Reviewerly/exHarmony huggingface dataset:
Infrastructure-as-Code in Open-Networking: Git, Ansible, and Cumulus-Linux Case Study

One of the necessities for modern connectivity infrastructures is decreasing devices setup-times and giving configuration visibility to troubleshoot complex scenarios more easily. This proof-of-concept and case study scenario demonstrates programmability in Open-Networking through an emulation of Cumulus Linux as an Open NOS (Network Operating System) with Ansible acting as a NAS (Network Automation Server), and Git as a VCS (Version Control System) for networking scripts, giving a global view of the technological evolution in data networks, focusing on the importance of IaaC (Infrastructures-as-a-Code) and NetDevOps.
Experimental Analyses of a Noise-Based True Random Number Generator

This work presents a fast, low power and simple structure noise-based true random number generator (TRNG) using discrete components and devices such as resistor., capacitor, diode., transistor., operational amplifier etc. Two NPN 2N3904 BJT transistors are employed in the noise-source circuit whereby the collector terminal of one transistor is unconnected. A high electric field is induced by reverse biasing the emitter-base junction under high voltage. Current is injected into the base which lowers the noise voltage at the output node and then removes the transistor from breakdown region. Another random pulse also puts the transistor back into breakdown and the cycle continues. Subsequently, the noise signal generated is amplified by two LM741 op-amp each with a gain of 100. We employed 4007 series as digitizer to convert the amplified noise to digital pulses. The post processing technique is implemented with XOR operation., and the result shows the output data generated by TRNG pass all NIST SP800-22 tests.
Consumer review Analysis using NLP and Data Mining

The popularity and development of the Internet have greatly increased the amount of information that is easily available. For a sizable portion of the population, it has evolved into the primary forum for opinion expression. These viewpoints can be discussed in reviews, online forums, and social media websites like Twitter and Facebook. Sentiment analysis and information extraction are made possible by the ease of access to all of these viewpoints, which is free. Sentiment analysis is concerned with automatically ascertaining, through the use of computer tools, if a reviewer's purpose is positive, negative, or neutral with regard to specific goods, services, and problems. Since they have such a positive impact on every particular organization, these strategies are rapidly gaining favor. As a result, we have researched multiple methods for classification, data preprocessing, and feature extraction in this study and compared the outcomes using the logistic regression, multinomial naive bayes, and other accuracy measures. The results of the simulation demonstrate that, when we have applied to a dataset with features that the model has retrieved, SVM is the most accurate classifier for the given data set.
The Precise 3D Reconstruction of Human Faces Based on 2D Photograph

3D object reconstruction is a broad research area in the computer vision field including complicated image processing techniques and existing open problems. One of the existing challenges in this field lies with accuracy, thus most works are designed for architecture reconstruction where accuracy is not the key. The purpose of this work is to discuss a flexible scan system using multiple cameras that can correctly reconstruct 3D objects ‐ ‐ a human face with expression. Further, a mathematical model is proposed in this literature to automate the 3D object reconstruction process.
Assessing the Impact of Matched Fragments' Relative Locations on Application Artifact Inference

One challenge in digital forensics is finding evidence of applications that have been uninstalled. When an application is uninstalled, the files making up the application are deleted. However, deleting a file doesn't necessarily immediately overwrite all the data (sectors) of the file. Investigators can find these orphaned file fragments (sectors) at some time after an application has been uninstalled and use them to infer the prior presence of whole files and hence associated applications. Prior work used a simple TF-IDF based weighting scheme to infer the prior presence of deleted application files and hence uninstalled applications. However, this method has limited accuracy because matching sectors not actually associated with a previously installed application result in application inference false positives. Most file systems write file sectors contiguously and files are cluster-aligned, suggesting a revised computation that might exploit these known and common behaviors to filter out many of these false matches. In this work, we add consideration of the relative locations of the matched sectors, where relative means (a) matching sector alignment relative to the original file of interest, and (b) matching sector alignment relative to cluster boundaries. We also add exponential weighting for file matches based on other work suggesting that the additional inferential value of each matching sector declines as more sectors are matched (i.e., two matches are not twice as useful as one match, etc.). Test results show that our proposed approach produces more accurate results than the prior work.
Automation of Buildings using Sign Interpretation Language to include People with Hearing Disabilities

Communication difficulties in people with hearing disabilities can cause anxiety, isolation, and even frustration. Their environment plays an essential role in personal and professional development. This paper proposes the deployment of a sign language interpretation system with a high-availability computational architecture for environment automation, as a potential communication media to help people with hearing and communication disabilities. This proposal consists of four stages: the first belongs to data acquisition, in which we have used a webcam to capture the sign. The next stage consists of data channeling, in which we have distributed data traffic to the image processing stage. In the third stage, we used different machine-learning tools for sign recognition. Then, the notifications and events stage runs an action based on the identified sign, such as turning on/off the air conditioner, projector, or another object. Finally, the system will allow the automation of different work and personal environments, helping the inclusion of people to interact better with their environment.
Real Time Road Traffic Detection Using Computer Vision

Object detection using Machine learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems effects the object detection. The main objective is to detect objects using You Only Look Once (YOLO) algorithm. The YOLO method has several advantages when compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO, the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster as compared to other algorithms. We have used this algorithm for detecting different types of objects.
Image Caption Generator using Deep Learning Approach

The creation of captions for a picture is the focus of the image caption generator. The image's semantic meaning is extracted and translated into plain language. Also, there are built-in programs that create and offer a caption for a certain image. Image captioning is the process of creating a description of a picture in the form of a caption. The system must recognize and develop connections between things, people, and animals. This study uses deep learning to find, identify, and produce interesting captions for a given image. The practice of creating textual descriptions of a given image using computer vision and natural language processing methods is known as image captioning. This suggests a strategy for deriving a caption for a picture that highlights particular objects in the image.
E-Governance of Properties using Blockchain Technology

Blockchain technology can be utilized in various ways, and one of its potential uses is in the field of Real Estate. Real Estate transactions require high levels of security and transparency, which are characteristics provided by Blockchain technology. In the traditional process of buying a house, ownership is proven through a physical deed or agreement signed by the government, which is recorded in a ledger maintained by the government. However, if the ledger or deed is lost or damaged, there may not be any other concrete proof of ownership. To address this issue, we propose a system that allows for the creation of a digital deed through a smart contract, which is then recorded as a new block in the Blockchain. The distributed nature of Blockchain technology ensures the integrity of the records even in the case of a system failure or attack, as each node is present on multiple servers.
Blockchain-Powered Land Ownership Management System

The project involves using Block chain technology and non-fungible tokens (NFTs) to manage assets such as residential, commercial, and agricultural lands. Traditional methods of tracking ownership of these assets require long and complex processes, including the need to obtain title deeds from government offices. The proposed system aims to simplify this process by providing a secure and verifiable way to represent ownership of assets through the use of NFTs and smart contracts on the Ethereum network. This system not only makes it easier to track ownership of assets, but also helps to ensure that assets are properly insured and that regular payments are made for insurance, taxes, and subscriptions like electricity, water, and natural gas. By using NFTs, the system can also represent rare and unique assets that cannot be replicated or replaced, which increases their value and collectability.
Study on Underwater Image Processing Techniques

Image processing is extensively used to improve the quality of photos with scattering and poor contrast. Image processing is used in many fields of computer vision, including underwater image processing. It is necessary to process underwater photographs in order to improve the poor image quality caused by light scattering and refraction. As depth deepens, different colors are absorbed by the adjacent medium specific wavelengths. This research describes an effective approach for enhancing underwater photographs that have been deteriorated owing to medium absorption and scattering. Image processing has been used to develop certain approaches for improving the quality of underwater photographs. This study also discusses some of the algorithms for enhancing the quality and resolution of photographs. Using an edge detection robustness criterion performance of filtering will be evaluated and also the color correction.
Automating the Deployment of Cyber Range with OpenStack

Abstract Cyber Range is an experimental platform based on virtualization technology to construct a controlled simulation environment, providing a real-world simulation environment for cybersecurity personnel to conduct various practical exercises. The problem is that generating a virtual environment satisfying the requirements is labor-intensive and time-consuming. To resolve the above problem, this paper proposes a system to automate the deployment of a cyber range. In our method, the first step is to collect virtual machines (VMs) related to cybersecurity and extract relevant features. Then, machine learning is used to classify VMs to reduce the cost of manual VMs selection. Lastly, leveraging the popular OpenStack cloud platform as the deployment platform enhances the applicability of the cyber range. When it comes time to deploy a virtual environment, the instructor only needs to provide some brief description information of a virtual environment. Then, the system will automatically parse the description file to complete the automated deployment of the virtual environment. This system has been successfully applied to the cyber range of Sichuan University for daily teaching tasks.
Lightweight Semantic segmentation method based on GhostNet and Atrous Spatial Pyramid Pooling

Abstract Semantic segmentation is frequently utilized in computer vision projects like remote sensing picture segmentation, unmanned driving, and medical image segmentation. A lightweight semantic segmentation model is suggested by taking into account three components of the network - parameters, calculation, and performance - to address the issue of deploying embedded platforms with constrained processing power and hardware storage. Dual Attention is used with Atrous Spatial Pyramid Pooling (ASPP) to obtain precise relevant data utilizing the lightweight network GhostNet as the foundation, and to lighten ASPP’s computational burden, depthwise separable convolution is used. To obtain a distinct segmentation boundary, a multi-scale splicing technique is then used. With network parameters of 2.7810 6 , a floating-point calculation of 1.931GFLOPs, and a MIoU of 72.13% in the experiments just on PASCAL VOC 2012, the model achieves an excellent compromise between computational efficiency and segmentation precision.
Analysis of Communication Simulation Model of Urban Comprehensive Energy Network Based on the Internet of Things

Abstract In urban construction, all energy infrastructures have been realized by the information structure. However, due to the non-uniform structure of each energy network, the data cannot be more compatible, and the communication guarantee based on the Internet of Things is one of the most urgent problems to be solved. In this paper, a reasonable network computing offloading model is designed in the edge computing layer, and the computing offloading algorithm is introduced. Considering the dependency between subtasks, an optimization problem is established, and a central scheduling algorithm is proposed to solve it. An optimized distributed unloading algorithm is proposed according to the solution idea of the classical distributed unloading algorithm. Simulation results show that the optimized distributed unloading algorithm proposed in this paper has good solving efficiency and performance, and reduces the task processing delay. At the same time, the experimental simulation proves that the efficiency of multiple edge servers in processing subtasks is higher than that of a single server. It is established that changing the convergence speed and communication delay stability margin of the system by changing the system eigenvalue is beneficial to design a better communication network structure to keep the system stable.
Automatic Retracting and Paying-off System of Tethered Unmanned Aerial Vehicle

Abstract With the progress of unmanned aerial vehicle (UAV) technology, people have higher and higher requirements for the working endurance of rotary-wing UAVs. The tethered UAV can be wired to power through the tethered cable, which solves the problem of UAV endurance. During the descent of the UAV, the operator needs to manually rotate the take-up device for take-up manually. When the operator fails to put the redundant tied cables into the retracting and paying-off device timely, the redundant tied cables are prone to be entangled and knotted, causing potential safety hazards. To overcome the shortcomings of the above existing technologies, this project develops an automatic retracting and paying-off device based on the tethered UAV. The tension of the tethered cable is measured in real time by adding a tension monitoring unit to the traditional take-up device. According to the tension of the tethered cable and the takeoff and landing speed of the tethered UAV, the real-time automatic take-up of the tethered cable is realized.
Design and Analysis of the Mechanical Structure of Land-Air Logistics UAV

Abstract In the post-epidemic era, drone distribution has attracted increasing public attention because of its excellent contact-free characteristics and capacity to greatly improve the efficiency and reliability of material distribution. Combining land-air drones with logistics distribution, this design breaks through the limitations of the single-movement ability of traditional drones and land robots to fly in the air or move on the ground. Based on the collection and statistics of technical indicators related to logistics distribution, this paper focuses on the design of land-air conversion mode, and proposes an innovative land-air conversion mechanism composed of ball screw nut pair and multiple sets of gears to complete the design of the related mechanical structure of land-air logistics UAV. In addition, the mechanical system simulation of drones is carried out by ADAMS software. Moreover, the static and aerodynamic simulation analysis of UAVs is conducted by ANSYS software to optimize the overall structure.
Signal recognition of a Fiber Bragg grating-based orbit monitoring system based on the convolutional neural network

Abstract In order to adapt to the diversification of train and railway technology by combining the needs of rail transit and the advantages of a grating array fiber sensing system, this paper discusses an intelligent rail transit safety monitoring system based on grating array fiber. The vibration signal sensing system based on grating array fiber can accurately locate the time and place of vibration occurrence in real time by analyzing the train vibration signal obtained by the sensing system. The possible abnormal conditions and safety risks of the train or track can be judged. At the same time, in view of the problems of low processing efficiency, low accuracy, and high possibility of omission caused by resolving and processing vibration signals only by human resources or a single feature extraction technology, this paper introduces a data analysis and processing method in a deep learning framework to analyze the collected signal directly. Deep learning is conducted through the convolutional neural network to distinguish the normal vehicle crossing data from the abnormal data, and then the abnormal data are analyzed separately. The exception category is used according to the exception data upper opportunity.
Dataset Enhancement and Multilingual Transfer for Named Entity Recognition in the Indonesian Language

Named entity recognition in the Indonesian language has significantly developed in recent years. However, it still lacks standardized publicly available corpora; a small dataset is available but suffers from inconsistent annotations. Therefore, we re-annotated the dataset to improve its consistency and benefit the community. Our re-annotation led to better training results from an effective baseline model consisting of bidirectional long short-term memory and conditional random fields. To fully utilize the limited available data, we utilized better contextualization and transferred external knowledge by exploiting monolingual and multilingual pre-trained language models, such as IndoBERT and XLM-RoBERTa. In addition to the general improvement from the language models, we observed that the monolingual model is more sensitive, while the multilingual ones show advantages in rich morphological knowledge. We also applied cross-lingual transfer learning to utilize high-resource corpora in other languages. We adopted English, Spanish, Dutch, and German as the source languages for the target Indonesian language and found that Dutch plays a special role in the data transfer method due to morphological similarity attributable to historical reasons.
Jointly Optimizing Job Assignment and Resource Partitioning for Improving System Throughput in Cloud Datacenters

Colocating multiple jobs on the same server has been widely applied for improving resource utilization in cloud datacenters. However, the colocated jobs would contend for the shared resources, which could lead to significant performance degradation. An efficient approach for eliminating performance interference is to partition the shared resources among the colocated jobs. However, this makes the resource management in datacenters very challenging. In this paper, we propose JointOPT, the first resource management framework that optimizes job assignment and resource partitioning jointly for improving the throughput of cloud datacenters. JointOPT uses a local search based algorithm to find the near optimal job assignment configuration, and uses a deep reinforcement learning (DRL) based approach to dynamically partition the shared resources among the colocated jobs. In order to reduce the interaction overhead with real systems, it leverages deep learning to estimate job performance without running them on real servers. We conduct extensive experiments to evaluate JointOPT and the results show that JointOPT significantly outperforms the state-of-the-art baselines, with an advantage from 13.3% to 47.7%.
Digital Polling System through Face Recognition and UID Verification

The primary goal is to provide a manual voting system using a camera for facial recognition and OTP generation, as well as an online voting platform that will help reduce fraud in early online voting iterations. For voters who are unable to travel to the voting location (their hometown), we are also introducing a location-free voting mechanism. Here, we provide a system with many levels of authentication, including facial recognition and OTP verification with validation data, to guarantee the device's dependability. Every voter must be recognised and verified against the provided database of registered voters before they are allowed access to the system. The voter can move on to choose their chosen candidate from the panel once the matching face has been matched with the data given.
Interpretable web service recommendation based on disentangled representation learning

Most existing Web service recommendation models based on machine learning do not fully consider the high-order features interaction between users and services and with poor interpretability. In this paper, an Interpretable Web Service Recommendation model based on Disentangled Representation Learning (WSR-DRL) is proposed. First of all, to make full use of the service description information to improve the accuracy of Web service recommendation, the features representation of service name is obtained by using BERT model, and the local and global features representation of service description information is further obtained by combining 2-D CNN and Bi-LSTM. Then the disentangled convolution neural network is used to generate the high-order interaction features between users and services, and the neighborhood routing algorithm is used to mine the latent factors in these features. That improves the accuracy of Web service recommendation and make it interpretable. Finally, in order to verify the effectiveness of the model, several groups of experiments are carried out on real data sets. The experimental results show that compared with latest models such as DMF, DeepFM, DKN, GCMC, NDCG model and WSR-MGAT model, the WSR-DRL model proposed in this paper shows better performance on Precision@10, Recall@10, F1@10 and NDCG@10 evaluation metrics.