Reviewerly/exHarmony huggingface dataset:
Sparse approximation to discriminant projection learning and application to image classification

Subspace learning for dimensionality reduction is an important topic in pattern analysis and machine learning, and it has extensive applications in feature representation and image classification. Linear discriminant analysis (LDA) is a well-known subspace learning approach for supervised dimensionality reduction due to its effectiveness and efficacy in discriminant analysis. However, LDA is not stable and suffers from the singularity problem when addressing small sample size and high-dimensional data. In this paper, we develop a novel subspace learning model, named sparse approximation to discriminant projection learning (SADPL), to learn the sparse projection matrix. Different from the traditional LDA-based methods, we learn the projection matrix based on a new objective function rather than the Fisher criterion, which avoids the matrix singularity problem. In order to distinguish which features play an important role in discriminant analysis, we embed a feature selection framework to the subspace learning model to select the informative features. Finally, we can attain a convex objective function which can be solved by an effective optimization algorithm, and theoretically prove the convergence of the proposed optimization algorithm. Extensive experiments on all sorts of image classification tasks, such as face recognition, palmprint recognition, object categorization and texture classification show that our SADPL achieves competitive performance compared to the state-of-the-art methods.
Specific Emitter Identification Via Multiple Distorted Receivers

Specific emitter identification (SEI) is a technique that identifies different emitters based on specific characteristics of each individual emitter, which finds broad applications in wireless communication authentication, radio monitoring, electromagnetic environment sensing, and information warfare. In this paper, we consider the SEI problem with unknown receiver distortion, which has not been well studied in the existing work. Two cooperative SEI algorithms are proposed which are composed of two steps, i.e., the feature extraction and the cooperative identification. In the first step, the received signal at each receiver is processed by the intrinsic time-scale decomposition (ITD), and the non-Gaussian features, i.e., the kurtosis and the skewness, are extracted from the decomposed signal. In the second step, the support vector machine (SVM) and the back propagation (BP) neural network are applied to fuse the features extracted from multiple distorted receivers respectively, and then determine the unknown emitters. Simulation results show that the proposed two cooperative identifiers using multiple distorted receivers outperform that using any single receiver in terms of the performance of correct identification. The significance of this paper is that the receive diversity can be achieved by the proposed cooperative identifier by using multiple distorted receivers without receiver distortion compensation.
IGM‐Vis: Analyzing Intergalactic and Circumgalactic Medium Absorption Using Quasar Sightlines in a Cosmic Web Context

Abstract We introduce IGM‐Vis, a novel astrophysics visualization and data analysis application for investigating galaxies and the gas that surrounds them in context with their larger scale environment, the Cosmic Web. Environment is an important factor in the evolution of galaxies from actively forming stars to quiescent states with little, if any, discernible star formation activity. The gaseous halos of galaxies (the circumgalactic medium, or CGM) play a critical role in their evolution, because the gas necessary to fuel star formation and any gas expelled from widely observed galactic winds must encounter this interface region between galaxies and the intergalactic medium (IGM). We present a taxonomy of tasks typically employed in IGM/CGM studies informed by a survey of astrophysicists at various career levels, and demonstrate how these tasks are facilitated via the use of our visualization software. Finally, we evaluate the effectiveness of IGM‐Vis through two in‐depth use cases that depict real‐world analysis sessions that use IGM/CGM data.
Mitigating stragglers to avoid QoS violation for time-critical applications through dynamic server blacklisting

The straggler problem is one of the most challenging issues toward rapid and predictable response time for applications in cluster infrastructures, leading to potential QoS violation and late-timing failure. Straggler tasks occur due to reasons such as resource contention, hardware heterogeneity, etc., and become severe with increased system scale and complexity. Speculative execution and blacklisting are the major two straggler tolerant techniques, but each has its own limitations. The former creates replica task to catch up with the identified straggler, but normally with no selection toward nodes when deciding where to launch the backup. Ignoring server performance hinders the speculation success rate. The latter typically relies on manual configuration, despite the fact that the ability of nodes to effectively execute tasks changes over time. In addition, the misidentification of weak-performance nodes decreases system capacity. Combining these two techniques, we present DSB, a dynamic server blacklisting framework which takes into account both historical and current behavior of a server node to increase straggler mitigation effectiveness. Servers are ranked at each time interval according to their performance in fulfilling jobs instead of their physical capacities, and the worst performed ones got temporarily blacklisted. As a result, no new tasks/replications are assigned to those straggler-prone nodes within the following time window. DSB also provides an alternative API where adjustable top k worst nodes can be blacklisted according to the ranking. The optimal k is investigated as a trade-off between capacity loss and straggler mitigation efficiency. Results show that, the DSB scheme is capable of increasing successful speculation rate up to 89%. In addition, it can improve job completion time by up to 55.43% compared to the default speculator in the YARN platform. This helps to reduce the chance of QoS violation, which is particularly important for time-critical applications.
Towards Continuous Domain Adaptation For Medical Imaging

Deep learning algorithms have demonstrated tremendous success on challenging medical imaging problems. However, post-deployment, these algorithms are susceptible to data distribution variations owing to limited data issues and diversity in medical images. In this paper, we propose ContextNets, a generic memory-augmented neural network framework for semantic segmentation to achieve continuous domain adaptation without the necessity of retraining. Unlike existing methods which require access to entire source and target domain images, our algorithm can adapt to a target domain with few similar images. We condition the inference on any new input with features computed on its support set of images (and masks, if available) through contextual embedding to achieve site-specific adaptation. We demonstrate state-of-the-art domain adaptation performance on X-ray lung segmentation problem from three independent cohorts that differ in disease type, gender, contrast and intensity variations.
EmoLabel: Semi-Automatic Methodology for Emotion Annotation of Social Media Text

The exponential growth of the amount of subjective information on the Web 2.0. has caused an increasing interest from researchers willing to develop methods to extract emotion data from these new sources. One of the most important challenges in textual emotion detection is the gathering of data with emotion labels because of the subjectivity of assigning these labels. Basing on this rationale, the main objective of our research is to contribute to the resolution of this important challenge. This is tackled by proposing EmoLabel: a semi-automatic methodology based on pre-annotation, which consists of two main phases: (1) an automatic process to pre-annotate the unlabelled English sentences; and (2) a manual process of refinement where human annotators determine which is the dominant emotion. Our objective is to assess the influence of this automatic pre-annotation method on manual emotion annotation from two points of view: agreement and time needed for annotation. The evaluation performed demonstrates the benefits of pre-annotation processes since the results on annotation time show a gain of near 20 percent when the pre-annotation process is applied (Pre-ML) without reducing annotator performance. Moreover, the benefits of pre-annotation are higher in those contributors whose performance is low (inaccurate annotators).
Fast structured illumination microscopy with reflectance disturbance resistibility and improved accuracy

The conventional fast structured illumination microscopy (FSIM) without vertical scanning has been proposed by Lee for fast surface profiling. Nevertheless, this method is vulnerable to nonuniform surface reflectivity which is generally caused by the different reflection characteristic of hybrid materials and the environmental fluctuations. Consequently, traditional IFSIM cannot be used in the profile measurement for the samples with surface heterogeneity. In this paper, an improved FSIM configuration (IFSIM) is explored for improving the axial accuracy and suppressing the disturbance of the reflectivity. With this technique, a sinusoidal pattern produced by the digital micro-mirror device (DMD) is illuminated onto the sample. The reflected patterns are captured by two charge-coupled devices (CCDs) that are separately placed in and behind or before the focal plane. The subtraction and sum values of the two axial modulation response curves (AMRs) are divided as the new response function, which effectively suppresses the influence of the inhomogeneous reflectivity. Both wide dynamic range and enhanced axial accuracy can be selectively obtained by controlling the defocusing amount of the two differential detectors. The theoretical analysis and experiments are conducted to verify the feasibility of this method.
Argus: An End-to-End Framework for Accelerating CNNs on FPGAs

In this article, we present Argus, an end-to-end framework for accelerating convolutional neural networks (CNNs) on field-programmable gate arrays (FPGAs) with minimum user effort. Argus uses state-of-the-art methods to auto-generate highly optimized CNN accelerator designs for FPGAs, and includes software for running an FPGA-backed CNN inference microservice.
Temporal and spatial outlier detection in wireless sensor networks

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.
Topic Modeling for Text Classification

Topic models allude to statistical algorithms for finding out an extensive text body’s latent semantic structures. Standing here in today’s world, the measure of the textual data and information we come across in our day-to-day lives is basically beyond our handling limit. Topic models can provide a way out for us to understand and manage the vast accumulations of unstructured textual data and information. Initially emerged as a text-mining instrument, topic models have found applications in various other fields. This paper makes a thorough comparative study of LSA with that of commonly used TF-IDF approach for text classification and proves that LSA yields better accuracy in classifying texts. The novelty of the paper lies in the fact that we are using a much sparser representation than usual TF-IDF and also, LSA can get from the topic if there are any synonym words. This paper proposes a method, using the concept of entropy, which further increases the accuracy of text classification.
Bridging the demand and the offer in data science

Summary During the last several years, we have observed an exponential increase in the demand for Data Scientists in the job market. As a result, a number of trainings, courses, books, and university educational programs (both at undergraduate, graduate and postgraduate levels) have been labeled as “Big data” or “Data Science”; the fil‐rouge of each of them is the aim at forming people with the right competencies and skills to satisfy the business sector needs. In this paper, we report on some of the exercises done in analyzing current Data Science education offer and matching with the needs of the job markets to propose a scalable matching service, ie, COmpetencies ClassificatiOn (E‐CO‐2), based on Data Science techniques. The E‐CO‐2 service can help to extract relevant information from Data Science–related documents (course descriptions, job Ads, blogs, or papers), which enable the comparison of the demand and offer in the field of Data Science Education and HR management, ultimately helping to establish the profession of Data Scientist.
People Counting in a Public Event—Use Case: Free-to-Ride Bus

In the case of traditional bus travel, there are many ways to gather statistics on bus passengers. Paid tickets can directly contain information about the stop at which the passengers came aboard and where they got off. Alternatively, stamping or reading travel cards can be used to obtain similar statistics. In the case of a free-to-ride bus service, these simple methods are generally not available. Yet, for further route and capacity planning, producing exact - or at least informative - statistics is crucial. This paper presents a real-life use case of collecting statistics about bus passengers on a free-to-ride bus route at a large public event in the summer of 2018 in Pori, Finland. The use case utilized cost-effective and off-the-shelf components such as the Raspberry Pi 3 computer, position sensors, cameras, and the Open Source Computer Vision Library version 3. The hardware and software components of the system, which was based on image analysis and shape detection, as well the results of the study, are explained in this paper. Furthermore, this paper presents a discussion on the challenges faced while developing and implementing the system.
Local optima network analysis for MAX-SAT

Local Optima Networks (LONs) are a valuable tool to understand fitness landscapes of optimization problems observed from the perspective of a search algorithm. Local optima of the optimization problem are linked by an edge in LONs when an operation in the search algorithm allows one of them to be reached from the other. Previous work analyzed several combinatorial optimization problems using LONs and provided a visual guide to understand why the instances are difficult or easy for the search algorithms. In this work we analyze for the first time the MAX-SAT problem. Given a Boolean formula in Conjunctive Normal Form, the goal of the MAX-SAT problem is to find an assignment maximizing the number of satistified clauses. Several random and industrial instances of MAX-SAT are analyzed using Iterated Local Search to sample the search space.
Super resolution enhancement of underwater sonar images

Image resolution is determined by pixels per square inches. Pixel is defined as a small unit of an image. The resolution improvement of the sonar images still remains a problem and it is compensated by the interpolation techniques. This work helps to improve the resolution of the sonar images and thereby it makes target object recognition with higher accuracy. The super resolution algorithm of the proposed work uses sparse representation for enhancing the resolution. This gives sharper edges and textures to highlight the boundaries of the target object. The low resolution image is converted into high resolution image that is of high pixel density. The peak signal to noise ratio and Structural Similarity Index is the performance metric employed to assess the different interpolation techniques for resolution enhancement.
Hidden in the Shadow: The Dark Web - A Growing Risk for Military Operations?

A multitude of leaked data can be purchased through the Dark Web nowadays. Recent reports highlight that the largest footprints of leaked data, which range from employee passwords to intellectual property, are linked to governmental institutions. According to OWL Cybersecurity, the US Navy is most affected. Thinking of leaked data like personal files, this can have a severe impact. For example, it can be the cornerstone for the start of sophisticated social engineering attacks, for getting credentials for illegal system access or installing malicious code in the target network. If personally identifiable information or sensitive data, access plans, strategies or intellectual property are traded on the Dark Web, this could pose a threat to the armed forces. The actual impact, role, and dimension of information treated in the Dark Web are rarely analysed. Is the available data authentic and useful? Can it endanger the capabilities of armed forces? These questions are even more challenging, as several well-known cases of deanonymization have been published over recent years, raising the question whether somebody really would use the Dark Web to sell highly sensitive information. In contrast, fake offers from scammers can be found regularly, only set up to cheat possible buyers. A victim of illegal offers on the Dark Web will typically not go to the police. The paper analyses the technical base of the Dark Web and examines possibilities of deanonymization. After an analysis of Dark Web marketplaces and the articles traded there, a discussion of the potential risks to military operations will be used to identify recommendations on how to minimize the risk. The analysis concludes that surveillance of the Dark Web is necessary to increase the chance of identifying sensitive information early; but actually the `open' internet, the surface web and the Deep Web, poses the more important risk factor, as it is - in practice - more difficult to surveil than the Dark Web, and only a small share of breached information is traded on the latter.
A New Steganography Method Based on Generative Adversarial Networks

In this paper, we propose a new steganography method based on generative adversarial networks (GAN). In using steganography, the third party can extract the embedded secret easily by comparing the cover data and the hidden message if the cover data are publicly available (e.g. accessible in the internet). To avoid this problem, we need a unique cover datum for each piece of secret messages. As the cover data, we focus on digital images in this study. To make a lot of unique and natural looking images, we can use GAN. Actually, training of GAN results in two neural networks namely the generator and the discriminator. The generator makes virtual images, and the discriminator evaluates the naturalness of the virtual images. In the proposed method, we use both the generator and the discriminator to guarantee the naturalness of the cover data on the sender side, and to filter out stego data sent from malicious third party. In this experiments, we first confirmed the capability of the generator for producing unlimited number of cover data, and then investigated the possibility of naturalness checking using the discriminator. We believe that the proposed method can provide a better way for information hiding.
Privacy-Preserving Compressive Model for Enhanced Deep-Learning-Based Service Provision System in Edge Computing

Advancements in the Internet of Things promote edge computing, which provides enhanced IoT services.IoT applications remarkably benefit from deep learning (DL) based on neural networks.Considering edge nodes with the limited ability of processing, the small-scale compressive DL model is applicatively deployed into them.However, the model can expose the privacy of training dataset.Thus, the model should be regarded as confidential due to the sensitive training dataset when applied in commercial and security fields.In this paper, we design a deep-learning-based service provision system for protecting the privacy and enhancing services in edge computing.We propose a practical approach for building the private compressive DL model by incorporating differential privacy mechanism on the cloud server side.For protecting the privacy of training dataset, the approach is composed of the private dense training step and the private compressive training step, where differential privacy is used to construct the private pre-trained dense model and the private compressive model, respectively.The private compressive model with 1/9 of the dense model's size can be practically embedded into edge servers.Edge servers provide enhanced services to the near IoT devices.Finally, we mainly execute a set of experiments on MNIST dataset.Compared to the dense model with no privacy constraints and with differential privacy (train and test accuracies of 98.0% and 98.2%, 95.5%, and 97.2%, respectively), the private compressive model perturbation with (6, 10 -5 )differential privacy (train and test accuracies of 96.3% and 95.8%) can achieve high utility while upholding tight privacy.
Siamese Network for Learning Genuine and Forged Offline Signature Verification

This work aims to use a Siamese network to verify between genuine and forged signatures by making signature embeddings more robust. Currently, the Siamese network is most widely used in many applications such as Dimensionality reduction, Learning image descriptor, Face recognition, Image ranking, etc. This network is termed as twin network since it consists of two similar neural networks which take two input images and shares same weights. The critical task in signature verification is to discriminate between genuine and skilled forger since forged signature differs by some precise kind of deformation. Embedding vector is generated by Siamese network and to make embedding vector more robust we propose to add statistical measures to it, which are calculated on the embedding vector itself. The contrastive loss function is then applied on the resultant embedding vector.
Mesh-Based Encryption Technique Augmented with Effective Masking and Distortion Operations

One of the main distinctive contributions of the digital era is that almost all of our information are either digitally stored on machines or transmitted over the networks. This information will be in a great danger unless properly secured. This paper addresses this problem by offering an encryption technique. The proposed technique possesses several properties that make it effective. First, it proposes an effective substitution process that highly eliminates the relation between the result of the substitution and the original plaintext. Second, it offers diffusion techniques that highly increase the avalanche effect. Third, it intelligently inserts random noises in the encryption to greatly increase the ciphertext confusion. Experiments with our prototype implementation showed that the proposed technique is effective and passed essential security tests.