Reviewerly/exHarmony huggingface dataset:
Robust Real-Time Group Activity Recognition of Robot Teams

Recognition of group activities is critical for the success of applications that depend on effective human-robot teaming. Awareness of these group activities (also referred to team behaviors in some literature), including the individual activities of human teammates and the overall team intent, allows robotic teammates to work alongside humans without explicit commands and to offer proactive assistance towards the overall mission. In this letter, we present a novel approach to robot recognition of team activities, simultaneously learning a projection from multi-sensory input data to a latent representation of individual activities and a projection from this representation to the overall activities. We introduce a smoothed iterative reweighted algorithm to solve this formulated optimization problem, guaranteed to converge to an optimal solution. We evaluate our approach extensively on benchmark group and team activity datasets, showing that our approach achieves state of the art performance while operating in real-time on mobile robots.
Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation

Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.
An Ontology to Promote Interoperability between Cyber-physical Security Systems in Critical Infrastructures

Critical infrastructures, like airports and hospitals, provide essential services and are considered huge investments in any country. Ensuring the security of these critical infrastructures is a necessity in every possible aspect. These infrastructures, however, currently make use of different technological solutions with distinct security levels, features and knowledge representation formats which often do not communicate directly with each other, leaving the task of processing the global view of the institution's security to the end-users. In order to have a complete view of the current cybersecurity status of a given system, interoperability between these tools must be addressed. Different ontologies have been proposed to deal with subdomains of cybersecurity. Similarly, these fail to provide a full view of cyber and physical security that would be necessary to achieve a global security approach. In this paper, we explore the current status and activities of cybersecurity as a basis for the development process, propose an integrated ontology for combined cyber and physical security. After that, we suggest several evaluation methods to be applied to the resulting ontology in order to check its usefulness in achieving the interoperability goal. The focus will be on the different systems needed for an integrated security of airports.
Optimized FPGA Implementation of Elliptic Curve Cryptosystem over Prime Fields

Elliptic curve cryptosystem has been widely applied in a lot of fields, such as finance, E-commerce and E-government. In this paper, we propose an optimized FPGA implementation of elliptic curve cryptosystem over 256-bit prime fields, which has high computational performance and low resource consumption. Specifically, we design a novel modular multiplier supporting four-level pipelining, which only needs 7 clock cycles to complete a single modular multiplication. It can process 4 modular multiplication operations (4 MMPO) simultaneously. We further design, on the basis of 4MMPO, a parallel architecture to efficiently implement point doubling and point addition operation. Finally, we testify the validity of our ECC processor on Xilinx's Virtex-7 FPGA platform. The result shows that it takes only 0.15ms for an elliptic curve point multiplication, the maximum frequency of the processor is about 123.27Mhz and the resource only needs 22938 look-up tables (LUTs).
Application of integrated geophysical methods in geothermal exploration: a case in Zhangjiakou

Abstract Geothermal energy has become increasingly important in our energy structure as it is a kind of clean and sustainable resource. Zhangjiakou, along with Beijing, is the host city of Winter Olympic Game 2022 and has the demand of geothermal resources. In the central-south part of Zhangjiakou area, integrated geophysical exploration was carried out in 2019, including regional and local high-precision gravity, magnetotelluric (MT), controlled source audio magnetotelluric (CSAMT) and seismic measurement. Combined with geothermal geological survey, new secondary faults, the contour maps of depth of geothermal reservoir and basement are achieved, which plays a vital role in delineating geothermal anomalous area. Based on the analysis of geophysical interpretation results, geological and hydrological information, the geothermal favorable zone and drilling target areas are determined. The work done in the year of 2019 is the beginning and basis of this geothermal resource survey project, and will provide technical support for the drilling work.
A Comparative Study of Deep Learning-based RGB-depth Fusion Methods for Object Detection

The object detection task which attempts to predict bounding boxes of all interest objects in an RGB image is of paramount importance for many real-world applications and has attracted much attention within the computer vision community. However, RBG cameras cannot directly provide depth information and RGB-based object detector can not achieve an accurate performance under complex environment. To address this problem, we make two contributions in this paper. Firstly, the performances of four state-of-the art unsupervised depth estimation methods were thoroughly evaluated in the context of object detection, which can serve as a baseline for other researchers to develop even more sophisticated methods. Secondly, we investigated whether fusing depth information and RGB can improve the performance of object detection networks. The obtained results on the KITTI dataset show that RGB-depth fusion approach with MonoDepth as depth estimation method outperforms the RGB-based and depth-based detectors.
Improved CTR Prediction Algorithm based on LSTM and Attention

Click-through rate prediction is a core research issue in recommendation systems and online advertising, playing a pivotal role in the entire Internet field. This article mainly integrates the LSTM module and the attention module into the DeepFM framework, and proposes LDeepFM and LADeepFM which can extract time series information more effectively. We use real data sets to evaluate the prediction performance of the model. The experiment results demonstrate that our proposed approach can increase the accuracy of click-through rate prediction more effectively than traditional models.
A robust watermarking hybrid algorithm for color image

In order to improve the anti-attack performance of the watermark to meet the requirements of copyright protection and content forensics. This paper proposes a digital watermarking hybrid algorithm based on color images. The specific process is to adopt the idea of multi-algorithm layered embedding, choose the algorithm based on discrete cosine transform (DCT) algorithm, discrete wavelet transform_singular value decomposition (DWT_SVD) algorithm, and hologram algorithm, these three algorithms with robust complementary functions, then embed the same watermark image into the color image R, G and B layers to complete the watermark embedding. Compared with the single-algorithm embedded watermark, the hybrid algorithm can achieve blind extraction, at the same time, the algorithm has better robustness and can resist more types and higher intensity attacks. In the process of digital image transmission, the integrity of the watermark information of the carried picture can be guaranteed to achieve copyright protection, content forensics and other purposes.
Industrial Energy Assessment Training Effectiveness Evaluation: An Eye-Tracking Study

It is essential to understand the effectiveness of any training program so it can be improved accordingly. Various studies have applied standard metrics for the evaluation of visual behavior to recognize the areas of interest that attract individuals’ attention as there is a high correlation between attentional behavior and where one is focusing on. However, through reviewing the literature, we believe that studies that applied eye-tracking technologies for training purposes are still limited, especially in the industrial energy assessment training field. In this paper, the effectiveness of industrial energy assessment training was quantitatively evaluated by measuring the attentional allocation of trainees using eye-tracking technology. Moreover, this study identifies the areas that require more focus based on evaluating the performance of subjects after receiving the training. Additionally, this research was conducted in a controlled environment to remove the distractions that may be caused by environmental factors to only concentrate on variables that influence the learning behavior of subjects. The experiment results showed that after receiving the training, the subjects’ performance in energy assessment was significantly improved in two areas: production, and recycling and waste management, and the designed training program enhanced the knowledge of participants in identifying energy-saving opportunities to the knowledge level of experienced participants.
Mixed Relay Placement for Quantum Key Distribution Chain Deployment over Optical Networks

Four mixed trusted/untrusted relay placement strategies for quantum key distribution chain deployment over optical networks are proposed, which can improve a security level up to 119% relative to the conventional purely trusted relay placement.
LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi-class Classification

Convolutional Neural Networks (CNNs) demonstrate state of the art performance in large scale multi-class image classification tasks. CNNs consist of convolution layers that progressively construct features and a classification layer. Typically, a softmax function is used in the classification layer to learn joint probabilities for the classes, which are subsequently used for class prediction. We refer to such an approach as the joint approach to multi-class classification. There exists another approach in the literature which determines the multi-class prediction outcome through a sequence of binary decisions, and is christened the class binarization approach. A popular type of class binarization is Error Correcting Output Codes (ECOC). In this paper, we propose to incorporate ECOC into CNNs by inserting a latent-binarization layer in a CNN's classification layer. This approach encapsulates both encoding and decoding steps of ECOC into a single CNN capable of discovering an optimal coding matrix during training. The latent-binarization layer is motivated by the family of latent-trait and latent-class models used in behavioral research. We call the proposed CNNs with Latent Binarization as LB-CNNs, and develop algorithms combining EM and back-propagation to train LB-CNNs. The proposed models and algorithms are applied to several image recognition tasks, producing excellent results. Furthermore, LB-CNNs can also enhance the interpretability of the decision process of CNNs.
Research on Vehicle Detection Based on Faster R-CNN for UAV Images

Since the vehicle targets in unmanned aerial vehicle (UAV) images are generally small, the existing neural network calculations will cause the detailed information to be seriously lost, which leads to the poor effects of existing recognition algorithms. Here we improved one of the most representative algorithms in the field of target recognition refer to the characteristics of UAV image datasets. There are two main tasks: first, to improve the Faster R-CNN network structure so that it can supplement the detailed information of small targets; second, to verify that it has higher network accuracy, model discrimination and robustness through experiments.
WITHDRAWN: Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation

The occurrence of defect over the soft tissues and nervous system is gradually increasing where Magnetic Resonance Imaging (MRI) is the most preferred method for performing the examination. The brain tumor MR image segmentation performs functionalities like image reconstruction of affected (diseased tissues) and qualitative analysis of infected and normal tissues. The image segmentation accuracy with the physician’s perspective relies over the shape, size, and location of lesions tissues, appropriate diagnostic strategies, and disease determination. The outcomes of this investigation rely over Multi-Perspective Scaling Convolutional Neural Networks (MPS-CNN) model for segmenting brain tumors more effectually and accurately. The multi-scale inputs are given to the proposed CNN model to overcome the necessity to select the appropriate input scale based on the tumor size, neighborhood tumor analysis based on scaled images, and adoption towards various tumor sizes. Therefore, the segmentation accuracy can be increased based on the input multi-scale brain tumor images. Also, the faster segmentation with multi-scaling process accelerates the speed of ensuring real-time segmentation process. This scaling process can effectively segment the brain images in the MRI which enhances the generalization process. It is utilized for predicting the brain lesion tissue of MRI. The simulation is carried out in MATLAB environment. The anticipated MPS-CNN is compared with prevailing approaches like CNN, FCN, U-Net, SegNet, Deep V3, and Deep FCN. And the MPS-CNN shows better trade-off in contrary to other approaches.
Generalized Newton methods for graph signal matrix completion

The matrix completion problem can be found in many applications such as classification, image inpainting and collaborative filtering. In recent years, the emerging field of graph signal processing (GSP) has shed new light on this problem, deriving the graph signal matrix completion problem which incorporates the correlation of data elements. The nuclear-norm based methods possess satisfactory recovery performance, while they suffer from high computational cost and usually have slow convergence rate. In this paper, we propose two new iterative algorithms for solving the nuclear-norm regularization based graph signal matrix completion (NRGSMC) problem. By adopting approximate diagonalization approaches to estimate singular value decomposition (SVD), we obtain two generalized Newton algorithms, the generalized Newton with truncated Jacobi method (GNTJM) and the generalized Newton with parallel truncated Jacobi method (GNPTJM). The proposed methods are with low complexity and fast convergence by using the second-order information associated with the problem. Numerical results on three real-world data sets demonstrate that our schemes have evidently faster convergence rate than the gradient method with exact SVD, while maintain the similar completion performance.
Construction of Binary Sequences With Low Correlation via Multiplicative Quadratic Character Over Finite Fields of Odd Characteristics

In literature, there are several methods to construct Gold sequences. One of the constructions is via the trace function from extension field of \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Estimation of correlation of this construction is based on number of rational points of elliptic curves. In this article, we generalize this construction from finite fields of even characteristic to odd characteristics by using multiplicative quadratic character. Again, estimation of correlation of this construction is based on number of rational points of elliptic curves. Thus, we obtain binary sequences which have more flexibility on length while still possessing low correlation property. Moreover, some of the sequences are optimally balanced.
Classification and Evaluation of Privacy Preserving Data Mining Methods

In the recently age, the volume of information is growing exponentially. This data can be used in several fields such as business, healthcare, cyber security, etc. Extracting useful knowledge from raw information is an important process. But the challenge in this process is the sensitivity of this information, which has made owners unwilling to share sensitive information. This has led the study of the privacy of data in data mining to be a hot topic today. In our paper, an aim is made to prepare a framework for qualitative analysis of methods. This qualitative framework consists of three main sections: a comprehensive classification of proposed methods, proposed evaluation criteria and their qualitative evaluation. Our main purpose of presenting this framework is 1) systematic introduction of the most important methods of privacy preserving in data mining 2) creating a suitable platform for qualitative comparison of these methods 3) providing the possibility of selecting methods appropriate to the needs of application areas 4) systematic introduction of points Weakness of existing methods as a prerequisite for improving methods of PPDM.
A Product Authentication Scheme for Supply Chain system via Smart Contracts using Blockchain Technology and Facial Recognition

Abstract The supply chain management industry is struggling with inadequate resources for efficient authenticity verification. Blockchain technology and smart contracts can overcome such conventional limitations to authenticate products in an easy, economical and secure manner. Decentralized and immutable blockchain systems allow product tracking to its origin.In this paper we have proposed and implemented a system for product authentication using blockchain technology based on ethereum platform by making use of smart contracts. We have analysed the existing centralized system and the need to shift from the existing centralized system to a decentralized blockchain based ledger technology. The use of blockchain technology in supply chain technology has reduced the complexity in product authentication by making the entire history of the product available from its production stage till it reaches the customer.
Non-Foster Matching Circuit Synthesis Using Artificial Neural Networks

In this work, we study the use of artificial neural networks for non-Foster circuit synthesis. Non-Foster matching circuits are often used in military and civilian communication systems to provide wide instantaneous bandwidth. The design of active non-Foster circuits is very challenging and only a few practical designs have been reported to date due to the complexity, the non-linearity, and the inherent instability of these circuits. We propose using an artificial neural network algorithm to allow for automated non-Foster circuit synthesis. Within the examined input impedances, where the equivalent negative inductance of the circuit ranges from -91.6 nH to -51.9 nH, and the positive resistance ranges from 6.5 Ω to 15.4 Ω, the mean synthesis error of the inductance and the resistance are 0.061% and 0.078%, respectively. The synthesized circuits are stable.
A Smart Surveillance Prototype Ensures the Respect of Social Distance During COVID19

At the moment, the best and only way to reduce the spread of coronavirus disease 2019 is by limiting close contact with others. Respecting social distance, infection is less probable. Since this is a new lifestyle for everyone, it's hard to be distance all the times. People forget to keep distance or they are not taking seriously the actual situation. That's why in this paper, we propose a smart surveillance solution. Our test prototype ensures the respect of social distancing by detecting persons, calculating distances between them and generating loud vocal alerts. The smart surveillance prototype is based on Raspberry Pi and Camera Pi. Then, we make a comparison study of object detection pretrained models. SSD-MobileNet gives the most satisfying result using Raspberry pi with limited computing resources. Despite that implementing CNN based model on the Raspberry Pi is such a challenging work, we reach a value of 1,1 FPS on real-time object detection and distance analysis system.
Research on path planning of cleaning robot based on an improved ant colony algorithm

The conventional ant colony algorithm is easy to fall into the local optimal in some complex environments, and the blindness in the initial stage of search leads to long searching time and slow convergence. In order to solve these problems, this paper proposes an improved ant colony algorithm and applies it to the path planning of cleaning robot. The algorithm model of the environmental map is established according to the grid method. And it built the obstacle matrix for the expansion and treatment of obstacles, so that the robot can avoid collision with obstacles as much as possible in the process of movement. The directional factor is introduced in the new heuristic function, and we can reduce the value of the inflection point of paths, enhance the algorithm precision, and avoid falling into the local optimal. The volatile factor of pheromones with an adaptive adjustment and the improved updating rule of pheromones can not only solve the problem that the algorithm falls into local optimum, but also accelerate the running efficiency of the algorithm in the later stage. Simulation results show that the algorithm has the better global searching ability, the convergence speed is obviously accelerated, and an optimal path can be planned in the complex environment.
A Deep Neural Network Pruning Method Based on Gradient L1-norm

The deep neural network model usually has a large number of redundant weight parameters. When calculating the deep neural network model, it needs to occupy a large amount of computing resources and storage space, which makes it difficult to deploy on some edge devices and embedded devices. In order to solve this problem, we propose a deep network pruning algorithm based on gradient L1 norm (GLNP). The core idea of GLNP algorithm is to judge the importance of the filter based on the L1-norm of gradient. Then, according to the pruning ratio, we remove the filter and its connection feature diagram layer by layer, and retrain the precision of the depth network model after pruning. We show that the GLNP algorithm can reduce 71% parameters on VGG-16, with guaranteeing the accuracy 90.81%; Similar experiments with ResNet-56 reveal that the GLNP algorithm can reduce 35.2% parameters on ResNet-56, with guaranteeing the accuracy 93.11%. Compared with the current popular PFEC algorithm, the GLNP algorithm compresses of both network model while improving the accuracy, which has better performance of deep network compression and acceleration.