity and variability factors.The CD issue may render the DLN inapplicable by degrading the accuracy of classification results in DLN which is a very serious issue that needs to be addressed.Therefore, these DLN need to quickly adapt to changes for maintaining the accuracy level of the results.To overcome classification accuracy, we need some dynamical changes in the existing DLN.Therefore, in this paper, we examine some of the existing Shallow Learning and Deep Learning models and their behavior before and after the Concept Drift (in experiment 1) and validate the pre-trained Deep Learning network (ResNet-50).In future work, this experiment will examine the most recent pre-trained DLN (Alex Net, VGG16, VGG19) and identify their suitability to overcome Concept Drift using fine-tuning and transfer learning approaches.
Robust Unsupervised Flexible Auto-weighted Local-coordinate Concept Factorization for Image Clustering

We investigate the high-dimensional data clustering problem by proposing a novel and unsupervised representation learning model called Robust Flexible Auto-weighted Local-coordinate Concept Factorization (RFA-LCF). RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model. The adaptive weighting is driven by including thejoint manifold preserving constraints on the recovered clean data, basis concepts and new representation. Specifically, our RFA-LCF uses a L2,1-norm based flexible residue to encode the mismatch between clean data and its reconstruction, and also applies the robust adaptive sparse local-coordinate coding to represent the data using a few nearby basis concepts, which can make the factorization more accurate and robust to noise. The robust flexible factorization is also performed in the recovered clean data space for enhancing representations. RFA-LCF also considers preserving the local manifold structures of clean data space, basis concept space and the new coordinate space jointly in an adaptive manner way. Extensive comparisons show that RFA-LCF can deliver enhanced clustering results.
Word Embeddings (Also) Encode Human Personality Stereotypes

Word representations trained on text reproduce human implicit bias related to gender, race and age.Methods have been developed to remove such bias.Here, we present results that show that human stereotypes exist even for much more nuanced judgments such as personality, for a variety of person identities beyond the typically legally protected attributes and that these are similarly captured in word representations.Specifically, we collected human judgments about a person's Big Five personality traits formed solely from information about the occupation, nationality or a common noun description of a hypothetical person.Analysis of the data reveals a large number of statistically significant stereotypes in people.We then demonstrate the bias captured in lexical representations is statistically significantly correlated with the documented human bias.Our results, showing bias for a large set of person descriptors for such nuanced traits put in doubt the feasibility of broadly and fairly applying debiasing methods and call for the development of new methods for auditing language technology systems and resources.1 Such knowledge will for example make it possible to accurately interpret the pragmatic meaning of a person exclaiming "You have a spider on your shoulder!" 2 https://bit.ly/2HXkipB 3https://bit.ly/
Electromagnetic radiation detectors based on Josephson junctions: Effective Hamiltonian

We theoretically analyze two setups of low energy single-photon counters based on Josephson junctions (JJ). For this, we propose two simple and general models, based on the macroscopic quantum tunneling formalism (MQT). The first setup is similar to the photon counter based on the "cold electron bolometer" (CEB), where the JJ replaces the CEB in the center of the superconducting antenna. In the second setup, the JJ is capacitively coupled to the antenna. We derive the Hamiltonians for the two setups and we write the Schr\"odinger equations taking into account both, the antenna and the JJ. The quantum particles of the MQT models are in two-dimensional potential landscapes, which are parabolic along one direction and may have the form of a washboard potential along another direction. Such a potential landscape has a series of local minima, separated by saddle points. If the particle is prepared in the initial state in the metastable "ground state" of a local minimum, then the photon absorption causes it to jump into an excited state. If the excitation energy is bigger than the potential barrier seen by the quantum particle (the difference between the "ground state" and the saddle point), the photon is detected. The models are simple and allow us to do mostly analytical calculations. We show that the two setups are equivalent form the MQT point of view, since one Hamiltonian can be transformed into the other by changes of variables. For typical values of the JJ and antenna parameters, the setups may work as counters of photons of wavelengths up to 1 cm, at least.
Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets

This paper describes the Duluth systems that participated in SemEval-2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval).For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data.However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black-list approach, and we also experimented with combining the training data from two different but related SemEval tasks.Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57 th of 103 in task A, 39 th of 75 in task B, and 44 th of 65 in task C.
Collaborative Generative Adversarial Network for Recommendation Systems

Recommendation systems have been a core part of daily Internet life. Conventional recommendation models hardly defend adversaries due to the natural noise like misclicking. Recent researches on GAN-based recommendation systems can improve the robustness of the learning models, yielding the state-of-the-art performance. The basic idea is to adopt an interplay minimax game on two recommendation systems by picking negative samples as fake items and employ reinforcement learning policy. However, such strategy may lead to mode collapse and result in high vulnerability to adversarial perturbations on its model parameters. In this paper, we propose a new collaborative framework, namely Collaborative Generative Adversarial Network (CGAN), which adopts Variational Auto-encoder (VAE) as the generator and performs adversarial training in the continuous embedding space. The formulation of CGAN has two advantages: 1) its auto-encoder takes the role of generator to mimic the true distribution of users preferences over items by capturing subtle latent factors underlying user-item interactions; 2) the adversarial training in continuous space enhances models robustness and performance. Extensive experiments conducted on two real-world benchmark recommendation datasets demonstrate the superior performance of our CGAN in comparison with the state-of-the-art GAN-based methods.
Floating solar PV—hydroelectric power plants in Brazil: Energy storage solution with great application potential

Photovoltaic (PV) solar farms and hydropower stations can create a plant that do more than the two resources acting independently as long as, with the addition of a solar project, hydroelectric plants increase its annual availability of power and economic efficiency, taking advantage of the storage capacity of energy that a hydroelectric reservoir can provide.The objective of this paper is to investigate the potential of Brazilian hydropower plants regarding these issues using the large amount of available data recorded.There exists a potential increase in the energy production of 53.3 TWh per year based on the proposed optimized solution for the Brazilian hydropower stations with significant water storage capacities assessed in this paper.This energy is equivalent to an additional capacity factor of approximately 20% to the original 31.5 GW installed hydro capacity or almost 10% of the Brazilian electricity demand in 2018.This result would be at the expenses of a huge deployment of 34 GW of solar PV floating power plants installed close to the reservoir dams that must yet prove to be economically feasible.However, on the other hand, this proposal can create a virtuous cycle for the solar industry in Brazil and speed up the viability of solar cell costs, since setting up and connecting the PV plants are greatly simplified when done in existing hydropower stations facilities.
Feature Reduction Method for Cognition and Classification of IoT Devices Based on Artificial Intelligence

In recent years, the scale and application scenarios of the Internet of Things (IoT) have been expanding. Since traditional algorithms are unable to meet wireless networks computing capability requirement in the IoT, more and more research institutions and scholars have turned their eyes to artificial intelligence (AI) methods. Because the IoT device uses wireless networks to communicate in most scenarios, this paper systematically studies the method of feature dimension reduction of wireless communication signals. In this paper, we will take the power amplifier radio frequency (RF) fingerprinting as an example. Focusing on reducing the high dimensionality of RF fingerprint features and the uncorrelated or redundant features in the features space, the RF fingerprint feature dimension reduction method is mainly studied. Based on the principal component analysis (PCA), linear discriminant analysis (LDA), and auto encoder (AE) research, this paper studies the PCA-LDA method and uses the distance ratio criterion to evaluate the separability of features. The simulation results show that the classification accuracy of PCA-LDA is superior to PCA, LDA, and AE in most SNR, and the characteristics of PCA-LDA is more separable.
BREAST CANCER DETECTION USING ANN NETWORK AND PERFORMANCE ANALYSIS WITH SVM

According to the World Health Organization (WHO) breast cancer is the major reason of death among women and its impact on women is 2.1 million per year.Only in 2018 approximately 15% (62700) of women are died due to breast cancer.To detect this breast cancer oncologist rely on two methods i.e. early diagnosis and screening.To identify cancers before any symptoms appear screening plays an important role and in screening Mammography is heart of breast cancer detection.Apart from this Clinical Breast Exams, Breast Self-Exam and many other methodologies are used.Screening for breast cancer is too long and time consuming process if approach is manual analysis and it's performed on medical images.It's also unfeasible for huge data sets.That's the reason we required self-automated, efficient and more accurate machine to identify or capture the breast cancer as minimum as possible amount of time.We found the solution of this problem is Deep Learning Method.It provides the results in short period of time as compare to other techniques and giving the better accuracy for detection of Breast cancer.In this paper we focuses on, by using which methodology we got the more accurate results and how much amount of time is required to do this process.In this project we are going to deal with different classifiers like CNN, KNN, Inception V3, SVM and ANN.By using ANN we are going to detect the Breast Cancer.We are also going to compare the results of SVM with ANN Technique.
Underwater Bearings-Only Multitarget Tracking Based on Modified PMHT in Dense-Cluttered Environment

Underwater bearings-only multitarget tracking has many advantages, and the probabilistic multiple hypothesis tracking (PMHT) is an elegant algorithm for multitarget tracking problem.However, the basic PMHT has following weakness: 1) the targets' posterior probability may convergent to the local maximum, which would degrade the tracking accuracy and 2) the tracking performance is sensitive to the targets' initialization.In order to overcome these weaknesses, this paper proposes a modified PMHT algorithm.The key idea of the modified PMHT algorithm is that it allows the bearings measurements at one scan come from any Gaussian density, which has the same mean and different covariance with the same target.In addition, the modified PMHT algorithm uses the deterministic annealing to reduce the dependence on the targets' initialization.To deal with the nonlinear bearings measurements, the paper uses the unscented Kalman smoother to update the target states.The simulation treats the cross moving targets and closely spaced targets for both multiple stationary sensors and single maneuvering sensor scenarios in a dense clutter environment.The simulation results show the superiority of the modified PMHT algorithm over the basic PMHT algorithm respect to accuracy and robustness for underwater bearings-only multitarget tracking problem when setting a relatively bad initialization value.
Including widespread geometry formats in semantic graphs using RDF literals

The exchange of building data involves both geometric and non-geometric data.A promising Linked Data approach is to embed data from existing geometry formats inside Resource Description Framework (RDF) literals.Based on a study of relevant specifications and related work, this toolset-independent approach was found suitable for the exchange of geometric construction data.To implement the approach in practice, the File Ontology for Geometry formats (FOG) and accompanying modelling method is developed.In a proof-of-concept web application that uses FOG, is demonstrated how geometry descriptions of different existing formats are automatically recognised and parsed.
A Comparison of Embedded Deep Learning Methods for Person Detection

Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish. Person detection is fundamental preliminary operation for several high level computer vision tasks. One industry that can significantly benefit from person detection is retail. In recent years, various studies attempt to find an optimal solution for person detection using neural networks and deep learning. This study conducts a comparison among the state of the art deep learning base object detector with the focus on person detection performance in indoor environments. Performance of various implementations of YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessed using our in-house proprietary dataset which consists of over 10 thousands indoor images captured form shopping malls, retails and stores. Experimental results indicate that, Tiny YOLO-416 and SSD (VGG-300) are the fastest and Faster-RCNN (Inception ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectors investigated in this study. Further analysis shows that YOLO v3-416 delivers relatively accurate result in a reasonable amount of time, which makes it an ideal model for person detection in embedded platforms.
Time dynamics of quantum coherence and monogamy in a non-Markovian environment

Abstract The time evolution of the distribution and shareability of quantum coherence of a tripartite system in a non-Markovian environment is examined. The total coherence can be decomposed into various contributions, ranging from local, global bipartite and global tripartite, which characterize the type of state. We identify coherence revivals for non-Markovian systems for all the contributions of coherence. The local coherence is found to be much more robust under the environmental coupling due to an effective smaller coupling to the reservoir. This allows us to devise a characterization of a quantum state in terms of a coherence tuple on a multipartite state simply by examining various combinations of reservoir couplings. The effect of the environment on the shareability of quantum coherence, as defined using the monogamy of coherence, is investigated and found that the sign of the monogamy is a preserved quantity under the decoherence. We conjecture that the monogamy of coherence is a conserved property under local incoherent processes.
Improving Forensic Triage Efficiency through Cyber Threat Intelligence

The complication of information technology and the proliferation of heterogeneous security devices that produce increased volumes of data coupled with the ever-changing threat landscape challenges have an adverse impact on the efficiency of information security controls and digital forensics, as well as incident response approaches. Cyber Threat Intelligence (CTI)and forensic preparedness are the two parts of the so-called managed security services that defendants can employ to repel, mitigate or investigate security incidents. Despite their success, there is no known effort that has combined these two approaches to enhance Digital Forensic Readiness (DFR) and thus decrease the time and cost of incident response and investigation. This paper builds upon and extends a DFR model that utilises actionable CTI to improve the maturity levels of DFR. The effectiveness and applicability of this model are evaluated through a series of experiments that employ malware-related network data simulating real-world attack scenarios. To this extent, the model manages to identify the root causes of information security incidents with high accuracy (90.73%), precision (96.17%) and recall (93.61%), while managing to decrease significantly the volume of data digital forensic investigators need to examine. The contribution of this paper is twofold. First, it indicates that CTI can be employed by digital forensics processes. Second, it demonstrates and evaluates an efficient mechanism that enhances operational DFR.
Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task

The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora.It would also open the avenue to answering new research questions: Collecting annotations from a larger number of individuals per instance would allow to investigate the distribution of inferred relations, and to study individual differences in coherence relation interpretation.However, annotating coherence relations with untrained workers is not trivial.We here propose a novel two-step annotation procedure, which extends an earlier method by Scholman and Demberg (2017a).In our approach, coherence relation labels are inferred from connectives that workers insert into the text.We show that the proposed method leads to replicable coherence annotations, and analyse the agreement between the obtained relation labels and annotations from PDTB and RST-DT on the same texts.