Reviewerly/exHarmony huggingface dataset:
A feature map aggregation network for unconstrained video face recognition

Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art.
Efficient quantum state tracking in noisy environments

Abstract Quantum state tomography, which aims to find the best description of a quantum state—the density matrix, is an essential building block in quantum computation and communication. Standard techniques for state tomography are incapable of tracking changing states and often perform poorly in the presence of environmental noise. Although there are different approaches to solve these problems theoretically, experimental demonstrations have so far been sparse. Our approach, matrix-exponentiated gradient (MEG) tomography, is an online tomography method that allows for state tracking, updates the estimated density matrix dynamically from the very first measurements, is computationally efficient, and converges to a good estimate quickly even with very noisy data. The algorithm is controlled via a single parameter, its learning rate, which determines the performance and can be tailored in simulations to the individual experiment. We present an experimental implementation of MEG tomography on a qutrit system encoded in the transverse spatial mode of photons. We investigate the performance of our method on stationary and evolving states, as well as significant environmental noise, and find fidelities of around 95% in all cases.
Wiener model identification of a pH plant with ELM

A methodology of identification in two stages is applied to model a nonlinear process with the block-oriented modelling structure. We find a Wiener model using a network Extreme Learning Machine to obtain the nonlinear block and Laguerre filters to represent the linear block. The methodology is applied to a pH neutralization plant, finding very good performance results. Insights in select some parameters to get better results are highlighted.
Deepfake Generation, Detection and Datasets: a Rapid-review

Deepfake is the general expression of fake images or videos. Images and videos created with Deep Neural Networks (DNN) can cause both personal and national problems. Deepfake technology has been making progress day by day. Accordingly, both the detection methods and the diversity of the data sets to be trained are increasing. In this article, datasets created in deepfake technology and applications that use datasets for deepfake detection are evaluated. The data sets used are produced with different models. There exist both traditional and deep learning-based detection methods for deepfake detection. Due to the recent malicious use of deepfake technology, detection methods and methods to be used in applications are of great importance. When the detection methods are compared, the accuracy of the deep learning-based detection method is higher for each data set. The method that provides the highest accuracy among two different data sets is the Xception method.
Oversquashing in GNNs through the lens of information contraction and graph expansion

The quality of signal propagation in message-passing graph neural networks (GNNs) strongly influences their expressivity as has been observed in recent works. In particular, for prediction tasks relying on long-range interactions, recursive aggregation of node features can lead to an undesired phenomenon called “oversquashing”. We present a framework for analyzing oversquashing based on information contraction. Our analysis is guided by a model of reliable computation due to von Neumann that lends a new insight into oversquashing as signal quenching in noisy computation graphs. Building on this, we propose a graph rewiring algorithm aimed at alleviating oversquashing. Our algorithm employs a random local edge flip primitive motivated by an expander graph construction. We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy. Synthetic experiments show that while our algorithm in general has a slower rate of expansion, it is overall computationally cheaper, preserves the node degrees exactly and never disconnects the graph.
Benign and Malignant Breast Mass Detection and Classification in Digital Mammography: The Effect of Subtracting Temporally Consecutive Mammograms

Breast cancer remains one of the leading cancers worldwide and is the main cause of death in women with cancer. Effective early-stage diagnosis can reduce the mortality rates of breast cancer. Currently, mammography is the most reliable screening method and has significantly decreased the mortality rates of these malignancies. However, accurate classification of breast abnormalities using mammograms is especially challenging, driving the development of Computer-Aided Diagnosis (CAD) systems. In this work, subtraction of temporally consecutive digital mammograms and machine learning were combined, to develop an algorithm for the automatic detection and classification of benign and malignant breast masses. A private dataset was collected specifically for this study. A total of 196 images were gathered, from 49 patients (two time points and two views of each breast), with precisely annotated mass locations and biopsy confirmed malignant cases. For the classification, ninety-six features were extracted and five feature selection techniques were combined. Ten classifiers were tested, using leave-one-patient-out and 7-fold cross-validation. The classification performance reached 91.7% sensitivity, 89.7% specificity and 90.8% accuracy, using Neural Networks, an improvement, compared to the state-of-the-art algorithms that utilized sequential mammograms for the classification of benign and malignant breast masses. This work demonstrates the effectiveness of combining subtraction of temporally sequential digital mammograms, along with machine learning, for the automatic classification of benign and malignant breast masses.
Permutation Codes for Correcting a Burst of at Most $t$ Deletions

Codes over permutations have received significant attention due to their applications in memory devices and powerline transmission systems. In this paper, we construct a family of permutation codes that are capable of correcting a burst of at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> deletions with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\log n+\mathcal{O}(\log\log n)$</tex> bits of redundancy where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> is a constant, which significantly improves upon the state-of-the-art construction with redundancy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2t\log n$</tex> bits.
On Sample Complexity of Learning Shared Representations: The Asymptotic Regime

Federated learning and multi-task learning have achieved great successes in recent works, which exploit the knowledge contained in different tasks to alleviate the influence of lacking training samples. In order to understand the knowledge utilization mechanism, we study the sample complexity of the federated learning algorithms by computing their expected population risks, where the asymptotic regime is considered that the number of tasks is large. Specifically, we focus on several typical algorithms, including (i) training personalized models merely by local data, (ii) jointly training a single model by all the data of devices, and (iii) jointly training the shared feature representation and learning individual classifiers of different devices, where the latter one is recently widely-adopted. The results reveal that the validity of the shared representation algorithm is affected by the task similarities, the model dimensionality, and the sample size, which quantify the sample size range where such algorithm is preferable. Moreover, we develop a new algorithm in consideration of collaborating the classifiers of different devices under the shared representations, which can have lower sample complexity than conventional methods under the asymptotic regime of large sample size. This provides theoretical guidance for practical algorithm designs.
Evaluating interpretations of deep-learned error-correcting codes

Deep-learned error-correcting codes have recently been shown to match and sometimes improve upon analytically derived codes in certain regimes. One such code, TurboAE, mimics the structure of a Turbo code. In a recent paper, we interpreted the encoder of TurboAE and matched it with a BCJR decoder, creating a complete interpretation of the TurboAE code. In this paper, we expand on that work and study various combinations of deep-learned and interpretable encoders and decoders with the goal of evaluating the interpretations and identifying features of the code that benefit from deep learning. In doing so we pursue a novel direction in Explainable AI (XAI) research, whereby different components of a deep-learned system can be replaced with their interpretable counterparts to better understand aspects of the original system. The initial observations based on a single code suggest that further similar studies for other codes might be of interest. Our experiments on the deep-learned TurboAE suggest the following: the nonlinear nature of two out of three TurboAE constituent encoders does not seem to play a role, and neither do distinct encoding functions used for the boundary bits. We also observe that TurboAE performs poorly if it is converted to a recursive systematic code, to the point that it trails a randomly generated convolutional code. Further, while TurboAE trained on the AWGN channel performs remarkably well on the Additive T-noise channel, it is outperformed when traditional codes use the correct channel model for decoding. A discussion that attempts to provide some insight into these results is included.
How to Charge Lightning: The Economics of Bitcoin Transaction Channels

Off-chain transaction channels represent one of the leading techniques to scale the transaction throughput in cryptocurrencies. However, the economic effect of transaction channels on the system has not been explored much until now. We study the economics of Bitcoin transaction channels, and present a framework for an economic analysis of the lightning network and its effect on transaction fees on the blockchain. Our framework allows us to reason about different patterns of demand for transactions and different topologies of the lightning network, and to derive the resulting fees for transacting both on and off the blockchain. Our initial results indicate that while the lightning network does allow for a substantially higher number of transactions to pass through the system, it does not necessarily provide higher fees to miners, and as a result may in fact lead to lower participation in mining within the system.
Byzantine Resilience With Reputation Scores

Training distributed machine learning algorithms is prone to Byzantine attacks where the adversarial workers send corrupted model updates to derail the training. In this paper, we propose a reputation score based gradient aggregation as a possible solution. We introduce two novel stochastic gradient descent algorithms, ByGARS (Byzantine Gradient Aggregation using Reputation Scores) and By <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{GARS++}$</tex> that involve computing reputation scores (of workers) using an auxiliary dataset at the server. These reputation scores are then used for aggregating the gradients (model updates) at the server. Under reasonable assumptions, we show that using these reputation scores is robust to any number of adversaries and prove the convergence of By <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{GARS++}$</tex> for strongly convex objective functions using results from two-timescale stochastic approximation theory. The computational complexity of By <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{GARS++}$</tex> is the same as the usual distributed stochastic gradient descent method with only an additional inner product computation in every iteration. We also demonstrate the effectiveness of the algorithms for non-convex learning problems using MNIST and CIFAR-10 datasets against almost all state-of-the-art Byzantine attacks.
Finding the Optimum Horizon for Short-Term Solar Irradiance Forecasting Using Long Short-Term Memory (LSTM) Network

The emerging negative effects of non-renewable fuels have compelled many nations to promote using Renewable Energy Sources (RES). Because it is affordable and readily available, solar power is among the RES that has achieved acceptance globally. Solar energy is, however, infamously unpredictable. This is why research fields that involve predictive analysis continue to have a considerable demand for improving the accuracy of global solar radiation prediction. Although Artificial Intelligence (AI) models have been quite helpful, researchers are still developing algorithms that can produce the least amount of prediction errors. This paper presents a successful improvement in estimating solar irradiance using the Long Short-Term Memory (LSTM) network. The input dataset used consisted of historical Cape Town meteorological data collected at five different horizons. Results obtained show that LSTM performed better than the Support Vector Regression (SVR) model, with an nRMSE value of 2.4 percent at 5-minute intervals of prediction. It is suggested that the real-time application of these findings to the operation of solar power plants in the study location produce reliable electricity for the consumers.
Research on Path Planning Method of Intelligent Robot Based on A* Algorithm

With the development of science and technology, a new handling method has emerged in the industrial automation industry - handling robots. Handling robot vehicles are called AGVs (Automatic Guided Vehicles), which are multi-wheel drive robots. Produced by the interdisciplinary application, it integrates automatic control, mechanical devices, image transmission and processing, computer communication, electronic information and other disciplines and technologies. It is widely used in various aspects. The path planning and obstacle avoidance problem is the leading research problem in applying the handling robot system. It is of great theoretical and practical significance to study the handling robot's path planning and obstacle avoidance algorithm. In this paper, the grid method is used to simulate the environment of the formal path, and the A* algorithm is used to improve the algorithm. We establish the global congestion model and local collision model of the robot and establish the dynamic path planning problem of the robot. At the same time, the global congestion problem is done Analysis and processing, complete the anti-collision prediction and automatic obstacle avoidance strategy analysis, and use the A* algorithm to improve the ant colony algorithm results for comparison.
Research on Database Encryption Technology of Industrial Network Monitoring System

This paper mainly studies a database encryption system and technology. The scheme mainly includes: the expansion model of the database system is based on whether the database system operation requirements contain sensitive data, and the relevant data contained in the database system operation requirements Appropriate information management is made for the data information, and the operation requirements of the database system and related data information are sent to the database system; the key management module expands the template to the database system according to the requirements of the expansion model of the database system Returns the encryption key. This method can meet the requirements of scalability, concurrency and stability of the server software of the industrial remote monitoring system, and meet the various requirements of the industrial remote monitoring system in terms of function and performance.
A Vision Perception Module on Quadruped Robot in Metro Inspection

Metro inspection is important but costly. This work aims to achieve intelligent metro inspection by equipping a vision perception module on the quadruped robot to save labor costs. In this paper, the implementation of detection methodologies of fire extinguishers as well as other specific objects in metro carriages by quadruped robot was studied. A simulated virtual metro scenario is built to complete the collection of the dataset. In total, there are 33542 images of training data and 1151 images of validation data in the experiment. The PP-YOLO algorithm is employed to complete the object detection function on a quadruped robot and a pruning approach is used to lighten the model in order to accommodate the memory and computing power requirements of the quadruped robot. The algorithm is then integrated on Jetson Xavier NX module which is embedded on the quadruped robot. Finally, the metro inspection is proceeded with the SLAM function. The experimental results show that the detection accuracy of the algorithm reached 97% while the detection speed reached a frame rate of 30 fps.
Research on On-Site Security Management and Control Technology Based on Smart Wearable Devices

This paper designs a substation automation remote operation and maintenance architecture based on the general service protocol of the power system, and establishes an information exchange framework between the main and substations. The whole-process in-depth security management and control system architecture covering the communication layer, the protocol layer and the application layer is designed, and the whole-process management and control process oriented to the service life cycle is proposed. The wireless wearable device can feed back the information of the human body and the environment to the self-organized network, and can monitor all aspects of the power construction site information at any time, ensuring the safety of the power construction site. Precise positioning to ensure the safety of staff. Finally, according to the changeable characteristics of human posture, action and viewing angle, this paper designs a HOG feature extraction algorithm based on human body. In view of the low running rate and low detection performance of the traditional SVM classifier, an improved cascade is adopted. The Adaboost classifier is used for training, learning and classification, which greatly improves the operation efficiency and detection performance of the original algorithm.
Improved GAN Model for Image Animation

Image-to-image translation is a meaningful and challenging task in computer vision and artistic style transfer. It aims to learn a function which can transfer an image with other style. Animation of real human face photos is one of the most popular image-to-image tasks. Our paper proposed a model which was based on U-GAT-IT for human face image animation task. U-GAT-IT is a GAN-based model which can efficiently transfer the style of picture from different domains. It uses a novel method to implement unsupervised image-to-image translation. Furthermore, detecting face key points is one of the important steps to generate animated face images. Using DLIB is an efficient way to achieve this. However, when faces were occluded by something such as hands and masks, the results were terrible. In our model, a practical facial landmark detector (PFLD) was selected to locate human faces. As PFLD has a special loss function, this model can direct human faces more accurately. Moreover, this change enables to realize the target of image-to-image even when faces were blocked. It could validly distinguish between human face and veils. The experimental results show that our model can obtain good animation effect, and can get high-quality generation results even when the face is occluded.
Power Grid Inspection Image Denoising for Mixed Poisson-Gaussian Noise

In order to solve the influence of mixed Poisson- Gaussian noise (MPGN) on power grid inspection image, an image denoising algorithm based on generalized Anscombe trans-form (GAT) is proposed to improve the details of the image. Since the actual image noise distribution is unknown, the Gaussian noise variance is a necessary parameter of the GAT. Accurate estimation of Gaussian noise variance becomes one of the keys to improve denoising performance. Therefore, a fast Gaussian noise variance estimation method is adopted. After performing the GAT, we adopt the Stein's unbiased risk estimator-linear expansion of threshold (SURE-LET) method for one-pass image denoising considering the computational cost and time cost of practical applications. Finally, inverse GAT is used to obtain the denoised image. Experimental results show that the proposed method achieves higher PSNRs and SSIMS, and can significantly improve the visual effect of power grid patrol image.
Single-Server Delegation of Small-Exponent Exponentiation from Quasilinear-Time Clients and Applications

Motivated by reducing the cost of public-key encryption on resource-constrained devices, we investigate the problem of delegating operations in cryptography schemes from client devices that only perform quasilinear-time or lower-order computations (e.g., modular additions and subtractions, multiplications with a small modulus, etc.) to a single, possibly malicious, server. Almost all of previous work considered clients capable of computing higher-order operations, such as fully homomorphic encryption, group exponentiations or several group multiplications with a large modulus. We show the (first in this model) single-server protocols to efficiently delegate the computation of small-exponent ring exponentiation, while satisfying desirable result correctness, input privacy and result security requirements. Small-exponent exponentiation is part of, for instance, the exponential El Gamal encryption algorithm. The asymptotic improvements in our delegation protocols are also backed up by concrete implementation results, showing that the improvements hold also for parameter values of practical interest. As an application example, we show that our protocols can be used to delegate encryption in the exponential El Gamal public-key cryptosystem, which can in turn be used for confidential transfer of, for instance, the average of multiple data values measured by a group of IoT client devices, with remarkable communication efficiency (i.e., a single public-key encryption ciphertext for each group).