Reviewerly/exHarmony huggingface dataset:
Greedy Centroid Initialization for Federated K-means

K-means is a widely used data clustering algorithm which aims to partition a set of data points into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> clusters through finding the best <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> centroids representing the data points. Initialization plays a vital role in the traditional centralized K-means clustering algorithm where the clustering is carried out at a central node accessing the entire data points. In this paper, we focus on K-means in a federated setting, where the clients store data locally, and the raw data never leaves the devices. Given the importance of initialization on the federated K-means algorithm, we aim to find better initial centroids by leveraging the local data on each client. To this end, we start the centroid initialization at the clients rather than at the server, which has no information about the clients' data initially. The clients first select their local initial clusters, and they share their clustering information (cluster centroids and sizes) with the server. The server then uses a greedy algorithm to choose the global initial centroids based on the information received from the clients. Numerical results on synthetic and public datasets show that our proposed method can achieve better and more stable performance than three federated K-means variants, and similar performance to the centralized K-means algorithm.
Teaching Reinforcement Learning Agents via Reinforcement Learning

In many real-world reinforcement learning (RL) tasks, the agent who takes the actions often only has partial observations of the environment. On the other hand, a principal may have a complete, system-level view but cannot directly take actions to interact with the environment. Motivated by this agent-principal capability mismatch, we study a novel “teaching” problem where the principal attempts to guide the agent's behavior via implicit adjustment on her observed rewards. Rather than solving specific instances of this problem, we develop a general RL framework for the principal to teach any RL agent without knowing the optimal action a priori. The key idea is to view the agent as part of the environment, and to directly set the reward adjustment as actions such that efficient learning and teaching can be simultaneously accomplished at the principal. This framework is fully adaptive to diverse principal and agent settings (such as heterogeneous agent strategies and adjustment costs), and can adopt a variety of RL algorithms to solve the teaching problem with provable performance guarantees. Extensive experimental results on different RL tasks demonstrate that the proposed framework guarantees a stable convergence and achieves the best tradeoff between rewards and costs among various baseline solutions.
Differentially Private Community Detection over Stochastic Block Models with Graph Sketching

There has been significant recent progress in under-standing the fundamental limits of community detection when the graph is generated from a stochastic block model (SBM). In this paper, we study the community detection problem over binary symmetric SBMs while preserving the privacy of the individual connections between the vertices. We present and analyze the associated information-theoretic tradeoff for differentially private exact recovery of the underlying communities through deriving sufficient separation conditions between the intra-community and inter-community connection probabilities while taking into account the privacy budget and graph sketching as a speed-up technique to improve the computational complexity of maximum likelihood (ML) based recovery algorithms
A Comprehensive Study of Gradient Inversion Attacks in Federated Learning and Baseline Defense Strategies

With a greater emphasis on data confidentiality and legislation, collaborative machine learning algorithms are being developed to protect sensitive private data.Federated learning (FL) is the most popular of these methods, and FL enables collaborative model construction among a large number of users without the requirement for explicit data sharing.Because FL models are built in a distributed manner with gradient sharing protocol, they are vulnerable to "gradient inversion attacks," where sensitive training data is extracted from raw gradients.Gradient inversion attacks to reconstruct data are regarded as one of the wickedest privacy risks in FL, as attackers covertly spy gradient updates and backtrack from the gradients to obtain information about the raw data without compromising model training quality.Even without prior knowledge about the private data, the attacker can breach the secrecy and confidentiality of the training data via the intermediate gradients.Existing FL training protocol have been proven to exhibit vulnerabilities that can be exploited by adversaries both within and outside the system to compromise data privacy.Thus, it is critical to make FL system designers aware of the implications of future FL algorithm design on privacy preservation.Motivated by this, our work focuses on exploring the data confidentiality and integrity in FL, where we emphasize the intuitions, approaches, and fundamental assumptions used by the existing strategies of gradient inversion attacks to retrieve the data.Then we examine the limitations of different approaches and evaluate their qualitative performance in retrieving raw data.Furthermore, we assessed the effectiveness of baseline defense mechanisms against these attacks for robust privacy preservation in FL.
Net Load Forecast based on Behind-the-Meter Disaggregation of Smart Meter Data

As the penetration of behind-the-meter (BTM) rooftop solar energies continues to increase in power distribution systems, it is of paramount importance for load serving entities and system operators to forecast net loads in the system. In this paper, novel algorithms for training high-performance predictors for day-ahead net load forecasting are developed. Importantly, the overall method only utilizes metered net load data and does not require any monitoring data of solar generation. Methodologically, the net load data trace is disaggregated into estimated BTM solar and load traces, based on which separate predictors are then trained for solar generation and load forecasting exploiting their distinct natures, respectively. For solar generation forecasting, time data, weather forecast, and potentially solar irradiance forecast are used as input features of the predictor. For load forecasting, time data, weather forecast, and judiciously chosen load data in the recent past are used as input features of the predictor. The two predictors' outputs are combined to produce the final net load forecast. The developed method is comprehensively evaluated based on two real-world smart meter data sets from Ithaca, NY and Clifton park, NY, respectively. High accuracy of day-ahead net load forecast is demonstrated.
Stochastic Mean-Shift for Speaker Clustering

This work is a continuation of our previous work on short segments speaker clustering. We have shown that mean-shift clustering algorithm with probabilistic linear discriminant analysis (PLDA) score as the similarity measure, can be a good approach for this task. While the standard mean-shift clustering algorithm is a deterministic algorithm, in this work we suggest a stochastic version to train the mean-shift. The quality of the clustering is measured by the value K, which is a geometric mean of average cluster purity (ACP) and average speaker purity (ASP). We test the proposed algorithm in the range of 3 to 60 speakers and show that it outperforms the deterministic mean-shift in all cases.
Research on Heterogeneous Data Consistency Comparison System Based on Computer Mobile Database

This paper introduces heterogeneous redundant function executor based on mimicry defense technology in router architecture. Through dynamic scheduling mechanism, the system randomly selects multiple heterogeneous executor jobs. The database space management architecture is designed and the consistent hash algorithm is applied to the constructed database space management architecture. The output results of multiple heterogeneous function performers are compared under the same external excitation. In this paper, the abnormal detection of the function executor is carried out. The feasibility and effectiveness of the method are verified by experiments. This method can provide a reference for 3D model conversion between various heterogeneous systems.
Research on EEG signal emotion recognition based on improved GAPSO-SVM

This study improves and combines genetic algorithm (GA) and particle swarm optimization (PSO), optimizes the penalty parameter C and kernel function parameter g of support vector machine (SVM), and proposes an emotion classification and recognition model of EEG based on improved GAPSO-SVM. The classification accuracy of γ band energy data was up to 88.8889 %, and the classification accuracy of SampEn data was up to 77.7778 %.
Attribute prediction of Pokémon images based on convolutional neural network

Image recognition has long been a fundamental research problem in computer vision, where the goal is to predict the class of a given image. Existing researches have focused on the category level while pay no attention on predicting object fine attribute information of object. To this end, in this paper, in order to investigate the classification effect of convolution neural network on Pokémon image data set, we use convolution neural network to classify the attributes of Pokémon, which can judge the attributes of a certain elf by its image. We used the pictures of the Pokémon in a scorching game "Pokémon " on the market as our data set. Specifically, we first a structure of the convolutional neural network and apply it to the Pokémon image data set, which can realize automatic recognition and attribute classification of elf images. Then the data enhancement method is used to preprocess the image data, which is combined with a convolutional neural network to improve the effect of attribute recognition and classification. Results show the effectiveness of our proposed method.
Multi-label Image Transient Background Information Recognition Based on Graph Convolutional Network

Transient background label recognition is an important computer vision application technology in daily life. However, most of the previous work regards transient background recognition as a single label classification task. Due to the complex co-occurrence dependencies between different transient background labels, single-label classification cannot fully describe the image background information. In this paper, we propose a new end-to-end attention-driven memory graph convolutional network (AM-GCN) model for multi-label image transient background information recognition. AM-GCN adopts the content-aware categories generated by semantic attention module (SAM), and obtains rough label dependencies of transient background through the static graph convolution network (S-GCN). Finally, the dynamic graph convolution network (D-GCN) obtains accurate classification information. Compare to other models, this model results in better performance by the experiment of AM-GCN on the transient background label dataset.
Research on intention management and intention switching technology of task-based dialogue based on hierarchical attention network

In the context of informatization, the electric power field is also undergoing continuous digital transformation. In the face of the redundancy problem in daily operation and maintenance and customer service, an intelligent and efficient dialogue system can play an essential role in it. In the dialogue system, task-oriented multi-round dialogue for complex scenes is the focus of research. For an actual conversation scenario, the system should first identify the user's intention and give feedback according to the identification results. At the same time, there are situations where users ask multiple questions simultaneously. Maintaining multiple intentions and judging the current conversation's intention attribution based on the corresponding conversation history is the key to whether the system can cover more conversation scenarios. Aiming at the problems in the current task-based dialogue system, this paper studies intention management and intention switching methods. In terms of solution strategy, this paper turns the problem into a semantic matching problem and uses a hierarchical attention network to fuse the dialog history and current input to achieve accurate intention matching. The experimental results show that this method effectively manages intention and switching tasks.
A Study on Improved Face Recognition Method Based on Multi-Detection Network and Facial Abnormal Part Beautification Technique

This paper designs a modified face recognition method based on RetinaFace and RetinaNet fusion networks and facial abnormal part beautification technology to address the privacy protection issues involved in face recognition technology. The method uses fusion networks and face feature point localization to achieve the extraction of the implied pose information and the localization of facial skin abnormal targets for facial abnormality recognition. After extracting the coordinates of facial abnormal parts, the parts are replaced by using small face elements, and at the same time, for skin abnormalities, the image local beauty method based on the inverse cut Gamma light compensation is used to beautify the abnormal parts of the face. Finally, this paper implements face recognition based on the modified FaceNet with the hybrid attention mechanism CBAM. The improved face recognition method designed in this paper ensures the smooth operation of the basic functions of face recognition, achieves the beautification of abnormal facial parts and data privacy protection based on information encryption technology and abnormal part beautification algorithm, and avoids the problem of face information leakage during the application of face recognition technology.
An Improved Pointer Chinese Text Summarization Model

The pointer generation network solves the problem of unregistered words in generative text summaries by extending unregistered words directly into the dictionary to enable the copying of unregistered words from the input sequence to the generated sequence. However, it suffers from the problems of insufficient semantic understanding, low accuracy rate and repetition of summary words. To address these problems, a pointer generation network improvement model is proposed, which firstly introduced a self-attention mechanism to obtain more contextual semantic information; secondly introduced Convolutional Gated Unit to extract and filter the features of contextual semantic information; finally proposed a coverage mechanism to reduce the re-attention of the same words and solve the problem of word repetition in the generation sequence. The experimental results on the LCSTS text summarization dataset show that the improved model obtains higher ROUGE scores, the accuracy and readability of the generated summaries are improved.
An oil painting image retrieval strategy based on feedback mechanism

Image retrieval is mainly a text-based image retrieval technology, which uses text description to describe the characteristics of images, such as the author, age, genre and size of paintings. Later, image retrieval techniques that analyze and retrieve the content semantics of images, such as color, texture, layout, etc. Feedback technology has become an important technology to improve retrieval efficiency in content-based image retrieval. However, in oil painting image retrieval, there is a huge semantic difference between high-level semantics and low-level features. The traditional relevance feedback technology requires multiple feedbacks to obtain satisfactory results, which makes the retrieval task of users time-consuming and cumbersome. In order to further improve the accuracy of retrieval, many systems combine relevant feedback technology to collect the feedback information of users on retrieval results. What they achieve is a gradual refinement of image retrieval process. In the same retrieval process, they need to constantly interact with users. Therefore, this paper proposes a retrieval technology of oil painting image through the feedback mechanism of oil painting image retrieval. On the one hand, the analysis and transformation of user needs form questions that can be retrieved from the index database. On the other hand, collect and process oil painting image resources, extract features, analyze and index, and establish image index database. The last aspect is to calculate the similarity between user questions and records in the index database according to the similarity algorithm, extract records that meet the threshold as the result, and output them in descending order of similarity.
Mask Wear Detection Algorithm Based on YOLOv5m

Wearing a mask during the COVID-19 epidemic can effectively prevent the spread of the virus. In view of the problems of small target size, crowd blocking each other and dense arrangement of targets in crowded places, a target detection algorithm based on the improved YOLOv5m model is proposed to achieve efficient detection of whether a mask is worn or not. This paper introduces four attention mechanisms in the feature extraction network based on the YOLOv5m model to suppress irrelevant information, enhance the information representation of the feature map, and improve the detection capability of the model for small-scale targets. The experimental results showed that the introduction of the SE module increased the mAP value of the original network by 9.3 percentage points, the most significant increase among the four attention mechanisms. And then a dual-scale feature fusion network is used in the Neck layer, giving different weights to the feature layers to convey more effective feature information. In the image pre-processing, the Mosaic method was used for data enhancement, and the CIoU loss function was used for coordinate frame positioning in the prediction layer. Experiments on the improved YOLOv5m algorithm demonstrate that the mean recognition accuracy of the method improves by 10.7 percentage points over the original method while maintaining the original model size and detection speed, and better solves the problems of small scale, dense arrangement and mutual occlusion of targets in mask wearing detection tasks in crowded places.
Optimization of Vehicle Blind Zone Monitoring (BSD) Evaluation Scheme Based on Image Processing and Radar Ranging Algorithm

In recent years, the country has gradually paid attention to the intelligent and connected automobile industry, from emergency driving to the current auxiliary driving, the blind spot detection system is also gradually upgraded. For the early warning BSD system, how to have a better alarm signal definition and a higher accuracy is the basis of upgrade direction. In the actual driving process, the change in road conditions and the driver’s negligence make the BSD system has the upgrade space. First of all, according to the BSD test of GB / T 39265-2020, evaluates its rationality and propose an improvement plan. The evaluation scheme of key functions that can be applied to blind spot monitoring in L2 driving assistance is designed to effectively guarantee the further standardization and upgrading of the blind spot detection system.
The Comparison and Analysis of Autonomous Food Delivery Robot Based on Artificial Potential Field and Breadth-First Search Methods

The autonomous food delivery robot, as one of the most popular products of Artificial Inteligence, is to plan the path accurately from the chef to the customer’s table while avoiding unnecessary collisions with the obstacles like tables, chairs and pedestrians. This paper makes a comparative analysis of the path planning technology in the process of robot food delivery in the restaurant environment. Firstly, the restaurant environment is analyzed and simulated in Webots. Then, according to the environmental requirements, the effects of Artificial Potential Field(APF), improved APF and Breadth- First Search (BFS) on path planning and obstacle avoidance are compared and analyzed respectively. Subsequently, the whole scene is simulated in Webots and the comparison is generated based on the operation time, traveling distance, completeness, and the smoothness of the robot’s motion. Finally, by comparing the experimental results, the advantages and disadvantages of each algorithm are analyzed. It is concluded that in small-scale restaurant environment, BFS algorithm will achieve better results in route planning.
Quantum Pattern Classification in a Three-Qubit System

The problem of pattern classification in quantum data has been of great importance over the past few years. This study investigates the effect of deploying Grover’s, the partial diffusion, and the fixed-phase algorithms separately to amplify the amplitudes of a desired pattern in an unstructured dataset. These quantum search operators were applied to symmetric and antisymmetric input superpositions on a three-qubit system for 20 iterations each. After each iteration, different probabilities of classification were calculated in order to determine the accuracy of classification for each of the three quantum search operators. The results indicated that, in the case of applying the three quantum search operators to incomplete superposition input states, the partial diffusion operator outperformed the other operators with a probability of correct classification that reached 100% in certain iterations. It also showed that the classification accuracy of the fixed-phase operator exceeded the accuracy of the other two operators by 40% in most cases when the input state was a uniform superposition, and some of the basis states were phase-inverted.
Deep Spread Multiplexing and Study of Training Methods for DNN-Based Encoder and Decoder

We propose a deep spread multiplexing (DSM) scheme using a DNN-based encoder and decoder and we investigate training procedures for a DNN-based encoder and decoder system. Multiplexing for multiple orthogonal resources is designed with an autoencoder structure, which originates from the deep learning technique. Furthermore, we investigate training methods that can leverage the performance in terms of various aspects such as channel models, training signal-to-noise (SNR) level and noise types. The performance of these factors is evaluated by training the DNN-based encoder and decoder and verified with simulation results.
Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior

Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance. Recent works further incorporate different regularization terms to enhance the performance of DIP and successfully show notable improvements. However, most DIP-based methods for HSI denoising rarely consider the distribution of complicated HSI mixed noise. In this paper, we propose the asymmetric Laplace noise modeling deep image prior (ALDIP) for HSI mixed noise removal. Based on the observation that real-world HSI noise exhibits heavy-tailed and asymmetric properties, we model the HSI noise of each band using an asymmetric Laplace distribution. Furthermore, in order to fully exploit the spatial–spectral correlation, we propose ALDIP-SSTV, which combines ALDIP with a spatial–spectral total variation (SSTV) term to preserve more spatial–spectral information. Experiments on both synthetic data and real-world data demonstrate that ALDIP and ALDIP-SSTV outperform state-of-the-art HSI denoising methods.