Reviewerly/exHarmony huggingface dataset:
Image Inpainting with Cascaded Modulation GAN and Object-Aware Training

Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partially due to the lack of effective network structures that can capture both the long-range dependency and high-level semantics of an image. We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level . In each decoder block, global modulation is first applied to perform coarse and semantic-aware structure synthesis, followed by spatial modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios. Extensive experiments are conducted to show that our method significantly outperforms existing methods in both quantitative and qualitative evaluation . Please refer to the project page: https://github.com/htzheng/CM-GAN-Inpainting .
Modulation Signal Classification Algorithm Based on Denoising Residual Convolutional Neural Network

Traditional denoising algorithms are easy to lose signal details, resulting in low recognition accuracy of modulated signals.A algorithm for modulation signal classification based on denoising residual Convolutional Neural Network (DRCNet) is proposed.DRCNet inserts a soft threshold function as a nonlinear transformation layer into the deep architecture to build a soft threshold learning network (STLNet).STLNet obtains an appropriate threshold according to the signal-to-noise ratio of the input signal samples, which is used to remove useless noise features, thereby effectively denoising the signal.This paper builds a serial network (RCTLNet) connected by Convolutional Neural Networks and Long-Short-Term Memory Networks.RCTLNet uses the convolutional neural network to extract the spatial features of the signal samples, and uses the long and short-term memory network to extract the temporal features of the signal samples.And then realize the classification and identification of the modulated signal.Experiments show that the recognition accuracy of the proposed modulated signal classification model is 92%, which is 7% higher than that of the Convolutional Long-Short Term Memory Network (CNN-LSTM).
Analyzing Human Observer Ability in Morphing Attack Detection—Where Do We Stand?

Morphing attacks are based on the technique of digitally fusing two (or more) face images into one, with the final visage resembling the contributing faces. Morphed images not only pose a challenge to Face-Recognition Systems (FRS) but also challenge experienced human observers due to high quality, postprocessing eliminating any visible artifacts, and further the printing and scanning process. Few studies have concentrated on examining how human observers can recognize morphing attacks, even as several publications have examined the susceptibility of automated FRS to morphing attacks and offered morphing attack detection (MAD) approaches. MAD approaches base their decisions either on a single image with no reference to compare against (Single-Image MAD (S-MAD)) or using a reference image (Differential MAD (D-MAD)). One prevalent misconception is that an examiner’s or observer’s capacity for facial morph detection depends on their subject expertise, experience, and familiarity with the issue. No works have reported the specific results of observers who regularly verify identity (ID) documents for their jobs. As human observers are involved in checking ID documents having facial images, a lapse in their competence can result in significant societal challenges. To assess the observers’ proficiency, this research first builds a new benchmark database of realistic morphing attacks from 48 different subjects, resulting in 400 morphed images. Unlike the previous works, we also capture images from Automated Border Control (ABC) gates to mimic realistic border-crossing scenarios in the D-MAD setting with 400 probe images, to study the ability of human observers to detect morphed images. A new dataset of 180 morphing images is also produced to research human capacity in the S-MAD environment. In addition to creating a new evaluation platform to conduct S-MAD and D-MAD analysis, the study employs 469 observers for D-MAD and 410 observers for S-MAD who are primarily governmental employees from more than 40 countries, along with 103 control group members who are not examiners. The analysis offers intriguing insights and highlights the lack of expertise and failure to recognize a sizable number of morphing attacks by experienced observers. Human observers tend to detect morphed images to a lower accuracy as compared to the four automated MAD algorithms evaluated in this work. The results of this study are intended to aid in the development of training programs that will prevent security failures while determining whether an image is bona fide or altered.
Modeling and optimizing the voice assistant behavior in Virtual Reality

In this article, we present the design and development process of the robot voice assistant in Virtual Reality, created within the NUX project which aims at exploring the potential of voice interaction in VR. For the experiments, the testing environment was prepared in two variants: with graphical communication and with voice communication with an assistant. The environment is designed as a futuristic office on the space station and it is implemented in Unreal Engine 4. The verbal and nonverbal behavior of the assistant and the interactions chains were designed and optimized in 3 iterations and investigated using a mix of qualitative and quantitative methods (N=28). The impact of the features of the assistant on the perception and attitudes of the users and the resulting design decisions are discussed.
The effect of information content and length on name recollection

Memorable function and variable names are useful for developers: they reduce the need to re-check how objects are named when one wants to use them, and they enhance comprehension when encountered when reading code. We look at the possible interplay between the information contained in names and how memorable they are. We show in two independent experiments involving a total of 190 subjects that informative names are usually easier to recollect than similar-length names which contain less focused information. Interestingly, we find that less-experienced and female participants are better at remembering the less informative names. We also find that short names, which are not just abbreviated but actually contain less information, are significantly more memorable. Hence a good choice would be to use the the shortest name that includes the most focused and pertinent information.
Hulls of Reed-Solomon Codes via Algebraic Geometry Codes

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ RS}}_{k}(\mathbf {a})$ </tex-math></inline-formula> be a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -dimensional Reed-Solomon (RS) code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> associated with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {a}=(\alpha _{1},\cdots,\alpha _{n})$ </tex-math></inline-formula> and let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$h=\prod _{i=1}^{n}(z-\alpha _{i})$ </tex-math></inline-formula> be a polynomial in variable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula> . In this paper, by expressing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ RS}}_{k}(\mathbf {a})$ </tex-math></inline-formula> as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}$ </tex-math></inline-formula> -construction algebraic geometry code, we completely determine the dimension of the hull <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ RS}}_{k}(\mathbf {a})\bigcap {\mathrm{ RS}}_{k}(\mathbf {a})^{\perp} $ </tex-math></inline-formula> in terms of the degree of the derivative of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$h$ </tex-math></inline-formula> and some relevant polynomials. As applications, we explicitly determine the parameters of MDS entanglement-assisted quantum error-correcting codes constructed from RS codes, and all linear complementary dual (resp. self-dual) RS codes are also fully described.
A prototype system for supporting a network of information graphs with the ability to assess the nature of the subject's knowledge

The task of modeling a subject whose cognitive activity is directed under the influence of the information environment continues to remain relevant. The difficulties in solving this problem are due to (1) the dependence of the truth of information on time, as a result of which the subject can make decisions based on outdated information, and (2) the heterogeneous nature of the information itself, which, in particular, can be intentionally false (fake). The paper proposes a model of interaction of subjects, including the exchange of information marked both by the time of its creation (receipt) and the quality of information. The model is based on a system of interacting processes and is presented as a set of information graphs describing the stages of interaction. A system of typing graphs and their constituent elements is proposed. The composition tools that allow one to assemble complex graphs from simpler ones are presented. The paper also presents a set of tools corresponding to the proposed model. The tools include a process language interpreter that provides computation of model constructs, as well as an information graph editor, type control tools, debugging tools, and configuration management tools for the computing environment. The proposed tools have been experimentally tested in the development of application systems from the field of implementing the best available technologies.
A Novel Approach for Automatic Recognition of LPI Radar Waveforms Based on CNN and Attention Mechanisms

Low probability of intercept (LPI) radar waveform identification is a challenging task in modern radar and electronic warfare (EW) systems. A high precision LPI radar waveform automatic recognition method based on CNN and attention mechanism is proposed in this paper. Firstly, using the smoothed pseudo-Wigner-Ville distribution (SPWVD) to convert the reconnaissance signal to a time-frequency image. Then the time-frequency images are preprocessed by using bicubic interpolation. Finally, the automatic recognition of the LPI radar waveform is realized by LRCNet which fused the convolutional neural network (CNN) and convolutional block attention mechanism (CBAM). The results show that when the signal-to-noise ratio (SNR) is −4 dB, the model achieved an overall average recognition rate of 99.2% for 13 LPI radar waveforms (LFM, Rect, Costas, Barker, Fank, P1-P4, T1-T4 codes). Compared with other typical waveform recognition methods, the proposed method has an obvious improvement in recognition accuracy under low SNR.
A Distributed and Privacy-Aware High-Throughput Transaction Scheduling Approach for Scaling Blockchain

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.
Adaptive Resonance Theory-based Clustering for Handling Mixed Data

This paper proposes an Adaptive Resonance Theory (ART)-based clustering algorithm for a dataset which contains numerical and categorical attributes simultaneously. In the proposed algorithm, similarity between numerical attributes is calculated by the correntropy-based nonlinear similarity measurement, while similarity between categorical attributes is defined by a hamming distance-based approach. One advantage of the proposed algorithm is that the algorithm continually and adaptively generates a sufficient number of nodes for clustering from given data points. Empirical studies on various datasets show that the proposed algorithm has comparable clustering performance to the representative mixed data clustering algorithms.
Free-living Ambulatory Activity Classification: A Comparative Analysis of Wrist-worn, Insole-embedded, and Phone-embedded Sensors

Wrist-worn and smartphone-embedded inertial sensors are among the most widely used sensing modalities for activity classification. Insoles instrumented with inertial and force sensors have also become available to researchers and the general public. However, little is known about how classification accuracy is affected by the combination of these three sensing modalities. This study compares the performances of 7 activity classification models, each relying on a combination of 3, 2, or a single sensing modality, under unstructured and free-living conditions. In each model, a genetic algorithm was applied for optimal feature selection, and multi-session leave-one-out cross-validation was used to evaluate model performance. Results for the unstructured condition indicated that the insole-embedded sensors can classify six common ambulatory activities with at least 95% accuracy when used alone or in combination with any of the other sensing modalities. In free-living conditions, sensor combinations that included the insole-embedded sensors demonstrated high levels of agreement with a silver-standard activity tracker. These results provide new insights into the feasibility of using instrumented insoles in combination with phone-embedded or wrist-worn sensors to enhance the accuracy of conventional methods for ambulatory activity classification.
A Novel Echo State Network Autoencoder for Anomaly Detection in Industrial IoT Systems

The industrial Internet of Things technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of artificial intelligence models running on edge devices, such as intelligent cyber physical systems typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, echo state networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this article, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best tradeoff in terms of memory footprint and inference time.
Symmetrical Epipolar Features Over Normalized Camera/Projector Calibration Matrices for Real-Time Structured Light Illumination

Structured light illumination is a 3D scanning technique based on projecting a series of striped patterns and reconstructing depth based on the observed warping of the pattern across the target surface. Extensively studied, real-time performance is of paramount importance in many applications. In this Letter, we build on prior researches with epipolar geometry to further simplify the computations of 3D point clouds, with the symmetrical epipolar features derived by extending epipolar geometry over normalized calibration matrices of the camera and projector. Experiments show that the proposed two new processes are of the same accuracy over the normalized calibration matrices with substantially fewer calculations.
A Self-Attentional ResNet-LightGBM Model for IoT-Enabled Voice Liveness Detection

Voice user interface (VUI) brings high efficiency and convenience for the applications of Internet of Things (IoT), meanwhile, it can also cause increasingly serious security issues. The word-level voice liveness detection is proved to be the promising solution to thwart spoofing attacks. However, the complex acoustic feature, diversified attacks, and different interaction distance can severely affects the improvement of detection accuracy. To alleviate this issue, we develop a novel pop noise-based word-level voice liveness detection framework. First, a new voice frame selection method is proposed for determining optimal frames, including short time Fourier transform, low-frequency average energy computation, and sequencing. Then, the acoustic features of the selected frames are calculated by the Gammatone frequency cepstral coefficient (GFCC). Finally, based on these features, a newly built joint voice detector, fusing the self-attentional residual network (ResNet), and light gradient boosting machine (LightGBM), can achieve accurate voice classification. On the popular voice spoofing attack data sets, experimental results show that our proposal significantly outperforming the baseline and the state-of-the-arts models, and it is gender dependent. Moreover, our proposal has good generalization ability for far-field replay voice attack, speech synthesis and voice conversion attacks, and partial fake voice attack. Finally, its effectiveness is verified by the ablation study.
GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

In order to automatically generate multiple labels for the event text of the Chinese government hotline, this paper propose a multi-label classification framework based on graph convolutional network (GCN), BERT, and memory network. The framework consists of three modules: label count prediction module, label semantic insert module, and label selection module. In the label count prediction module, this paper constructs the event graph with the abstract meaning representation (AMR) and extract the event topic information vector with GCN. To predict the label count, this paper first use BERT to extract the event semantic information vector and then fuse it with the event topic information vector (GCN-BERT fusion vector) with a dynamic fusion gate. In the label semantic insert module, to obtain the event label candidate set, this paper uses a multi-hop memory network to store the event label semantic information, and then use the answer selection framework, which matches the GCN-BERT fusion vector with the event label semantic memory vector. In label selection module, this paper uses the label count based multi-label selection to sort the event label candidate set and guide to output the optimal multi-label set of the event. Comparison experimental results show that the proposed framework outperforms all baselines and ablation studies demonstrate the effectiveness of each module.
Elevation State-Space: Surfel-Based Navigation in Uneven Environments for Mobile Robots

This paper introduces a new method for robot motion planning and navigation in uneven environments through a surfel representation of underlying point clouds. The proposed method addresses the shortcomings of state-of-the-art navigation methods by incorporating both kinematic and physical constraints of a robot with standard motion planning algorithms (e.g., those from the Open Motion Planning Library), thus enabling efficient sampling-based planners for challenging uneven terrain navigation on raw point cloud maps. Unlike techniques based on Digital Elevation Maps (DEMs), our novel surfel-based state-space formulation and implementation are based on raw point cloud maps, allowing for the modeling of overlapping surfaces such as bridges, piers, and tunnels. Experimental results demonstrate the robustness of the proposed method for robot navigation in real and simulated unstructured environments. The proposed approach also optimizes planners' performances by boosting their success rates up to 5x for challenging unstructured terrain planning and navigation, thanks to our surfel-based approach's robot constraint-aware sampling strategy. Finally, we provide an open-source implementation of the proposed method to benefit the robotics community.
Making Smart Contract Classification Easier and More Effective

Nowadays, as an emerging and powerful technology, smart contract supports decentralized trusted computing and is widely used in various business fields such as finance, exchange and game. Although smart contracts provide a convenient way to manage funds in the business, they also bring economic loss to users. Particularly, there are a huge number of malicious smart contracts running on blockchain, which defraud users of money. Therefore, identifying the intention of smart contracts is of great significance, which will help users have a better understanding of smart contracts and reduce their financial losses. In this paper, we purpose a supervised classification method to detect the intention of smart contracts. Specifically, we extract the features of smart contracts from codes and transactions, and then use XGBoost to classify smart contracts. We conduct extensive experiments on on-chain smart contracts. The experimental results demonstrate that our features and model are capable of effectively identifying the types of smart contracts.