Reviewerly/exHarmony huggingface dataset:
A Hybrid Method of Sentiment Analysis and Machine Learning Algorithm for the U.S. Presidential Election Forecasting

U.S. Presidential Election forecasting has been a research interest for several decades. Currently, election prediction consists of two main approaches: traditional models that incorporate economic data and poll surveys, and models that leverage X and other social media platforms due to their increasing popularity in the past decade. However, traditional approaches have predominantly focused on national-level predictions, while social media-based approaches often oversimplify the nuanced differences between online discourse and the broader voting population’s political landscape.In this work, we perform a hybrid method of both the machine learning algorithm and the sentiment analysis on the state level with various independent variables including census data, economic indicators, polling averages, and the newly defined average sentiment scores from X. Our prediction for the 2020 U.S. Presidential Election yielded promising results. Most of our models successfully predicted a victory for the Democratic candidate with 96% accuracy using Gradient Boosting Trees and Multi-Layer Perceptron algorithms. This novel prediction framework addresses the limitations of existing U.S. Presidential Election forecasting approaches, particularly in terms of state-level predictions. It provides a valuable foundation for future research in this field and contributes to advancing our understanding of election dynamics.
Image recognition and age estimation based on the graph neural network

Image recognition and age estimation has always been one of the important problems in computer vision and image detection. Accurately estimating the age of face image is of great significance for face recognition, security monitoring, age-specific recommendation of products and other fields. However, existing age estimation methods based on CNN or transformer-based face images have poor modeling power for irregular objects. To solve the above problems, this paper proposes a face image age estimation model, which uses the graph convolutional neural network to extract features from the face image, and model and predict through the node connection of the graph neural network and the features of the node itself. Furthermore, to further enhance the representational power of the model, the multi-head attention mechanism is introduced. The multi-head attention mechanism can adaptively learn the degree of attention between different features and fuse information from different attention heads to improve the accuracy and robustness of age estimation. The experimental results show that the proposed method achieves excellent age estimation performance on the MORPH dataset. The methods based on graph neural networks and multi-head attention mechanism have obvious advantages in terms of accuracy and robustness. Moreover, the method exhibits good robustness for difficult situations such as noise, occlusion and expression changes. The method proposed in this paper introduces two novel ideas of graph neural network and multi-head attention mechanism in the field of face image age estimation, which provide new directions and enlightenment for research in this field.
Image Quality Analysis and Verification for Vehicle Photos Using Deep Learning

This paper offers a thorough overview of the state of the art in image quality assessment, vehicle detection, and number plate extraction. This research would serve as the foundation for building an automated system for vehicle verification in online vehicle Leasing applications. In order to accurately assess vehicle conditions, confirm vehicle identity, and guarantee compliance with rules, these duties are essential in avoiding bottlenecks that could occur during manual verification in vehicle leasing applications. The best approaches for each task are determined through a thorough examination and comparison of the available models, taking into account elements like accuracy, resilience, and real-time processing capabilities. In order to support the creation and application of effective and precise systems for picture quality evaluation, vehicle detection, and number plate extraction, this research paper seeks to serve as a useful resource for professionals working in the auto leasing industry or for other vehicle related purposes. In this Paper, We Use SRGAN(Super Resolution Generative Adversarial Network) for Image Quality Evaluation of Vehicle with Threshold Value Of 0.93 i.e Lower value than Threshold indicated Poor Image Quality And Greater Value than Threshold Indicates Good Image Quality. We also use three different pre-trained models (Inception, Xception and Mobile Net) for Vehicle Detection, where we use a “Vehicle Detection image Dataset” from kaggle containing Vehicle and Non - Vehicle Images. After Training and Testing the data set with all three models, We come into conclusion that Mobile -net give most accurate result with about 99.4% accuracy. For Number Plate Extraction we use LPRNet Algorithm which give us a mean accuracy of about 0.9.
Improving Adversarially Robust Sequential Recommendation through Generalizable Perturbations

Sequential recommendation is of great importance for a variety of purposes, such as application engineering, resource optimization, and marketing. Yet, existing sequence-based recommendation models are susceptible to adversarial attacks, which aim to perturb input sequences and mislead trained models, resulting in incorrect predictions. Defense methods are accordingly adopted to enhance model robustness. Nevertheless, these methods encounter challenges, such as error propagation (from the model output to generate adversarial samples), the high system complexity, and the difficulty of maintaining the model generalizability. To bridge this gap, this paper introduces a simple yet effective adversarial defense algorithm, termed Perturbation-Driven Sequential Recommendation (PDSR). In the training process, PDSR leverages a simple perturbation-generation module to create adversarial samples, eliminating the need for gradient estimation, thus streamlining the process. Additionally, it also incorporates a robust encoder designed to increase tolerance towards representation variations by ensuring alignment between original and perturbed representations, thereby boosting model generalizability. Comprehensive experiments are conducted based on a combination of five benchmark datasets, two attack methods, and four sequential recommendation models. When compared to four state-of-the-art defense baselines, PDSR demonstrates notable improvements in defense performance.
Novel Approach for Real-Time Exercise Posture Correction Using Computer Vision and Cnn

In recent years, there has been an increased focus on physical fitness, this underscores the need for proper exercise form to prevent injuries and maximize benefits. This paper presents a system that employs Computer Vision and Machine Learning for Real-Time Exercise correction. By analyzing exercise videos and utilizing pose estimation algorithms, the system identifies incorrect postures and provides immediate Feedback to users, ensuring correct exercise execution. The study's findings demonstrate that the proposed system achieves a high level of accuracy in detecting incorrect postures, thereby enhancing exercise safety and efficacy. By ensuring proper form, our system has the potential to be an effective tool for individuals looking to optimize their workout routines for a healthier lifestyle.
Multi-label Co-training using a Graph neural network.

Text data has grown exponentially due to social media, e-commerce, and online reviews. Recent studies have explored graph neural networks (GNNs) for text classification, since they are not capable of handling complex structures and preserving global information. To overcome these issues, we propose a GNN-based co-training model that creates graphs based on global parameters instead of single parameters It retains global information while eliminating dependency on text and corpus. In addition, we use smaller text windows to extract more local features. Graph neural networks (GNN) have focused on natural language processing in recent years, outperforming existing models while consuming less memory. Co-training enhances the limited amount of labeled data used to train each classifier with pseudo-labeled data derived from predictions from both the classifiers. Moreover, pseudo-labels cannot be controlled by the co-training of the data. Wrongly trained classifiers can generate inaccurate pseudo-labels that cause noticeable errors when used as training data for other classifiers. This can lead to a model overfitting the training data, which can result in poor generalization of the model when applied to new data. To address this issue, we propose a deep multi-label co-training framework for semi-supervised GNNs (MLD-GNN). Two GNNs are trained simultaneously on the same training data. GNNs use original labeled and pseudo-labeled data for training the model. In order to minimize the negative impact of inaccurate pseudo-labels on learning data augmentation, label augmentation strategies make pseudo-labels available earlier during training.
Human Activity Recognition with Smartphone Sensors Using RNN

In recent time, different hardware, smart devices, machine learning model and deep learning model are used for Human Activity Recognition (HAR) applications. The main implication of this question is that which creates HAR models with high accuracy, uses low cost. Smartphones have become an important part of persons life; they play vital role in human activity recognition systems to detect human actions. To recognize human activity, this article proposes the use of Logistic Regression and Recurrent Neural Networks (RNNs), which is used for handling time series data or sequential data. The long-term memory network model recognizes six basic human activities: sitting, standing, walking up and down, lying down, and walking. The LSTM model is an advanced form of Recurrent Neural Network that eliminates leaky gradients and burst gradients of RNN. It helps us to examine past information to perform current tasks or give output based on current input.
SmartDT: An Effective Vulnerability Detection System of Smart Contracts Based on Deep Learning

In recent years, blockchain technology has received widespread attention. Smart contracts are programs that run on the blockchain, and their security faces serious challenges for blockchain applications. Inspired by the success of artificial intelligence technology, some smart contract vulnerability detection methods based on deep learning have been proposed and achieved meaningful progress. However, a closer look reveals three flaws in these works. First, some works simply use LSTM modules to perform sequence learning on pre-processing smart contracts, which lacks the ability to extract long-range features and does not support parallel processing of inputs. Secondly, smart contract vulnerability detection methods based on deep learning lack interpretability of results. From the perspective of system design, in order to solve these problems, we propose SmartDT, a more effective, faster, and interpretable smart contract vulnerability detection system. Specifically, (i) we propose an attention-based deep learning smart contract detection module, which is able to learn long-range dependencies in inputs and supports parallel processing of inputs. (ii) After the deep learning module, we stack an optional symbolic execution module for enhancing the interpretability of the classification results. Compared with general symbolic execution detection, our method can achieve faster detection because we can call a specific symbolic analysis model to detect input based on the classification results of the deep learning module. Extensive experiments demonstrate the effectiveness of our proposed method. Compared with other machine learningbased methods, our method achieves better performance and better interpretability, which demonstrates SmartDT’s outstanding feature learning capabilities. Compared with symbolic execution methods, our method achieves faster and more effective detection. In addition, we conducted ablation experiments to verify the effectiveness of each module.
A Human-Machine Collaborative Video Summarization Framework Using Pupillary Response Signals

This paper proposes a novel, human-machine collaborative, pupillary response-based video summarization framework. Considering that humans are the end-users and evaluators of video content, it is natural to establish the links between video features and real-time viewers' attentive response for designing a video summarization framework. In this paper, pupil size, a pupillary response, is introduced as a real-time indicator for assessment of viewers' engagement and attention. Firstly, we augmented the TVSum dataset by replacing manual annotations with attention scores that converted from pupillary size signals to generate the perceptually driven dataset. Secondly, we developed a video summarization framework which uses cues from pupillary size signal of humans to predict frame-level attention scores, and then extracts key shots from videos. On the perception-driven dataset, the average F-measure of the proposed summarization method is 69.71%, while the precision and recall are 69.67% and 69.77%, respectively, which is a significant improvement compared to random summarization. Experimental results initially demonstrated that our modeling approach can learn the dynamic attention mechanism of viewers and apply it to video summarization. In addition, the experimental results validate the effect.
PGAN-KD:Member Privacy Protection of GANs Based on Knowledge Distillation

Generative adversarial networks (GANs) have been widely used for creating diverse data such as images, audio, and videos. However, as the training data of GANs often contain sensitive information, they are vulnerable to privacy attacks on the training dataset, such as membership inference attacks (MIAs). To improve the resistance of GANs to MIA while ensuring their performance, we design a novel GAN framework PGAN-KD (member Privacy protection of GANs based on Knowledge Distillation). PGAN-KD prevents the discriminator from leaking membership information of the training data by introducing knowledge distillation and gradient clipping. Specifically, it adopts an extra teacher discriminator to distilling knowledge and then transfer it to a student discriminator, and thereby isolating the attacker from indirectly obtaining private information through the generator. In addition, the teacher discriminator prevents itself from MIAs through gradient clipping. To evaluate the performance of PGAN-KD, we conducted experiments on both real and simulated datasets. The results indicate that PGAN-KD achieves a 7.8% improvement in privacy protection levels while maintaining similar generation performance with the baselines.
Practice-Oriented Approaches for Information and Metadata Management in a Content Management System - Learnings from the Smart City Project LOKAL-digital

Metadata play an important role in the organization of information. As data about data, they explain how information relates to one another, in what chronological sequence it arises, or what hierarchical structures can be formed. In the content management system of the smart city project “LOKAL-digital”, metadata is utilized for the structuring of information in the areas of housing, health and care. This paper gives practical approaches for the implementation of metadata management in the development of the information portal and reports on the lessons learned in building the knowledge management solution. The use of metadata for structuring and searching content is discussed in various use cases.
Apriori Algorithm Based Exam Information Service Web App Design

This academic paper presents the design and implementation of an Apriori Algorithm-Based Exam Information Service Web App. The web app aims to provide personalized recommendations and valuable insights to users to enhance their exam preparation process. The system architecture, data preprocessing techniques, Apriori algorithm implementation, and evaluation of system performance and user satisfaction are discussed in detail.The system architecture of the web app is designed to efficiently retrieve exam-related information and generate personalized recommendations based on user preferences. The components and functionality of the system are outlined, emphasizing the importance of data preprocessing, association rule mining, and recommendation generation.The results of the evaluation indicate that the Apriori Algorithm-Based Exam Information Service Web App shows promising performance. The recommendation system achieves high precision and recall, indicating the relevance and retrieval of relevant recommendations. The coverage metric demonstrates the system's ability to recommend items from a diverse set. User satisfaction ratings further validate the effectiveness of the system in meeting user needs and expectations.
NASEREX: Optimizing Early Exits via AutoML for Scalable Efficient Inference in Big Image Streams

We investigate the problem of smart operational efficiency, at scale, in Machine Learning models for Big Data streams, in context of embedded AI applications, by learning optimal early exits. Embedded AI applications that employ deep neural models depend on efficient model inference at scale, especially on resource-constrained hardware. Recent vision/text/audio models are computationally complex with huge parameter spaces and input samples typically pass through multiple layers, each with large tensor computations, to produce valid outputs. Generally, in most real scenarios, AI applications deal with big data streams, such as streams of audio signals, static images and/or high resolution video frames. Deep ML models powering such applications have to continuously perform inference on such big data streams for varied tasks such as noise suppression, face detection, gait estimation and so on. Ensuring efficiency is challenging, even with model compression techniques since they reduce model size but often fail to achieve scalable inference efficiency over continuous streams. Early exits enable adaptive inference by extracting valid outputs from any pre-final layer of a deep model which significantly boosts efficiency at scale since many of the input instances need not be processed at all the layers of a deep model, especially for big streams. Suitable early exit structure design (number + positions) is a difficult but crucial aspect in improving efficiency without any loss in predictive performance, especially in context of big streams. Naive manual early exit design that does not consider the hardware capacity or data stream characteristics is counterproductive. We propose NASEREX framework that leverages Neural architecture Search (NAS) with a novel saliency-constrained search space and exit decision metric to learn suitable early exit structure to augment Deep Neural models for scalable efficient inference on big image streams. Optimized exit-augmented models perform $\approx 2.5 \times$ faster having $\approx 4 \times$ aggregated lower effective FLOPs, with no significant accuracy loss.
Dual-Path Side Information Fusion for Sequential Recommendation

Sequential recommendations are designed to capture user preferences based on their past actions and predict the items they may interact with in the next moment. Benefiting from the self-attention mechanism, methods that utilize side information (such as item categories or brand) to improve the prediction performance of sequential recommendation have yielded promising results. Previous approaches typically directly fuses side information embeddings into item embeddings as inputs to the model. However, this fusion approach overlooks the distinctions in various types of information in sequential pattern inference, and also failing to fully model the relationship between items and side information. In this work, we propose a Dual-Path Side Information Fusion method (DPIF) to better utilize side information for improved recommendation performance. Our model employs two parallel paths for side information fusion modeling. One path obtains the relationship representation within the items and the side information, and the other path obtains the relationship representation between the items and the side information. Subsequently, an attention-based adaptive fusion module is utilized to combine inter-attribute relationship and intra-attribute relationship representation, generating the final user preferences. Extensive experiments were conducted on four real-world datasets, demonstrating the effectiveness of the introduced model. Our source code is available at https://github.com/ZhangYu-x/DPIF.
Realization of Positive Feedback Source Coupled Logic Even Parity Generator/Checker

In this paper, the realization of a even parity generator/checker (3-bit) in positive feedback source coupled logic style (PFSCL) is proposed. Three different realizations for even parity generator/checker (3-bit) are proposed. The first realization is based on employing conventional PFSCL-based NOR/OR gate, second realization use PFSCL fundamental cell (FC), and third realization is based on involving memristor in PFSCL style. The proposed implementations are simulated and validated using LT SPICE simulations with PTM 90nm CMOS technology parameters. A comparative analysis is carried out to evaluate the performance based on gate count, transistor count, average delay, power consumption, power delay product (PDP), and area. It is observed that proposed realization-3 outperforms over the other proposed realizations. The proposed realization -3 involving the use of memristors in PFSCL 3-bit even parity generator realization achieves a maximum reduction of 48%, 87.5%, 93%, 95.66% in average delay, power, PDP and area values respectively in comparison to other realizations that are based on existing architecture. The simulation results for the checker follows the similar trend. The impact of technology scaling is also studied for all the proposed realizations.
Analysis of Dementia Prognostication Using Computational Models

Dementia is a progressive neurological condition characterized by a decline in cognitive function. Predicting dementia early offers numerous benefits by allowing for timely interventions and planning. Early diagnosis enables individuals to access treatments and support services, potentially slowing the disease's progression and improving their quality of life. Ultimately, early prediction enhances the overall well-being of individuals affected by this challenging condition. The focus of this investigation centers on the prediction of Dementia by employing a range of Machine Learning algorithms to identify the most suitable one for the task. The dataset employed encompasses 373 individual imaging sessions that were conducted on each patient, spanning a minimum of two visits with a time gap of at least one year between them. We applied algorithms both with and without ensemble techniques, namely the Light Gradient Boost machine, XG Boost Model, Random Forest, and the Support Vector Machine Model (SVM). Notably, the ensemble Gradient Boosting Model demonstrated the most promising performance, achieving an accuracy rate of 91.2%.