Reviewerly/exHarmony huggingface dataset:
Cross Fusion-Based Low Dynamic and Saturated Image Enhancement for Infrared Search and Tracking Systems

Unmanned aerial vehicles and battleships are equipped with the infrared search and tracking (IRST) systems for its mission to search and detect targets even in low visibility environments. However, infrared sensors are easily affected by diverse types of conditions, therefore most of IRST systems need to apply advanced contrast enhancement (CE) methods to cope with the low dynamic range of sensor output and image saturation. The general histogram equalization for infrared images has unwanted side effects such as low contrast expansion and saturation. Also, the local area processing for saturation reduction has been studied to solve the problems regarding the saturation and non-uniformity. We propose the cross fusion based adaptive contrast enhancement with three counter non-uniformity methods. We evaluate the proposed method and compare it with conventional CE methods using the discrete entropy, PSNR, SSIM, RMSE, and computation time indexes. We present the experimental results for images from various products using several datasets such as infrared, multi-spectral satellite, surveillance, general gray and color images, as well as video sequences. The results are compared using the integrated image quality measurement index and they show that the proposed method maintains its performance on various degraded datasets.
Model-Based Deep Network for Single Image Deraining

For current learning-based single image deraining methods, deraining networks are usually designed based on a simplified linear additive rain model, which may not only cause unreal synthetic rainy images for both training and testing datasets, but also adversely affect the applicability and generality of corresponding networks. In this paper, we use the screen blend model of Photoshop as the nonlinear rainy image decomposition model. Based on this model, we design a novel channel attention U-DenseNet for rain detection and a residual dense block for rain removal. The detection sub-network not only adjusts channel-wise feature responses by our novel channel attention block to pay more attention to learn the rain map, but also combines the context information with the precise localization by the U-DenseNet to promote pixel-wise estimation accuracy. After rain detection, we use the nonlinear model to get a coarse rain-free image, and then introduce a deraining refinement subnetwork consisted of the residual dense block to obtain a fine rain-free image. For training our network, we apply the nonlinear rain model to synthesize a benchmark dataset called as RITD. It contains 3200 triplets of rainy images, rain maps, and clean background images. Our extensive quantitative and qualitative experimental results show that our method outperforms several state-of-the-art methods on both synthetic and real images.
PENGARUH MODEL PEMBELAJARAN ROLE PLAYING TERHADAP KETERAMPILAN BERBICARA SISWA KELAS IV SD PADA PEMBELAJARAN BAHASA INDONESIA

Based on the results of field studies conducted by researchers, the quality of teaching and speaking of students in grade IV SDN Sukamaju is still less active in terms of speaking, they prefer to talk during lessons, do not want to ask questions, say nothing and if the teacher asks to speak, students are still stammering, embarrassed and doubtful. With this in mind, it can be seen that fourth-grade students still do not have the speaking skills, and it is difficult to communicate the information obtained. Researchers are learning to role-play learning models for class IV student learning in Indonesian language learning and for class IV students' understanding by applying role-playing learning models to speaking learning in Indonesian language learning. The method used in this study is a quantitative study kind of quasi experiment conducted on students in class IV SDN Sukamaju with a total of 42 students, consisting of IV A 21 people and class IV B 21 people. This research uses the Nonequivalent Control Group Design. Data The results of the study were obtained from interviews, tests, questionnaires, observations, and documentation. The average value of the post-test class using conventional learning has an average value of 74.90. In contrast, the experimental class that uses the role-playing model has an average value of 80.19. It can simplify learning by using role learning models that can help students in learning to read in learning Indonesian.
MASGQF with application to SINS alignment

To effectively improve the computation efficiency in the strapdown inertial navigation system (SINS) alignment process, the authors propose a marginalised adaptive sparse-grid quadrature filter (MASGQF). The filtering method combines the virtues of marginalisation technique and accuracy level switching structure to decrease the computational burden and improve the filtering precision simultaneously. With the adaptation criterion and the predefined tolerance value, different integration rules corresponding to the different accuracy levels nested in the proposed filtering method can be adjusted autonomously. It is proved that the MASGQF is exponentially bounded by theoretical analysis. The performance comparison of different filtering methods is demonstrated by the numerical results of the simulation and offline experiment. On the basis of the data performance of the online test, the validity of the proposed MASGQF is verified in the SINS alignment application.
Bayesian network-based high-dimensional crowdsourced data publication with local differential privacy

Third-parties图 1 (网络版彩图) 群智感知系统示意图 Figure 1 (Color online) A concept figure of crowd sensing systems甚至高维的特性.对其中属性维度间的相关性挖掘是实现群智感知数据价值的重要方式.例如, 通过 对患者健康记录中不同身体特征的相关性分析, 可以发现或预测其潜在的患病风险 [5] ; 通过对手机用 户的购物、浏览行为进行关联分析, 可以实现个性化的智能推荐系统 [6] .群智感知数据中通常包含了感知用户自身及其环境信息 (如 GPS 数据) 和日常行为信息 (如计步 数据) 等敏感信息.这些敏感信息如果被超出感知目的地滥用或发布, 或者在数据产生到消亡的生命 周期内无法得到有效保护, 都可能会造成感知用户隐私的暴露 [7∼9] , 给他们带来广告滋扰, 经济利益受 损, 甚至是人身安全的威胁.因此, 对群智感知数据的隐私保护尤为重要, 并已受到业界和学界的广泛 关注 [10] .目前, 常用的隐私保护技术包括基于匿名的方法 [11] (如 K-匿名, L-多样性和 T-邻近等) 和 基于加密的方法 [12,13] (如同态加密, 秘密共享, 安全多方计算等).然而, 基于匿名的方法通常缺乏严 格的隐私安全保证, 仅适用于小规模数据的隐私保护 [14] .基于加密的方法虽然具有较好的安全保证, 但是加密操作会带来较大的计算开销, 难以适用于资源受限的感知设备 [15] .近年来, 差分隐私的概念 被提出 [16] , 并由于其严谨的数学定义和灵活的组合特性, 受到学界和业界的广泛关注, 已成为隐私保 护的一种事实标准.由于其轻量性, 差分隐私特别适合于群智感知数据等大数据的处理和分析场景.然而, 将差分隐私应用于群智感知系统中的高维感知数据发布, 仍然面临以下两个研究挑战.
Hyperspectral image quality evaluation using generalized regression neural network

In order to alleviate the overfitting problem caused by image quality evaluation (IQA) model learning under intolerably small dataset, this paper proposes a multi-feature fusion-based deep architecture for hyperspectral image quality assessment. First, eight key IQA-related features, which are descriptive to the mean noise of multi-band images, spatial correlation, inter-spectral correlation, blur, and the phase-consistent map of images, are extracted from each hyperspectral image within the dataset. Based on this, a carefully-designed generalized regression neural network (GRNN) with a limited number of parameters is hierarchically trained by the feature vectors from samples in the training IQA data set. Comprehensive experimental evaluations on the hyperspectral IQA images from the DOTA dataset and the EO-1 Hyperion dataset have shown that the proposed model can indicate the subjective/objective quality-aware images regions precisely In addition, we observe that our designed IQA method has received impressive IQA performance than the other state-of-the-art non-reference methods.
SlimRGBD: A Geographic Information Photography Noise Reduction System for Aerial Remote Sensing

In the past ten years, civil drone technology has developed rapidly, and UAV (Unmanned Aerial Vehicle) has been widely used in various industries. Especially in the field of aerial remote sensing, the emergence of UAV technology has enabled the geographical information of remote areas that are not concerned to be quickly presented. However, UAV aerial photography is greatly affected by the weather. Pictures that use aerial drones for aerial photography in rainy weather will appear noise. In this paper, how to eliminate the noise of aerial image is to be talked, the multi-channel pruning technology is used to pruning the RnResNet network. Based on this, a new anti-convergence-convolution neural network noise reduction system for the operation of UAV airborne embedded equipment is proposed. The system is used to eliminate noise in the aerial image. This type of noise reducer has got rid of the current situation that the neural network noise reducer consumes too much power and is inefficient, and has certain advantages.
Temperature Compensation Method for Mechanical Base of 3D-Structured Light Scanners

The effect of temperature on three-dimensional (3D) structured light scanners is a very complex issue that, under some conditions, can lead to significant deterioration of performed measurements. In this paper, we present the results of several studies concerning the effect of temperature on the mechanical base of 3D-structured light scanners. We also propose a software compensation method suitable for implementation in any existing scanner. The most significant advantage of the described method is the fact that it does not require any specialized artifact or any additional equipment, nor access to the thermal chamber. It uses a simulation of mechanical base thermal deformations and a virtual 3D measurement environment that allows for conducting virtual measurements. The results from the verification experiments show that the developed method can extend the range of temperatures in which 3D-structured light scanners can perform valid measurements by more than six-fold.
An exact line search scheme to accelerate the EM algorithm: Application to Gaussian mixture models identification

This paper tackles the slowness issue of the well-known expectation-maximization (EM) algorithm in the context of Gaussian mixture models. To cope with this slowness problem, an Exact Line Search scheme is proposed. It is based on exact computation of the step size required to jump, for a given search direction, towards the final solution. Computing this exact step size is easily done by only rooting a second-order polynomial computed from the initial log-likelihood maximization problem. Numerical results using both simulated and real dataset showed the efficiency of the proposed exact line search scheme when applied to the conventional EM algorithm as well as the anti-annealing based acceleration techniques based on either the EM or the expectation conjugate gradient algorithm.
Managing Gender Change Information on Immutable Blockchain in Context of GDPR

The transgender community faces serious socio-economic predicaments due to the discrepancy that its members face between their current gender expression and the assigned gender identity at birth.Even though, a considerable amount of work has been done to protect their basic human rights such as security, equality and social acceptance; trans people are still large victims of hate-related crimes.With general data protection regulation (GDPR) and other data protection laws and policies in place, now it is ever more important to protect the confidentiality of gender change information as well as to establish technical solutions that can prevent from inferring any sense of gender change from historical data.In this context, distributed ledger technologies such as blockchain present great opportunities for information integrity, security, privacy and access.However, at the same time provenance information extracted from immutable blockchain can be exploited to infer gender change.Addressing this paradox here, we propose recommendations for managing gender change information in the blockchain environment in the context of the present socio-political, legislative and technical challenges associated with gender change.
Information Theoretic Objective Function for Genetic Software Clustering

Software clustering is usually used for program comprehension. Since it is considered to be the most crucial NP-complete problem, several genetic algorithms have been proposed to solve this problem. In the literature, there exist some objective functions (i.e., fitness functions) which are used by genetic algorithms for clustering. These objective functions determine the quality of each clustering obtained in the evolutionary process of the genetic algorithm in terms of cohesion and coupling. The major drawbacks of these objective functions are the inability to (1) consider utility artifacts, and (2) to apply to another software graph such as artifact feature dependency graph. To overcome the existing objective functions’ limitations, this paper presents a new objective function. The new objective function is based on information theory, aiming to produce a clustering in which information loss is minimized. For applying the new proposed objective function, we have developed a genetic algorithm aiming to maximize the proposed objective function. The proposed genetic algorithm, named ILOF, has been compared to that of some other well-known genetic algorithms. The results obtained confirm the high performance of the proposed algorithm in solving nine software systems. The performance achieved is quite satisfactory and promising for the tested benchmarks.
Distributed Fusion Estimation for Unstable Systems With Quantized Innovations

This article is concerned with the distributed networked fusion estimation problems for unstable systems with limited communication capacity, where the system and measurement noises are unknown but bounded. To overcome the unboundedness of measurement information for unstable systems, it is proposed to quantize the innovations that are sent to the fusion center over bandwidth constrained channels. By using the bounded recursive optimization idea, the design problems of local stable estimators and distributed fusion criterion under the quantized innovations are converted into two different convex optimization problems that can be easily solved by the standard software. The target tracking system is employed to demonstrate the effectiveness of the proposed methods.
CloudBench: an integrated evaluation of VM placement algorithms in clouds

A complex and important task in the cloud resource management is the efficient allocation of virtual machines (VMs), or containers, in physical machines (PMs). The evaluation of VM placement techniques in real-world clouds can be tedious, complex and time-consuming. This situation has motivated an increasing use of cloud simulators that facilitate this type of evaluations. However, most of the reported VM placement techniques based on simulations have been evaluated taking into account one specific cloud resource (e.g., CPU), whereas values often unrealistic are assumed for other resources (e.g., RAM, awaiting times, application workloads, etc.). This situation generates uncertainty, discouraging their implementations in real-world clouds. This paper introduces CloudBench, a methodology to facilitate the evaluation and deployment of VM placement strategies in private clouds. CloudBench considers the integration of a cloud simulator with a real-world private cloud. Two main tools were developed to support this methodology, a specialized multi-resource cloud simulator (CloudBalanSim), which is in charge of evaluating VM placement techniques, and a distributed resource manager (Balancer), which deploys and tests in a real-world private cloud the best VM placement configurations that satisfied user requirements defined in the simulator. Both tools generate feedback information, from the evaluation scenarios and their obtained results, which is used as a learning asset to carry out intelligent and faster evaluations. The experiments implemented with the CloudBench methodology showed encouraging results as a new strategy to evaluate and deploy VM placement algorithms in the cloud.
Detection of Road Surface Identifiers Based on Deep Learning

Road markings are an important guarantee for road infrastructure and driving safety. In recent years, the rapid development of autonomous driving technology, and the identification ofroad identifiers has become an indispensable part of this technology. The traditional target detection algorithm in computer vision has poor regional selectivity, high time complexity and poor feature robustness. The deep learning algorithm can effectively solve these problems. At present, deep learning is widely used in audio data and images, and it is feasible and meaningful to identify commonroad identifiers. Based on the self-builtroad marking database, in view of the problems existing in the currentroad marking detection research, the latest deep learning method is used to detect the six common types ofroad signs such as the right turn arrow, the straight right turn arrow and the crosswalk, and the SSD is trained. Modifying the four target detection models of focal loss SSD, FSSD and R-FCN and testing the road surface. Through data analysis and comparison, the following conclusions can be drawn: R-FCN has higher accuracy, but slower speed; The accuracy of SSD model is low whilst the detection speed is fast. The accuracy of the SSD model after modifying the focal loss is improved compared with the SSD model. The accuracy of the FSSD model is between R-FCN and SSD, and it can also maintain a faster speed. Comparing the experimental results, the R-FCN model with 18-layer depth residual network is the best for road marking detection.
YouTube as Praxis? On BreadTube and the Digital Propagation of Socialist Thought

In this paper we discuss the rise of BreadTube and what it means for the spread and normalization of socialist ideas online. We aim to focus on four major YouTube content creators – Contrapoints, Philosophy Tube, Shaun, and Hbomberguy – to outline how they construct their videos to entertain, inform, as well as debunk both alt-right and (economically) liberal talking points, helping to prevent potential radicalization of a mostly young audience who stand at a crossroads in their ideological development. Aside from examining the content of produces by the creators, we also hope to investigate the unique configuration of their platform use, emphasizing such elements as distributions, financing, and audience interaction.
Sequential Quantum Multiparty Signature Based on Quantum Fourier Transform and Chaotic System

Motivated by the quantum Fourier transform (QFT), a sequential quantum multiparty signature (QMS) scheme is proposed. Several signatories jointly accomplish the task in a chaotic encryption system. Alice generates and sends the encrypted message with the quantum Fourier transform. Signatories provide individual signatures in a certain order, while Bob verifies the authenticity of the combined multiple signatures after the eavesdropping check phase with help of the arbitrator. Analysis shows the correctness and security of the proposed scheme. This QMS protocol increases the efficiency of verification compared to arbitrated quantum signatures and broadcasting multiparty signature. This QMS protocol will be widely used for online e-government and e-business systems.
A Privacy-preserving BGN-type Parallel Homomorphic Encryption Algorithm Based on LWE

Although the rapid development of cloud computing brings many conveniences to people’s lives, it also leads to the problems of user data privacy protection and the massive bandwidth resource consumption caused by frequent access to cloud servers. A feasible solution is to combine the Homomorphic Encryption (HE) technique to realize the efficient operation of ciphertext without decryption. The low encryption efficiency is a common issue faced by both Partially Homomorphic Encryption (PHE) algorithm and Fully Homomorphic Encryption (FHE) algorithm. To this end, based on the cryptosystem of Boneh, Goh and Nissim (BGN), we propose an efficient BGN-type parallel homomorphic encryption algorithm to address this issue, which security is based on the hardness of the Learning with Errors problem (LWE). Specifically, the proposed algorithm utilizes the characteristics of multi-nodes in cloud environment to conduct parallel encryption through block matrix multiplication, and simultaneously conduct the group-wise ciphertext computations. The experimental results show that, in a 16-core 4-node cluster with MapReduce environment, the proposed encryption algorithm achieves the maximum speedup up to 5.3, which meets the practical requirements for the implementing efficient homomorphic encryption in cloud computing environment.
Deep Ensemble Object Tracking Based on Temporal and Spatial Networks

In recent years, correlation filtering and deep learning have achieved good performance in object tracking. Correlation filtering is an efficient and real-time method because its formula provides a fast solution in the Fourier domain, but it does not benefit from end-to-end training. Although deep learning is an effective method for learning object representations, training deep networks online with one or a few examples is challenging. To address these problems, we propose a deep ensemble object tracking algorithm that fuses temporal and spatial information to improve algorithm precision and robustness. The framework of our algorithm includes four aspects: feature extraction, a baseline network, a branch network and adaptive ensemble learning. Feature extraction extracts the general object representation. The baseline network integrates feature extraction and a correlation filtering algorithm into a convolutional neural network for end-to-end training. The branch network is composed of a temporal network and a spatial network. The temporal and spatial networks capture the object temporal and spatial information and further refine the object position. Our algorithm only needs an initial frame to train all networks. Adaptive ensemble learning compensates for the object information deficiency and improves tracking accuracy. Many experiments on tracking benchmark datasets demonstrate that our algorithm performs favourably compared with state-of-the-art tracking algorithms.
Types and sources of medical and other related data

This chapter presents the origin of medical data that comprises the core of any federated cloud platform that deals with medical data sharing and analytics. The different types and sources of medical data are extensively described along with applications and standard data acquisition protocols. Emphasis is given on the definition and the impact of the cohorts in clinical research, as well as the importance of the big data in medicine. The impact of the big data in medicine is also discussed along with emerging opportunities and challenges. The need to develop data standardization protocols across heterogeneous and dispersed data sources is finally highlighted, to enable the analysis of different types of medical big data.