Reviewerly/exHarmony huggingface dataset:
A Comprehensive Analysis of Fusion-based Image Enhancement Techniques

Image enhancement is an important aspect to improve the efficiency of any vision-based techniques. Image enhancement has a variety of applications, ranging from mobile phone cameras to medicinal visualization, law enforcement, ultrasonic imaging, underwater exploration, etc. Various kinds of methods are available to improve the quality of an image, out of which fusion-based image enhancement is one category. Fusion-based techniques focus on the weighted combination of multiple images based on the required features from those particular images. Moreover, fusion-based techniques require less time as compared to optimization-based or learning-based approaches. This paper presents a detailed analysis of image enhancement techniques based on multi-fusion. It gives an insight into the algorithm used in each method, along with its implementation framework. Finally, a comparison is drawn between the various techniques based on various parameters.
Visual Commonsense Representation Learning via Causal Inference

We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). We extensively apply VC R-CNN features in prevailing models of two popular tasks: Image Captioning and VQA, and observe consistent performance boosts across all the methods, achieving many new state-of-the-arts <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Testing the equality of the spectral densities of several uncorrelated almost cyclostationary processes

In this work, a new approach to compare the spectral densities of several uncorrelated almost cyclostationary processes is proposed. The asymptotic distribution of the discrete Fourier transform is used to establish a powerful test statistic. The behavior of the introduced technique is examined in both simulation study and real data case. The results verify the suitability and applicability of the given approach.
Towards efficient and automated side-channel evaluations at design time

Models and tools developed by the semiconductor community have matured over decades of use. As a result, hardware simulations can yield highly accurate and easily automated pre-silicon estimates for, e.g., timing and area figures. In this work, we design, implement, and evaluate CASCADE, a framework that combines a largely automated full-stack standard cell design flow with the state-of-the-art techniques for side-channel analysis. We show how it can be used to efficiently evaluate side-channel leakage prior to chip manufacturing. Moreover, it is independent of the underlying countermeasure and it can be applied starting from the earliest stages of the design flow. Additionally, we provide experimental validation through assessment of the side-channel security of representative cryptographic circuits. We discuss aspects related to the performance, scalability, and utility to the designers. In particular, we show that CASCADE can evaluate information leakage with 1 million simulated traces in less than 4 h using a single desktop workstation, for a design larger than 100 kGE.
Detecting Hardware-Assisted Virtualization With Inconspicuous Features

Recent years have witnessed the proliferation of the deployment of virtualization techniques. Virtualization is designed to be transparent, that is, unprivileged users should not be able to detect whether a system is virtualized. Such detection can result in serious security threats such as evading virtual machine (VM)-based malware dynamic analysis and exploiting vulnerabilities for cross-VM attacks. The traditional software-based virtualization leaves numerous artifacts/fingerprints, which can be exploited without much effort to detect the virtualization. In contrast, current mainstream hardware-assisted virtualization significantly enhances the virtualization transparency, making itself more transparent and difficult to be detected. Nonetheless, we showcase three new identified low-level inconspicuous features, which can be leveraged by an unprivileged adversary to effectively and stealthily detect the hardware-assisted virtualization. All three features come from the chipset fingerprints, rather than the traces of software-based virtualization implementations (e.g., Xen or KVM). The identified features include i) Translation-Lookaside Buffer (TLB) stores an extra layer of address translations; ii) Last-Level Cache (LLC) caches one more layer of page-table entries; and iii) Level-1 Data (L1D) Cache is unstable. Based on the above features, we develop three corresponding virtualization detection techniques, which are then comprehensively evaluated on three native environments and three popular cloud providers: i) Amazon Elastic Compute Cloud, ii) Google Compute Engine and iii) Microsoft Azure. Experimental results validate that these three adversarial detection techniques are effective (with no false positive) and stealthy (without triggering suspicious system events, e.g., VM-exit) in detecting the above commodity virtualized environments.
ROQ: A Noise-Aware Quantization Scheme Towards Robust Optical Neural Networks with Low-bit Controls

Optical neural networks (ONNs) demonstrate orders-of-magnitude higher speed in deep learning acceleration than their electronic counterparts. However, limited control precision and device variations induce accuracy degradation in practical ONN implementations. To tackle this issue, we propose a quantization scheme that adapts a full-precision ONN to low-resolution voltage controls. Moreover, we propose a protective regularization technique that dynamically penalizes quantized weights based on their estimated noise-robustness, leading to an improvement in noise robustness. Experimental results show that the proposed scheme effectively adapts ONNs to limited-precision controls and device variations. The resultant four-layer ONN demonstrates higher inference accuracy with lower variances than baseline methods under various control precisions and device noises.
You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design

DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific characteristics. However, it may fails in promising the highest composite score which combines the goals of accuracy and other hardware-related constraints (e.g., latency, energy efficiency) when building a specific neural-network-based system. In this work, we present a single-stage automated framework, YOSO, aiming to generate the optimal solution of software-and-hardware that flexibly balances between the goal of accuracy, power, and QoS. Compared with the two-stage method on the baseline systolic array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or 1.79x~3.07x latency reduction at the same level of precision, for different user-specified energy and latency optimization constraints, respectively.
Hybrid deep neural networks for recommender systems

More recently, deep neural networks received much attention from the recommender systems community where CNNs and RNNs were applied in different recommendation contexts and achieved state-of-the-art results on many publicly available benchmarks. While the success of the neural models came from their high expressiveness, the uninterpretability problem still one of their main drawbacks, which can have a negative side effect on the whole learning process. A good candidate to reduce the uninterpretability is using Probabilistic Soft Logic (PSL), which showed a strong performance in dealing with this problem. Moreover, sparsity is another major problem of the most previous recommendation systems. In this paper, we introduce a new recommender system framework based on the generalized distillation principle that combines two modeling approaches: PSL for the knowledge-driven modeling approach and deep neural networks for the data-driven approach. Experimental results on publicly available datasets show that our method significantly outperforms the previous state-of-the-art based on deep neural networks emphasizing the utility of PSL rules in handling inconsistencies, reducing the uninterpretability of the neural models and shows promising results in dealing with sparsity.
HECTORSLAM 2D MAPPING FOR SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)

Abstract This paper presents an application of LiDAR sensor for 2D mapping construction in an unkwown environments and capability to localize its own location based on landmark detected. Previously, there are various research actively conducted by others researchers for SLAM application. In general, it can be categorized based on three different type of sensor measurement and technique such as Vision based SLAM, RGB-D based SLAM and also Laser based SLAM. The main focus in this project is to present an experiment result conducted of a Simultaneous Localization and Mapping (SLAM) application based on laser sensor which is LiDAR in term of capability of mapping construction and localization it self. LiDAR sensor is put on the vehicle that will operate in real-world environments and computational processing done by using Robotic Operating System (ROS). This project is tested and verified in a curtain room with several parameter by using Robot Operating System (ROS). SLAM was implemented to provide localization estimates in environments, where there are static landmarks that are only rarely recognized by the vehicle or robot. This project also consider the features that enter and leave the environment as temporary landmarks that can be used in combination with the rarely seen static landmarks. As conclusion, performance of SLAM by using LiDAR sensor can be apply for several robotic system such as flight control, obstacle avoidance, navigation and other function in the future application.
Homogeneous Linear Inequality Constraints for Neural Network Activations

We propose a method to impose homogeneous linear inequality constraints of the form Ax ≤ 0 on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.
Designing a GPU-parallel algorithm for raw SAR data compression: A focus on parallel performance estimation

When a Synthetic Aperture Radar (SAR) acquires raw data using a satellite or airborne platform, it must be transferred to the ground for further processing. For example, SAR raw data need a so-called ’focusing’ signal processing to render it into a visible image. Such processing is time and computing consuming, and it is commonly carried out in computing centres. Since the data transfer rate is a typical limitation when communicating with the ground station, compression is necessary to reduce transmission time. So far, this procedure has been implemented in application-specific hardware, but recent adoption of avionic computational GPUs opened to new high-performance onboard perspectives. Due to the limited availability of avionic GPUs, we focused on parallel performance estimation starting from measures relative to a similar off-the-shelf solution. In this paper, we present a GPU algorithm for raw SAR data compression, which uses 1-dimensional DCT transforms, followed by quantisation and entropy coding. We evaluate results using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 data by measuring compression rates, statistical parameters, and distortion on decompressed and then focused images. Moreover, by evaluating the Algorithmic Overhead induced by the parallelisation strategy, we predict the best thread-block configuration for possible adoption of such a GPU algorithm on one of the most available avionic hardware.
On the choice of the parameter control mechanism in the (1+( <i>λ, λ</i> )) genetic algorithm

The self-adjusting (1 + (λ, λ)) GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter λ that governs the mutation strength and the number of offspring. However, on multimodal problems, the parameter control mechanism tends to increase λ uncontrollably.
Evaluating an Interactive Memory Analysis Tool: Findings from a Cognitive Walkthrough and a User Study

Memory analysis tools are essential for finding and fixing anomalies in the memory usage of software systems (e.g., memory leaks). Although numerous tools are available, hardly any empirical studies exist on their usefulness for developers in typical usage scenarios. Instead, most evaluations are limited to reporting performance metrics. We thus conducted a study to empirically assess the usefulness of the interactive memory analysis tool AntTracks Analyzer. Specifically, we first report findings from assessing the tool using a cognitive walkthrough, guided by the Cognitive Dimensions of Notations Framework. We then present the results of a qualitative user study involving 14 subjects who used AntTracks to detect and resolve memory anomalies. We report lessons learned from the study and implications for developers of interactive memory analysis tools. We hope that our results will help researchers and developers of memory analysis tools in defining, selecting, and improving tool capabilities.
Risk communication in cyberspace: a brief review of the information-processing and mental models approaches

Effective risk communication in cyberspace is critical for users to understand the potential security risks and make secure decisions. Two approaches to risk communication originating from psychology, the human information-processing approach and the mental-models approach, have been widely applied in other research fields of risk communication. The human information-processing approach characterizes the human as a communication system, with risk-communication information from a source delivered to the receiver, who processes the information via various stages. The mental-models approach emphasizes the importance of understanding experts' and non-experts' mental models, comparing these models, and drafting and evaluating risk-communication messages. With an overview of these two approaches and their applications, the goal of this paper is to provide insights for future use of these approaches in cybersecurity.
Hamming and simplex codes for the sum-rank metric

Sum-rank Hamming codes are introduced in this work. They are essentially defined as the longest codes (thus of highest information rate) with minimum sum-rank distance at least 3 (thus one-error-correcting) for a fixed redundancy r, base-field size q and field-extension degree m (i.e., number of matrix rows). General upper bounds on their code length, number of shots or sublengths and average sublength are obtained based on such parameters. When the field-extension degree is 1, it is shown that sum-rank isometry classes of sum-rank Hamming codes are in bijective correspondence with maximal-size partial spreads. In that case, it is also shown that sum-rank Hamming codes are perfect codes for the sum-rank metric. Also in that case, estimates on the parameters (lengths and number of shots) of sum-rank Hamming codes are given, together with an efficient syndrome decoding algorithm. Duals of sum-rank Hamming codes, called sum-rank simplex codes, are then introduced. Bounds on the minimum sum-rank distance of sum-rank simplex codes are given based on known bounds on the size of partial spreads. As applications, sum-rank Hamming codes are proposed for error correction in multishot matrix-multiplicative channels and to construct locally repairable codes over small fields, including binary.
Un/Making in the Aftermath of Design

This paper takes as its starting point the fact that we live in the aftermath of previous making and design. For participatory design to adequately answer to this aftermath, we suggest building on a combination of participatory and speculative design approaches in everyday life settings and exploring the practice of un/making matters. The paper draws on two cases where participants have been invited to engage with recent scientific findings and practices - one where they explore the practice of un/making plastic waste through composting, and one on un/making polluted soil through plants that can accumulate metals. By not primarily aiming at feeding into new iterations of a design process, there is an openness for speculating beyond the given systems, and to bring into question imaginaries of constant progress, which have been part of generating these lingering matters.
LandLedger: Blockchain-powered Land Property Administration System

The current land administration system of many countries including India is plagued with incomplete and damaged records. Different departments pertaining to land administration system store their own copy of records, which lead to incomplete verification and document forgery. In this paper, we present a blockchain-powered land administration system, termed as LandLedger, which provides accountable, transparent, efficient, secure and scalable land property administration. The proposed architecture of LandLedger realizes property verification, registration and revocation using specially designed transactions on a permissioned blockchain, which is managed by various departments such as Registrar's office, The Income tax department, The Revenue department and so on. LandLedger uses Merkle Patricia Tree to implement ownership verification and property history checking efficiently. The implementation of LandLedger shows its practicality with enhanced features in comparison to the current practice used in many countries including India.
The language of bullying: Social issues on Chinese websites

The growing use of information and communication technology has led to the development of the global phenomenon of cyberbullying. Causes and consequences of cyberbullying, and the detection and prevention of it have been widely analysed in the fields of psychology and computer science. However, research on cyberbullying in China to date has been extremely limited, despite its recent growth in significance. As such, this research seeks to use the principles of semantics and pragmatics, and language policy and planning to analyse cyberbullying through the content analysis method. This research focuses primarily on cyberbullying word categories, the relationship between word use and forms of cyberbullying, the consequences of cyberbullying, and potential protocols for addressing this phenomenon. The primary findings reveal that internet users prefer strong or directly offensive words to mild or indirectly offensive language when the intention is to attack others or to express anger online. Contextual factors are primary and influential elements which may enable detection and/or prevention of cyberbullying. Informed by these findings, this paper considers the potential social problems caused by word usage in cyberbullying, and contributes to the development and refinement of media regulation and governance guidelines.
Do economic freedom matters for finance in developing economies: a panel threshold analysis

Economic freedom is an important determinant of economic development, yet little academic research has considered its financial perspectives. Using a relatively new measure of finance, we find that a high-freedom environment fosters finance in developing economies. Unlike prior literature, the advanced panel threshold estimator shows a single significant economic freedom threshold level of 4.282 as a benchmark, where the relationship turns positive. The findings are robust to alternative estimators and split-sample analysis. The findings are useful for policymakers to attain optimal financial benefits by assuring a high-freedom environment.
Learning a Spatial Field in Minimum Time With a Team of Robots

In this article, we study an informative path-planning problem where the goal is to minimize the time required to learn a spatially varying entity. We use Gaussian process (GP) regression for learning the underlying field. Our goal is to ensure that the GP posterior variance, which is also the mean square error between the learned and actual fields, is below a predefined value. We study three versions of the problem. In the placement version, the objective is to minimize the number of measurement locations while ensuring that the posterior variance is below a predefined threshold. In the mobile robot version, we seek to minimize the total time required to visit and collect measurements from the measurement locations using a single robot. We also study a multirobot version where the objective is to minimize the time required by the last robot to return to a common starting location called depot. By exploiting the properties of GP regression, we present constant-factor approximation algorithms. In addition to the theoretical results, we also compare the empirical performance using a real-world dataset, with other baseline strategies.