Reviewerly/exHarmony huggingface dataset:
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models

Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics. To address these limitations, we introduce AutoKG, a lightweight and efficient approach for automated knowledge graph (KG) construction. For a given knowledge base consisting of text blocks, AutoKG first extracts keywords using a LLM and then evaluates the relationship weight between each pair of keywords using graph Laplace learning. We employ a hybrid search scheme combining vector similarity and graph-based associations to enrich LLM responses. Preliminary experiments demonstrate that AutoKG offers a more comprehensive and interconnected knowledge retrieval mechanism compared to the semantic similarity search, thereby enhancing the capabilities of LLMs in generating more insightful and relevant outputs.
UniMHe: Unified Multi Hyperedge Prediction A Case Study on Crime Dataset

Edge prediction is a fundamental challenge in network science, with broad applications, notably in social networks. It plays a crucial role in unveiling complex system dynamics by forecasting connections between entities. Our paper introduces UniMHe (Unified Multi Hyperedge Prediction), a novel framework for predicting multiple hyperedges associated with each node using hypergraph representations. We present a case study focused on crime network analysis, where UniMHe reveals intricate patterns in criminal activities, including crime types, locations, and seasonal variations. Our research leverages extensive historical crime data encompassing geographical information, timestamps, points of interest, and crime categories. In an extensive evaluation, we benchmark UniMHe against state-of-the-art hypergraph deep learning techniques, highlighting its superior performance. These findings underscore the significance of UniMHe across various domains and problem-solving scenarios.
Uncovering and Understanding Smart TV Users’ Picture Quality Preferences via Big Data Analytics

We analyzed one year’s device usage log data collected from Smart TVs launched in the US to uncover and understand user-preferred picture quality previously unknown. Through K-means cluster analysis, we first clustered similar users into groups based on their historical picture setting activities on TVs. We then performed K-prototypes clustering upon data instances from a specific group of users, who were reasonably active in customizing picture quality, to discover new picture setting configurations distinguished from the factory default. Lastly, we conducted mixed effects modeling on the collected datasets to determine contextual factors impacting the user preferences towards the discovered picture settings. The analysis yielded several implications for the consumer electronics industry in providing users with the picture quality best suited for their current situation and personal characteristics.
OrthoNets: Orthogonal Channel Attention Networks

Designing an effective channel attention mechanism implores one to find a lossy-compression method allowing for optimal feature representation. Despite recent progress in the area, it remains an open problem. FcaNet, the current state-of-the-art channel attention mechanism, attempted to find such an information-rich compression using Discrete Cosine Transforms (DCTs). One drawback of FcaNet is that there is no natural choice of the DCT frequencies. To circumvent this issue, FcaNet experimented on ImageNet to find optimal frequencies. We hypothesize that the choice of frequency plays only a supporting role and the primary driving force for the effectiveness of their attention filters is the orthogonality of the DCT kernels. To test this hypothesis, we construct an attention mechanism using randomly initialized orthogonal filters. Integrating this mechanism into ResNet, we create OrthoNet. We compare OrthoNet to FcaNet (and other attention mechanisms) on Birds, MS-COCO, and Places356 and show superior performance. On the ImageNet dataset, our method competes with or surpasses the current state-of-the-art. Our results imply that an optimal choice of filter is elusive and generalization can be achieved with a sufficiently large number of orthogonal filters. We further investigate other general principles for implementing channel attention, such as its position in the network and channel groupings. Our code is publicly available at (https://github.com/hady1011/OrthoNets)
A migration learning based binary classification method for small datasets

With the rapid development of digital technology, people increasingly use images to express and convey information, and image classification, as a core problem in the image field, has shown its great potential and value. Therefore, the use of deep learning techniques to achieve accurate and fast classification of images is a problem that needs to be solved urgently nowadays. However, deep learning methods at the current stage rely too much on huge data samples and detailed data annotations. In the above context, this paper proposes a binary classification method based on migration learning for small datasets. In the construction of the migration model, the Swin MAE model is adopted for the upstream task, and the strategy of the Swin Transformer model is adopted for the downstream task with global identification. Comparing with the mainstream deep learning network at the current stage, the experimental results show that on the relevant small dataset, the method proposed in this paper has a better classification effect, with an accuracy of 97.98%, a sensitivity of 96.2%, and a specificity of 95.6%, which is a certain advantage compared with the mainstream deep learning classification method at the current stage.
The Nexus Between Big Data Analytics and the Proliferation of Fake News as a Precursor to Online and Offline Criminal Activities

This paper presents a novel framework for the thorough analysis of fake news and disinformation campaigns, which have the potential to result in both offline and online criminal activities. Its primary focus relies on the spread analysis of disinformation across social media and online platforms, aiming to uncover the underlying dynamics and mechanisms driving the dissemination of false information. The framework integrates state-of-the-art Natural Language Processing (NLP) techniques for sentiment analysis, Deep Learning (DL) algorithms for prediction of criminal activties related to the disiformation spread and graph analysis to identify key actors and propagation pathways. To address the emerging challenges of disinformation that transcend the online realm and have tangible real-world consequences, the framework extends its analysis to potential offline actions incited by disinformation, such as acts of violence and public unrest or the disruption of public health efforts especially in case of pandemics. By exploring the complex interconnections between disinformation and crimes, our research aims to contribute to a deeper understanding of the societal implications of false information and provide actionable insights for policymakers, security practitioners and the broader public.
Students’ Classroom Behavior Recognition Based on Behavior Pose and Attention Mechanism

Artificial intelligence technology drives the reform of traditional teaching concepts, models, content, and methods, providing assistance for the informatization and intelligence of education. Classroom teaching activities have always been a focus of research in the field of education. In complex classroom behavior recognition scenarios, it is significant to recognizing students' classroom behavior by using computer vision techniques. However, the prevailing behavior recognition methods currently demonstrate poor performance in classroom behavior recognition. Consequently, this article introduces a college student classroom behavior recognition model that integrates behavior pose and attention mechanisms to enhance the accuracy of recognizing student classroom behavior in high school classrooms. To validate the effectiveness of the proposed model, experiments were conducted using a labeled high school classroom behavior dataset. The experimental results show that the proposed model performs exceptionally well in recognizing college students' classroom behavior.
Research on Intelligent Oral English Teaching Training System Based on Computer Multimedia Technology

This paper proposes a feature-based oral English language correction system. The system solves problems that cannot be corrected and fed back to the learner in a timely manner. The whole speech recognition system includes speech feature extraction and correction. Error correction is the key link of the whole equipment. It performs distortion operations on the detected results and identifies inaccurate pronunciations. The effect of speech comparison can be obtained through feedback path and sequential mode. Rough mode is used to control the effect, so as to realize the correction of speech. The experimental results show that this method can quickly and accurately identify correct pronunciation, and can quickly achieve speech correction. It realizes the scientific and accurate pronunciation correction system of spoken English.
Breast Cancer Classification Using ANN and ML Techniques

Millions of individuals throughout the world are impacted by the significant health issue known as breast cancer. To improve patient outcomes, breast cancer must be identified early and correctly diagnosed. In this study, we used TensorFlow to create an artificial neural network (ANN) model to categorise breast cancer. To compare with our ANN model, we also employed seven alternative ML classifiers as basis models, including Line SNM, LR, KNN, NB, DT, RF, and Gradient Booster. For each model, we determined the precision, accuracy, recall, and F1 score. To improve performance, we ensembled all the classifiers. Each classifier's accuracy was analysed and contrasted, and the classification performance was demonstrated via a confusion matrix. Our results show that the ANN have given an accuracy of 97% and the ML model achieved the highest accuracy of 98.8% using LR, followed by SVM with 97.7%, KNN with 96.7%, RF with 95.6%, DT with 95.4%, NB with 95.3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and Gradient Booster with 94.2% The ensemble model gave the overall accuracy to 97.7%. These findings indicate that our proposed ANN model and ensemble approach are effective but LR gave the highest accuracy in classifying breast cancer and can serve as a useful tool for clinicians and researchers in this field.
Multi-Item Continuous Influence Maximization

Influence maximization in social networks plays an important role in many real-world applications, e.g., viral marketing and trend detection. Current research on influence maximization focuses on promoting one single item/product under discrete settings. The basic assumption is that each user should be chosen to receive either a full incentive (as seed users) or no incentive (as non-seed users) on one single product (e.g., iPhone XS). However, many online shopping platforms, such as Amazon, sell multiple products (e.g., iPhone, iPhone cases, Android phones, etc.) and promoters can give different percentage discounts to different users on different products. In this paper, we study the problem of multi-item influence maximization under continuous settings, where the influence diffusion includes multiple items and each user is given a different level of incentive (e.g., percentage discount) on each item. This problem is challenging and different from previous work on influence maximization since different levels of incentive can lead to different levels of influence on each user. Moreover, not only users can influence each other through a social network, but items can also influence each other through an item network. We propose a novel solution, called MMF (Multi-item Mean-Field), for the multi-item continuous influence maximization problem, based on the mean-field approximation of Ising networks. The proposed MMF model can effectively maximize the total purchase of multiple items by taking social influence as well as item influence into account. The experiments demonstrate the effectiveness of the approach on both simulated and real-world datasets.
A Novel Method to Enable Transfer Learning of Structural Graph Representations

Graph Representation Learning (GRL) methods which effectively capture a node’s neighborhood structure in their representations can show excellent performance on important machine learning tasks such as node and graph classification. Recent work has focused on scaling GRL to massive graphs, but existing methods are transductive (must be re-trained for unseen nodes) and are often geared to learn proximity rather than node structure. Graph Neural Network methods can learn structure, but are often supervised, prone to learn proximity, and do not scale well for massive graphs. Transfer learning has the potential to enable scaling to massive graphs, while preventing overfitting, and creating universal models for use on a wide variety of datasets. We propose a novel method that enables transfer learning. Our model performs better at tasks which require capture of nodes’ structural information and scales as well as the current state of the art to very large graphs.
Study of California Earthquake Prediction Using Machine Learning Approach

Earthquake prediction is a highly challenging and complex task that has been the subject of scientific inquiry for many years. Even though we have learned a lot about earthquakes, it is still hard to predict them accurately and reliably. This is because earthquakes are complicated and hard to predict, and because our scientific knowledge and technology are not as advanced as they could be. There have been many ideas about how to predict earthquakes, such as using seismological and geophysical data,. But a lot of work still needs to be done to make a reliable and effective earthquake prediction system that can warn people in time and lessen the damage caused by earthquakes. In this work, an intelligent earthquake prediction model is developed using various machine learning algorithms. The seismological data of USGS is used for the model training and validation. The system has shown an prediction accuracy of 92.7%
Research on Elderly Services Based on Matlab Image Processing and SPSSPRO

At present, the trend of aging in China is getting more and more serious, and it is of great significance to predict the number of beds demanded by the elderly. In this paper, we firstly investigated and analyzed, we chose the number of the elderly population, the "elderly model 9037", the constant level of Chinese household consumption and the degree of self-reliance of the elderly as the variables of the model of the demand for elderly service beds. By reviewing the number of elderly population and the number of labor employment in China from 2009-2018 using polynomial fitting to reasonably predict the values of the two variables for each year from 2019-2025, substituting them into the model of demand for senior care service beds to derive the values of the demand for senior care service beds. Secondly, the model of balance of payments and revenues is established to consider the costs and revenues, and the amount of revenues and expenditures is calculated according to the different types of elderly people. The potential revenue in different years is calculated to quantify the size of the market opportunity. Meanwhile, to further address the current situation of the prominent contradiction between supply and demand of elderly facilities in Jiancaoping District, Taiyuan City, this paper utilizes Matlab image processing technology and SPSSPRO software to carry out K-mean clustering algorithm, and designs a reasonable planning scheme for the elderly facilities in the district.
A Comprehensive Study on Authentication Systems

In present authentication systems on the web, users are compelled to interact with identity providers. Initially, they are required to register on a particular website wherein they fill all their details. After this stage, they get a user id and password or token which they can use for accessing the application and their respective features. However, from security point of view, this type of system can be challenging. In such systems, authentication of data is available with the identity providers. It could be hacked to obtain the user passwords and other details. Various current systems track the activity of users and users provide access to sensitive information for the same. For example, access to storage, files, contacts, etc. To make sure that data is available, third-party servers are required which need to be available during authentication. In current, various methods for authentication such as Single-Factor, Two-Factor Authentication (2FA), Single Sign-On, Multi-Factor Authentication, etc are used. In this paper, we will study the authentication systems, their advantages and flaws along with the protocols used.
Identification of Fake Products using Blockchain

Manufacturing and marketing of fake or knockoff items and goods puts consumers' finances, health, and safety at risk. Additionally, through missed sales, interruptions, cost of substitution, forcing many brands to spend money fighting counterfeits, potentially endangering the trust of business partners, stealing revenue, etc., it hurts the economic development of original manufacturers and companies. A blockchain-based system is utilized in the identification of original products and also detects duplicate products to assure the identification of original goods in order to overcome and stop these significant repercussions of counterfeiting. Due to the substantial developing trends in wireless communication, we used QR (Quick Response) codes in this research to give a solution to limit the activity of item counterfeiting. Utilizing the item's QR code, which is connected to a blockchain to store the item's details and guaranteed unique code as database blocks, fake goods can be detected. Customers won't have to depend entirely on businesses to determine whether products are real or phony thanks to this technique for minimizing counterfeit.
Active Data Reconstruction Attacks in Vertical Federated Learning

Vertical Federated Learning (VFL) stands out as a promising approach to safeguard privacy in collaborative machine learning, allowing multiple entities to jointly train models on vertically partitioned datasets without revealing private information. While recent years have seen substantial research on privacy vulnerabilities and defense strategies for VFL, the focus has primarily been on passive scenarios where attackers adhere to the protocol. This perspective undermines the practical threats since the attackers can deviate from the protocol to improve their inference capabilities. To address this gap, our study introduces two innovative data reconstruction attacks designed to compromise data privacy in an active setting. Essentially, both attacks modify the gradients computed during the training phase of VFL to breach privacy. Our first attack uses an Active Inversion Network exploiting a small portion of known data in the training set to coerce the passive participants into training an auto-encoder for the reconstruction of their private data. The second attack, Active Generative Network, utilizes the knowledge of the training data distribution to guide the system into training a conditional generative network (C-GAN) for feature inferences. Our experiments confirm the efficacy of both attacks in inferring private features from real-world datasets.
Unveiling the Anatomy of Phishing Attacks: Strategies, Targets, and Cybersecurity Solutions

This paper undertakes a comprehensive analysis of phishing attacks, a pervasive and sophisticated cyber threat facing individuals, organizations, and online systems. It examines the techniques employed by attackers, including deceptive emails, website spoofing, and social engineering tactics. Additionally, emerging trends such as spear phishing, clone phishing, and voice-based phishing are investigated to draw attention to the constantly shifting landscape of malicious campaigns. To mitigate the risks associated with these attacks, existing countermeasures are critically evaluated for their strengths and weaknesses. These include both technical solutions like email filters and anti-phishing tools as well as user awareness programs that can enhance human resilience against phishing attempts. The findings of this research illustrate the importance of a multi-faceted approach in order to effectively combat phishing attacks and minimize the impact on individuals and organizations.
Roughness Index for Loss Landscapes of Neural Network Models of Partial Differential Equations<sup>*</sup>

Loss landscape is a useful tool for characterizing and comparing neural network models. The main challenge for analysis of loss landscape for the deep neural networks is that they are generally highly nonconvex in very high-dimensional space. In this paper, we develop the “roughness” concept for understanding such landscapes in high dimensions and apply this technique to study two neural network models arising from solving differential equations. Our main innovation is the proposal of a well-defined and easy-to-compute roughness index (RI) which is based on the mean and variance of the (normalized) total variation for one-dimensional functions projected on randomly sampled directions. A large RI at the local minimizer indicates an oscillatory landscape profile and indicates a severe challenge for the first-order optimization method. Particularly, we observe the increasing-then-decreasing pattern for RI along the gradient descent path in most models. We apply our method to two types of loss functions used to solve partial differential equations (PDEs) when the solution of PDE is parametrized by neural networks. Our empirical results on these PDE problems reveal important and consistent observations that the landscapes from the deep Galerkin method around its local minimizers are less rough than the deep Ritz method.
Speech Synthesis: An Empirical Analysis of Various Techniques in Text to Speech Generation

Speech is the most important resource of communication and a part of human development. Speech Synthesis gives an opportunity to communicate with deaf and dumb people. The state of the art in speech synthesis is automatically generate speech from text. In the digital era, a text-to-speech synthesizer system is able to read any text clearly. This is a primary difference between any other talking machine and a text-to-speech synthesizer. The recent task of speech synthesis is to produce speech in the form of acoustic waveforms from text input. It is used in conversational and nonconversational applications also. In conversational applications like speech recognizers, telephone-based dialogues with people, current chatbots, etc., and in non-conversational applications like video games, children's toys, devices, or machines which are helpful for blind people to read out the text or messages loud. The purpose of this article is to analyze generation-wise growth in speech synthesis techniques. In the comparative analysis, we have done the analysis of speech synthesis methods and a few evaluation parameters such as corpus size, the complexity of each technique, intelligibility, the naturalness of sound, advantages, and limitations. We have highlighted a few research gaps in those techniques. As a contribution proposed a framework to produce speech from the text as well as images using the Lj speech dataset. We have adopted the Tacotron model to generate speech from the text in the English language. For an evaluation generated speech is evaluated with a Mean Opinion Scale parameter from 30 listeners.