Reviewerly/exHarmony huggingface dataset:
Weight Dropout for Preventing Neural Networks from Overfitting

This paper briefly introduces an enhanced neural network regularization method, so called weight dropout, in order to prevent deep neural networks from overfitting. In suggested method, the fully connected layer jointly used with weight dropout is a collection of layers in which the weights between nodes are dropped randomly on the process of training. To accomplish the desired regularization method, we propose a building blocks with our weight dropout mask and CNN. The performance of proposed method has been compared with other previous methods in the domain of image classification and segmentation for the evaluation purpose. The results show that the proposed method gives successful performance accuracies in several datasets.
Blockchain in 5G Networks: Perfomance Evaluation of Private Blockchain

Usage of blockchain in fifth generation mobile networks give opportunities to improve security, privacy and service quality. In spite of all these advantages blockchain technology has a set of disadvantages as low bandwidth and transactions' high latency. Private blockchain has a set of differences which can solve these challenges. In this paper experiment within which compare two popular blockchain (private and public) is described. The goal of experiment to show that private blockchain is able for usage in mobile networks.
Generating emotional response by conditional variational auto-encoder in open-domain dialogue system

An important goal in open-domain dialogue research is to make chatbot generate emotional responses given a context. To achieve this, some researchers have attempted to introduce affective information into neural dialogue models. However, these neural dialogue models containing affective information still suffer from the problem of generating safe but meaningless responses, such as I don't know, which makes users lose interest in chatting quickly. Fortunately, the latest research has proven that conditional variational auto-encoder (CVAE) can solve this problem and enhance the responses' diversity. In this paper, we combine affective knowledge into the CVAE-based model to generate diverse and affective responses. First, we use an affective lexicon to understand each word's emotion in the input sentences and feed the affective vector with its embedding vector together into the CVAE-based model. Next, we construct semantic and affective loss functions, enabling the model to simultaneously learn the response's semantic and affective distributions. Additionally, we formulate a ranking rule to help rank the candidate responses according to their syntax, semantics, and affection scores, thereby enhancing the emotion and relevance while retaining the response's diversity. Finally, we evaluate the proposed model on the DailyDialog dataset and Reddit dataset. The experimental results show that our model can generate more emotional, diverse, and context-relevant responses compared to the baselines.
Elliptic Curve Cryptography- Status, Challenges and Future trends

In the modern cryptography, the Elliptic Curve takes large and important part. Elliptic Curve usually uses in wireless environments, mobile network and credit card, because it uses a relatively small key with the rest of the public key algorithms. This paper introduces survey of Elliptic Curve by developed classification depends on the concept of the main objective of the studies and reviewed previous survives on this field.
Privacy-preserving reversible data hiding based on quad-tree block encoding and integer wavelet transform

Applications on the cloud server have matured, and protecting the privacy of the content owner has attracted more attention. Privacy-Preserving Reversible data hiding (PP-RDH) is an efficient technique for embedding additional data into an encrypted image. In this paper, we propose a privacy-preserving reversible data hiding scheme using the quad-tree partition and Integer Wavelet Transform (IWT) techniques. Our scheme focuses on improving the embedding rate and quality of the recovered image when a 2 × 2-sized, block-based image encryption method is applied to ensure relative higher security. On this basis, the IWT technique transforms the encrypted image, and coefficients in three high frequency subbands are converted into 8-bit binary system. Then, the quad-tree partition technique encodes each 8 × 8-sized coefficient block, since there are many zeroes in the front bit planes. The experimental results indicated that our proposed scheme significantly improved the embedding rate, and guaranteed lossless image recovery and data extraction.
GT2-CFC: General type-2 collaborative fuzzy clustering method

This paper presents a new algorithm called general type-2 collaborative fuzzy clustering (GT2-CFC), which focuses on uncertainty associated with the structures discovered for the same data stored in multiple data sites in different feature spaces while maintaining data-sharing restrictions. The uncertainty of membership degrees is directly controlled by using the available data rather than by reinforcing the lower-order fuzzy model through general type-2 fuzzy sets (GT2FS) including primary and secondary memberships. Primary membership degrees show the obtained membership without the knowledge gained from other data sites, and secondary membership degrees indicate the belonging degree to the data in the presence of external knowledge. In addition to the ability of GT2FS in uncertainty modeling, due to the lack of any type reduction and defuzzification during the proposed clustering process, there is no data loss, and time complexity becomes is reduced leading to more powerful uncertainty modeling. Finally, a new validity index is proposed to evaluate the quality of collaboration using the relative entropy-based global consistency. The superiority and efficiency of the proposed method are illustrated with several experiments.
Low-shot transfer with attention for highly imbalanced cursive character recognition

Recognition of ancient Korean–Chinese cursive character (Hanja) is a challenging problem mainly because of large number of classes, damaged cursive characters, various hand-writing styles, and similar confusable characters. They also suffer from lack of training data and class imbalance issues. To address these problems, we propose a unified Regularized Low-shot Attention Transfer with Imbalance τ-Normalizing (RELATIN) framework. This handles the problem with instance-poor classes using a novel low-shot regularizer that encourages the norm of the weight vectors for classes with few samples to be aligned to those of many-shot classes. To overcome the class imbalance problem, we incorporate a decoupled classifier to rectify the decision boundaries via classifier weight-scaling into the proposed low-shot regularizer framework. To address the limited training data issue, the proposed framework performs Jensen–Shannon divergence based data augmentation and incorporate an attention module that aligns the most attentive features of the pretrained network to a target network. We verify the proposed RELATIN framework using highly-imbalanced ancient cursive handwritten character datasets. The results suggest that (i) the extreme class imbalance has a detrimental effect on classification performance; (ii) the proposed low-shot regularizer aligns the norm of the classifier in favor of classes with few samples; (iii) weight-scaling of decoupled classifier for addressing class imbalance appeared to be dominant in all the other baseline conditions; (iv) further addition of the attention module attempts to select more representative features maps from base pretrained model; (v) the proposed (RELATIN) framework results in superior representations to address extreme class imbalance issue.
PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms

Path planning is a key component in mobile robotics. A wide range of path planning algorithms exist, but few attempts have been made to benchmark the algorithms holistically or unify their interface. Moreover, with the recent advances in deep neural networks, there is an urgent need to facilitate the development and benchmarking of such learning-based planning algorithms. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learned 2D and 3D path planning algorithms, while offering support for Robot Operating System (ROS). Many existing path planning algorithms are supported; e.g. A*, wavefront, rapidly-exploring random tree, value iteration networks, gated path planning networks; and integrating new algorithms is easy and clearly specified. We demonstrate the benchmarking capability of PathBench by comparing implemented classical and learned algorithms for metrics, such as path length, success rate, computational time and path deviation. These evaluations are done on built-in PathBench maps and external path planning environments from video games and real world databases. PathBench is open source <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation

Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poisoning attack and free-riding attack. To design the Byzantine-resilient FL paradigm in wireless networks with limited radio resources, we propose a novel communication-efficient robust model aggregation scheme via over-the-air computation (AirComp). This is achieved by applying the Weiszfeld algorithm to obtain the smoothed geometric median aggregation against Byzantine attack. The additive structure of the Weiszfeld algorithm is further leveraged to match the signal superposition property of multiple-access channels via AirComp, thereby expediting the communication-efficient secure aggregation process of FL. Numerical results demonstrate the robustness against Byzantine devices and good learning performance of the proposed approach.
A Non-Local Superpatch-Based Algorithm Exploiting Low Rank Prior for Restoration of Hyperspectral Images

We propose a novel algorithm for the restoration of a degraded hyperspectral image. The proposed algorithm exploits the spatial as well as the spectral redundancy of a degraded hyperspectral image in order to restore it without having any prior knowledge about the type of degradation present. Our work uses superpatches to exploit the spatial and spectral redundancies. We formulate a restoration algorithm incorporating structural similarity index measure as the data fidelity term and nuclear norm as the regularization term. The proposed algorithm is able to cope with additive Gaussian noise, signal dependent Poisson noise, mixed Poisson-Gaussian noise and can restore a hyperspectral image corrupted by dead lines and stripes. As we demonstrate with the aid of extensive experiments, our algorithm is capable of recovering the spectra even in the case of severe degradation. A comparison with the state-of-the-art low rank hyperspectral image restoration methods via experiments with real world and simulated data establishes the competitiveness of the proposed algorithm with the existing methods.
Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs

Uncertain graphs have been widely used to model complex linked data in many applications, such as guaranteed-loan networks and power grids. In these networks, a node usually has a certain chance of default due to self-factors or the influence from upstream nodes. For regulatory authorities, it is critical to efficiently identify the vulnerable nodes, i.e., nodes with high default risks, such that they could pay more attention to these nodes for the purpose of risk management. In this paper, we propose and investigate the top-k vulnerable nodes detection problem in uncertain graphs. We formally define the model and prove it hardness. A sampling-based approach is first proposed. Rigorous theoretical analysis is conducted to bound the quality of returned results. Novel optimization techniques and a bottom-k sketch based approach are further developed to scale for large networks. We demonstrate the performance of proposed techniques on 3 real financial networks and 5 benchmark networks. Moreover, to further verify the advantages of our model, we integrate the proposed techniques with our loan risk control system, which is deployed in the collaborated bank. Particularly, we show that our proposed model can better estimate the default risks of enterprises compared to the state-of-the-art techniques.
Sustainable Risk Management in IT Enterprises

A synthesis of literature studies covering the determinants of agile project management methods, risk management processes as well as factors influencing the shaping of project success and failure clearly indicates that in most publications on risk in agile managed projects, the human factor is heavily underestimated at the expense of often excessive favoring of procedures. Meanwhile, after analyzing the risk factors that arise in agile-managed IT projects, it became apparent that in addition to aspects such as technology, hardware, system, or even project schedule and cost, the project team is highlighted, which is also the second concept with the GPM P5 Standard for Sustainability in Project Management. Thus, the purpose of this article is to develop a model for risk management in IT projects. As a result of the empirical research carried out by means of an expert interview (108 experts) and a questionnaire survey (123 respondents), a risk management model was developed and six original risk management areas were identified, describing 73.92% of all risk factors that may occur during the implementation of an IT project. Furthermore, empirical studies confirm that basic processes such as risk factor identification, impact assessment, and key risk factor management are used by managers and/or team leaders during the implementation of IT projects.
Neural Camera Simulators

We present a controllable camera simulator based on deep neural networks to synthesize raw image data under different camera settings, including exposure time, ISO, and aperture. The proposed simulator includes an exposure module that utilizes the principle of modern lens designs for correcting the luminance level. It also contains a noise module using the noise level function and an aperture module with adaptive attention to simulate the side effects on noise and defocus blur. To facilitate the learning of a simulator model, we collect a dataset of the 10,000 raw images of 450 scenes with different exposure settings. Quantitative experiments and qualitative comparisons show that our approach outperforms relevant baselines in raw data synthesize on multiple cameras. Furthermore, the camera simulator enables various applications, including large-aperture enhancement, HDR, auto exposure, and data augmentation for training local feature detectors. Our work represents the first attempt to simulate a camera sensor’s behavior leveraging both the advantage of traditional raw sensor features and the power of data-driven deep learning. The code and the dataset are available at https://github.com/ken-ouyang/neural_image_simulator.
Low-dose CT denoising using a Progressive Wasserstein generative adversarial network

Low-dose computed tomography (LDCT) imaging can greatly reduce the radiation dose imposed on the patient. However, image noise and visual artifacts are inevitable when the radiation dose is low, which has serious impact on the clinical medical diagnosis. Hence, it is important to address the problem of LDCT denoising. Image denoising technology based on Generative Adversarial Network (GAN) has shown promising results in LDCT denoising. Unfortunately, the structures and the corresponding learning algorithms are becoming more and more complex and diverse, making it tricky to analyze the contributions of various network modules when developing new networks. In this paper, we propose a progressive Wasserstein generative adversarial network to remove the noise of LDCT images, providing a more feasible and effective way for CT denoising. Specifically, a recursive computation is designed to reduce the network parameters. Moreover, we introduce a novel hybrid loss function for achieving improved results. The hybrid loss function aims to reduce artifacts while better retaining the details in the denoising results. Therefore, we propose a novel LDCT denoising model called progressive Wasserstein generative adversarial network with the weighted structurally-sensitive hybrid loss function (PWGAN-WSHL), which provides a better and simpler baseline by considering network architecture and loss functions. Extensive experiments on a publicly available database show that our proposal achieves better performance than the state-of-the-art methods.
Collaborative algorithms that combine AI with IoT towards monitoring and control system

Recently, a new IoT structure known as the Artificial Intelligence of Things (AIoT) comes into play. Crowd counting is a promising field in data analysis of AIoT, however, due to poor transparency and high data security risks, developing a novel network architecture that can precisely elevate the counting of heavy crowd is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. The focus of this work is on the effective design of IoT framework and deep learning algorithm towards security of smart city. The system can be used to estimate the crowd traffic in public places, and can prevent the occurrence of congestion, stampede and other accidents, such as stations, airports, large-scale exhibitions, tourist attractions and other places. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically tracks densely crowd areas by controlling the video monitoring device. Moreover, the cloud platform can be controlled through the network. Our proposed algorithms are composed of two main aspects, i.e., division and focus. Firstly, we propose a novel density-adaptive Gaussian kernel to elevate the quality of density maps. Then, we propose a module based on conditional random fields for feature fusion. Finally, we propose a block segmentation module to predict our segmentation results and extract the context-aware information in segmentation stage. Experiments on our captured data, the Shanghai Tech, UCF_CC_50 and UCF_QNRF datasets demonstrate that our solution has obtained better performance and lower count errors over the state of the art.
Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment

Neural networks for Image Aesthetic Assessment are usually initialized with weights of pretrained ImageNet models and then trained using a labeled image aesthetics dataset. We argue that the ImageNet classification task is not well-suited for pretraining, since content based classification is designed to make the model invariant to features that strongly influence the image’s aesthetics, e.g. stylebased features such as brightness or contrast.We propose to use self-supervised aesthetic-aware pretext tasks that let the network learn aesthetically relevant features, based on the observation that distorting aesthetic images with image filters usually reduces their appeal. To ensure that images are not accidentally improved when filters are applied, we introduce a large dataset comprised of highly aesthetic images as the starting point for the distortions. The network is then trained to rank less distorted images higher than their more distorted counterparts. To exploit effects of multiple different objectives, we also embed this task into a multi-task setting by adding either a self-supervised classification or regression task. In our experiments, we show that our pretraining improves performance over the ImageNet initialization and reduces the number of epochs until convergence by up to 47%. Additionally, we can match the performance of an ImageNet-initialized model while reducing the labeled training data by 20%. We make our code, data, and pretrained models available.
A novel adaptive lateral reentry guidance algorithm with complex distributed no-fly zones constraints

Aimed at complex distributed no-fly zones avoidance problems, a novel adaptive lateral reentry guidance algorithm is proposed. Firstly, by introducing the improved attractive and repulsive potential fields, an improved artificial potential field method is developed. Combined with the proposed judgment criterion for whether a no-fly zone has been avoided, the proposed improved artificial potential field method effectively solves the reference heading angle determination problem under the constraints of complex distributed no-fly zones. Then, based on the proposed no-fly zone’s threat quantitative evaluation method and the reference heading angle determined by the proposed improved artificial potential field method, the heading corridor is improved to increase its sensitivity to the threat changes of the no-fly zones. Finally, for satisfying the requirements of complex distributed no-fly zones avoidance, a novel guidance logic via improved heading corridor is proposed to update the reference heading corridor adaptively in real time according to the threat and constraint changes of the no-fly zones, and the bank reversal logic is employed to control the lateral motion. The simulation results for nominal and dispersed cases indicate that the proposed guidance algorithm has high robustness, stability, and applicability, and is feasible and effective to deal with the complex distributed no-fly zones avoidance problems.
Corner Case Data Description and Detection

As the major factors affecting the safety of deep learning models, corner cases and related detection are crucial in AI quality assurance for constructing safety- and security-critical systems. The generic corner case researches involve two interesting topics. One is to enhance DL models' robustness to corner case data via the adjustment on parameters/structure. The other is to generate new corner cases for model retraining and improvement. However, the complex architecture and the huge amount of parameters make the robust adjustment of DL models not easy, meanwhile it is not possible to generate all real-world corner cases for DL training. Therefore, this paper proposes a simple and novel approach aiming at corner case data detection via a specific metric. This metric is developed on surprise adequacy (SA) which has advantages on capture data behaviors. Furthermore, targeting at characteristics of corner case data, three modifications on distanced-based SA are developed for classification applications in this paper. Consequently, through the experiment analysis on MNIST data and industrial data, the feasibility and usefulness of the proposed method on corner case data detection are verified.