Reviewerly/exHarmony huggingface dataset:
Residual Enhanced Multi-Hypergraph Neural Network

Hypergraphs are a generalized data structure of graphs to model higher-order correlations among entities, which have been successfully adopted into various researching fields. Meanwhile, HyperGraph Neural Network (HGNN) is currently the de-facto method for hypergraph representation learning. However, HGNN aims at single hypergraph learning and uses a pre-concatenation approach when confronting multi-modal datasets, which leads to sub-optimal exploitation of the inter-correlations of multi-modal hypergraphs. HGNN also suffers the over-smoothing issue, that is, its performance drops significantly when layers are stacked up. To resolve these issues, we propose the Residual enhanced Multi-Hypergraph Neural Network, which can not only fuse multi-modal information from each hypergraph effectively, but also circumvent the over-smoothing issue associated with HGNN. We conduct experiments on two 3D benchmarks, the NTU and the ModelNet40 datasets, and compare against multiple state-of-the-art methods. Experimental results demonstrate that both the residual hypergraph convolutions and the multi-fusion architecture can improve the performance of the base model and the combined model achieves a new state-of-the-art. Code is available at https://github.com/OneForward/ResMHGNN.
Inter-team communication in large-scale co-located software engineering: a case study

Abstract Large-scale software engineering is a collaborative effort where teams need to communicate to develop software products. Managers face the challenge of how to organise work to facilitate necessary communication between teams and individuals. This includes a range of decisions from distributing work over teams located in multiple buildings and sites, through work processes and tools for coordinating work, to softer issues including ensuring well-functioning teams. In this case study, we focus on inter-team communication by considering geographical, cognitive and psychological distances between teams, and factors and strategies that can affect this communication. Data was collected for ten test teams within a large development organisation, in two main phases: (1) measuring cognitive and psychological distance between teams using interactive posters, and (2) five focus group sessions where the obtained distance measurements were discussed. We present ten factors and five strategies, and how these relate to inter-team communication. We see three types of arenas that facilitate inter-team communication, namely physical, virtual and organisational arenas. Our findings can support managers in assessing and improving communication within large development organisations. In addition, the findings can provide insights into factors that may explain the challenges of scaling development organisations, in particular agile organisations that place a large emphasis on direct communication over written documentation.
Application of the Metric Learning for Security Incident Playbook Recommendation

The article describes an algorithm for the automated selection of the most relevant playbook for responding to computer security precedents. The proposed approach is based on the methodology of metric learning. During the execution of the algorithm, it analyzes the precedents recorded in the past and the playbooks used for them. A trained neural network maps the entire set of precedents into a vector space, in which precedents with the same playbooks are closer to each other than to precedents with different playbooks. This method does not require the involvement of object domain experts and additional training of the network when expanding the set of precedents or playbooks. The developed approach was tested on real data. Experiments show that the proposed method can be effectively used to playbook's recommendation.
RLS-PSM: A Robust and Accurate Password Strength Meter Based on Reuse, Leet and Separation

Password strength meters (PSMs) are being widely used, but they often give conflicting, inaccurate and misleading feedback, which defeats their purpose. Except for fuzzyPSM, all PSMs assume passwords are newly constructed, which is not true in reality. FuzzyPSM considers password reuse, six major leet transformations and initial capitalization, and performs the best as evaluated by Golla and Dürmuth at ACM CCS’18. On the basis of fuzzyPSM, we propose a new PSM based on <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> euse, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> eet and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> eparation, namely RLS-PSM. First, we classify password reuse behaviors into capitalization and those that use special characters for leet or separation, and calculate the corresponding probabilities. Then, to balance efficiency and precision, we use Long Short-Term Memory to calculate the probabilities of alphanumeric strings. Besides, we propose to use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">benchmark passwords</i> to show the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relative strength</i> of a password. Due to the varied impacts of different service types and diversified economic value of websites, we consider parameter settings of RLS-PSM under six different service types. Finally, we use the Monte Carlo method and weighted Spearman coefficient to measure and compare the robustness and accuracy of RLS-PSM, leading PSMs (including Markov-based PSM, PCFG-based PSM, fuzzyPSM, RNN, and Zxcvbn), and password cracking tools (including JtR and Hashcat). We find that the robustness of RLS-PSM is significantly higher than all counterparts when <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evaluating attempts</i> > 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> (e.g., on average, Fraction of Successfully Evaluated passwords of RLS-PSM is 18.9% higher than fuzzyPSM). The accuracy of RLS-PSM is also better than other mainstream PSMs used for comparison in this paper, except for fuzzyPSM.
Image Denoising Based on Dual-tree Complex Wavelet Transform and Convolutional Neural Network

Abstract In view of the problem that the noise in the image will have a negative impact on the subsequent image processing, this paper proposes a noise reduction algorithm based on the dual-tree complex wavelet transform and convolutional neural network by using the characteristics of the DTCWT and CNN. Firstly, the two-dimensional image is decomposed into two parallel wavelet trees by double tree complex wavelet transform, which forms six high-frequency detail parts and two same low-frequency parts in different directions. Then, it is put into the designed deep convolution neural network for training, Finally, the six high-frequency components and one of the low-frequency components are recombined into the image after noise reduction by using the inverse transform of double tree complex wavelet. The simulation results show that PSNR and MS-SSIM of the image are better than other noise reduction algorithms, and the details of the original image can be restored well while noise reduction. It has practical application value in the case that the collected image has a large noise due to various reasons.
An Adaptive Pyramid Single-View Depth Lookup Table Coding Method

As depth maps show unique characteristics like piecewise smooth regions bounded by sharp edges at depth discontinuities, new coding tools are required to approximate these signal characteristics. Moreover, the number of bits to signal the residual values for each segment can be further reduced by integrating a Depth Lookup Table (DLT), which maps depth values to valid depth values of the original depth map. The DLT is constructed based on an initial analysis of the input depth map and is then coded in the sequence header. In this paper, an adaptive pyramid single-view depth lookup table coding method is proposed, with the purpose of designing a clean syntax structure in the sequence header with reasonably good performance. Experiments show that the proposed method can reduce about 84.97% coding bits on average.
Research on Feature Enhancement for Small Object Detection

Abstract Small object detection is a challenging research direction in the field of computer vision, due to the low resolution and restricted information of small objects. At present, the general detectors only use appearance features to classify and locate objects, but they are prone to failure under the interference of background noise. On the other hand, the detector based on deep neural network has excellent performance on large scale, but it is difficult to extract enough information of small objects. This paper proposes a feature enhancement network (FENet), which contains two modules. The Residual feature enhancement (RFE) module combines residual learning and sub-pixel convolution to improve the resolution of input small objects and remove image noise. The Attention Feature Pyramid (AFP) module integrates the feature pyramid and attention mechanism, which can extract context information and filter redundant context information. At the same time, considering the imbalance of the contribution of large and small objects to the loss function during the training process, a feedback-driven function is introduced to solve the problem of uneven loss under multiple scales. Experimental results show that compared with the existing small object detection methods, our method has better performance.
Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network

This research features a deep learning based framework to address the problem of matching a given face sketch image against a face photo database. The photo-sketch matching problem is challenging because 1) modality gap between photo and sketch is very large, and 2) the number of paired photo/ sketch data is insufficient to train deep network. To circumvent the problem of large modality gap, our approach is to use an intermediate latent space between the two modalities. We effectively align the distributions of the two modalities in this latent space by employing a bidirectional (photo &#x2192; sketch and sketch &#x2192; photo) collaborative synthesis network. A StyleGAN-like architecture is utilized to make the intermediate latent space be equipped with rich representation power. To resolve the problem of insufficient training samples, we introduce a three-step training scheme. Extensive evaluation on public composite face sketch database confirms superior performance of our method compared to existing state-of-the-art methods. The proposed methodology can be employed in matching other modality pairs.
An Active Learning Framework for Constructing High-Fidelity Mobility Maps

Recent workat the U.S. Army CCDC Ground Vehicle Systems Center has shown that machine learning classifiers can quickly construct high-fidelity mobility maps. Training these classifiers, on the other hand, is still a challenge, since each data instance is labeled by performing a computationally intensive, physics-based simulation. In this paper we introduce an active learning framework, based on the query-by-bagging algorithm, that substantially reduces the number of simulations needed to train a classifier. Experimental results suggest that our sampling algorithm can train a neural network, with higher accuracy, using less than half the number of simulations when compared to random sampling.
Research on Distribution Route Planning Model and Algorithm of Unmanned Aerial Vehicle (UAV) based on Improved Multi-objective Genetic Algorithm

As an automatic and efficient delivery approach, the unmanned aerial vehicle (UAV) has received much attention in the field of electronic commerce and urban logistics for its natural ability to avoid traffic congestion. Regarding the arrangement of UAV delivery paths as a multi-objective path planning problem, we propose a novel genetic method based on the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) with constraints to optimize the multi-UAV path planning problem, which is to minimize the total cost while maximizing customer satisfaction. In this algorithm, decision vectors are represented as 1-dimensional chromosomes, and two specialized variation operators are employed. These designs, as well as the properties of NSGA-II, enable us to find more non-crowding Pareto optimal solutions with less calculation. In a case study on part of the Solomon dataset, this algorithm found three sparsely distributed non-dominant solutions in one Pareto frontier within a relatively small population size and a short time.
Time Dependence in Kalman Filter Tuning

In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional approaches to tuning (e.g. using normalized estimation error squared and covariance minimization) compute empirical measures of filter performance. The parameters are selected, either manually or by some kind of optimization algorithm, to maximize these measures of performance. However, there are two challenges with this approach. First, in theory, many of these measures do not guarantee a unique solution due to observability issues. Second, in practice, empirically computed statistical quantities can be very noisy due to a finite number of samples. We propose a method to overcome these limitations. Our method has two main parts to it. The first is to ensure that the tuning problem has a single unique solution. We achieve this by simultaneously tuning the filter over multiple different prediction intervals. Although this yields a unique solution, practical issues (such as sampling noise) mean that it cannot be directly applied. Therefore, we use Bayesian Optimization. This technique handles noisy data and the local minima that it introduces. We demonstrate our results in a reference example and demonstrate that we are able to obtain good results. We share the source code for the benefit of the community <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
DRQN-based 3D Obstacle Avoidance with a Limited Field of View

In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural network with LSTM units in a 3D simulator of mobile robots to approximate the Q-value function in double DRQN. We also use a curriculum learning strategy to accelerate and stabilize the training process. Then we deploy the trained model to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate the proposed approach both in the simulated environment and on a robot in the real world. The experimental results show that the approach is efficient and easy to be deployed, and it performs well for 3D obstacle avoidance with a narrow observation angle, which outperforms other existing DRL-based models by 15.5% on success rate.
Scalable Coding Of Motion And Depth Fields With Shared Breakpoints

A new breakpoint adaptive DWT, referred to as tri-break, is currently being considered by standardization efforts in relation to JPEG 2000 Part 17 extensions. We first provide a summary of the tri-break transform and then explore its performance for coding motion fields. Experimental results show that significant gains can be achieved for coding piecewise smooth motion flows by employing the tri-break transform. We demonstrate the feasibility of utilising a common set of breakpoints for compressing depth maps and motion fields anchored at the same frame. We also extend prior work to decode motion vector fields directly onto a triangular mesh, enabling operations such as view warping to be defined on triangular cells which can be efficiently processed by GPU based architectures.
A 28 nm, 397 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e688" altimg="si8.svg"><mml:mi>μ</mml:mi></mml:math>W real-time dynamic gesture recognition chip based on RISC-V processor

This paper presents a low-power programmable dynamic gesture recognition chip based on a RISC-V processor using RGB images. The chip uses two gesture recognition algorithms to recognize the most commonly used dynamic gestures (up, down, left, and right) with any shape of a hand at the distance of 10 cm ∼ 90 cm and speed of 10 cm/s ∼ 150 cm/s. The chip is fabricated in a 28 nm process and occupies an area of 640μm x 640μm. The accuracy reaches 93% ∼ 99% at complex backgrounds with 320x240 RGB image. It operates either in the recognition mode or in the standby mode that can be automatically switched to the other. The power consumption of the recognition mode is 397 μW at 0.584 V supply voltage, 25 MHz clock, and 30 FPS. And the power consumption of the standby mode at room temperature is 78.3 μW.
Artificial Intelligence for Dynamical Systems in Wireless Communications: Modeling for the Future

Dynamical systems are no strangers in wireless communications. Our story will necessarily involve chaos, but not in the terms secure chaotic communications have introduced it: we will look for the chaos, complexity and dynamics that already exist in everyday wireless communications. We present a short overview of dynamical systems and chaos before focusing on the applications of dynamical systems theory to wireless communications in the past 30 years, ranging from the modeling on the physical layer to different kinds of self-similar traffic encountered all the way up to the network layer. The examples of past research and its implications are grouped and mapped onto the media layers of ISO OSI model to show just how ubiquitous dynamical systems theory can be and to trace the paths that may be taken now. When considering the future paths, we argue that the time has come for us to revive the interest in dynamical systems for wireless communications. It did not happen already because of the big question: can we afford observing systems of our interest as dynamical systems and what are the trade-offs? The answers to these questions are dynamical systems of its own: they change not only with the modeling context, but also with time. In the current moment the available resources allow such approach and the current demands ask for it. Reservoir computing, the major player in dynamical systems-related learning originated in wireless communications, and to wireless communications it should return.
Efficient transfer learning for multi-channel convolutional neural networks

Although most convolutional neural networks architectures for computer vision are built to process RGB images, more and more applications complete this information with additional input channels coming from different sensors and data sources. The current techniques for training models on such data, generally leveraging transfer learning, do not take into account the imbalance between RGB channels and additional channels. If no specific strategy is adopted, additional channels are underfitted. We propose to apply channel-wise dropout to inputs to reduce channel underfitting and improve performances. This improvement of performances may be linked to how much new information is brought by additional channels. We propose a method to evaluate this complementarity between additional and RGB channels. We test our approach on three different datasets: a multispectral dataset, a multi-channel PDF dataset and an RGB-D dataset. We find out that results are conclusive on the first two while there is no significant improvement on the last one. In all cases, we observe that additional channels underfitting decreases. We show that this difference of efficiency is linked to complementary between RGB and additional channels.
Painting Style-Aware Manga Colorization Based On Generative Adversarial Networks

Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, which incurs high labor costs. Although automatic colorization methods have been recently proposed, most of them are designed for illustrations, not for comics. Unlike illustrations, since comics are composed of many consecutive images, the painting style must be consistent. To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data. The proposed method takes a pair of a screen tone image and a flat colored image as input, and outputs a colorized image. Experiments show that the proposed method achieves better performance than the existing alternatives.
People name recognition from ancient Chinese literature using distant supervision and deep learning

Ancient Chinese literature records many great historical figures, which is significant to the understanding of Chinese history and culture. An important step in studying historical figures for researchers is to find out the names of their interest from ancient Chinese literature. In this paper, we propose an advanced neural network method of automatic name recognition from ancient Chinese literature based on distant supervision and deep learning. To address the limitation of insufficient annotated corpus, we propose a distant-supervision-based method which matches the original text of ancient Chinese literature with the dictionary of historical figure names to generate a training corpus. In terms of the deep learning model, we adopted a state-of-the-art BiLSTM+CRF model, introducing attention mechanism to determine the correlation between other Chinese characters and the target Chinese characters, and further identify the boundaries of people's names. Experimental results indicate that our model achieves an F1-score of 66.9, which outperforms the most commonly used LSTM+CRF model and Baidu's deep learning Chinese lexical analysis tool LAC (the only publicly available tool for comparison). Therefore, our model can effectively extract people's names from ancient Chinese literature.
GIID-NET: Generalizable Image Inpainting Detection Network

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting, which could produce visually plausible results. Meanwhile, the malicious use of advanced image inpainting tools (e.g. removing key objects to report fake news) has led to increasing threats to the reliability of image data. To fight against the inpainting forgeries, in this work, we propose a novel end-to-end Generalizable Image Inpainting Detection Network (GIID-Net), to detect the inpainted regions at pixel accuracy. Extensive experimental results are presented to validate the superiority of the proposed GIID-Net, compared with the state-of-the-art competitors. Our results would suggest that common artifacts are shared across diverse image inpainting methods.