Reviewerly/exHarmony huggingface dataset:
Poet Attribution of Urdu Ghazals using Deep Learning

Poet attribution focuses on determining ownership of a piece of poetry by insights obtained from analyzing his existing poetry. Its significance is immense including in detection of plagiarism and characterization of poetry of a poet. Urdu, Pakistan's lingua franca with the richest poetic tradition, has been a subject of misinformation and misattribution. This paper presents a novel approach to poet attribution in Urdu Ghazals through the application of machine and deep learning models. Our aim is to establish an accurate and comprehensive characterization of ghazals that captures the unique writing style of each poet. To achieve this, we trained and tested a range of machine learning, deep learning, and transformer-based classification models on a dataset containing 17,609 couplets of 15 notable ghazal poets. We used classifiers such as SVM and logistic regression to obtain preliminary results, achieving an accuracy of 64% with SVM. However, to achieve even better results, we employed deep learning models such as MLP, CNNs, and GRUs, with LSTMs resulting in the highest accuracy of 59.96%. We then used transformer-based models, including roBERTa and BERT, which achieved an outstanding accuracy of approximately 80% in classifying 15 poets. This work represents a significant contribution to the field of computational poetry analysis, as it is the first to explore poet attribution in Urdu Ghazals using deep learning and transformer-based models. Our analytical approach enables us to examine and analyze each model's capabilities in capturing the writing style of Urdu Ghazal poets, leading to a more comprehensive and accurate characterization of these works.
Deep neural network based interpolation of sparse samples for time-dense power load forecasting

Large amount of power load data with high-sampling rate plays a crucial role in the training of time-dense power load forecasting network. However, in many cases, there is a large amount of data with low-sampling rate, which can't directly train a time-dense forecasting network. Therefore, this paper proposes a deep learning based dataset interpolation method, which uses the available high-sampling rate power load datasets to train a dataset interpolation network, then interpolates the low--sampling rate datasets with trained network to obtain high-sampling rate datasets, and finally uses the interpolated datasets for time-dense forecasting network training. The results show that the data obtained based on the method proposed in this paper fits the ground-truth closer than the traditional interpolation, and the results of the network trained based on this dataset are more accurate in the subsequent forecasting tasks.
Exploring Satisfaction Level of Customers in Restaurants by Using Latent Dirichlet Allocation(LDA) Algorithm

Social media has become an essential means for communicating the review/opinions of people around the world due to the rapid expansion and availability of the internet. Reviews and opinions are expressed both as text and audio. But text communication via networking media is overwhelming. Each and every second, a vast amount of information is produced online because of social media sites. However, online reviews on social media provides an excellent and trustworthy channel for examining the areas that require improvement and for understanding the needs of customers. This paper tries to understand the various topics that are discussed by customers about food with the aim of providing an insight to improve the area where there are negative comments. By doing this, customer retention will increase and gradually the business also. Topic modeling is done to find the hidden topic in a set of comments or customer reviews to find out on which topic customers are talking, thinking, or discussing more about restaurant services. It helps to read customers' mindsets, and accordingly, improvements can be made to increase consumer demand for a restaurant. So, a system is proposed to detect topics of textual reviews of different restaurants using the latent Dirichlet allocation (LDA) modelling approach. A probabilistic, statistical strategy for document designing called latent document analysis (LDA) identifies latent semantic topics in sizable corpora. The dataset used for this study was taken from Kaggle, which had 111,105 reviews. First, topics from reviews were extracted using the LDA model, then data was visualised using the LDAvis tool. The experiment's findings show that the analysis obtained a successful topic division outcome especially in long-to-short text level topic classification.
Encrypted Traffic Classification Model Based on SwinT-CNN

With the rapid development of the network, the proportion of encrypted traffic in the network is increasing, which brings great challenges to traffic classification. The traditional encrypted traffic classification algorithm extracts the port number of the traffic or the load information of the traffic packet for feature matching, which has no effect in the current encrypted traffic. The encrypted traffic classification algorithm based on machine learning especially relies on the features extracted by professionals for encrypted traffic, which is inefficient and has low accuracy. And, The accuracy and overall performance of existing deep learning methods also need to be improved. Therefore, in order to improve the accuracy of encrypted traffic classification, this paper proposes an encrypted traffic classification method based on deep learning, which tries to combine the core modules of CNN and Swin Transformer with the encrypted traffic classification model to realize the identification of the application type of encrypted traffic. Among them, the CNN module is able to capture the local spatial information characteristics of encrypted traffic data. The multi-head attention mechanism of Swin Transformer can capture global information about the association between attributes of the data. The model is tested on the public ISCX VPN-nonVPN encryption dataset to evaluate the performance of the proposed model. Experimental results show that the accuracy of our proposed model reaches 96.7%. Compared with the other best-performing deep learning models, the proposed model improves the accuracy by 3.1% and the F1 value by 2.7%.
Automated Malaria Parasite Detection for Legal Blindness Accessibility using Hybrid Deep Learning Techniques

Mosquitoes carry malaria, a serious blood disease that can be fatal, contagious, and life-threatening. The most common and accepted method of diagnosing malaria involves looking at blood smears under a microscope to visually check for red blood cells that are infected with parasites. The results of this system depend on the examiner's knowledge and experience, are limited in scope, and take a lot of time to complete. In order to form an opinion, blood smears from malaria patients were subjected to automatic image recognition technologies based on image processing. However, the performance in practice has been underwhelming so far. This encourages us to develop the most straightforward, efficient, and rapid malaria diagnosis methods. Using image processing and prompt testing, our main goal is to develop a model that can categorize cells as infected or uninfected. This model will be able to distinguish between various cell types in thin blood smears on typical microscope slides. In order to find the parasite on the picture of the infected cell using deep learning techniques.
Keyword mask pre-training model for the review of standard declaration

The review of standard declaration of import and export commodities refers to the evaluation of whether the declaration information of the commodities filled in by enterprises conforms to the customs filling standards according to the standardized declaration catalogue The manual review method cannot meet the actual work needs. Therefore, using text classification algorithm to conduct this task is necessary. The classification method based on the pre-training model would rely on the keywords. However, for the review of standard declaration task, excessive reliance on keywords will reduce the classification effect because of ignoring the overall information of the text. Keyword mask pre-training model is proposed. In the fine-tuning stage, the model designs a keyword masking strategy and a keyword blinding strategy. The keyword masking strategy regularizes the BERT model with a mask mechanism, that is, the keywords are reconstructed with other text information except keywords, so that the model focuses on non-keywords information; Keyword blinding strategy makes the model unable to distinguish the categories of text information without contextual text information, which can eliminate the influence of keywords on model decision-making. The experimental results on the real customs declaration data set showing that the method in this thesis exceeds the baseline method in terms of precision and true negative rate.
Deep search based prototype estimation method for few-shot image classification

Few-shot learning aims to transfer training models for base classes with sufficient labeled data to adapt to novel classes with only a few training examples. In this paper, we propose a deep search based prototype estimation method to improve few-shot learning. Based on the deep search, the similarity between the data in the base class can be used to better complete the prototype of the support set category. After obtaining similar samples, we can generate a prototype and then use it to estimate the distribution of the category to improve the few-shot learning task. A comparative experiment is carried out on three benchmark datasets to evaluate the effectiveness of our algorithm for few-shot learning. The results show that our method can generate more robust samples and significantly improve the few-shot learning, which is superior to the most advanced methods on these datasets.
Data Security in Cloud using Advanced Encryption Standard

This research study gives an overview on cloud security to understand the security concerns and solutions. The internet has revolutionised how people live and work, and cloud computing is considered as the next logical progression. Cloud security is a series of security precautions employed to safeguard cloud-based architecture, services, and information. These precautions cover client and hardware authentication, information and resource access management, and data confidentiality. People who utilize the internet should also be concerned about cloud computing security. Despite the hype, cloud computing has also created new vulnerabilities, such as unwanted access to individual's personal data and the ability to control sensitive information, which includes an underlying weak point. The encryption algorithm guarantees that the data will be kept confidential while in transit, ensuring that it can only be decoded by the authorised user. Advanced Encryption Standard (AES) is the symmetric encryption algorithm that is more frequently utilised and more generally known nowadays. Here, the data may be accessed and exploited in a variety of ways, and it is also critical to understand the dangers. This study discusses about the different ways to guarantee that user data is secure and confidential. Finally, the future potential strategies for cloud security are examined. Moreover, the cloud computing will not only save the world, it will alter the way users engage with digital/mobile applications.
Design of data retrieval system for distributed energy

Distributed energy is the key to developing smart energy. Taking the distributed energy system in an industrial park as an example, the system generates massive structured time series data, such as productivity data, energy consumption data, and equipment data, and also generates unstructured data files, such as documents, forms, audio and video data. Because of the heterogeneity of data sources, the system uses HBase database as a storage carrier. Therefore, a secondary index scheme based on ElasticSearch+HBase is proposed and a data retrieval system is built based on the positive order index adopted in HBase. The experimental results show that this data retrieval design scheme meets the retrieval requirements.
Dual-stream architecture and improved action recognition based on 3D convolutional neural network fusion

Due to the rich content of video data and the excessive parameters of 3D convolutional networks, how to reduce the difficulty of parameter tuning and reduce the training time is a difficult problem in action recognition. To solve these difficulties, a new action recognition algorithm based on dual-stream architecture and improved three-dimensional convolutional aggregation is proposed. The network structure of the 3D convolution kernel is split into two types of convolution kernels, spatial flow and time flow, and the data flow is fused based on the dual-stream network, and the residual network is introduced for optimization. To verify the effectiveness of the algorithm, it is verified on the large public behavior dataset UCF101. Experimental results show that the algorithm has good spatiotemporal feature modeling ability, which effectively improves the accuracy of action recognition while shortening the training time.
SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning

Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data. However, by design, MPC protocols faithfully compute the training functionality, which is susceptible to poisoning and privacy attacks, as shown by the adversarial ML community. In this work, we argue that model ensembles, implemented in our framework called SafeNet, are a highly MPC-amenable way to avoid many adversarial ML attacks. The natural partitioning of data amongst owners in MPC training allows this approach to be highly scalable at training time, provide provable protection from poisoning attacks, and provable defense against a number of privacy attacks. We demonstrate SafeNet's efficiency, accuracy, and resilience to poisoning on several machine learning datasets and models trained in end-to-end and transfer learning scenarios. For instance, SafeNet reduces backdoor attack success significantly, while achieving 39× faster training and 36× less communication than the four-party MPC framework of Dalskov et al. [31]. Our experiments show that ensemble learning retains these benefits even in many non-iid settings. The simplicity, cheap setup, and robustness properties of ensemble learning make it a strong first choice for training ML models privately in MPC.
Rethinking the Entropy of Instance in Adversarial Training

Adversarial training, which minimizes the loss of adversarially-perturbed training examples, has been extensively studied as a solution to improve the robustness of deep neural networks. However, most adversarial training methods treat all training examples equally, while each example may have a different impact on the model's robustness during the course of adversarial training. A couple of recent works have exploited such unequal importance of adversarial samples to the model's robustness by proposing to assign more weights to the misclassified samples or to the samples that violate the margin more severely, which have been shown to obtain high robustness against untargeted PGD attacks. However, we empirically find that they make the feature spaces of adversarial samples across different classes overlap and thus yield more high-entropy samples whose labels could be easily flipped. This makes them more vulnerable to adversarial perturbations, and their seemingly good robustness against PGD attacks is actually achieved by a false sense of robustness. To address such limitations, we propose simple yet effective re-weighting scheme that weighs the loss for each adversarial training example proportionally to the entropy of its predicted distribution to focus on examples whose labels are more uncertain.
An Attribute-enhanced Method based on Latent Space Factorization for Face Image Style Transfer

In recent years, arbitrary style transfer (AST) with high quality develops rapidly and the application is widespread. Customers desire to stylize their portraits and edit face attributes simultaneously. To satisfy people's demands, we design a whole framework to complete both global style transfer in color and local style transfer in attributes. First, a target attribute enhancement module guided by the latent space factorization is presented to alleviate over-editing problems and obtain style codes. Then, a local-global fusion module is proposed to integrate target style codes and global features. Besides, a contrastive coherence preserving loss is built in a pre-trained style transfer network to better adapt to face images. The experiments show that our model can generate pleasant images.
Improving temporal question answering using temporal knowledge graph embedding

Knowledge graph question answering is an important research direction for question answering tasks. In recent years, there has been an increasing amount of research on knowledge graph question answering, but temporal knowledge graph question answering is still a relatively unexplored area. In question answering tasks, many natural language questions have explicit or implicit temporal constraints, and most existing research methods lack the temporal awareness to deal with complex temporal questions. To address these challenges, this paper proposes a question answering model based on temporal knowledge graph embedding (TKGETQA). The model uses TKG embeddings to root a question in the entities, relations and time horizons it references, and perceives temporal information from the question to improve the accuracy of answer prediction. Experiments on the dataset CronQuestions in this paper show that the TKGETQA model exhibits better results compared to existing temporal knowledge graph question answering approaches.
Enhancing Home Security: user Authentication Techniques for Home Automation

Several user authentication methods are being used for safe-guarding the home automation but each method has its own vulnerabilities. Especially with the evolution of the internet of things, home automation systems are becoming increasingly popular. These systems give consumers the ability to remotely manage many aspects of their homes, such as the lighting, heating, and security cameras. However, Security has become a significant challenge due to the increase in linked devices. Home automation systems are susceptible to several threats, such as hacking, data leakage, and malware attacks. Understanding the threats and taking the necessary steps to reduce them is crucial for protecting the smart systems. Enhancing the security of home automation systems using user authentication methods is the primary topic of this research study. The strengths and limitations of various user authentication systems, such as password-based authentication, biometric authentication, and two-factor authentication are analyzed in this research study. Furthermore, this study also analyses the efficiency of each method in limiting unauthorized access and preserving the confidentiality and security of home automation systems.
Pretrained Sentence Embedding and Semantic Sentence Similarity Language Model for Text Classification in NLP

A key task in the field of Natural Language Processing (NLP) is determining semantic similarity between text sentences. Sentence pair modeling, Textual similarity and language modeling are few important tasks in NLP. Traditional machine learning algorithms require an enormous quantity of training data, but it is a timeconsuming process. Pre-trained models can be modified for a variety of downstream applications since they use methods for generically learning the characteristics of neural network topologies and language representations. Bidirectional Encoder Representations from Transformers- BERT & GPT are the popular architectures in NLP which enable to use minimal fine-tuning effort to produce effective results. In this work a fine-tuned BERT model that is suitable for semantic sentence similarity which predicts the entailment, neutral and contradictory categories of sentence pairs is presented. The fine-tuning feature promotes the training phase of model whichis widely effective across different types of semantic similarity models. The performance analysis of our system shows that the fine-tuned model reduces the number of neurons in the neural network there by reducing storage and time spent in expensive training task to create deep learning model.
KTGAT: Improving the Robustness of Knowledge-enhanced Text Generation via Adversarial Training

The shortage of information in text generation has been a prominent area of research in Natural Language Processing (NLP). Current research endeavors aim to combine pre-trained models with rich open-world knowledge from external sources to increase the priori information and thereby enhance the informativeness of text generation. While recent studies suggest that integrating open-world and task-specific knowledge can improve text generation by addressing specific knowledge gaps in downstream tasks, the inherent semantic ambiguity in natural language remains a significant challenge that may impede knowledge acquisition and text generation. To overcome this challenge and improve the model's semantic comprehension and overall robustness, we propose a novel framework, the Knowledge Augmentation Text Generation model via Adversarial Training (KTGAT). Our method adds perturbations to the embedding layer, which are equivalent to constructing unstable samples. This approach improves the model's robustness to adversarial samples and the generalization performance of the original samples. Our experiments demonstrate that the proposed KTGAT framework outperforms the baseline model, thus proving its effectiveness in improving text generation. The generated text cases illustrate that our method enhances the model's semantic comprehension and enables it to search for knowledge items more effectively and accurately.
Real-time object detection in deep foggy conditions using transformers

Transformers have been extensively employed in various vision issues, particularly visual recognition and detection. Detection transformers are connected to end-to-end networks for object detection. Self-attention modules in the transformer give huge efficiency, making excellent object detection models. The decoder transformer fails to initialize query content properly and also fails to provide specific prior knowledge, which might potentially enhance inductive bias. This paper uses encoder and decoder transformers for object detection in deep foggy conditions. High-Resolution Network (HRNet) has been used in the backbone of this architecture to extract deep feature representation. The proposed method validates and compares with other detection techniques in terms of average precision (AP), the variety of factors, and frames per second (FPS) using the Foggy Cityscapes dataset. The qualitative results indicate that the proposed technique improves detection accuracy in deep foggy conditions.