Reviewerly/exHarmony huggingface dataset:
Safety-Critical Rapid Aerial Exploration of Unknown Environments

This paper details a novel approach to collision avoidance for aerial vehicles that enables high-speed flight in uncertain environments. This framework is applied at the controller level and provides safety regardless of the planner that is used. The method is shown to be robust to state uncertainty and disturbances, and is computed entirely online utilizing the full nonlinear system dynamics. The effectiveness of this method is shown in a high-fidelity simulation of a quadrotor with onboard sensors rapidly and safely exploring a cave environment utilizing a simple planner.
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc. Some learning-based frameworks have been proposed for this purpose due to their inherent end-to-end optimization mechanisms. However, it is still quite challenging to integrate these task-driven metrics seamlessly into traditional hybrid coding framework. To the best of our knowledge, this paper is the first work trying to solve this challenge based on reinforcement learning (RL) approach. Specifically, we formulate the bit allocation problem as a Markovian Decision Process (MDP) and train RL agents to automatically decide the quantization parameter (QP) of each coding tree unit (CTU) for HEVC intra coding, according to the task-driven semantic distortion metrics. This bit allocation scheme can maximize the semantic level fidelity of the task, such as classification accuracy, while minimizing the bit-rate. We also employ gradient class activation map (Grad-CAM) and Mask R-CNN tools to extract task-related importance maps to help the agents make decisions. Extensive experimental results demonstrate the superior performance of our approach by achieving 43.1% to 73.2% bit-rate saving over the anchor of HEVC under the equivalent task-related distortions.
PAC-Bayes control: learning policies that provably generalize to novel environments

Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between generalization of control policies to novel environments and generalization of hypotheses in the supervised learning setting. In particular, we utilize the probably approximately correct (PAC)-Bayes framework, which allows us to obtain upper bounds that hold with high probability on the expected cost of (stochastic) control policies across novel environments. We propose policy learning algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved using convex optimization (relative entropy programming in particular) in the setting where we are optimizing over a finite policy space. In the more general setting of continuously parameterized policies (e.g., neural network policies), we minimize this upper bound using stochastic gradient descent. We present simulated results of our approach applied to learning (1) reactive obstacle avoidance policies and (2) neural network-based grasping policies. We also present hardware results for the Parrot Swing drone navigating through different obstacle environments. Our examples demonstrate the potential of our approach to provide strong generalization guarantees for robotic systems with continuous state and action spaces, complicated (e.g., nonlinear) dynamics, rich sensory inputs (e.g., depth images), and neural network-based policies.
Learning Bayesian networks based on order graph with ancestral constraints

We consider incorporating ancestral constraints into structure learning for Bayesian Networks (BNs) when executing an exact search based on order graph (OG); this is thought to be impossible because ancestral constraints are non-decomposable. In order to adapt to the constraints, the node in an order graph is generalized as a series of directed acyclic graphs (DAGs). Then, we design a novel revenue function to breed out infeasible and suboptimal nodes to expedite the graph search. A breadth-first search algorithm is implemented in the new search space, verifying the validity and efficiency of the proposed framework. It has been demonstrated that, when the ancestral constraints are consistent with the ground-truth network or deviate from it, the new framework can navigate a path that leads to a global optimization in almost all cases with less time and space required for orders of magnitude than the state-of-the-art framework, such as EC-Tree.
Synergistic use of ASTER, L-band ALOS PALSAR, and hyperspectral AVIRIS-NG data for exploration of lode type gold deposit – A study in Hutti Maski Schist Belt, India

In this study, we have explored the utility of advanced hyperspectral, dual-polarized microwave, and advanced multispectral data to delineate potential areas of mineralization associated with the lode type gold deposit in Hutti-Maski Schist Belt Karnataka, India. We derived different image-enhanced products using visible-near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate major rock types of gold-bearing greenstone belt such as metabasalt, amphibolite, younger alkali feldspar rich granite, granodiorite, and tonalitic-granodioric gneiss (TTG). The principal component (PC) image composite of ASTER VNIR-SWIR bands was not only used to distinguish different granitoids but also confirmed their sharp instructive nature of contact with the greenstone rocks. We also used an index image composite using ASTER SWIR bands to discriminate granitoid, basalt and amphibolite. We could demarcate few lineaments extending through the mineralized zone (e.g., Hutti, Heera-Buddine mines, etc.) using the fused products of ASTER and two polarization channels of the Phased-array L band Synthetic Aperture Radar (PALSAR) sensor of the Advanced Land Observation Satellite (ALOS). All these image-enhanced products are validated using the reference geological map of the study area and field data collected along selected field -traverses. The mineralogical and petrographical data of the altered rocks from the study site revealed the formation of calcite, scapolite, and other minerals. Represenattive reflectance spectrum of the altered rock was used to spatially delineate the surface distribution of the patchy and small alteration zones using airborne hyperspectral data of the Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) sensor. In this regard, a unified spectral map of altered rocks was derived by integrating two alteration maps prepared independently using two different partial sub-pixel algorithms (constrained energy minimization and matched filtering). Occurrences of altered rocks in the vicinity to favorable geological lineaments and their spatial proximity to metavolcanic-younger felsic intrusive contacts suggest their importance as the potential sites for detailed exploration.
Policy Evaluation in Continuous MDPs With Efficient Kernelized Gradient Temporal Difference

We consider policy evaluation in infinite-horizon discounted Markov decision problems with continuous compact state and action spaces. We reformulate this task as a compositional stochastic program with a function-valued decision variable that belongs to a reproducing kernel Hilbert space (RKHS). We approach this problem via a new functional generalization of stochastic quasi-gradient methods operating in tandem with stochastic sparse subspace projections. The result is an extension of gradient temporal difference learning that yields nonlinearly parameterized value function estimates of the solution to the Bellman evaluation equation. We call this method parsimonious kernel gradient temporal difference learning. Our main contribution is a memory-efficient nonparametric stochastic method guaranteed to converge exactly to the Bellman fixed point with probability 1 with attenuating step-sizes under the hypothesis that it belongs to the RKHS. Further, with constant step-sizes and compression budget, we establish mean convergence to a neighborhood and that the value function estimates have finite complexity. In the Mountain Car domain, we observe faster convergence to lower Bellman error solutions than existing approaches with a fraction of the required memory.
Comparing U-Net Based Models for Denoising Color Images

Digital images often become corrupted by undesirable noise during the process of acquisition, compression, storage, and transmission. Although the kinds of digital noise are varied, current denoising studies focus on denoising only a single and specific kind of noise using a devoted deep-learning model. Lack of generalization is a major limitation of these models. They cannot be extended to filter image noises other than those for which they are designed. This study deals with the design and training of a generalized deep learning denoising model that can remove five different kinds of noise from any digital image: Gaussian noise, salt-and-pepper noise, clipped whites, clipped blacks, and camera shake. The denoising model is constructed on the standard segmentation U-Net architecture and has three variants—U-Net with Group Normalization, Residual U-Net, and Dense U-Net. The combination of adversarial and L1 norm loss function re-produces sharply denoised images and show performance improvement over the standard U-Net, Denoising Convolutional Neural Network (DnCNN), and Wide Interface Network (WIN5RB) denoising models.
An Enhanced Round Robin (ERR) algorithm for Effective and Efficient Task Scheduling in cloud environment

Efficient and Effective scheduling methods can result in more desired services to the consumers and also can improve the performance of a cloud computing environment. The primary job of a task scheduler is to ensure reduction in execution time of the tasks and to fulfill maximum resource utilization. This paper proposes an Enhanced version of Round Robin algorithm (ERR) for improved performance without affecting the good features of traditional RR and which can bring out more efficiency. The proposed algorithm is implemented and tested using CloudSim toolkit and the initial results prove that the average waiting time for the tasks in a given number of cloudlets is reduced in MRR than the conventional RR in the same conditions. The proposed method also outperforms the other existing algorithms such as ACO, GA, MPA, Min-Min and PSO in terms of execution time and residue energy.
Obstacle Detection and Avoidance System for Small UAVs using a LiDAR

This paper presents the obstacle detection and avoidance system for a multicopter UAV. A Velodyne VLP-16 LiDAR is used for obstacle detection. The LiDAR generates a 360° view of the environment around the multicopter, enabling the multicopter to navigate around any obstacles in any direction. An Intel NUC processor is used as an onboard flight computer, which processes the LiDAR data and obstacle avoidance algorithm. The flight computer communicates with the flight controller (Pixhawk2 autopilot) using a Python library called DroneKit to send the obstacle avoidance commands. This results in the multicopter safely navigating around obstacles autonomously. Simulation and ground test results are shown.
EncodeORE: Reducing Leakage and Preserving Practicality in Order-Revealing Encryption

Order-preserving encryption (OPE) is a cryptographic primitive that preserves the order of plaintexts. In the past few years, many OPE schemes were proposed to solve the problem of executing range queries in encrypted databases. However, OPE leaks some certain information (for example, the order of ciphertext), so it is vulnerable to many attacks. Subsequently, order-revealing encryption (ORE) was proposed by Boneh <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (Eurocrypt 2015) as a generalization of order-preserving encryption. It breaks through the limitation of the numeric order of OPE plaintext. It implements ciphertext comparison for any specific form of plaintext through a publicly computable comparison function. In this article, we aim to design a new ORE scheme which reduces the leakages and preserves the practicality in terms of ciphertext length and encryption time. We first propose the hybrid model named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HybridORE</i> . Then, we propose an improved scheme named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EncodeORE</i> which achieves acceptable security and appropriate ciphertext length. They both explore the encode strategy of encoding plaintext into different parts and apply suitable ORE algorithms to each part according to its security characteristics to reduce leakages. Compared with the typical CLWW scheme (FSE 2016) and Lewi-Wu (CCS 2016) in large domain, they have fewer leakages. The experiment shows that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EncodeORE</i> is very practical.
Subcarrier modulation identification of underwater acoustic OFDM based on block expectation maximization and likelihood

The low identification rate of the Orthogonal frequency division multiplexing (OFDM) based subcarrier modulation in underwater acoustic multipath channel is an important issue. Therefore, we proposed a novel Expectation Maximization-Block-Quasi Hybrid Likelihood Ratio Test (EM-Block-QHLRT) method which effectively improved the identification rate while using the blind channel impulse response (CIR) estimation and likelihood. Initially, CIR is obtained by using clustering, and then the CIR is updated iteratively by EM-Block method. Further, the subcarrier modulation is identified using QHLRT. The influence of iteration times, the length of symbol and influence of the number of blocks in EM are analyzed by using extensive simulation. The identification rate of subcarrier modulation of binary phase shift keying (BPSK), QPSK, 8PSK and 16QAM are presented using different signal noise ratio (SNR). In addition, the identification rate of subcarrier modulation based on Average Likelihood Rate Test (ALRT) as well as QHLRT with EM are compared. Simulation results showed that the identification rate of the proposed EM-Block-QHLRT method can reach to more than 90% when SNR is higher than 5 dB. Finally, the performance of the proposed EM-Block-QHLRT method is verified using sea trial data.
Egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic

Education field is affected by the COVID-19 pandemic which also affects how universities, schools, companies and communities function. One area that has been significantly affected is education at all levels, including both undergraduate and graduate. COVID-19 pandemic emphasis the psychological status of the students since they changed their learning environment. E-learning process focuses on electronic means of communication and online support communities, however social networking sites help students manage their emotional and social needs during pandemic period which allow them to express their opinions without controls. The paper will propose a Sentiment Analysis Model that will analyze the sentiments of students in the learning process with in their pandemic using Word2vec technique and Machine Learning techniques.The sentiment analysis model will start with the processing process on the student's sentiment and selects the features through word embedding then uses three Machine Learning classifies which are Naïve Bayes, SVM and Decision Tree. Results including precision, recall and accuracy of all these classifiers are described in this paper. The paper helps understand the Egyptian student's opinion on learning process during COVID-19 pandemic.
BitPruner: Network Pruning for Bit-serial Accelerators

Bit-serial architectures (BSAs) are becoming increasingly popular in low power neural network processor (NNP) design. However, the performance and efficiency of state-of-the-art BSA NNPs are heavily depending on the distribution of ineffectual weight-bits of the running neural network. To boost the efficiency of third-party BSA accelerators, this work presents Bit-Pruner, a software approach to learn BSA-favored neural networks without resorting to hardware modifications. The techniques proposed in this work not only progressively prune but also structure the non-zero bits in weights, so that the number of zero-bits in the model can be increased and also load-balanced to suit the architecture of the target BSA accelerators. According to our experiments on a set of representative neural networks, Bit-Pruner increases the bit-sparsity up to 94.4% with negligible accuracy degradation. When the bit-pruned models are deployed onto typical BSA accelerators, the average performance is 2.1X and 1.5X higher than the baselines running non-pruned and weight-pruned networks, respectively.
CPC Algorithm: Exact Area Coverage by a Mobile Robot Using Approximate Cellular Decomposition

SUMMARY A new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.
An exotic multi-dimensional conceptualization for medical image encryption exerting Rossler system and Sine map

Ensuring data security is a hot-button-issue at present in medical field because the sufferer information are stored and transmitted widely through communication networks. To vanquish the awful conditions a novel Multi-dimensional Medical Image Encryption (MMIE) scheme is proposed exploiting Rossler dynamical system and Sine map. Chaotic systems gained its popularity in the last few decades for their complex properties and in particular their sensitiveness to initial conditions. The proposed encryption system incorporates a substantial key space and robustness to withstand diverse attacks and beats out the conventional encryption methodologies. Simulation results such as histogram analysis, entropy analysis, differential analysis, correlation analysis, computational complexity analysis, comparison analysis, key space analysis, key sensitivity analysis, robustness to various noise and data-loss analysis turns up the system to admit a substantial amount of security.
Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Abstract In the era of “big data,” a huge number of people, devices, and sensors are connected via digital networks and the cross‐plays among these entities generate enormous valuable data that facilitate organizations to innovate and grow. However, the data deluge also raises serious privacy concerns which may cause a regulatory backlash and hinder further organizational innovation. To address the challenge of information privacy, researchers have explored privacy‐preserving methodologies in the past two decades. However, a thorough study of privacy preserving big data analytics is missing in existing literature. The main contributions of this article include a systematic evaluation of various privacy preservation approaches and a critical analysis of the state‐of‐the‐art privacy preserving big data analytics methodologies. More specifically, we propose a four‐dimensional framework for analyzing and designing the next generation of privacy preserving big data analytics approaches. Besides, we contribute to pinpoint the potential opportunities and challenges of applying privacy preserving big data analytics to business settings. We provide five recommendations of effectively applying privacy‐preserving big data analytics to businesses. To the best of our knowledge, this is the first systematic study about state‐of‐the‐art in privacy‐preserving big data analytics. The managerial implication of our study is that organizations can apply the results of our critical analysis to strengthen their strategic deployment of big data analytics in business settings, and hence to better leverage big data for sustainable organizational innovation and growth. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Fundamental Concepts of Data and Knowledge &gt; Data Concepts
Impeccable Circuits II

Protection against active physical attacks is of serious concerns of cryptographic hardware designers. Introduction of SIFA invalidating several previously-thought-effective counter-measures, made this challenge even harder. Here in this work we deal with error correction, and introduce a methodology which shows, depending on the selected adversary model, how to correctly embed error-correcting codes in a cryptographic implementation. Our construction guarantees the correction of faults, in any location of the circuit and at any clock cycle, as long as they fit into the underlying adversary model. Based on case studies evaluated by open-source fault diagnostic tools, we claim protection against SIFA.
PFcrowd: Privacy-Preserving and Federated Crowdsourcing Framework by Using Blockchain

Crowdsourcing is a promising computing paradigm that utilizes collective intelligence to solve complex tasks. While it is valuable, traditional crowdsourcing systems lock computation resources inside each individual system where tasks cannot reach numerous potential workers among the other systems. Therefore, there is a great need to build a federated platform for different crowdsourcing systems to share resources. However, the security issue lies in the center of constructing the federated crowdsourcing platform. Although many studies are focusing on privacy-preserving crowdsourcing, existing solutions require a trusted third party to perform the key management, which is not applicable in our federated platform. The reason is that it is difficult for a third party to be trusted by various systems. In this paper, we present a secure crowdsourcing framework as our initial effort toward this direction, which bridges together the recent advancements of blockchain and cryptographic techniques. Our proposed design, named PFcrowd, allows different crowdsourcing systems to perform encrypted task-worker matching over the blockchain platform without involving any third-party authority. The core idea is to utilize the blockchain to assist the federated crowdsourcing by moving the task recommendation algorithm to the trusted smart contract. To avoid third-party involvement, we first leverage the re-writable deterministic hashing (RDH) technique to convert the problem of federated task-worker matching into the secure query authorization. We then devise a secure scheme based on RDH and searchable encryption (SE) to support privacy-preserving task-worker matching via the smart contract. We formally analyze the security of our proposed scheme and implement the system prototype on Ethereum. Extensive evaluations of real-world datasets demonstrate the efficiency of our design.