Reviewerly/exHarmony huggingface dataset:
Age Gap Reducer-GAN for Recognizing Age-Separated Faces

In this paper, we propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification. The key idea of this approach is to learn the age variations across time by conditioning the input image on the subject's gender and the target age group to which the face needs to be progressed. The loss function accounts for reducing the age gap between the original image and generated face image as well as preserving the identity. Both visual fidelity and quantitative evaluations demonstrate the efficacy of the proposed architecture on different facial age databases for age-separated face recognition.
Graph convolutional networks for skeleton-based action recognition with LSTM using tool-information

Skeleton-based action recognition using a Graph Convolutional Network (GCN) achieved remarkable results by reconstructing a person's skeleton into a graph. However, there are fundamental problems with existing GCN-based models. Generally, human action is greatly affected by the tools used, but in traditional GCN models, action recognition is performed without using the information in the tools. For example, a person holding a pen is limited to the act of writing. A person holding a ball is limited to the action of throwing or receiving the ball. In other words, it is an inaccurate method to judge action only by recognizing the movements of bones used in existing methods. Therefore, a graph was made to reflect the information on the tool. We identify tool-information using the LSTM classifier and propose GCNs for skeleton-based action recognition with LSTM using tool-information. Additionally, we apply a new graph construction and utilize the Learnable Adjacency Matrix. The proposed method is applied to the existing model and comparative evaluation was performed between the model with and without the applied algorithm. The evaluations showed consistent performance improvement, and the proposed method applied to the baseline models achieved state-of-the-art performance.
Sentiment-Aware Emoji Insertion Via Sequence Tagging

Due to the booming popularity of online social networks, emojis have been widely used in online communication. As nonverbal language units, emojis help to convey emotions and express feelings. In this article, we focus on the sentiment-aware emoji insertion task, which predicts multiple emojis and their positions in a sentence conditioned on the plain texts and sentiment polarities. To facilitate future research in this field, we construct a large-scale emoji insertion corpus named “MultiEmoji,” which contains 420 000 English posts with at least one emoji per post. We formulate the insertion process as a sequence tagging task and apply a BERT-BiLSTM-CRF model to the insertion of emojis. Extensive experiments illustrate that our model outperforms existing methods by a large margin.
Discriminative Auto-Encoding for Classification and Representation Learning Problems

Auto-Encoders (AE) play an important role in feature extraction, fusion and representation learning. Particularly, in case of medical datasets, where labeled data is often scarce, they perform better than transfer-learning approaches. Many auto-encoding methods have been proposed, however, present methods either preserve the latent space (with lower predictive power) or provide good predictive performance (with distorted latent space). This work presents a Discriminative Auto-Encoding (DiscAE) approach which provides better representations with decent reconstructions. Clustering constraint is imposed through a discriminator on the latent space of a simple auto-encoder to enforce Gaussian distribution of the latent space. The network is trained by alternating between decoder and the discriminator. Furthermore, the role of noise is explored in improving the reconstructions and regularization of the latent space. Unlike Variational Auto-Encoders (VAE), DiscAE provides better performance by utilizing the labeled data. The working of DiscAE under a semi-supervised setting is also discussed. To support our claims, extensive experiments and ablations were carried out on three benchmark datasets. It was found that DiscAE outperforms the existing auto-encoding approaches on predictive tasks while maintaining the quality of reconstructions.
Research on 3D global path planning technology for UUV based on fusion algorithm

Abstract Aiming at the global path planning problem of unmanned underwater vehicle (UUV) in three-dimensional environment, a fusion algorithm named PACO based on particle swarm optimization (PSO) and ant colony optimization (ACO) is proposed. In order to meet the needs of three-dimensional path planning, the grid method is used to model the actual marine environmental data. Besides, PSO and ACO are optimized respectively. Taking the path length as the measurement index, the simulation is realized by using MATLAB. The simulation results show that the initial path search ability of the fusion algorithm is significantly improved, and the effectiveness of the algorithm is verified.
Unsupervised Learning of Depth from Monocular Videos Using 3D-2D Corresponding Constraints

Depth estimation can provide tremendous help for object detection, localization, path planning, etc. However, the existing methods based on deep learning have high requirements on computing power and often cannot be directly applied to autonomous moving platforms (AMP). Fifth-generation (5G) mobile and wireless communication systems have attracted the attention of researchers because it provides the network foundation for cloud computing and edge computing, which makes it possible to utilize deep learning method on AMP. This paper proposes a depth prediction method for AMP based on unsupervised learning, which can learn from video sequences and simultaneously estimate the depth structure of the scene and the ego-motion. Compared with the existing unsupervised learning methods, our method makes the spatial correspondence among pixel points consistent with the image area by smoothing the 3D corresponding vector field based on 2D image, which effectively improves the depth prediction ability of the neural network. Our experiments on the KITTI driving dataset demonstrated that our method outperformed other previous learning-based methods. The results on the Apolloscape and Cityscapes datasets show that our proposed method has a strong universality.
Introduction to Model Compression Knowledge Distillation

In recent years, with the rapid development of deep neural networks, deep learning has been applied to the fields of medicine, industry, education, and so on. However, due to a large number of neural network parameters and huge storage space consumption, the problem makes it is difficult to be ported to mobile devices. As a result, deep learning model compression method can effectively alleviate the problem. Among them, knowledge distillation adopts the idea of transfer learning, using the teacher network model to guide student network, the model allows the student model to learn from the teacher, thereby to improve the robustness of the model. This article presents primarily the process of development and the trend of distilling knowledge.
Improving the Performance of Convolutional Neural Networks by Fusing Low-Level Features With Different Scales in the Preceding Stage

The width of convolutional neural networks (CNNs) is crucial for improving performance. Many wide CNNs use a convolutional layer to fuse multiscale features or fuse the preceding features to subsequent features. However, these CNNs rarely use blocks, which consist of a series of successive convolutional layers, to fuse multiscale features. In this paper, we propose an approach for improving performance by fusing the low-level features extracted from different blocks. We utilize five different convolutions, including 3×3, 5×5, 7×7,5×3 ∪ 3×5 and 7×3 ∪ 3×7, to generate five low-level features, and we design two fusion strategies: low-level feature fusion (L-Fusion) and high-level feature fusion (H-Fusion). Experimental results show that the L-Fusion is more helpful for improving the performance of CNNs, and the 5×5 convolution is more suitable for multiscale feature fusion. We summarize the conclusion as a strategy that fuses multiscale features in the preceding stage of CNNs. Furthermore, we propose a new architecture to perceive the input of CNNs by using two self-governed blocks based on the strategy. Finally, we modify five off-the-shelf networks, DenseNet-BC (depth = 40), ALL-CNN-C (depth = 9), Darknet 19 (depth = 19), Resnet 18 (depth = 18) and Resnet 50 (depth = 50), by utilizing the proposed architecture to verify the conclusion, and these updated networks provide more competitive results.
One case study shows an important phenomenon: Active fault can cause subtle spectral features change of soil

Buried fault identification in plains is one of the important and difficult problems in current active fault research. Seismic activity forces crustal fluid to migrate up, especially along faults, thereby altering the geochemical characteristics of faults. Hyperspectral remote sensing has an extremely high spectral resolution; data derived from hyperspectral remote sensing have the capacity to detect physicochemical characteristics for mining or the subtle recognition of different underground objects. It provides a new method to detect changes in the geochemical characteristics in the fault. This study is trying to locate the fault by spectrum feature. The Xiadian fault was chosen as the study area. Several samples across the fault were collected, and these samples were measured by an ASD Field Spec 3 spectrometer(Analytical Spectral Devices, Boulder, USA, produced in 2008). Continuum removal, the spectral classification method and the wavelet transform technique have been used for spectral analysis. It has been shown that the spectrum of soil in the fault zone has some special characteristics that are different from those of the spectrum away from fault. Four special spectral regions, 550–580 ​nm, 600–700 ​nm, 700–800 ​nm and 800–900 ​nm, have been extracted as the singular ranges. These individual ranges could be used for fault location in the plain area. There is weak absorption near 880 ​nm of the sample in the fault, indicating that Fe3+ content plays an important role in fault identification and that the iron valences can indicate the fault environment. This study also found that the spectral anomalies in space could reflect the intensity of active tectonics to a certain extent. Therefore, spectral features have the potential to identify a buried faults. This study is the first to use spectroscopic techniques to analyze active tectonics, and there is a new discovery. This new discovery provides the basis for the application of remote sensing in the study of active tectonics.
Environmental Risk Assessment of Petroleum Activities in Surface Sediments, Suez Gulf, Egypt

The present study focuses on the risk assessment of heavy metal contamination in aquatic ecosystems by evaluating the current situation of heavy metals in seven locations (North Amer El Bahry, Amer, Bakr, Ras Gharib, July Water Floud, Ras Shokeir, and El Marageen) along the Suez Gulf coast that are well-known representative sites for petroleum activities in Egypt. One hundred and forty-six samples of surface sediments were carefully collected from twenty-seven profiles in the intertidal and surf zone. The hydrochemical parameters, such as pH and salinity (S‰), were measured during sample collection. The mineralogy study was carried out by an X-ray diffractometer (XRD), and the concentrations of Al, Mn, Fe, Cr, Cu, Co, Zn, Cd, and Pb were determined using inductively coupled plasma mass spectra (ICP-MS). The ecological risks of heavy metals were assessed by applying the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution load index (PLI), and potential ecological risk index (RI). The mineralogical composition mainly comprised quartz, dolomites, calcite, and feldspars. The average concentrations of the detected heavy metals, in descending order, were Al &gt; Fe &gt; Mn &gt; Cr &gt; Pb &gt; Cu &gt; Zn &gt; Ni &gt; Co &gt; Cd. A non-significant or negative relationship between the heavy metal concentration in the samples and their textural grain size characteristics was observed. The coastal surface sediment samples of the Suez Gulf contained lower concentrations of heavy metals than those published for other regions in the world with petroleum activities, except for Al, Mn, and Cr. The results for the CF, EF, and Igeo showed that Cd and Pb have severe enrichment in surface sediment and are derived from anthropogenic sources, while Al, Mn, Fe, Cr, Co, Ni, Cu, and Zn originate from natural sources. By comparison, the PLI and RI results indicate that the North Amer El Bahry and July Water Floud are considered polluted areas due to their petroleum activities. The continuous monitoring and assessment of pollutants in the Suez Gulf will aid in the protection of the environment and the sustainability of resources.
Cryptanalysis of the Binary Permuted Kernel Problem

In 1989, Shamir presented an efficient identification scheme (IDS) based on the permuted kernel problem (PKP). After 21 years, PKP was generalized by Lampe and Patarin, who were able to build an IDS similar to Shamir's one, but using the binary field. This binary variant presented some interesting advantages over Shamir's original IDS, such as reduced number of operations and inherently resistance against side-channel attacks. In the security analysis, considering the best attacks against the original PKP, the authors concluded that none of these existing attacks appeared to have a significant advantage when attacking the binary variant. In this paper, we propose the first attack that targets the binary PKP. The attack is analyzed in detail, and its practical performance is compared with our theoretical models. For the proposed parameters originally targeting 79 and 98 bits of security, our attack can recover about 100% of all keys using less than $$2^{63}$$ and $$2^{77}$$ operations, respectively.
Pre-trained Deep Convolutional Neural Network for Detecting Malaria on the Human Blood Smear Images

Malaria is a disease caused by the Plasmodium falciparum parasite carried by female Anopheles mosquitoes. This disease is still a severe threat in eastern Indonesia which is an endemic area of Malaria. A data-driven computer-aided diagnostic approach can be an innovative solution. From the experiment results using the Pre-trained Deep Convolutional Neural Network algorithm that was trained with the transfer learning method, the GoogLeNet model was able to achieve a detection accuracy of 93.89%. In comparison, the ShuffleNet V2 model gained 95.20% accuracy with training times three times faster than GoogLeNet.
Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering

In this paper, we propose a configurable topic modeling framework named Familia. Familia supports an important line of topic models that are widely applicable in text engineering scenarios. In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models. Simply through changing the data organization of Familia, software engineers are able to easily explore a broad spectrum of existing topic models or even design their own topic models, and find the one that best suits the problem at hand. With its superior extendability, Familia has a novel sampling mechanism that strikes balance between effectiveness and efficiency of parameter inference. Furthermore, Familia is essentially a big topic modeling framework that supports parallel parameter inference and distributed parameter storage. The utilities and necessity of Familia are demonstrated in real-life industrial applications. Familia would significantly enlarge software engineers' arsenal of topic models and pave the way for utilizing highly customized topic models in real-life problems. Source code of Familia have been released at Github via https://github.com/baidu/Familia/ .
Bagging k-dependence Bayesian network classifiers

Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.
Design and Implementation of Drilling Engineering Simulation System Based on Unity3D

Abstract In this paper, a drilling engineering simulation system is designed and developed aiming at some problems existing in traditional well control safety operation training. The overall design and process of the virtual simulation system were given, optimized and rendered the models of the drilling engineering equipment and the wellsite scene were created using the software 3Ds Max. With the aid of the Unity3D development platform, the oil drilling virtual simulation system was constructed, which realized the interaction between the wellsite roaming system and drilling operations. The system has the characteristics of low development cost, strong real-time interaction, realistic immersion and meets the needs of oilfield drilling engineering training.
Model Optimization Method Based on Vertical Federated Learning

When Vertical Federated Learning is used to classify tasks, a large number of invalid parameters are produced. In view of the above problems, we propose a general method of parameter sharing and gradient compression for both sides of communication, and improve the homomorphic encryption transfer parameters. The experimental results show that the evaluation index of the classification model is greatly improved compared with the traditional longitudinal federated learning logic regression algorithm.
Adiabatic Quantum Computing for Solving the Weapon Target Assignment Problem

Quantum computing promises significant improvements of computation capabilities in various fields such as machine learning and complex optimization problems. Recent technological advancements suggest that the adiabatic quantum computing ansatz may soon see practical applications. In this work, we adopt this computation paradigm to develop a quantum computation based solver of the well–known weapon target assignment problem, an NP-hard nonlinear integer programming optimization task. The feasibility of the presented model is demonstrated by numerical simulation of the adiabatic evolution of a system of quantum bits towards the optimal solution encoded in the model Hamiltonian. Overall, the described method is not limited to the context of weapon management but is, with slight modifications to the model Hamiltonian, applicable to relevant problems in data fusion, for example sensor allocation and multi-target data association in tracking applications.
Experimental vulnerability analysis of QKD based on attack ratings

Abstract Inspired by the methodology used for classical cryptographic hardware, we consider the use of attack ratings in the context of QKD security evaluation. To illustrate the relevance of this approach, we conduct an experimental vulnerability assessment of CV-QKD against saturation attacks, for two different attack strategies. The first strategy relies on inducing detector saturation by performing a large coherent displacement. This strategy is experimentally challenging and therefore translates into a high attack rating. We also propose and experimentally demonstrate a second attack strategy that simply consists in saturating the detector with an external laser. The low rating we obtain indicates that this attack constitutes a primary threat for practical CV-QKD systems. These results highlight the benefits of combining theoretical security considerations with vulnerability analysis based on attack ratings, in order to guide the design and engineering of practical QKD systems towards the highest possible security standards.
Structure Preserved Graph Reordering for Fast Graph Processing Without the Pain

By optimizing the data layout ahead-of-time, graph reordering can effectively improve the memory access locality in graph processing. The reordered graphs derived by sophisticated graph reordering approaches can greatly speedup the executions of most graph algorithms, while they incur huge computation overheads. Although lightweight approaches indeed reduce the reordering costs, they cannot achieve the best speedup performances. This is because they merely operate vertices of high-degrees and inadvertently destroy the community structures hidden in the graph. In this paper, we thus propose Sorder to balance the speedup performance and the reordering overhead. Sorder achieves better locality by preserving structural properties of graphs. Specifically, it mainly exploits neighborhood relations to renumber vertices and preferentially reorders vertices of high-degree ahead of the other vertices. We further enhance the design with the hypernode concept, which gathers neighboring vertices of low-degree to form a virtual vertex. Therefore, Sorder can consecutively rearrange more neighboring vertices, such that protecting the community structures. Extensive experiments with 5 representative graph algorithms and 7 real-world graphs demonstrate that Sorder can achieve comparable speedup performance as Gorder, the state-of-the-art approach, while significantly reducing the reordering overhead.
Distributed Supervised Discrete Hashing With Relaxation

The data-dependent hash methods are becoming more and more attractive because they perform well in fast retrieval and storing high-dimensional data.Most existing supervised hashes are centralized, such as supervised discrete hashing (SDH) and supervised discrete hashing with relaxation (SDHR).The SDH algorithm determines the regression target by using ordinary least squares regression and the traditional zeroone matrix encoding of class label information.And SDHR is a constraint to the regression target matrix so that each example is correctly classified and satisfies a larger margin, so as to achieve the purpose of optimizing the regression target.In real environment, a large amount of data will be distributed in different nodes.Therefore, the centralized hash method has great limitations.In this article, we propose distributed supervised discrete hashing algorithm with relaxation (DSDHR) based on SDHR.The SDHR algorithm is introduced into the distributed network.In this framework, all nodes share a centralized hash learning model.At the same time, in order to ensure that the distributed hash algorithm is updated in parallel on multiple nodes, consistency constraints are introduced.In each node, alternative iterative methods are used to obtain the binary hash code, regression target and hash function.Experiments show that DSDHR has certain competitive advantages over some centralized hashing algorithms and some distributed hashing algorithms.