Reviewerly/exHarmony huggingface dataset:
Cross-Site Scripting (XSS)

This paper discusses what is Cross-Site Scripting (XSS) and what can it do when the attack is launched successfully. A Cross-Site Scripting attack is a well-known attack that is used by hackers to attack web applications for many purposes but mostly is used to steal confidential information from the client or the database of the webserver. There will be a demonstration on how to perform cross-site scripting attack with several tools such as XSSer, FoxyProxy and Burp Suite. The whole demonstration is performed by using a virtual machine with Kali Linux Operation System and the target web application is a buggy web application that is used to perform penetration testing named, bWAPP. This demonstration shows how easy is the cross-site scripting attack can be performed in just a few steps in a few minutes. Besides the demonstration of cross-site scripting attack, there will also have some introduction to the tools used in the demonstration such as XSSer, FoxyProxy and Burp Suite. There will also be a discussion on the result of the cross-site scripting attack demonstration and the countermeasure against this kind of attack to avoid people falling into the cross-site scripting attack easily in this paper.
An FPGA Implementation of CNN-based Compression Artifact Reduction

This paper proposes a convolutional neural network (CNN) based compression artifact reduction hardware. The proposed CNN architecture is applied to re-parameterization and INT8 quantization methods for efficient inference in edge devices. As a result of applying the optimization methods, the model size was reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times 5.62$</tex> , and the number of operations was reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\times 1.72$</tex> . The proposed hardware achieves a frame rate of 33.33 FPS when implemented on a Xilinx ZCU104 SoC.
Using VR in Science Subjects: Popularity and Public Attitudes on Twitter

Following their surge in popularity during the height of the Covid-19 pandemic, e-learning and virtual education remain a noteworthy and rapidly developing part of the educational landscape. Still relatively immature in its development, virtual reality in particular promises extraordinary potential for positive educational outcomes. Given this potential, it is essential to deepen our understanding of the needs and attitudes of the public regarding its use. Based on Twitter data, our research focuses on the use of virtual reality in biology, chemistry and physics education, providing an analysis of popularity and other trends, along with public attitudes. Major results of our study include the finding that the application of virtual reality in chemistry was the most discussed among these three subjects between July 2016 and July 2022, and the discovery of a strong positive correlation between the frequency of tweets and the launch of hardware and software products. Our overview of the traffic on these three subjects is likely to be of particular value to software developers and virtual reality content creators.
A Novel Approach for Newspaper Block Segmentation using Run-Length Smoothing Algorithm

Region findings and analysis plays an important role in document understanding. Due to the existence of complex layouts, document understanding is a very challenging task for researchers. Layouts in newspapers are derived for the articles where article consists of multiple blocks. These various blocks must be segmented and identified within the whole newspaper which helps further in article segmentation. This paper proposes a novel method to identify and segment blocks found within the newspaper irrespective of its layout using simple image processing operations such as morphological dilation, run-length smoothing algorithm and rule based algorithm. These methods have been tested on dataset consisting of digital enewspaper images of recent years from the TOI and Financial Express Newspapers with different layouts and complexities. The experimental results exhibit our method proposed outperformed region findings with the precision 0.83, recall 0.72 and F1 score 0.76. Effectively these blocks will be used as features and evaluation measure for various document analysis.
English Machine Translation System based on Human-Computer Interaction Algorithm

With the development of corpus linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and has been widely used. However, the existing automatic machine translation technology is far from being completely practical. As a feasible alternative to automatic machine translation technology, computer-aided translation technology has been greatly developed, and a feasible mode of computer-aided translation is interactive machine translation technology. Therefore, this paper proposes a Human-Computer Interaction Algorithm(HCIA) and designs an English machine translation system(MTS). Based on the research and analysis of the existing interactive machine translation technology, a human-computer interactive English machine translation algorithm model is proposed; The experimental test and analysis of the English MTS proposed in this paper show that the translation system designed in this paper reduces the translator’s thinking time and cognitive burden, does not need to spend too much time and energy on the correct translation recognition, and improves the translation efficiency.
ClassHotel: Application of Data Analytic Techniques for Online Hotel Recommendation

Booking a luxury hotel should be one of your top priorities while organizing a trip. Booking a hotel online may be difficult because there are so many options for lodging in each location. We will thus investigate the best hotel nearby. As a result, there will be just one hotel. We will then focus on methods used in content-based recommendation systems to model the user's interests and evaluate an item collection using the representation of the items. This implies choosing a certain hotel over others. In order to complete this task, you must choose a hotel from among the numerous listings in the Airbnb dataset and suggest the user use a certain number of hotels, ranking them according to how likely is that the user would book a room.
Mobile robotic systems for sealing of underwater wells in case of oil and gas blowouts

The problem of sealing of emergency underwater flowing oil ang gas wells using mobile robotic systems is discussed in the article. Technologies for installing preventers on gushing wells using the underwater mobile robots (platforms) and pontoons using robotic anchor-cable system are proposed. The use of underwater mobile platforms with walking propulsions is considered. Using an orthogonal walking mover provides the necessary passability and maneuverability of the platform when working in an underwater field, positioning accuracy at the wellhead, and installation of a preventer without a crane device. The results of computer modeling of a blowing gas at underwater fields are presented. The advantages of the proposed sealing technologies are the ability to work in conditions of ice cover and significant sea disturbance, reduction of the duration of work to eliminate an accident at an underwater oil and gas field and the possibility of continuing oil and gas production. The main directions for future researches are outlined.
Oculus: A New Dimension to Virtual Reality

Immersive Technologies, these days have contributed a lot in solving various societal and technical problems. It aids in providing trainings to all the people irrespective of their gender, race, age and qualification. It helps them to experience a virtual world in spite of being present in the real world. It basically connects the virtual world with the real world. In order to bring out the objects into picture many devices are used such as HTC Vive, Oculus and many more. This paper presents various devices and the prerequisites required to use a particular device. In this paper our focus will be on Oculus and its yearly advancements till date.
Dataflow and Hardware Design for The Sharing of Feature Maps

Networking pruning is an effective way to reduce the memory storage requirement of a DNN (deep neural network). Previous works have shown that some channels in the same layer may have similar feature maps. These similar channels can share the same feature map (for network pruning) with only a small loss on the accuracy. However, previous works did not pay attention to the corresponding hardware design. In this paper, we address the dataflow for utilizing the sharing of feature maps. Based on the dataflow, a corresponding hardware circuit design is proposed. Experiments consistently show that the proposed approach works well in practice.
Penalty based Sentimental Text Generation Framework using Generative Adversarial Networks

Emotional Intelligence is the technology where computers are trained to handle, use, understand and manage emotions. Emotional Intelligence (EI) is used in sentimental text generation where sentimental texts like positive, negative and neutral texts are generated. Sentimental text generation is used in many Artificial Intelligence application such as Google Assistant, Siri, Chatbots and so on. Many Deep learning algorithms such as LSTM, Bi-LSTM, RNN and CNN focus on generation of sentimental texts. The research work proposed herein uses Generative Adversarial Networks (GAN) for generating texts based on the training dataset. The GAN network has 2 models; generator model which generates the text and discriminator model which evaluates the text and both are trained using Bi-LSTM algorithm. The model is trained using Amazon customer review dataset and the system generates sentimental text such as positive, negative and neutral text for a given product. The existing system uses LSTM algorithm for prediction, but the performance and quality of generated text is low. The proposed system uses the Bi-LSTM algorithm which increases the accuracy of the generated sentimental text. The performance of the framework is evaluated in terms of fluency, sentimental accuracy and context relevance and is compared to the existing system of Automatic Generation of Sentimental Texts using Mixture Adversarial Networks.
SVR: A Shard-aware Vertex Reordering Method for Efficient GNN Execution and Memory Access

Graph neural networks (GNNs) appear to be a powerful approach to analyzing non-euclidean data structures and achieving unprecedented performance on graph processing tasks. However, due to the inherent randomness and sparsity of graph structures, GNNs require irregular execution and memory access patterns. This paper proposed a lightweight shard-aware vertex reordering (SVR) algorithm to exploit data reuse and reduce memory access based on the adjacency of vertices. The vertices are divided into fixed-size groups and the vertex indices are updated in each group according to their adjacency relationship. The preprocessed dataset has fewer shards with any non-zero element and the sparsity is significantly decreased. The SVR algorithm can be used as a preprocessing extension to current GNN accelerators, improving performance without changing existing data flow. The proposed SVR is implemented to the baseline HyGCN and EnGN architecture for evaluation and we achieve an additional 1.43x memory access reduction on average in the aggregation phase and up to 2.52x sparsity reduction in the effectual shards.
Experimental Setup of Apache Spark Application Execution in a Standalone Cluster Environment using Default Scheduling Mode

This paper proposes the Apache Spark application execution on a local machine using covid dataset with the standalone cluster environment using default scheduling mode. Cloud computing has been widely used in various fields of organization due to its flexibility and scalability. Efficient job scheduling technique can apply in the cloud to increases the profit of Cloud Service Provider and also reduces various optimization technique was recently applied to increases the performance of cloud computing. Experimental setup of apache spark done on a local machine in a standalone cluster environment using FIFO default scheduling mode. Existing techniques have limitations of local optimal trap, lower convergence and over fitting in prediction. This research applies First In First Out (FIFO) technique to increases the efficiency of cloud, reduce the cost, easy to configure with simplest standalone cluster manager. In this proposed framework, the first phase configure the local machine with apache spark second phase launch master node and worker node and the final phase launch the history server and run the application. The FIFO technique has a cost value of 2.5 $ based on the results recorded with two parameters execution time and resources utilized (CPU cores and memory).
Network-on-Chip-Centric Accelerator Architectures for Edge AI Computing

In this paper, we present a Network-on-Chip-centric (NoC-centric) design technique for edge AI accelerator architectures. The technique enables NoC with compute capability, eliminates the need of using extra cores or frequent access to memory for neural network computing and eventually improves accuracy and energy efficiency. We demonstrate the dedicated, software-configurable NoCs with on-device inference and training tasks and show the architectures can achieve 2.8x and 2.1x lower energy per classification compared to state-of-the-art baselines, respectively.
Monocular Depth Estimation with Optical Flow Attention for Autonomous Drones

This work proposes a depth estimation method for monocular images for autonomous drone flight. Drones with high speed require depth information to determine which directions to avoid collisions with objects. Unlike existing work, our proposed model applies optical flow and an attention block in order to improve accuracy. In the experiments, we show the effectiveness of our proposed method compared to the state-of-the-art.
Research on Adaptive Information Fusion Algorithm Based on Multi-sensor

I proposed a multi-sensor adaptive state fusion technology that can improve the discovery capability and continuous observation capability of sensor networks. Focusing on the complementarity between the algorithms of adaptive state fusion technology to achieve some kind of optimization. The algorithm can automatically select the most effective algorithm according to the characteristics of the information source and the target, with transparency and openness, is a multi-level multi-model adaptive algorithm. Experiments confirm that: the proposed algorithm will bring greater benefits for command information system information fusion to improve accuracy, anti-stealth, anti-low-altitude intrusion, anti-loss target, etc., besides, it can overcome the shortcomings of sensor performance instability.
Breast Cancer Segmentation by K-Means and Classification by Machine Learning

Breast Cancer (BC) progression is currently a common health problem among modern women. It is the cause of death for a significant number of women. BC is the growth of malignant cells in the breast tissue. Adipose or connective tissue can also develop BC. Because of developments in medical technology, ultrasonography is one of many procedures utilised for the early identification of cancer. Ultrasound is a technique that uses high-frequency sound wave technology to create images of inside body structures such as organs and soft tissues. Because of the poor quality of the information, there is a lot of possibility for interpretational mistakes when diagnosing cancer based on ultrasound images. As a result of these concerns, this paper uses the idea of Machine Learning (ML) is employed for the classification and segmentation of BC. The K-means clustering approach is used as part of the segmentation procedure to detect where the cancer is present. A recent study has demonstrated that machine learning produces reliable findings, allowing specialists to make better decisions. Using standard BC datasets, the performance of three different Machine Learning algorithms—Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN)—is tested in this work. In terms of accuracy, RF outperformed the other algorithms, according to the finding. Future BC researchers will be able to utilise the findings of this study to guide their investigations and influence their efforts to improve the efficiency of specific algorithms.
Distribution Characters Improvement and ALN-LSTMP approach in enabling big data analytics for serving precise load classification

Accurate load classification can provide support for scenarios such as demand response scheme design, load characteristic analysis and high-precision forecasting. Aiming at the low classification accuracy of traditional load classification methods, a distribution characters improvement and ALN-LSTMP approach in enabling big data analytics for serving precise load classification is proposed. Firstly, considering the data missing and category imbalance in the load data set, a data distribution improvement strategy is proposed to improve the model training effect; Secondly, an improved ALN-LSTMP model is proposed to further enhance the accuracy of load classification in view of the low calculation efficiency and the shortage of over fitting easily of traditional machine learning models when dealing with load big data; Finally, an example is given to verify the effectiveness of the proposed method.
A Survey on Application of Metaheuristics Techniques for Ensemble Feature Selection (EFS)

Unprecedented increase in the quantum of data generated through online social, financial, business and learning transaction have necessitated the incorporation of indigenous techniques to intelligently preprocess/trim the data to a level that suits the learning algorithm in all dimensions with improved classification accuracy and performance. Intelligent Feature Preprocessing techniques are deployed to address the issue of "curse of dimensionality" that is persistent in the current voluminous databases. Individual feature selection techniques when applied to the dataset may face several deficiencies viz. may get stagnated in the local optima of the complex feature search space, tends to yield unstable feature subsets missing the feature interaction etc. These challenges have encouraged the implementation of Ensemble Feature Selection (EFS) techniques where multiple feature selection models (Base Feature Selectors) are integrated to form a composite model (stronger version) to yield an optimal and robust feature subset. Eventual application of these features to the learning/classification algorithm produces better results with high accuracy and efficiency. High quantum of feature subsets generated through EFS necessitates the replacement of conventional feature searching/ranking/combining strategies with stochastic metaheuristic techniques exclusively designed for tackling high dimensional data issues to generate a global optimal solution. The voluminous intermediate feature subsets from the EFS mandates the deployment of Population based multi-objective Metaheuristics algorithm viz. Minimizing the number of features, classification error, minimizing the total misclassification cost and maximizing the classification accuracy. Population based Metaheuristics techniques serves as correct fit for seamlessly finding optimal feature subsets that sieves through multiple feature subsets generated through EFS. This paper aims to present a survey encompassing the adoption of metaheuristics techniques for efficient feature selection with an aim to improve the detection rate of various attack classification accuracy and efficiency.
Person Recognition Based On Deep Learning Using Convolutional Neural Network

Matching the same subject across various cameras is the aim of the person recognition task. Earlier, person recognition problems were primarily addressed by image-based techniques. However, as the use of cameras and monitoring increases, image-based solutions are being employed. Image-based systems yield better results for person recognition because they take into account the person’s spatial and temporal information, which are not included in a single image. The CNN properties for person recognition are improved in this work. On huge annotated datasets like Image Net, recent studies have demonstrated the effectiveness of features produced from pre-trained Convolution Neural Network (CNN) top layers. In this study, we use a dataset of pedestrian attribute values to fine-tune the CNN features. We propose new labels that were produced by integrating a number of attribute labels included to the classification loss for the different pedestrian attribute labels. CNN is forced to develop more discriminative features that can identify more personally identifiable information as a result of combination attribute loss. Using traditional metric learning, we further enhance discriminative performance on a target identification dataset after extracting features from the trained CNN.
Deep IoU Network for Dense Rebar Object Detection

Typically, dense rebar detection scenes comprise cross-sections of hundreds or even thousands of rebars. We demonstrate that most commonly used object detectors still have trouble detecting objects accurately in such settings. We present a novel deep-learning-based approach for tackling this problem, which is combined with a useful soft-Iou layer to predict the Iou of a detected bounding box and its ground truth and an efficient EM - Merger unit to resolve a single detection per object, enabling the accurate detection of the bounding box of tiny objects such as rebars. Experiments show that the proposed method can achieve excellent results in our collected rebar images while our network is trained on the RebarDSC dataset.
Time Series Analysis with Noise-Mixing Effects Using Neural Networks

One-dimensional convolutional neural networks (1D-CNNs) are used for time series analysis. However, noise mixed in the data can interfere with time series analysis. We propose a method of injecting noise into the original data and the autocorrelation function data for feature extraction of training data. The test accuracy of neural network is compared with the original data and the autocorrelation function data.
Research on product sales forecasting based on multi-value chain collaborative data management system in manufacturing industry

Manufacturing data is a massive amount of data generated in the application of manufacturing informatization based on the multi-value chain of the manufacturing industry. The multi-source heterogeneity of data poses a great challenge to manufacturing data management. Based on the analysis of data space theory, this paper constructs a collaborative data management system architecture of manufacturing multi-value chain based on data space, and establishes an intelligent prediction model of product sales based on data management system. Taking the products of power equipment manufacturing enterprises as an example, this paper first extracts and filters the data sets related to product sales from the multi-value chain collaborative data space of the manufacturing industry. The sparrow search algorithm was used to optimize the extreme learning machine model to predict product sales, which verifies the effectiveness of the multi-value chain collaborative data management system based on data space. This provides guidance for making the next production plan.