Reviewerly/exHarmony huggingface dataset:
Bandit-based Variable Fixing for Binary Optimization on GPU Parallel Computing

This paper explores whether reinforcement learning is capable of enhancing metaheuristics for the quadratic unconstrained binary optimization (QUBO), which have recently attracted attention as a solver for a wide range of combinatorial optimization problems. In particular, we introduce a novel approach called the bandit-based variable fixing (BVF). The key idea behind BVF is to regard an execution of an arbitrary metaheuristic with a variable fixed as a play of a slot machine. Thus, BVF explores variables to fix with the maximum expected reward, and executes a metaheuristic at the same time. The bandit-based approach is then extended to fix multiple variables. To accelerate solving multi-armed bandit problem, we implement a parallel algorithm for BVF on a GPU. Our results suggest that our proposed BVF enhances original metaheuristics.
Treasure hunt

The first U.S. nationwide geological survey in a generation could reveal badly needed supplies of critical minerals.
A spatiotemporal multi-channel learning framework for PCMA signal detection and recognition algorithm

Paired Carrier Multiple Access signal (PCMA) signals use the same frequency to transmit the modulation waveforms of both sides of the communication. With the characteristics of high-frequency band utilization and strong anti-interception ability, PCMA signals are widely used in military and important commercial communication systems. Blind separation of PCMA signals is an important part of radio spectrum monitoring, high-precision modulation parameter information is the necessary basis for separation. In order to process the PCMA signal normally, it is necessary to distinguish the PCMA signal from the common single-channel satellite digital modulation signal, and then identify the modulation mode of the PCMA signal. The detection and modulation identification of the PCMA signal provides the necessary guidance for parameter estimation and blind processing. In this paper, the detection and modulation identification of the PCMA signal with low SNR are implemented by using the multi-channel feature to extract the different feature information of the signal, adopting the idea of feature fusion, and using the time and space features of the signal simultaneously. Simulation results show that the proposed algorithm can be widely applied to the blind detection and modulation identification of the PCMA signal with BPSK, QPSK, 8PSK and their mixtures, and the classification performance is better than the traditional deep learning network based on CNN and the traditional detection modulation identification algorithm.
Research on Convolution Decomposition and Hardware Acceleration based on FPGA

Convolutional neural network (CNN) is an artificial intelligence algorithm with a wide application foundation. In recent years, to improve the computational speed of the computation-intensive CNN models, many researchers have accelerated hardware implementation on FPGA. However, the acceleration algorithms are mostly unfriendly to hardware or have poor flexibility, which leads to a decrease in computing power and an increase in data access. In this paper, we propose a convolution decomposition method in the time domain, which fully considers the behavior of convolution operation and the difference between data streams in hardware. Besides, we design a hardware acceleration architecture. The results show that this design can be compatible with convolution and full connection operations of various sizes and strides. Hardware tailoring and cascade expansion are proposed for different application scenarios. Experiments show that the computing performance per DSP is 0.39 GOP/s, which demonstrates that the hardware design has a better multiplier utilization than similar works.
A scheme of long short-term memory (LSTM) -CNN features extraction and spatial relationship to anonymize image and video for Privacy-Preserving Human Activity Recognition of multiple sequences of data

Safeguarding security from stowed away pictures and recorded recordings is a significant social issue. To store information related picture or video data the arrangement of human movement can be recognized for examining the different issues of exercises. And yet guarantee that it doesn’t record the significant video or catch the activities of human it is abusing the security of human. This article presents a crucial way to deal with tackling such clashing undertakings: acknowledgment of human movement while utilizing just mysterious low-goal recordings (Example: lSx16). We are presenting the Deep learning, CNN, Computer Vision OpenCV worldview, the calculation making sense of the insignificant picture transformation and to make a few low-goals of profoundly goal informative recordings from video. Our ideas is concentrating on different kinds of sub-pixel changes that are streamlined for arranging exercises, which permits the study hall expert to utilize existing high-goal recordings by bringing a few LR preparing recordings adjusted for this issue. The commonsense result results are affirmed that the OpenCV in PC models can profit from movement acknowledgment from very human action recordings. In this paper we are seeing as the negligible of human exercises from the pictures or recordings taken from the different public where the high goal can be distinguished and getting the exceptionally low goal and protection can be stowed away from such pictures or recordings.
A Feature Selection Method based on Sparse Regularization Deep Neural Network

Data dimensionality reduction is a technology that converts high-dimensional data into low-dimensional data. With the continuous growth of data dimensions, data dimensionality reduction is essential in both industrial applications and scientific research. Feature selection is an effective method to achieve dimensionality reduction. However, most existing feature selection methods can only be applied to small datasets, and they cannot be used in big datasets or have poor performance. In order to overcome this dilemma, A novel feature selection method based on sparse regularization deep neural network, called Sparse Regularization Deep Feature Selection (SRDFS), is proposed. SRDFS utilizes deep neural network and sparse regularization to realize feature selection, which has a good performance on big datasets. In addition, this paper proposes an optimization algorithm based on soft threshold function for sparse regularization. Finally, we compare the proposed method with the related state-of-the-art feature selection methods on six big datasets, and the experimental results show the effectiveness and superiority of the method.
An analysis of Software Maintainability Prediction Using Ensemble Learning Algorithms

Software Maintenance is a long process and is the longest phase in the software development life cycle. Once the software is developed and delivered, maintenance plays an important role in the success of the software. The prediction of the effort required for software maintainability would result in effective management. In this paper, we used ensemble machine learning techniques to predict accurate effort required to maintain a software. Given two datasets, we implemented various machine learning algorithms to predict accuracy. The results show that the prediction using ensemble learning methods is more accurate due to less error. The least error in prediction is obtained while using Gradient Boost Classifier. Therefore, a more accurate prediction is obtained by using Gradient Boost machine learning algorithm.
Infrastructure as a Code Chatbot using Natural Language Processing

Cloud Mind is an innovative chatbot that provides infrastructure code templates for AWS CloudFormation, making it easier and faster for cloud developers and engineers to create and manage infrastructure on AWS. The chatbot is built using modern technologies such as Flask, NLTK, Keras, Python, and AWS services. Cloud Mind leverages the power of natural language processing to understand and interpret user input, providing an intuitive and interactive way for users to specify their infrastructure requirements. The chatbot generates CloudFormation templates based on the user's input and provides the necessary code to create, update, or delete CloudFormation stacks. The chatbot's backend is built using Flask, a Python web framework, and Python code, providing a reliable and scalable foundation for the chatbot's functionality. The AWS CloudFormation service is integrated with the chatbot, allowing it to create, update, and delete CloudFormation stacks based on user requirements. CloudMind is an innovative tool that simplifies and streamlines the process of creating and managing infrastructure on AWS. Its use of modern technologies and the intuitive conversational interface makes it a valuable addition to any cloud developer or engineer's toolkit. The chatbot's user-friendly interface makes it easy to create, update, and delete infrastructure resources like virtual machines, databases, and storage systems. This simplifies the administration of infrastructure resources.
Social Distancing Detector Using Deep Learning

A visual surveillance system is a tool used for analyzing and explaining object behaviors. A visual surveillance system typically includes the detection of static and moving objects. Video tracking is also used to better understand what’s happening in a scene, making it an important part of computer vision theory. This paper describes about how detecting and tracking humans is done and check whether they are following social distance or not. This paper uses the Yolo V3 object detection model, which segregates people and then attempts to determine whether they are adhering to social distance standards based on the bounding boxes that track them. This paper also shows the number of violations, and also how differently colored boxes bound around the people depending on the distance. To monitor and manage social distancing protocols, various technologies such as computer vision, deep learning approaches, and the programming language Python are often used. The motivation of this paper is to develop a method for identifying and informing officials in charge of breaches in social distance regulations in public areas.
Quantitative Evaluation of China's Scientific Data Management Policies Based on The PMC-AE Index Model

In the process of promoting the open sharing of scientific data management, a standardized and effective scientific data management policy is crucial, which could provide effective theoretical support for the improvement of the scientific data management policy system. To address this problem of imperfect evaluation mechanism and lack of quantitative analysis, we take Chinese scientific data management policies as the research nut. Firstly, we construct an evaluation index system with nine causal variables and a PMC-AE index calculation model, and conduct a more comprehensive evaluation and quantitative analysis of the seven mainstream scientific data management policies in China. The analysis results show that the overall distribution of Chinese scientific data management policies show “national-level-provincial and municipal-level-universities/research institutions”.
Panoramic Analysis of Video Defogging

Video defogging is a process that aims to improve the visibility and quality of video captured in foggy or hazy conditions. It involves several steps, including analyzing the captured video, estimating the amount of fog or haze in each frame, and applying algorithms to remove or reduce the effect of fog or haze. There are various techniques used for video defogging, such as image fusion, dark channel prior, and atmospheric scattering models. Image fusion involves combining multiple images of the same scene to create a clear image, while dark channel prior exploits statistical regularities to estimate the haze layer. Atmospheric scattering models use light scattering properties in the atmosphere to estimate the amount of haze. Video defogging has numerous applications, including surveillance, traffic monitoring, and outdoor video recording. It can improve the visibility of captured video, making it easier to identify objects and events in the scene.
Research on named entity recognition method for rich text elements of test questions

The lack of some rich text information such as graphs, tables and expressions for solving the questions in important examinations directly affects the specification of the examination paper. Therefore, this paper uses the method of named entity recognition to get the text description of pictures, tables and expressions from the test text, and gives a warning when the examination paper is examined. The baseline model was the BiLSTM-CRF model, aiming at the problem of rare entities and unclear boundary information in the self-built test question data set, an unsupervised BERT pre-training model was incorporated at the character level to obtain the dynamic character vector representation. Three dictionaries are introduced into the word set, and the multi-head attention mechanism is utilized to modify the weight of words in various word sets. As the input of the basic model, characters and words are finally combined to create more semantic information. According to the experimental data, the model's F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> value on the created test data set may reach 89.6%, which is 1.4% and 0.6% higher than those of BERT-BiLSTM-CRF and Softlexicon, respectively.
Dependency Tree Distance for Aspect Level Sentiment Analysis

When dealing with complex statements containing multiple aspects in the existing model, most of them ignore the dependencies between context and aspect words, this makes it difficult for the model to learn local information about aspects. To solve this problem, an aspect-level emotion analysis model based on dependency tree distance is proposed. It can calculate the dependency tree distance between aspect words and other context words according to their position in the syntactic dependency tree structure, then the sentence is divided into local information based on dependency tree distance. In the design of the model structure, the fusion learning method is used. The multi-head attention mechanism is used to fuse the local information, global information and aspect information, and the hidden state containing rich sentence expression is obtained as the classification basis. To verify the effectiveness of the model, experiments are carried out on three benchmark datasets, including Restaurant and Laptop datasets of SemEval 2014, and Twitter datasets. In addition, a comparative experiment is designed for the two processing methods and the multi-group dependency tree distance threshold, and the processing method and the optimal threshold suitable for different datasets are selected.
Backdoor Attacks on Time Series: A Generative Approach

Backdoor attacks have emerged as one of the major security threats to deep learning models as they can easily control the model's test-time predictions by pre-injecting a backdoor trigger into the model at training time. While backdoor attacks have been extensively studied on images, few works have investigated the threat of backdoor attacks on time series data. To fill this gap, in this paper we present a novel generative approach for time series backdoor attacks against deep learning based time series classifiers. Backdoor attacks have two main goals: high stealthiness and high attack success rate. We find that, compared to images, it can be more challenging to achieve the two goals on time series. This is because time series have fewer input dimensions and lower degrees of freedom, making it hard to achieve a high attack success rate without compromising stealthiness. Our generative approach addresses this challenge by generating trigger patterns that are as realistic as real-time series patterns while achieving a high attack success rate without causing a significant drop in clean accuracy. We also show that our proposed attack is resistant to potential backdoor defenses. Furthermore, we propose a novel universal generator that can poison any type of time series with a single generator that allows universal attacks without the need to fine-tune the generative model for new time series datasets.
Knowledge Graph Recommendation Combining Message Passing and Preference Propagation

To address the issues of sparsity and cold-start in recommendation systems, knowledge graphs are often used as supplementary information. In this paper, we propose a Message Passing and Preference Propagation Knowledge graph Recommendation (MPPPKR) algorithm that combines message passing and preference propagation techniques to improve recommendation performance on sparse and incomplete knowledge graphs. Our algorithm uses alternate relation message passing to complete the knowledge graph and extracts user response embeddings with preference propagation. Finally, we predict recommendations based on user response embeddings and item feature embeddings. Experimental results demonstrate that MPPPKR outperforms other baselines.
Publishing Efficient On-device Models Increases Adversarial Vulnerability

Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mechanisms enable the construction of a small, efficient version of commercial-scale models with comparable accuracy, accelerating their deployment to resource-constrained devices. In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. In evaluations across 19 DNNs, by exploiting the published on-device models as a transfer prior, the adversarial vulnerability of the original commercial-scale models increases by up to 100x. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase. Based on the insights, we propose a defense, similarity-unpairing, that fine-tunes on-device models with the objective of reducing the similarity. We evaluated our defense on all the 19 DNNs and found that it reduces the transferability up to 90% and the number of queries required by a factor of 10-100x. Our results suggest that further research is needed on the security (or even privacy) threats caused by publishing those efficient siblings.
Tibetan Aspect Extraction based on syllable feature

The paper proposes a new approach to evaluation target extraction for Tibetan news texts, which integrates Tibetan syllable components and character features to address segmentation errors propagation issues caused by previous models utilizing word embedding. The model used Bi-LSTM for feature extraction after feature fusion in the encoder and CRF(Conditional Random Field) for target output. The experimental results showed that the proposed model performed better than previous models
Cost Analysis and Optimization of Virtual Machine Allocation in the Cloud Data Center

IoT sensors and devices are being used in more and more applications every day. Cloud computing has been introduced in order to meet the storage and processing demands for time-sensitive applications on low-powered end devices that leverage IoT devices and sensors. Processing and analysis of IoT workloads are carried out by virtual machines (VMs) housed inside of actual computers. It is an NP-complete issue to allocate VMs inside a physical machine (PM). If a VM is unsatisfied with the provided service, the VM may decide to re-join the line as a feedback customer. Whenever the system is empty, the physical machine may take a working vacation rather than remaining in the system idle. Working vacations and vacation service times are both independent random variables with exponential distributions. An M/M/1 feedback queue in steady-state with balking, reneging, and recurrent working vacations (WV) have been applied in the VM allocation to the PMs. The matrix approach is used to determine the model's steady-state system length distributions. A population based meta-heuristic approach teaching learning based optimization (TLBO) technique is provided for cost optimization and performance features of the VM allocation. Numerical findings presented as tables are used to perform sensitivity analysis.
Information Retrieval for Japanese Character Database based on LeNet-5-Saliency Algorithm

With the increasing number of Internet character database size, the efficient analysis regarding the information is becoming the principle task for the intelligent analysis, hence, in this manuscript, information retrieval for Japanese character database based on LeNet-5-Saliency algorithm is studied. In the designed algorithm, the Hash model is considered, in order to learn more robust hash codes, the feature fusion operation is performed on global features and fine-grained features in the feature space, and the attention algorithm is used to fuse features for hash code learning. Furthermore, this study performs binary tree hash encoding on the searched first word string, that is, the first word detection and binary tree hash encoding run in parallel, which can save a lot of retrieval for text retrieval time. Through above operations with combination of the feature selection, the model is implemented and the comparison simulation is then conducted. The designed model has the Map value of better that 0.9 in most cases, that mean, the accuracy is higher than most of the state-of-the-art algorithms.
Secure File Sharing System Using Image Steganography and Cryptography Techniques

Secure file sharing is a critical aspect in the digital world, where confidential information must be protected from unauthorized access. This research study presents a system to integrate image steganography and cryptography techniques to enhance the security of file sharing. The system uses image steganography to hide the secret file within an image cover file, and then applies cryptography algorithms to encrypt the image cover file. The encrypted image cover file is then shared securely between the sender and receiver. The receiver can retrieve secret file by first decrypting the encrypted image cover file and then extracting the secret file from the image cover file using steganography techniques. This system provides high security for file sharing by combining the strengths of both steganography and cryptography.
Research on Intelligent Recommendation Technology for Complex Tasks

The value of personalized recommendation lies in mining potential association rules from massive data. In order to provide users with high-quality and personalized items. Due to the complex and changeable tasks, scenarios, and environments in the military field, traditional recommendation algorithms based on collaborative filtering have poor recommendation accuracy in the face of such highly variable and highly sparse data. Therefore, this paper proposes a design idea based on events/tasks. It builds a heterogeneous information network consisting of four types of nodes and seven types of links through many users historical behavior records. Then use heterogeneous network representation learning technology to extract the relationship features between users and items, thus retaining the complex topological relationship between users and items. Finally, the obtained user embedding vector and item embedding vector are combined with user characteristics and item characteristics, and then input into the neural network to predict the degree of preference.