Reviewerly/exHarmony huggingface dataset:
CP-ABE with multi-Authorization Centers and Supporting Outsourcing Encryption and Decryption

The traditional ciphertext-policy attribute-based encryption (CP-ABE) has the problems of poor security of key distribution by a single attribute authorization center and too much calculation on the client in the process of encryption and decryption. A CP-ABE scheme that can outsource encryption and decryption and support multi-authorization centers is introduced to solve the above two problems. In the key generation stage, the user's private key is generated by the attribute authorization center and the key generation center jointly executing the two-party secure computing protocol; In the encryption and decryption stage, the cloud encryption server and cloud storage server are used to handle most of the computing work. Security proof and performance analysis show that the scheme not only can effectively make up for the defect of all key leakage when the attribute authorization center is broken, but also can enhance the security of the system; Moreover, after using the cloud server to process data, users only need to perform a simple calculation on the client to complete encryption or decryption, thus reducing the user's computing workload.
Evaluation of Methods for Estimating Noise Floor of MHD Angular Rate Sensors in Operation

Noise floor plays a key role in evaluating the quality of MHD Angular Rate Sensors (MHD-ARSs). It determines the smallest angular rate signals that can be detected. Consequently, how to measure the noise floor accurately has become a critical issue. To the best of our knowledge, little research has been conducted into the noise floor measurement of MHD-ARSs. Moreover, noise floor estimators which have been used are not clearly defined and assessed. In this research, we aim at the evaluation of two methods for estimating noise floor of MHDARSs in operation, namely two-channel method and three-channel method. We present mathematical models and derive bias errors of both two methods. We set up a noise floor measurement system for two identical MHD-ARSs and test the two methods under the condition in which the two sensors operate in a high output SNR regime. We have obtained significant results demonstrating that: 1) Normalized bias error of two-channel method is approximately 80% to 100% in the case of SNR value greater than 10 dB. 2) Three-channel method exhibits no bias errors in theory, but it suffers from random errors originated from normalized random errors of PSD estimates.
Research and Application of Identification Method for Production and Shutdown Status of High-Risk Enterprises Based on Power Big Data

Safety accidents in high-risk industries have deep negative impact and great economic loss, which has always been a concern of the society, and the construction of urban emergency management system is urgent. Due to the large granularity and poor timeliness of existing regulatory measures, it is difficult to control the whole process of safe production of high-risk enterprises, and cannot meet the regulatory requirements of early warning and prediction and accident prevention. Power big data has the characteristics of full coverage and short data delay, which can be systematically monitored for enterprise safety production. However, high-risk industry production enterprises belong to characteristic difference is big, current technology cannot achieve high risk enterprises targeted variance analysis, cause widespread application lacks scientific support, how there may be a significant risk of safety in production for high-risk enterprises effectively mining and timely prevention is the key of the current need to study. Based on electric power big data for high-risk enterprise abnormal production production stop state recognition as the research object, put forward an improved algorithm based on k - means clustering enterprises produce abnormal state recognition method, through isolated forests for special subclass overlay, achieve enterprise safety production range biggest electricity utilization index threshold value and the minimum index threshold precision lock. The results of an example show that the model has high accuracy and differentiated universality, and can effectively reflect the production and shutdown status of enterprises and promote the construction of urban emergency management system.
Content-based Commodity Search Engine Using Convolutional Image Retrieval

Affected by the continuous advancement of science and technology, environmental changes, and the evolution of the times, the development of e-commerce has gradually become mainstream. The amount of commodities on the e-commerce platform is large. How to find the commodities the user is looking for is its most important function. The commodity search engine is the key to this function on the ecommerce platform. The traditional way which is the most common method of product retrieval is to use the keywords entered by the user to search for products in a large amount of data in text mode. However, to find the exact product in a large number of products through text retrieval rely on only text descriptions. There may exist semantic gaps among the input keywords, the user’s intention, and the description of the product itself. Using a content-based image retrieval system as the commodity searching engine, users may more easily find the product they want. We established a product search engine based on content-based image retrieval. After using the United Nations Standard Products and Services Code (UNSPSC) as the definition of product categories, the product images on momo shopping online are downloaded as the database for the learning of product classification and the target image database in retrieval. The learning for classification was based on the convolutional neural network to extract image features. Then, the extracted image features were used to match the similarities between the query product image and the target image database. The retrieval result using DenseNet had a mean average precision of 0.93. Using a deep convolutional neural network as the image retrieval base can achieve effective commodity search.
Radio-Emitting Objects State Recognition Based on Formal Systems under a Priori Uncertainty

On the basis of formal systems the problem of creating knowledge models for the task of recognition of radio-emitting objects and their states in terms of a priori uncertainty is considered. As a formal system the first order predicate calculus extended by aletic modal logic operators is used. The mechanism of logical inference is developed using a modified method of resolutions. The examples of construction of knowledge models, implemented with the use of expert system are given and experimental studies, which confirmed their effectiveness and high reliability of the results achieved, are carried out.
vGraph: Memory-Efficient Multicore Graph Processing for Traversal-Centric Algorithms

To lower the monetary/energy cost, single-machine multicore graph processing is gaining increasing attention for a wide range of traversal-centric graph algorithms such as BFS, SSSP, CC, and PageRank, of which the processing is relatively simple and the topology data (vertices and edges) dominates the memory footprint. This paper presents <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex> Graph, a NUMA-aware, memory-efficient multicore graph processing system for traversal-centric algorithms. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex> Graph proposes an ultralight NUMA-aware graph preprocessing scheme which eliminates almost all complex preprocessing steps and pipelines per-NUMA graph loading and compressing, to effectively reduce inter-NUMA memory accesses while keeping both preprocessing cost and peak memory footprint low. We further optimize <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex> Graph with effective HPC techniques including prefetching and work-stealing. Evaluation on a 384GB-memory, four-NUMA machine shows that compared to the state-of-the-art NUMA-aware/-unaware systems, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$v$</tex> Graph can process much larger real-world and synthetic graphs with various traversal-centric algorithms, achieving significantly higher memory efficiency and lower processing time.
A comparative analysis of the effectiveness of feature engineering techniques on thyroid disease prediction

The thyroid gland’s edge experiences an abnormal proliferation of thyroid tissue, which causes thyroid illness. The two primary types of thyroid disorders are hypothyroidism and hyperthyroidism which typically result when this gland releases excessive amounts of hormones. To identify and diagnose thyroid disease, this study suggests employing effective classifiers and feature selection strategies that consider accuracy and other performance evaluation measures. This study offers a thorough examination of various classifiers that includes the support vector machine, logistic regression, and extreme gradient boosting algorithms. The algorithms use three feature removal strategies that is recursive feature elimination, Pearson’s correlation and chi-squared statistics. To determine thyroid illness, thyroid data from the Kaggle datasets were used. Numerous aspects of the experiment have been evaluated and analyzed, including accuracy, precision, and the receiver operating curve’s area under the curve. The outcome showed that classifiers that use feature selection have a greater overall accuracy(Xtreme Gradient Boost 98%and support vector machine 95%) compared to without feature selection technique (support vector machine 89%). Logistics regression performed better without at 95% than 94% with feature selection.
VSGM: View-Based GPU-Accelerated Subgraph Matching on Large Graphs

Subgraph matching is a fundamental building block in graph analytics. Due to its high time complexity, GPU-based solutions have been proposed for sub graph matching. Most existing GPU-based works can only cope with relatively small graphs that fit in GPU memory. To support efficient subgraph matching on large graphs, we propose a view-based method to hide communication overhead and improve GPU utilization. We develop VSGM, a sub graph matching framework that supports efficient pipelined execution and multi-GPU architecture. Ex-tensive experimental evaluation shows that VSGM significantly outperforms the state-of-the-art solutions.
ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy. The key idea is to represent sets of vertices using probabilistic set representations such as Bloom filters. These representations are much faster to process than the original vertex sets thanks to vectorizability and small size. We use these representations as building blocks in important parallel graph mining algorithms such as Clique Counting or Clustering. When enhanced with ProbGraph, these algorithms significantly outperform tuned parallel exact baselines (up to nearly 50 x on 32 cores) while ensuring accuracy of more than 90% for many input graph datasets. Our novel bounds and algorithms based on probabilistic set representations with desirable statistical properties are of separate interest for the data analytics community. Proofs of theorems & more results: http://arxiv.org/abs/2208.11469
A Lightweight Broad Learning System for Wireless Traffic Prediction

Currently, traffic trends to grow explosively under the scenarios of advanced wireless communication networks and diversified services. How to design suitable model to accurately and efficiently predict wireless traffic has become a significant technical challenge. To get over this dilemma, this paper proposes a lightweight broad learning system (LBLS) for wireless traffic prediction. Specifically, the LBLS firstly adopts restricted Boltzmann machine (RBM) to perform feature extraction. Then, gated recurrent unit (G RU) is introduced into enhancement layer to describe temporal relations of multivariate time series. Finally, elastic-net considering both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> 1-norm and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{2}$</tex> -norm regularization is used to realize connection weight estimation. Experimental results on two typical traffic datasets show that the proposed LBLS can not only reduce the complexity of existing BLS and its variants, but also has strong modeling and prediction ability for wireless traffic prediction.
Local Area Network Based Collaboration Using Distributed Computing

Collab is an application that uses distributed computing techniques for effective, real-time collaboration over a Local Area Network (LAN) in either a home, educational, or workplace environment. Users can collaborate by joining a secure collaboration session in the absence of an internet connection. Within a session, users can share messages, digital content, and reviews. Most existing LANbased applications lack important collaboration features. It is against this backdrop that we propose the use of distributed computing techniques in a small-scale, serverless LAN to empower users to collaborate effectively in local teams. Thus, we design and implement a computer application that can be used for collaboration on a LAN in the absence of a centralised web service.
Blaze: Fast Graph Processing on Fast SSDs

Out-of-core graph processing is an attractive solution for processing very large graphs that do not fit in the memory of a single machine. The new class of ultra-low-latency SSDs should expand the impact and utility of out-of-core graph processing systems. However, current out-of-core systems cannot fully leverage the high IOPS these devices can deliver. We introduce Blaze, a new out-of-core graph processing system optimized for ultra-low-latency SSDs. Blaze offers high-performance out-of-core graph analytics by constantly saturating these fast SSDs with a new scatter-gather technique called online binning that allows value propagation among graph vertices without atomic synchronization. Blaze offers succinct APIs to allow programmers to write efficient out-of-core graph algorithms without the burden to manage complex IO executions. Our evaluation shows that Blaze outperforms current out-of-core systems by a wide margin on seven datasets and a set of representative graph queries on Intel Optane SSD.
Underwater Image Restoration Based on An Improved Underwater Image Formation Model

Underwater images often show characteristics of low contrast, low visibility and color distortion. To improve underwater image quality, a novel underwater image restoration algorithm is proposed in this paper. First of all, we put forward an underwater image restoration network (UIRnet) based on CNN(Convolutional Neural Network) for transmission estimation in the underwater imaging model. Besides, the Leaky ReLU(Rectified Linear Units) activation function and SENet(Squeeze-and-Excitation Networks) unit are used to improve the ability of extracting image features and training parameters. Secondly, the fuzzy patch dataset which is used as input dataset is synthesized by the underwater optical imaging model and randomly generated transmission. Meanwhile, the corresponding transmission value is used as label dataset. Then, we use the dataset to train and optimize all parameters in the UIRnet. Finally, the trained UIRnet is used in underwater image restoration. The experimental results show that the proposed algorithm can restore the details of the image, improve the contrast, and have a more natural visual effect.
Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing

Vehicular edge computing (VEC) is a promising technology to support real-time applications through caching the contents in the roadside units (RSUs), thus vehicles can fetch the contents requested by vehicular users (VUs) from the RSU within short time. The capacity of the RSU is limited and the contents requested by VUs change frequently due to the high-mobility characteristics of vehicles, thus it is essential to predict the most popular contents and cache them in the RSU in advance. The RSU can train model based on the VUs' data to effectively predict the popular contents. However, VU s are often reluctant to share their data with others due to the personal privacy. Federated learning (FL) allows each vehicle to train the local model based on VUs' data, and upload the local model to the RSU instead of data to update the global model, and thus VUs' privacy information can be protected. The traditional synchronous FL must wait all vehicles to complete training and upload their local models for global model updating, which would cause a long time to train global model. The asynchronous FL updates the global model in time once a vehicle's local model is received. However, the vehicles with different staying time have different impacts to achieve the accurate global model. In this paper, we consider the vehicle mobility and propose an Asynchronous FL based Mobility-aware Edge Caching (AFMC) scheme to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. Experimental results show that AFMC outperforms other baseline caching schemes.
Measurement-Device-Independent Three-party Quantum Secure Direct Communication Based on Entanglement Swapping

Three-party (3P) quantum secure direct communication (QSDC) enables two parties to directly transmit secure messages to the third party through quantum channels without keys. Previous 3P-QSDC protocols all require ideal measurement devices, which are difficult to realize under current experimental condition. For enhancing 3P-QSDC's security under practical imperfect measurement device condition, we propose the measurement-device-independent (MDI) 3P-QSDC protocol, which can resist all possible attacks from imperfect measurement devices. In this protocol, the parties construct the entanglement channel with the help of entanglement swapping. Our MDI-3P-QSDC protocol is unconditionally secure in theory. It has application potential in future quantum communication field.
Object Detection Model Based on Dual Visual Pathway

Object detection has made great progress with the development of deep learning. However, existing object detection algorithms still have limitations compared with human brain. In addition, the interpretability of the model becomes more and more inefficient with the increasing accuracy of the model. Most of the object detection models are considered as “black box” that cannot be explained. Considering that the visual information processing system is composed of two pathways spatially distributed in the ventral and dorsal sides of the cerebral cortex when the brain detects objects, this paper proposes object detection model based on dual visual pathway by simulating the process of detecting objects in the brain. Intra layer recursion and remote connections of different modules are added to the feedforward network to achieve feature extraction for image, since the ventral flow pathway is a recurrent network composed of loops and feedback. Experimental results carried out on PASCAL VOC show that the performance of object detection has been further improved, and the interpretability of the model has been enhanced.
Curiosity-driven Exploration in VizDoom

Efficient exploration remains one of the most challenging aspects of Reinforcement learning. The enormous number of studies devoted to this topic propose different complex methods for enhancing overall performance. For vanilla reinforcement learning algorithms, it can take a tremendous amount of time just to find out the right action, especially for sparse reward environments. One of the solutions that could improve the agent behavior is applying the effective exploration strategy architecture to the baseline methods. This research suggests the examination of several advanced exploration approaches, based on the curiosity bonuses idea. Intrinsic Curiosity Module (ICM) and Random Network Distillation (RND) exploration architectures are applied to the Asynchronous Advantage Actor-Critic (A3C) algorithm. The constructed models are validated in VizDoom environment. This study compares the implemented models with vanilla A3C and Deep Q-Networks algorithms and shows state-of-the-art results in the most complicated scenario Deathmatch.
Facial Recognition and Motion Tracking in Dynamic Environment by Drone Control Software

In this paper face recognition and the tracking process of the selected face are described. In the beginning, face recognition is detailed in a static environment, where different effective face recognition methods are shown, such as LBP and HOG combination. A method of selecting, following, and tracking a face in a dynamic environment is further discussed. For this, a CNN was used (trained) for correct recognition instead of the initially tested HOG method, as it proved more adaptable to shifts in perspective and lighting on faces. The methods have also been evaluated in practice using the drone described in chapters II.B, and III.
Attack Graph Implementation in Graph Database

Security Operation Centers (SOC) are essential to identify and analyze cyberattacks and defend networks from security incidents. Graph representations are frequently used nowadays in order to correctly identify vulnerabilities and weaknesses in the system, as well as attack paths and potential methods of defense. This paper demonstrates a Labeled Property Graph data model based on STIX that makes efficient storage and querying of security-related objects possible, including a method for importing a sample STIX data set into Neo4j graph database.
Multi-Agent Path Finding with Priority Transfer Accompanied with Moving Obstacles

In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph must reach their respective destinations without inter-agent collisions. In the MAPF problem, when agents plan a route, they compete for some resources on a specific route, and such contention sometimes causes deadlock. However, unpredictable obstacles like warehouses may appear in real scenes, making route planning difficult. Based on the existing MAPF algorithm, this paper proposes a new algorithm that relies on an adaptive prioritization scheme to pay effort to the adjacent movements of multiple agents in the presence of unpredictable and moving obstacles while avoiding collisions.
Parallel Compound Index Arrays for Flexible Classification

The goal of this research is to develop a new type of classification method that can be used to infer the value of any attribute of the dataset it has been trained on (given the values of the other attributes) without the need to retrain its model. For this reason, a new classifier named Parallel Compound Index Arrays (PCIA) is introduced that aims to achieve this by creating an association between the known attribute values of the training data and the appropriate training samples, which can be used to quickly find the sample that is the most similar to any given input data. Furthermore, the proposed implementation applies parallel computing procedures in order to enable the classifier process larger volumes of data. The efficacy of the proposed classifier is shown using 10 benchmark datasets, compared to other contemporary classification methods.