Reviewerly/exHarmony huggingface dataset:
High dynamic range infrared image compression and denoising

Detail enhancement and noise removal are major issues in high dynamic range (HDR) infrared image processing. The main focus is how to effectively compress the high dynamic range infrared image to display on the low dynamic range monitors, and in this compression process, both the image details can be enhanced and the noises can be removed. In this paper, we propose a new method to achieve high dynamic range infrared image compression. In the proposed framework, the Laplace differential and Histogram projection are respectively used to sharpen and compress the raw image. Because the image details and noises are enhanced, we design a denoising deep convolutional neural network (DnDCNN)to remove the noises of the image processed in the previous step. By training an additive white Gaussian noise (AWGN) model with different noise levels, our model is capable of processing Gaussian noise at unknown noise levels. Specifically, residual learning and skip connection strategy are utilized to speed up the training process as well as boost the denoising performance. Final extensive experiments demonstrate that our CNN model is very effective in removing such infrared image noise, and the output image has excellent visualization effect.
Open Source PowerShell-Written Post Exploitation Frameworks Used by Cyber Espionage Groups

Cyber espionage groups are sophisticated adversary groups that conduct complex attack campaigns against their targets. As cyber espionage activities increase, there will be an increased pressure on these groups to quickly and effectively conduct their cyber operations against their targets. Using open source hacking tools can allow these groups to meet this demand by lowering resources which would otherwise be used to develop customized tooling. Using a language such as PowerShell which is widely available on target systems, cyber espionage groups can use tools that require less setup and manipulation to operate. Open source PowerShell-written post exploitation frameworks allow cyber espionage groups to leverage open source tooling on the PowerShell platform. This makes an ideal attack platform to conduct cyber operations from. This paper details cyber espionage groups that use open source PowerShell-written post exploitation frameworks and describe how they are used. The goal of this research is to understand how this tooling is used and identify trends that can lead to insights on future usage.
Partition Pruning: Parallelization-Aware Pruning for Dense Neural Networks

As recent neural networks are being improved to be more accurate, their model's size is exponentially growing. Thus, a huge number of parameters requires to be loaded and stored from/in memory hierarchy and computed in processors to perform training or inference phase of neural network processing. Increasing the number of parameters causes a big challenge for real-time deployment since the memory bandwidth improvement's trend cannot keep up with models' complexity growing trend. Although some operations in neural networks processing are computational intensive such as convolutional layer computing, computing dense layers face with memory bandwidth bottleneck. To address the issue, the paper has proposed Partition Pruning for dense layers to reduce the required parameters while taking into consideration parallelization. We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7.72× speedup of performance and a 2.73× reduction in the energy used for computing pruned fully connected layers in TinyVGG16 model in comparison to running the unpruned model on a single accelerator. Besides, our method showed a limited reduction in accuracy while partitioning fully connected layers.
Uncertainty Characterization for Predictive Analytics with Clinical Time Series Data

Recurrent neural networks (RNNs) offer state-of-the-art (SOTA) performance on a variety of important prediction tasks with clinical time series data. However, meaningful translation to actionable decisions requires capability to quantify confidence in the predictions, and to address the inherent ambiguities in the data and the associated modeling process. We propose a Bayesian LSTM framework using Bayes by Backprop to characterize both modelling (epistemic) and data related (aleatoric) uncertainties in prediction tasks for clinical time series data. We evaluate our approach on mortality prediction tasks with two public Intensive Care Unit (ICU) data sets, namely, the MIMIC-III and the PhysioNet 2012 collections. We demonstrate the potential for improved performance over SOTA methods, and characterize aleatoric uncertainty in the setting of noisy features. Importantly, we demonstrate how our uncertainty estimates could be used in realistic prediction scenarios to better interpret the reliability of the data and the model predictions, and improve relevance for decision support.
Lattice Ideals, Semigroups and Toric Codes

Let X be a complete n-dimensional simplicial toric variety over a finite field with homogeneous coordinate ring S. In this survey, we review algebraic methods for studying evaluation codes defined on subsets of the algebraic torus TX. The key object is the vanishing ideal of the subset and its Hilbert function. We also explore the nice correspondence between subgroups of the group TX and lattice ideals as their vanishing ideals. We present recent results for obtaining a basis for the lattice and for computing a minimal generating set of its ideal.
The Internet of Things (IoT) in Tunisian Education and Healthcare: Prospects and Barriers to Improved Educational and Medical Outcomes

The importance of Internet technology cannot be overstated in this era of globalization. The Internet is revolutionizing every sector of our life, and with the introduction of low-cost sensor technologies, the Internet of Things (IoT) will lead to greater integration between the physical and virtual worlds. In Tunisia IoT has very recently been launched in many sectors, namely in Education and Healthcare. This paper will give a general overview of The Internet of Things (IoT) in Tunisian Education and Healthcare to explore how robust IoT ecosystems with proper Privacy, Security and Trust (PST) provisions can improve both educational and health outcomes. The paper additionally analyzes the barriers that hinder the application of IoT in the country.
Engaging Participants during Selection Studies in Virtual Reality

Selection studies are prevalent and indispensable for VR research. However, due to the tedious and repetitive nature of many such experiments, participants can become disengaged during the study, which is likely to impact the results and conclusions. In this work, we investigate participant disengagement in VR selection experiments and how this issue affects the outcomes. Moreover, we evaluate the usefulness of four engagement strategies to keep participants engaged during VR selection studies and investigate how they impact user performance when compared to a baseline condition with no engagement strategy. Based on our findings, we distill several design recommendations that can be useful for future VR selection studies or user tests in other domains that employ similar repetitive features.
Panoramic Image Quality-Enhancement by Fusing Neural Textures of the Adaptive Initial Viewport

With the development of virtual reality (VR) technology, panoramic image has been widely applied in our life. Due to its large size, the existing streaming methods prefer only transmitting contents corresponding to the audience’s current viewport in high quality. This viewport-based transmission, however, suffers from severe delay as the viewport changes. In this paper, to fill in the time gap between the switch of viewport and the arrival of high-resolution content, we introduce an end-to-end network at the receiver. The main idea is to use the neural textures in the adaptive initial viewport to improve the quality of regions around it. When the viewport changes but the high-resolution content has not arrived, the image enhanced by our strategy is acceptable to satisfy visual experience as the experiment results show. To the best of our knowledge, this is the first panoramic image quality-enhancement method considering content continuities and internal features.
Auto-Scale Resource Provisioning In IaaS Clouds

Abstract Users of cloud computing technology can lease resources instead of spending an excessive charge for their ownership. For service delivery in the infrastructure-as-a-service model of the cloud computing paradigm, virtual machines (VMs) are created by the hypervisor. This software is installed on a bare-metal server, called the host, and acted as a broker between the hardware of the host and its VMs. The host is responsible for the allocation of required resources, such as CPU, RAM and network bandwidth, for VMs. Therefore, allocating resources to a VM is equivalent to finding the location of the VM on the hosts. In this paper, we propose a model for resource allocation of a datacenter that includes clusters of hosts. This model is based on the birth–death process of queueing systems and continuous-time Markov chains. We will focus on RAM-intensive VMs and consider the allocation of RAM for a VM as a job in the queueing systems. The purpose of this modeling is to keep the number of running hosts minimum while guaranteeing the quality of service in terms of response. When the utilization of active hosts reaches a predefined threshold value, a new host is added to prevent response time violation, and when host utilization is reduced to a certain threshold, one of the hosts can be deactivated. The experimental results show that, in the long run, the odds of working with more jobs are increased.
A type-assignment of linear erasure and duplication

We introduce LEM, a type-assignment system for the linear λ-calculus that extends second-order IMLL2, i.e., intuitionistic multiplicative Linear Logic, by means of logical rules that weaken and contract assumptions, but in a purely linear setting. LEM enjoys both a mildly weakened cut-elimination, whose computational cost is cubic, and Subject reduction. A translation of LEM into IMLL2 exists such that the derivations of the former can exponentially compress the dimension of the derivations in the latter. LEM allows for a modular and compact representation of boolean circuits, directly encoding the fan-out nodes, by means of contraction, and disposing garbage, by means of weakening. It can also represent natural numbers with terms very close to standard Church numerals which, moreover, apply to Hereditarily Finite Permutations, i.e. a group structure that exists inside the linear λ-calculus.
A Consumer-Centric Conceptual Framework for Trust Assessment in Cloud Computing

Consumers are heavily dependent on secure and reliable cloud computing services. However, there are various shortcomings in cloud services, such as those concerning performance, security, trust and privacy, among others. Cloud services consumers do not have enough information on these critical issues neither on compliance with laws and regulations. We present a conceptual framework for trust assessment of cloud computing environments. Our proposal is based on a consumer-centric approach, since it deals with cloud trust aspects from the perspective of end users. For this purpose, metrics and indicators are proposed to allow consumers to assess the trust of cloud services providers. Our contributions are: (1) a conceptual framework with indicators and processes for trust assessment; (2) a lightweight ontology of key concepts of trust assessment; and (3) an application scenario to illustrate the practical adequacy of the conceptual framework.
New Perspective of Interpretability of Deep Neural Networks

Deep neural networks (DNNs) are known as black-box models. In other words, it is difficult to interpret the internal state of the model. Improving the interpretability of DNNs is one of the hot research topics. However, at present, the definition of interpretability for DNNs is vague, and the question of what is a highly explanatory model is still controversial. To address this issue, we provide the definition of the human predictability of the model, as a part of the interpretability of the DNNs. The human predictability proposed in this paper is defined by easiness to predict the change of the inference when perturbating the model of the DNNs. In addition, we introduce one example of high human-predictable DNNs. We discuss that our definition will help to the research of the interpretability of the DNNs considering various types of applications.
Hybrid APSO–Spiral Dynamic Algorithm

The accelerated particle swarm optimisation (APSO) is an improved variant of the PSO algorithm that guarantees convergence through the use of only global best to update both velocity and position of particles.
Bio-sensory data warehouse with analytics for e-helath solutions

In this study, we will continue the description of our unique e-Health data warehouse implementation as part of the Med-i-Hub architecture. The research team implemented a system to collect and analyze health related data from different sources, e.g.: fitness trackers, smart scales, wearable devices and other external systems. This paper focuses on the reference architecture and its characteristics. The demonstrated system gives a solution to integrate sensor-based data into traditional health care systems via simple application programming interfaces, while it supports health related international standards. It is capable of effective data processing to serve reliably the integrated e-health applications and the integrated systems. The presented complex innovative solution is scalable, flexible and expansible. The detailed personal assistant application allows the users to monitor and follow its bio-sensory signals.
The Software Engineering Position Mapping From Personality Traits

There are high demands of software engineering now a day due to the digital evolution. Having good software engineers to the team is an advantage for the business. There are many research indicates that there are relationships between software engineer position and personal traits. The work position requires people with specific traits to achieve work. So this paper proposed the K-question position mapping technique to find a suitable job position for the candidate. The candidate needs to complete the Five-Factor Model (FFM) test to explore their dominated traits. Using the model data from the FFM test of people with various positions, the dominated traits are compared to each traits data with software position. The proposed technique suggests the appropriate software position for the candidate. Besides, the proposed approach has been compared with other techniques such as regression analysis, and the k-nearest neighbor. The result shows that the proposed approach returns the appropriate position more accuracy than the other.
Wavelet-Based Image Compression Encoding Techniques — A Complete Performance Analysis

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).
Privacy Preserving and Security Management in Cloud-Based Electronic Health Records—A Survey

The rise of cloud computing in the previous decade has seen an increase in integrated healthcare facilities. The cloud not only provides third-party infrastructure, but also acts as a Medical Record Storage Centre by facilitating the exchange of electronic health records among hospitals and physicians. This paradigm of shift has offered healthcare organizations flexibility with cost, development and infrastructure maintenance. The biggest success deciding factor of cloud on healthcare is the level of security and confidentiality it offers. Because electronic health records (EHRs) store sensitive data about a patient, it is the responsibility of cloud service providers and data centres to ensure confidentiality, privacy and trust-based security mechanisms. As more and more healthcare migrations are moving to the cloud day by day, ensuring safety of security breach and privacy preservations and ensuring trust remain a challenge as incidents of security attacks and hacking are widely reported.
The Geometry of Nonlinear Embeddings in Kernel Discriminant Analysis

Fisher's linear discriminant analysis is a classical method for classification, yet it is limited to capturing linear features only. Kernel discriminant analysis as an extension is known to successfully alleviate the limitation through a nonlinear feature mapping. We study the geometry of nonlinear embeddings in discriminant analysis with polynomial kernels and Gaussian kernel by identifying the population-level discriminant function that depends on the data distribution and the kernel. In order to obtain the discriminant function, we solve a generalized eigenvalue problem with between-class and within-class covariance operators. The polynomial discriminants are shown to capture the class difference through the population moments explicitly. For approximation of the Gaussian discriminant, we use a particular representation of the Gaussian kernel by utilizing the exponential generating function for Hermite polynomials. We also show that the Gaussian discriminant can be approximated using randomized projections of the data. Our results illuminate how the data distribution and the kernel interact in determination of the nonlinear embedding for discrimination, and provide a guideline for choice of the kernel and its parameters.
Predicting Issue Handling Process using Case Attributes and Categorical Variable Encoding Techniques

Software development process deals with the increasing complexity, needs for enhancement, and the reduction of bugs. The software project managers have a responsibility to maintain the software quality, e.g., by establishing Service Level Agreement (SLA) to the software development. However, there are difficulties to monitor SLA and to predict the software quality, due to a lot of issues should be handled. This work proposed prediction techniques for handling issues in the software development by using historical data from software repositories. There are two types of predictions in this work, namely 1) prediction of the remaining duration, and 2) prediction of the next activities. Event logs extracted from historical data in the software repositories exploited case and event attributes as predictors. Furthermore, the categorical variable encoding technique used in the preprocessing data phase. Some categorical variable encoding techniques also proposed in this study. The results showed that the use of case attributes as predictors could improve performance by 8.57% in the next activity prediction and 1.9% in the remaining duration prediction. Of the 4 (four) categorical variable encoding techniques used, one-hot encoding and sum encoding techniques can provide the best remaining duration prediction with a MAE of 19.72 and one-hot encoding technique can provide the best next activity prediction with an accuracy of 65.38%. All of these methods are widely evaluated using datasets from the Google Chromium Project. Further work including utilization of other attributes outside case and event attributes, e.g., component attributes, as predictor variables.
A semisupervised classifier based on piecewise linear regression model using gated linear network

Semisupervised classification aims to construct a classifier by making use of both labeled data and unlabeled data. This paper proposes a semisupervised classifier based on a piecewise linear regression model implemented by using a gated linear network. The semisupervised classifier is constructed in two steps. In the first step, instead of estimating the break points of a piecewise linear model directly, a label‐guided autoencoder‐based semisupervised gating mechanism is designed to generate binary gate control signals to realize the partitioning. In the second step, the piecewise linear model is first transformed into linear regression form, and the linear parameters are then optimized globally by a Laplacian regularized least squares (LapRLS) algorithm using a kernel function comprising the gate control signals obtained in the first step. Moreover, the composed kernel function is used as a better similarity function for the graph construction. As a result, we capture data manifold from both labeled and unlabeled data, and the data manifold is ingeniously incorporated into both the kernel and the graph Laplacian in LapRLS. Numerical experiments on various real‐world datasets exhibit the effectiveness of the proposed method. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Fast and robust visual tracking with hard balanced focal loss and guided domain adaption

Recently, Siamese networks based trackers have shown excellent performance in accuracy and speed. However, previous studies treat all training samples equally and use the general feature space without adapting to the specific video during tracking. These trackers ignore the class data imbalance during training and the feature space difference between the generic domain and the current tracking target domain, which limits the robustness of trackers. In this paper, we propose an algorithm for learning a discriminative and self-adaptive feature representation, in order to achieve accurate and robust tracking. During the off-line training stage, a hard balanced focal loss function is utilized to solve the positive–negative samples imbalance and the hard-easy negative samples imbalance. During the tracking phase, an off-line trained guided domain adaptation module is embedded into the Siamese networks, which can quickly transfer the feature space from the general domain to the current video domain by adjusting the search branch channel weights. Our networks are trained in an end-to-end manner and without online updating. Our tracker runs at 130 FPS while achieving favorable performance against the state-of-the-art methods on OTB-2013, OTB-2015, VOT-2016, VOT-2017, GOT-10 K and TC-128 benchmarks.