Reviewerly/exHarmony huggingface dataset:
LSTP: Long Short-Term Motion Planning for Legged and Legged-Wheeled Systems

This article presents a hybrid motion planning and control approach applicable to various ground robot types and morphologies. Our two-step approach uses a sampling-based planner to compute an approximate motion, which is then fed to numerical optimization for refinement. The sampling-based stage finds a long-term global plan consisting of a contact schedule and sequence of keyframes, i.e., stable whole-body configurations. Subsequently, the optimization refines the solution with a short-term planning horizon to satisfy all nonlinear dynamics constraints. The proposed hybrid planner can compute plans for scenarios that would be difficult for trajectory optimization or sampling planner alone. We present tasks of traversing challenging terrain that requires discovering a contact schedule, navigating nonconvex obstacles, and coordinating many degrees of freedom. Our hybrid planner has been applied to three different robots: a quadruped, a wheeled quadruped, and a legged excavator. We validate our hybrid locomotion planner in the real world and simulation, generating behaviors we could not achieve with previous methods. The results show that computing and executing hybrid locomotion plans is possible on hardware in real time.
Single-source UDA for privacy-preserving intelligent fault diagnosis based on domain augmentation

Abstract In practical applications of fault diagnosis, several factors, including fluctuations in load, changes in equipment condition, and environmental noise effects, could cause a classifier that’s been trained on the source domain to be ill-suited for matching data from the target domain. Unsupervised domain adaptation techniques have been developed to tackle this issue, but they typically demand access to fully labeled source domains, ignoring concerns of privacy regarding source domain data. Therefore, we consider a new research scene for source-free unsupervised domain adaptation (SFUDA), which exclusively relies on a source model trained on source domain sample without requiring access to fully labeled source domain data. This paper introduces a SFUDA approach that utilizes knowledge distillation (KD), which involves two stages: (1) generalizing the source model by applying domain augmentation techniques and LS methods that enhance the model’s potential to enhance its generalization capability; (2) adapting the target model using a KD framework to achieve knowledge migration; and in addition, mutual information structure regularization is added to consider the internal data structure, thus enhancing the model’s adaptability. To evaluate the efficacy of our approach, we perform experiments on two datasets—the Case Western Reserve University dataset and the Paderborn University dataset, comprising 24 transfer tasks. Our experiments demonstrate the effectiveness of the domain augmentation technique, mutual information regularization, and the proposed method.
Near-surface pedosediments of Takatokwane saline pan, southern Botswana: Properties, pedogenic processes, and inferences of environmental change

Natural pans are an important feature of dry landscapes worldwide. The purpose of this study was to characterize, for the first time, the near-surface pedosediments of the prominent Takatokwane pan in southern Botswana using their physical, geochemical, and mineralogical properties, to explore past and active pedogenic processes, and to interpret the properties of the pedosediments in light of environmental change. Pedogenic parameters including particle size distribution, reaction, electrical conductivity, and organic matter content were determined using routine laboratory procedures. The total elemental composition was analyzed using inductively coupled plasma optical emission spectroscopy and inductively coupled plasma mass spectrometry. The clay mineralogical compositions of the pedosediments were determined using a Bruker D8 Advance X-ray diffractometer. A suite of geochemical coefficients was used to quantify the weathering and pedogenic processes. Granulometric analysis showed that the pedosediments were dominated by fine sand (380 – 890 g/kg). The two pedons were greenish-gray in color at a depth of 140 cm. The elemental abundances followed the order of SiO2 > Al2O3 > Fe2O3 > TiO2. The TiO2/Al2O3 coupled with mineralogy indicated a uniform origin of the parent materials and feldspathic sandstone components, which were redeposited in the pan environment. The geochemical weathering coefficient indicates incipient weathering. Smectite-enriched clay deposits in the basin paved the way for the formation of the vertical layers. At the meso-morphological scale, reworked pedofeatures indicative of clay illuviation and gleisation processes were observed. The dominant pedogenic processes in soils include hydrolysis, lessivage, illuviation, and gleisation. This set of processes indicates the cycles of humid pedogenesis. This study confirmed three cycles of deposition, chemical precipitation, and post-depositional alteration under changing environmental conditions. Evidence from this study points to the seasonality of the environment, which could be explored for an improved understanding of the evolution of the Takatokwane pan and the Kalahari Basin.
Adaptive vulnerability-based risk identification software with virtualization functions for dynamic management

Over the years, risk management has become increasingly important for all companies, varying from large, medium-sized to micro-enterprises. The growing increase in cyber threats and the costs they cause has forced companies to consider new types of risks. This problem mainly affects medium, small, and micro-enterprises because of their limited resources or because they do not have sufficient technological knowledge. For this reason, this article studies different frameworks associated with risk management in different business areas. In addition, an open-source, automatic (plug and play) solution is proposed, which uses few resources thanks to virtualization through containerization. It takes care of identifying wired and wireless network devices. It generates a risk report with qualitative and quantitative values by reassessing the risk over time and provides mitigation if possible. Finally, a proof of concept was generated using a simulation of a medium-sized enterprise with various types of assets. As a result, we explored over 20 business days how network devices were detected and assessed without interacting with the framework, except to collect the results. It also showed how the risks evolve and increases if not addressed by the organization.
Petrogenesis and geological implications of the trachytic rocks in the South Eastern Desert, Egypt: mineralogical, geochemical, and radioactivity insights

The investigated Phanerozoic volcanic (Gabal Um Itly, G. El Beida, G. Um Domi and G. Um Doweila) areas in the south Eastern Desert of Egypt of the Northern Arabian-Nubian Shield (ANS), have been a peralkaline affinity and display a continuous composition trachyte rocks. Geochemically, the studied rocks show significant negative anomalies in Ba, Sr, and Ti on the primitive mantle normalized trace element plot, indicating an ancestry involving plagioclase and Fe-Ti oxide fractionation. The concentrations of uranium mineralization and rare earth elements in the trachyte rocks is related to the presence of uranophane, kasolite, umohoite, autunite, monazite and allanite. The distribution of uranium, thorium and their ratios in the trachyte rocks reflects a direct strong relation, which suggests that U distribution is, at least in part, governed primarily by magmatic processes and disturbed later by a hydrothermal solution.
Comprehensive Analysis of Spectral Mismatch Factor for Solar Cells Based on In Situ Observation of Aerosol Optical Depth Spectra and Solar Spectral Irradiance in Korea

The spectral mismatch factor for solar cells quantifies their relative performance in converting solar irradiance between the incident and reference solar spectra into electricity. This study attempted to evaluate the spectral mismatch factor for eight types of solar cells and investigate their sensitivity to changes in the solar spectral irradiance, which is dependent on the aerosol optical properties in a clear sky. Copper indium gallium diselenide cells have the highest mean value of the spectral mismatch factor, implying that they are less sensitive to changes in the solar spectral irradiance. In contrast, perovskite and amorphous silicon cells are more sensitive to atmospheric conditions, with broader distributions of the spectral mismatch factor values. Additionally, our study found that heterojunction with intrinsic thin-layer cells has the highest substantial efficiency, considering the nameplate efficiency. The spectral mismatch factor decreased with increasing aerosol optical depth at 500 nm and was proportional to the humidity. The effects of aerosol optical properties on the spectral mismatch factor for different solar cells were clarified using clustering analysis and back-trajectory modeling results. In the present study, the aerosol optical depth spectra were found to be more important in determining the spectral mismatch factor than the aerosol optical depth at 500 nm. This study recommends further research on the relationship between the aerosol optical properties and solar spectral irradiance to better predict or estimate the spectral mismatch factor in solar power forecasting.
DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters

Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NNs challenging to apply in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.
Multi-image blending holographic encryption system based on multi-source coherent diffraction and frequency-domain attention learning

The multi-image encryption method has the advantage of large information encryption capacity, which is conducive to the development of high-throughput cryptosystems. However, for multi-image encryption, the low PSNR caused by the crosstalk between images restricts the development of multi-image encryption methods. In our method, a novel optical multi-image encryption method based on multi-image coherent diffraction and frequency-domain attention learning is proposed. For encrypting, random aberrations brought about by diffraction are used instead of random phase masks to encrypt multiple source images into a single holographic ciphertext image based on holography and coherent diffraction imaging. A specially designed deep learning decryption network based on physical prior is proposed, which is used to resolve the source images of each channel respectively. Highlights and advantages include lower computational cost algorithm, higher real-time performance and better decryption quality. The feasibility, noise immunity, security and robustness of the proposed method are investigated through simulations and experiments. It is believed that the method we propose here can promote the further development of multi-optical image encryption systems.
On the self-adjustment of privacy safeguards for query log streams

Internet-based services process and store numerous search queries around the globe. The use of web search engines, such as Bing and Google, as well as personal assistants (e.g., Alexa and Cortana) and task specific systems (e.g., YouTube, Netflix, Amazon) are relevant examples. The queries associated to such services may be stored and sold out for profit. Before doing so, personal and sensitive information must be sanitized, as requested by current regulations. This can be cumbersome for some organizations. We present an automated solution for anonymizing unstructured data, like the one used within query logs. Our solution uses a light-weight probabilistic k-anonymity approach, which allows verifiable real-time privacy protection. It addresses previous limitations and improves performance. We validate the feasibility of the approach, under some evaluation metrics including data utility, privacy and speed.
Considering the Implications of Artificial Intelligence, Quantum Computing, and Cybersecurity

This paper considers the impact of using artificial intelligence and quantum computing as part of a cyber defense strategy and the deterrence, defense, and offensive capabilities that it provides. A model for this is presented and discussed.
ICANet: A Lightweight Increasing Context Aided Network for Real-Time Image Semantic Segmentation

Although existing real-time segmentation methods have reduced the network parameters, they still suffer from slow inference speed. To alleviate this problem, we propose a novel lightweight image segmentation method, called Increasing Context Aided Network (ICANet), which brings a significant increase in inference speed while maintaining competitive segmentation accuracy. To do this, we elaborately design both encoder and decoder. Considering that a large reduction in channel numbers brings poor performance, we propose a Inverted Depthwise Separable convolution block (IDS block) that enriches semantic information in channel and spatial dimensions. By stacking several such IDS blocks, we build an efficient encoder that can capture an increasing context by using different dilation rates in IDS blocks to further improve accuracy. Besides, we present a simple decoder that adopts a bottom-up pathway to effectively fuse multiscale feature maps from encoder. Extensive experiments show our ICANet achieves a SOTA balance between accuracy, speed and parameters.
Establishing a Stronger Baseline for Lightweight Contrastive Models

Recent research has reported a performance degradation in self-supervised contrastive learning for specially designed efficient networks, such as MobileNet and EfficientNet. A common practice to address this problem is to introduce a pretrained contrastive teacher model and train the lightweight networks with distillation signals generated by the teacher. However, it is time and resource consuming to pretrain a teacher model when it is not available. In this work, we aim to establish a stronger baseline for lightweight contrastive models without using a pretrained teacher model. Specifically, we show that the optimal recipe for efficient models is different from that of larger models, and using the same training settings as ResNet50, as previous research does, is inappropriate. Additionally, we observe a common issue in contrastive learning where either the positive or negative views can be noisy, and propose a smoothed version of InfoNCE loss to alleviate this problem. As a result, we successfully improve the linear evaluation results from 36.3% to 62.3% for MobileNet-V3-Large and from 42.2% to 65.8% for EfficientNet-B0 on ImageNet, closing the accuracy gap to ResNet50 with 5× fewer parameters. We hope our research will facilitate the usage of lightweight contrastive models. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Inference for Variance-Gamma Driven Stochastic Systems

In this work we present the variance-gamma driven state-space model (VGSSM) - a linear vector stochastic differential equation driven by the variance-gamma (VG) Lévy process, and propose a novel inference framework in such systems. There are closed form expressions for the first four moments of the marginals of the VG process, allowing for more flexible modelling than Brownian motion (BM), retaining BM as a limiting case. The conditionally Gaussian formulation of the variance-gamma process lends itself well to the use of a marginalised particle filter (MPF) which can include the estimation of model parameters as part of the sampling framework. As an example we present a state-space formulation of Langevin dynamics in the VGSSM for estimation of both the observed and the latent first-order dynamics of a system. We apply this specific Langevin formulation to synthetically generated data to validate the results of the MPF, followed by an application to foreign-exchange tick data to demonstrate the method for trend tracking in data sets that are irregularly sampled in time.
Histogram-guided Video Colorization Structure with Spatial-Temporal Connection

Video colorization, aiming at obtaining colorful and plausible results from grayish frames, has aroused a lot of interest recently. Nevertheless, how to maintain temporal consistency while keeping the quality of colorized results remains challenging. To tackle the above problems, we present a Histogram-guided Video Colorization with Spatial-Temporal connection structure (named ST-HVC). To fully exploit the chroma and motion information, the joint flow and histogram module is tailored to integrate the histogram and flow features. To manage the blurred and artifact, we design a combination scheme attending to temporal detail and flow feature combination. We further recombine the histogram, flow and sharpness features via a U-shape network. Extensive comparisons are conducted with several state-of-the-art image and video-based methods, demonstrating that the developed method achieves excellent performance both quantitatively and qualitatively in two video datasets.
Explicit Attention Modeling for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition have achieved significant improvements by utilizing complex networks and attention mechanisms. However, most of these studies learn the attention map implicitly through the class activation map. In this paper, we propose an explicit attention modeling approach for pedestrian attribute recognition. We construct a mask branch to learn the attention maps with a lightweight feature pyramid network. The features inside the specific mask are then averaged to obtain the scores for attribute recognition. Additionally, we introduce spatial and semantic distillation to improve the consistency of attention masks and attribute scores. Our experiments demonstrate that the proposed explicit attention modeling can achieve state-of-the-art performance on PA100K, PETA, and PAR datasets with negligible parameters.
Deniable Diffusion Generative Steganography

Steganography is used for converting communication via embedding secrets in cover media. Though steganography has developed many novel approaches based on GANs, etc., coverless generative steganography, sophisticated steganalysis can always perceive them. So there is a great possibility of coercive attacks, in which an attacker perceives steganography and coerces the sender or receiver to disclose secret information. Therefore, it is necessary to bring deniable steganography which mimics the deniable encryption. Usually training generative steganography suffers from an unstable training process and hard convergence, so deniable generative steganography becomes more difficult. Inspired by the fast-popular diffusion models known for their stability in training and their balance of diversity and high quality, we propose a deniable carrier-free generative steganography merging with diffusion models for the first time. Experiments demonstrate our deniable diffusion generative steganography achieves superior ability of image fidelity, sample diversity, reliable secret message extraction, and anti-steganalysis.
Scene Graph Generation using Depth-based Multimodal Network

Scene graph generation (SGG) provides an efficient way for scene understanding. However, it has been plagued by the inaccurate classification of relative spatial relationship and incorrect feature information aggregation from distant objects. In this paper, we innovatively introduce the depth information of objects into SGG and propose a multimodal edge-featured graph attention network (MEGA-Net). MEGA-Net primarily comprises three modules. First, the edge-aware message passing (EMP) module extracts multimodal features and fuses them as edge features in the graph network via a quadrilinear model. Multimodal features consist of depth features, visual features, spatial features, and linguistic features. The depth feature in EMP provides the relative spatial relationship among objects which prevents the tail spatial predicates from being recognized as the head predicates. Second, we propose a depth-based self-supervised graph attention (DSGAT) module to predict the correlation probability between object pairs. By encoding the depth ranking of different object pairs in 2D images, DSGAT learns more accurate directional attention to avoid unrelated neighbors. Third, we introduce a predicate aware loss (PA-Loss) to alleviate the feature redundancy problem caused by extra depth information. This is achieved by introducing semantic frequency information that reflects the priority between different types of relationships. Systematic experiments show that our method achieves state-of-the-art performance on two popular datasets, VG and VRD.
ShiftFormer: Spatial-Temporal Shift Operation in Video Transformer

Transformers have achieved great success in various tasks, especially that introducing pure Transformers into video understanding shows powerful performance. However, video Transformer suffers from the problem of memory explosion: it is difficult to be deployed on hardware due to the intensive computation. To address this issue, we propose ST-shift (spatial-temporal) operation with zero computation and zero parameter. We are only shifting a small portion of the channels along the temporal and spatial dimensions. Based on this operation, we build an attention-free ShiftFormer, where ST-shift blocks substitute the attention layers in video Transformer. ShiftFormer is accurate and efficient: it can reduce 56.34% of memory usage and achieve 3.41× faster training. When both using random initialization, our model performs even better than Video Swin Transformer for video recognition on Something-Something v2.
Adversarial Audio Watermarking: Embedding Watermark into Deep Feature

Audio watermarking is a promising technology for copyright protection, yet traditional methods are limited that must be combined with auxiliary techniques against attacks. This article proposes a new audio watermarking method that embeds watermarks through a trained neural network. It adds small imperceptible perturbations to the original audio so that its deep features point to specific watermark features. Data augmentation and error correcting coding are employed to guarantee its practicable robustness. This method is robust against many attacks without auxiliary techniques and shows better performance than other deep learning-based methods.