Reviewerly/exHarmony huggingface dataset:
Research and application of encrypted data transmission based on IPSec

In order to solve the security problem of data transmission between networks, Internet practitioners have been trying various solutions, trying to make data can be transmitted in multiple complex network environments, while security is also satisfied. The most classic solution is to use an encryption algorithm to encrypt and transmit data. The encryption algorithm includes international algorithms such as RSA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> and AES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</sup> . There are also SM1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</sup> , SM2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</sup> and SM4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</sup> in China to fill the vacancy of the encryption algorithm, but the above solutions are all aimed at encrypting the data itself. This article tries to solve the problem of data security transmission from another angle. Even if the data itself is not encrypted, it can still ensure that both the sender and receiver of the data can transmit the data safely. The idea of this article is to try to establish a proprietary encrypted channel at the sending and receiving ends of the data. Based on this encrypted channel, the security of data transmission can be guaranteed even though the data passes through multiple devices in the Internet environment. The data sender and receiver can base on the Site-to-Site <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</sup> VPN <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</sup> channel established by IPSec <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</sup> , cooperate with the domestic encryption algorithm, which not only ensures the security of the data, but also avoids the risk of using foreign algorithms, and realizes the encryption of the data packet during the transmission process. This method has the characteristics of safety and wide applicability, and can be used as a new idea for safe data transmission.
Online Examination Auto-Proctoring System

One of the biggest challenges with online examinations today is the identity verification and proctoring of students giving online exams. For examination, the authority must confirm the authenticity of the online students who are attempting the exam. Additionally, they must make sure that the students successfully complete all of the online examinations without cheating or using any inappropriate methods. So here we have proposed a detailed explanation of the Auto-Proctoring methodologies used in online examination. Currently, human proctoring is the most common assessment method, requiring teachers and students to join online meetings, where they are visually monitored via webcam. However, these methods are laborious and expensive. This system includes a face recognition and attendance system to estimate user authentication developed using LBPH and SVM classifier. Further a talking detection algorithm based on mouth opening algorithm is implemented using Viola Jones algorithm and Mouth Aspect Ratio calculated using Facial Landmarks library by dlib. Audio detection method comprises Speech to text conversion using Speech Recognition libraries and API along with Text summarization using NLP. Further to prevent cheating, tab switching detection and Two or more person detection or No face detection is implemented in the system. By integrating these advanced methodologies and technologies, the proposed AutoProctoring system establishes a secure and cheating-free examination environment.
Secure Peer-to-Peer Communication using Private Network Blockchain Technology

Although centralised chat applications are frequently used in modern communication, it is becoming more obvious that they have limitations. These systems are susceptible to censorship, privacy risks, and other problems that criminals may exploit against them. Decentralised chat apps that support peer-to-peer communication and disperse data among several nodes on a private blockchain network can alleviate these problems. Customers that use these programmes enjoy more reliable, secure, and confidential communication. Using GoEthereum (GETH) technology, a private blockchain network is created to build smart contracts and carry out transactions. The clique consensus method is used in this study to carry out the Proof of Authority (POA) and block validation on the blockchain. By using a private network, platform managers may impose stricter rules and regulations to protect users from undesirable behaviors while also enhancing the security and privacy of users. The proposed research provides users with a safe means of communication, enhancing their experience and placing the highest value on privacy.
Dual-path Residual UNet with Convolutional Attention based Swin-Spectral Transformer Network for Segmentation and Detection of Ovarian Cancer

In this paper, a new transformer based deep learning network (TDLN) model has designed for detecting ovarian cancer (OC) from the input samples. Primarily, the input images are pre-processed to remove the unwanted noises and thereby enhances the quality through HSV color channel conversion, block matching with triple dimensional filtering (BM_TriD-Fil) and improved gabor wavelet transform (IGWT). From the pre-processed images, the affected regions are gets segmented through a new dual-path residual UNet (DPResU-Net) structure. Using the segmented regions, the feature extraction and classification is performed by utilizing a novel convolutional adaptive-attention based swin-spectral transformer network (CA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> _S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TNet) model. In order to tune the parameters of CA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> _S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TNet model, chaotic fire hawks optimization (CFHO) has employed. The proposedTDLN model is implemented in the python platform through TCGA-OV dataset and assessed the performance in terms of different evaluation measures. The maximum classification accuracy obtained by TDLN is 99.91%, superior to the existing approaches for OC classification.
Based on the combination of CNN and LSTM, long and short sentence network education public opinion analysis

In recent years, the importance of education has been balanced in the “national economy and people’s livelihood”. The Internet has provided a convenient way for the public to express their opinions and demands, and people’s attention to education has continued to increase. Online education public opinion and real education influence each other, and have a far-reaching impact on education. By making full use of online education public opinion, education administrators can make more scientific decisions and enhance the public’s ability to supervise education. Emotional tendency is the vane of online education public opinion, which truly reflects the opinions and attitudes of all social strata. These emotional information play a decisive role in the development of online education public opinion, so how to accurately mine the emotional semantic information in the text of online education public opinion has become a research difficulty in the field of sentiment analysis. Deep learning technology is an effective method that can automatically perform the emotional feature generation stage to mine the emotional feature information of the text. This paper takes two different texts, short text comments and long news texts generated in online education public opinion, as the practical application background, and combines deep learning technology to study the sentiment analysis methods of different length texts. Existing short text sentiment analysis methods cannot comprehensively extract the emotional features of text, and rely heavily on a large amount of language knowledge and emotional resources, so it is necessary to make full use of these unique emotional information to make the model achieve the best performance. This paper proposes a novel text sentiment analysis model that combines the advantages of convolutional neural networks and LSTMs. The model first uses the multi-head attention mechanism to learn the dependencies between words and capture emotional vocabulary in short texts. Next, LSTM is used to extract the emotional features of different granularities of the text. Then, the features are fused and the instance feature representation of the text is obtained through the global average pooling layer. Finally, judge the emotional category of the text based on the properties of the capsule. Experimental results show that the model performs well on the short text sentiment classification task and outperforms other baseline models.
Homomorphic Equal and Less Than Query Protocol

In this paper, we propose a homomorphic protocol in which users can perform privacy-preserving equal query and less than query on a database while concealing both the query val-ues and the query results from the database. This homomor-phic protocol allows for a broader range of query types while preserving privacy, compared to the existing private information retrieval protocol that only allows for querying specific indexes. The proposed homomorphic query protocol based on equal and less than queries can be particularly applied to var-ious scenarios such as querying data produced before certain time.
A Viewport Adaptive Low-latency Streaming System for Large-scale Multi-view Service

A viewport adaptive low-latency streaming system for large-scale multi-view service is proposed. The proposed system is implemented based on MPEG-DASH and supports a total of 25 viewpoints. Because video segments for all 25 views cannot be transmitted at the same time due to network capacity limitations, the video segments for a main view and 8 surrounding views are transmitted. At this time, the video qualities for the main view and the surrounding views are 4K and 2K, respectively. The simulation results show the average latency of the proposed system is less than 1 msec.
Hate Speech Detection Using Transformers

One of the most significant difficulties in recent decades has been identifying hate speech. These last few years have contributed to research based on identifying hate speech on social networking platforms. It appears to be a divisive communication that expresses a hate ideology through misunderstandings. The protected characteristics that are the target of hate speech include gender, religion, race, and disability. For some, it might be just fun but for others, it can become the reason for depression, anxiety, etc. Therefore, it is crucial to keep an eye on what a user is posting thus filtering out any that may be related to hate speech before it spreads. However, the technology to detect hate speech is still under development. Although some detection approach has yet been proposed. This paper also proposes an approach to build a classifier based on deep learning using transformers, convolution neural network(CNN) and long-short term memory (LSTM) (TCL) to detect hate speech.
T-Fort:A Tree-Based Fast-Moving Object Trajectory Tracking Algorithm for Table Tennis

Table tennis is a popular sport with high popularity around the world. A key technology in table tennis analysis system is reconstructing the trajectory of table tennis by tracking the fast-moving from images or videos. As the ball is always barely visible and too tiny in the image or video to be detected directly by detection models such as YOLO and Fast- RCNN, researchers usually track it by analyzing their moving patterns. In this article, we propose a tree-based algorithm named T-FORT to tracking the ball and its trajectory. Specifically, we consider all the possibly in a tree-framework, and select the target by some known parameters. We then evaluate the proposed algorithm in a table tennis video dataset, the results show that our method is more precise than algorithm before, and can be widely used.
Cooperative Learning Strategy for Human Behavior Prediction Using Multi-Modal Data

Understanding human behaviors leads to fully-automated systems in the near future. This paper investigates a deep learning solution that forecasts human activity patterns based on sensing signals measured by internet-of-things devices. Practical limitations on these small-form-factor sensors request remote deep learning services at a distant edge computing server. Therefore, we need to involve impairments in sensor-server communication phases, such as random packet loss, resource constraint, and propagation noise, in the design of the remote learning architecture. To address these challenges, we propose a collaborative learning strategy among the sensors and server. Each sensor is equipped with its own encoding neural network that compresses high-dimensional sensing signals to communication messages. These are forwarded to the server through imperfect backhaul channels. Then, a classifier at the server infers desired labels. A joint training mechanism of the encoders and classifier is developed along with the channel impairment. By doing so, we can obtain a robust prediction model for arbitrary communication noises. Numerical results demonstrate the viability of the proposed methods.
Feature Space Manipulation and Fusion for Severe Imbalanced Data

Real-World-Data always follows natural patterns with various undesirable situations. Chinese language is more prone to susceptibility and influence for its rich semantic information. When confronted with severely imbalanced distributions, the existing methods always tend to favor the majority class while neglecting the minority class. However, the performance of the minority class cannot be ignored, and it is particularly crucial not to overlook the performance of the minority class when dealing with situations involving offensive text filtering. In this work, we mainly focus on three aspects, (i): We developed a Chinese Offensive Message Text Dataset, which includes seven categories: Normal, Spam, Advertisement, Fraud, Violent, Pornography, Politics, exhibiting a significant imbalance between Normal and Abnormal. (ii): We proposed a feature space manipulation which combines samples optimization and spatial position optimization for the majority / minority and a hybrid feature prediction module which integrates multi-level feature fusion for comprehensive utilization of sample information. (iii): Experimental results show that our method achieves significant improvement on the offensive classes in our dataset.
Face Recognition for Efficient Identification of Domain Experts at Professional Meetups

Making meaningful contacts and networking are essential in today’s corporate environment if one wants to succeed in their career. However, it can be difficult to recognize people who are pertinent to our area of expertise quickly in situations like conferences, meet-ups, and workshops. This article outlines the requirement for a system that can improve networking by speedily locating people with pertinent skills. The suggested technology, dubbed “People’s Lens,” uses a face recognition model and Firebase database to scan and recognize people’s faces in business gatherings and determine whether they are experts in a particular field. For the system’s front end, web development tools like HTML, CSS, and JavaScript are used, as well as the face-api.js service for face recognition and classification. Additionally, the system scrapes information from the backend database to give the user a summary of the corresponding individual. The proposed system has the potential to make networking at professional meet-ups more efficient, especially when time is limited.
A Social Media System for detecting Child Predators

This text discusses the risks of using the internet and seeing sexually explicit content by sexual predators, as well as the need for psychotherapists to be aware of these issues. It highlights the importance of detecting child predators making advantage of machine learning and a support vector machine (SVM) algorithm. The proposed system includes three modules: the user, training module, and cyber system, and uses SVM for image and text classification. The system aims to protect children from sexual abuse by detecting and reporting damaging comments and posts made by predators on social media platforms. Existing systems use various classification algorithms, while the proposed system offers higher accuracy with the use of SVM. The system’s function involves receiving input from users, going through posts and comments, classifying messages and pictures, and forwarding reports to cyber admins for further action. [6–11].
Elastic Deformable Augmentation for Autonomous Driving

Unlocking the information concealed in 3D point clouds by LiDAR is a key mission for autonomous driving. However, this task is challenging due to the sparsity, continuity, and unordered nature of point clouds. In addition, creating annotated data is time-consuming and expensive. This emphasizes the crucial necessity of data augmentation. Conventional object data augmentation methods such as rotation, scaling, and translation are not fully effective for 3D data. Therefore, we propose a novel augmentation method, Elastic Deformable Augmentation (EDA), which enhances data diversity for better model robustness. EDA applies deformation methods from 3D graphics to the objects, diversifying their shapes without violating occlusion or intensity properties. We demonstrate EDA on the KITTI dataset, where it improves object detection performance, particularly increasing the mean AP scores by 1.55% and 1.00%, respectively. Consequently, our research provides compelling evidence that EDA is a promising approach for augmenting 3D object detection tasks in autonomous driving.
Federated Learning in Prediction of Dementia Stage: An Experimental Study

Federated Learning(FL) has emerged as the optimal approach for training machine learning models when dealing with data containing sensitive information, making data sharing impractical. Particularly in contexts where privacy is a primary concern, such as medical applications, Federated Learning demonstrates its efficacy as a solution. Motivated by this, we have conducted comprehensive experiments using a medical image dataset. One of the key objectives of these experiments is to evaluate the influence of Non-IID data which is frequently encountered in Federated Learning, especially within the medial field. We present our exploration of Federated Learning in classification of OASIS medical images, along with the results obtained from various experiments.
Air Writing Gesture Recognition Using FMCW Radar and Deep Learning

Hand gesture recognition is getting huge attention amongst the research community in the domain of human-computer interaction (HCI). Amongst different HCI methods, air-writing recognition is gaining significant importance. In this paper, we present a deep learning framework to recognize alphabets written in the air using a Frequency Modulated Continuous Wave (FMCW) radar. Human subjects were asked to perform gestures in front of radar and the signal captured through radar is localized in two dimensions. Afterwards, a deep learning model is trained on the patterns generated by localizing hand movements. Data from eight human volunteers is captured and promising accuracy of above 90% is achieved.
LookTell: A Deep Learning Based Smart Assistant for Visually Impaired

Around 14 million people in our country are visually impaired, facing difficulties when travelling and requiring assistance. Family and friends often worry about their whereabouts, while emergencies can be challenging. Identifying currency and using online banking can also be problematic, leaving them vulnerable to fraud. To address this, we have developed a Currency Recognition System (CRS) using a Teachable Machine with smart Image-to-Text conversion. This enables blind individuals to recognize bank notes, while an android-based location tracking system helps loved ones monitor their real-time location. Our system uses transfer learning to provide fast and accurate results, making life easier for the visually impaired. The accuracy of proposed system is 97.33%.
Is LinkedIn going to become the next Facebook?

Nowadays many people spend substantial amount of time on social networks and tend to express their emotions and sentiments more on online platforms than anywhere else. Writing has always been an easy way for many people to express themselves and social media platforms like Facebook and LinkedIn pave a path. LinkedIn has always been a professional platform from its nascent days and it also provides a great dataset of posts to analyse the sentiment of people over time. More than 900 million profiles on LinkedIn justifies our statement and provides us with a good basis to analyse the sentiments of different people in different segments at different times. In this paper, we have analysed the sentiment of people over time using their LinkedIn posts with special focus over COVID-19 period, and its pre and post periods. A scrapping mechanism was formulated and implemented to get dataset of more than 40,000 user profiles and 50,000 posts on LinkedIn. LinkedIn has changed a lot over time and we have analysed its trends and patterns over time using various metrics to comment on its future and whether it will remain a professional platform or become a next social media platform like Facebook. Sentiments were classified in different categories using polarity and subjectivity data and trends were identified using patterns in keywords, hashtags and hashtag groups (formed from hashtags).
ORGANiser

With regards to listing patients, organ matching, procurement, transportation and transplantation, current organ procurement and transplantation systems provide various requirements and obstacles as well as technological, clinical, ethical, and legal limitations. Therefore, a complete system for donating organs and its process of transplantation, is needed to provide a just and effective procedure that will improve patient experience and confidence. The objective is to develop a website that will automate and secure the organ donation procedure while safeguarding private patient and donor health information to obviate any chance of manipulation.
Beyond Perspectives: Enhancing Pose Estimation via Viewpoint Transformation

A number of approaches have been proposed to address the occlusion and truncation issues in pose estimation, but they typically require additional devices or specific recording environments. In this paper, we propose a novel pose estimation mechanism that utilizes GAN-based viewpoint transformation. Our mechanism complements missing pose information without requiring additional devices or a pre-aligned camera setups. It achieves this by transforming a supplementary viewpoint video to the target viewpoint video utilizing GAN and using it to complement missing keypoints in the target viewpoint video. The evaluation results confirm that our mechanism effectively complements missing pose information and provides reliable viewpoint transformation performance.