Reviewerly/exHarmony huggingface dataset:
Robust color image watermarking algorithm based on synchronization correction with multi-layer perceptron and Cauchy distribution model

A robust color image watermarking algorithm based on the multi-layer Perceptron (MLP) and Cauchy distribution model is proposed. The algorithm has great performance against geometric attacks. Firstly, the central region of the Y-color channel is extracted, and the watermark is embedded into the low-frequency component by dual-tree complex wavelet transform (DTCWT). A synchronization correction technique based on the MLP model is used prior to the watermark extraction. When the MLP model is trained, the position and scale parameters of the Cauchy distribution are employed to generate the image feature vector after first calculating the quaternion discrete cosine transform (QDCT) of the original color host image. The new 3D hyperchaotic mapping is used during the algorithmic process to increase security, which is employed for scrambling, encryption, and embedding of watermarks, respectively. The experimental results demonstrate that the proposed algorithm has good imperceptibility, accurate model prediction, and excellent robustness against common image processing attacks and geometric attacks.
Histo-fusion: a novel domain specific learning to identify invasive ductal carcinoma (IDC) from histopathological images

On the global level, Invasive Ductal Carcinoma (IDC) is one of the leading malignancies among women undergoing breast cancer screening. A slight delay in detecting and diagnosing this disease may result in irreversible complications. Histopathological images obtained from a biopsy examination present enormous structural information, which helps significantly in improving the prognosis of the disease. Pathological analysis involving microscopic examination of the histopathological slides is a highly challenging task due to the limited abilities of conventional computer-aided detection (CAD) methods to reach a precise diagnosis. The problem has become more accentuated due to the less availability of medical data in the public domain. Lately, deep learning methods using artificial neural networks are being used consistently to improve the performance of these CAD methods. Despite less medical data availability, transfer learning has recently been commonly practised to facilitate a deep neural network to train on a specific dataset in resolving the problem. However, the performance of this method is not remarkably appreciable on small and low-resolution medical image datasets compared to those comprising whole slide images of higher resolutions. In this direction, this study proposes a domain-specific learning strategy, Histo-Fusion, with the objective of detecting the IDC more precisely. In the proposed method, the deep CNN model is initially trained on higher-resolution histopathological images of breast tissue which presents ample information for a model to learn the significant features for better discrimination between normal and malignant tissues. Subsequently, through a positive transfer of domain features, the model is further trained on the small and low-resolution images, enabling it to classify these histology images into IDC - and IDC + categories. Moreover, shallow and deep neural network architectures are utilized in the study to compare their performance across the two learning approaches: transfer learning and Histo-Fusion on the IDC dataset. As revealed by the present study results, the proposed Histo-Fusion learning approach has improved each deep CNN's discriminating abilities by yielding better accuracy scores of around 5% over and above those obtained by the commonly used transfer learning strategy. Therefore, the procedure is expected to reduce false-positive rates and help expert pathologists reach accurate diagnoses.
Hybrid partial-constrained learning with orthogonality regularization for unsupervised person re-identification

Person re-identification (re-ID) aims at determining whether there is a specific person in image sets or videos via computer vision technology. State-of-the-art unsupervised re-ID methods extract image features through CNNs-based networks and store these extracted features in memory for identity matching. However, extracted global features of these methods ignore the problem of information redundancy and the influence of the constraints between the internal features. To overcome these problems, a Hybrid Partial-constrained Learning (HPcL) network with orthogonality regularization is proposed to learn a discriminative visual representation by generating hybrid features. Specifically, the hybrid features are generated by our designed Dynamic Fusion Module (DFM) to initialize the memory dictionary and match the identity, which can constrain each part of the features extracted by our proposed Multi-Scale (M-S) module and learn robust visual representations. In addition, a new orthogonal regularization method is introduced to constrain orthogonality of the kernel weights and features, which reduces the correlations among features. Extensive experimental results on Market-1501, DukeMTMC-reID, PersonX, and MSMT17 datasets demonstrate that our method is effective and superior to the state-of-the-art methods.
Optimizing sentiment analysis of Nigerian 2023 presidential election using two-stage residual long short term memory

Sentiment analysis is the process of recognizing positive or negative attitudes in text. This technique makes use of computational linguistics, text analysis, and natural language processing. The 2023 presidential election in Nigeria is a significant event for the country, as it will determine the leader of the nation for the next four years. As such, it is important to understand the sentiment of the public towards the different candidates. In this research, we aimed to understand the sentiment of the public towards the three main candidates in the 2023 presidential election in Nigeria, Atiku, Tinubu, and Obi, by conducting a sentiment analysis on tweets related to the candidates. We used the long short-term memory (LSTM), peephole long short term memory (PLSTM), and two-stage residual long short-term memory (TSRLSTM) models to classify tweets as positive, neutral, or negative. Our dataset consisted of a large number of tweets that were preprocessed to remove noise and irrelevant information. Results showed that TSRLSTM performed excellently well in classifying the tweets and in identifying the sentiment towards each candidate individually. Our findings provide valuable insights into the public's opinion on the candidates and their campaign strategies, which can be useful for researchers, political analysts, and decision-makers. Our study highlights the importance of sentiment analysis in understanding public opinion and its potential applications in the field of political science.
Preprocessing Analysis of Medical Image: A Survey

The diagnosis process determines the disease of humans based on the symptoms/signs that possessed. Manual decisions are made being observations and recognition of the medical test are considered upon the condition of the diseased. It is observed that manual decisions in Cancer therapy, tumor detection heading the life of the patients at high risk. The medical industry has made significant technological advancements that reduce human error in disease detection. Computer-aided diagnosis systems like magnetic resonance imaging (MRI) and CT scan play a significant part in medical diagnosis. In this paper, we are presenting the various approaches that analyze the use cases of the medical images; the major effect is that, images obtained from the computerized diagnosis systems may produce the results with some artifacts like impulse noise, Gaussian noise, Speckle noise etc., so the preprocessing techniques that are proposed by the different researchers to improve the accuracy of diagnosis process by reducing the different noise mentioned earlier.
An Optimized System for Human Behaviour Analysis in E-Learning

In several industries, artificial intelligence is becoming more and more popular. Artificial intelligence is commonly used in the interactions between individuals and objects. Technologies for human activities, occurrences, and behavior have been designed and deployed in surveillance equipment. However, human observation should be prioritized more in academic settings or e-learning. In this, we ended a useful technique for examining how people behave in various learning environments. Primarily, crowd statistics are used as a parameter, and pre-processing is carried out to reduce noise and identify objects. After that, layout collection, object separation, and human design authentication are carried out. Then the human structure is validated using image enhancement. The characteristics vector is retrieved at the levels of micro and macro following human layout confirmation. Furthermore, a particle swarm estimator is used to determine the most ideal features, and an Adaboost classification is used to categorise a people's behaviour. The suggested approach outperformed state-of-the-art algorithms, achieving validity of 83.25% and 84.25% across the MED dataset and EduNet dataset, correspondingly.
An Automatic Speech Recognition System: A systematic review and Future directions

The process of recognizing a person through his/her speech signals can be termed Speech recognition and the model used to do this task is known as the speech recognition system. It permits the system to take the speech in an understandable form and identify the speaker based on the feature extracted. These types of systems ease the communication between humans and machines. In this paper, we have discussed the core terminologies like Utterance, Speaker dependence, Vocabulary, Accuracy, and Training which help to understand the speech recognition system in a better way. Further, the paper elaborates on the flowchart which needs to be followed for speech recognition with some research work done by various researchers in this field. Lastly, some future directions are shown which depict the use of the speech recognition system.
Topic Modeling on Customer Feedback from an Online Ticketing System using Latent Dirichlet Allocation and BERTopic

This project aims to use Topic Modeling on Customer Feedback from an Online Ticketing System using Latent Dirichlet Allocation and BERTopic. The experiment started with analysis of the Topics generated from a base LDA model and computing its coherence score and fine-tuning the LDA model and comparing the coherence score with the base Model. It was found that the fine-tuned LDA model increased the cohesion score by 8.33%. This project also explored the use of BERTopic, using the multilingual model and found that it was able to extract Tagalog words from the data composed of English and Tagalog. As such, BERTopic can be further experimented on using data with more relevant Tagalog words. Also, using human judgements or “eye-balling” techniques, it was able to evaluate that the topic distribution generated by both the LDA and BERTopic model was able to capture the information needed by the organization.
DP-Denclue: An outlier detection algorithm with differential privacy preservation

A key data preparation method is the Denclue-based outlier identification algorithm in data mining. Howev-er, the conventional Denclue algorithm does not provide priva-cy protection and has a low outlier identification accuracy. In this paper, we use differential privacy technology, applying the Laplace mechanism to the process of the Denclue algorithm to add noise to achieve privacy preservation. Unfortunately, the noise addition has a downward trend on the Denclue outlier detection algorithm's accuracy. Therefore, the information entropy weight distance, which weights the attributes and amplifies the influence of significant attributes on the outlier data, is used instead of the Euclidean distance in the Denclue algorithm to measure the distance between two data points. This makes it simpler for the algorithm to identify outlier data. A differential privacy-based Denclue outlier detection algorithm (DP-Denclue) is proposed. Experiments with the UCI dataset show that the DP-Denclue algorithm has excellent performance.
A Blockchain-based approach to securing data in smart agriculture

Digital transformation in the socioeconomic sectors has become a requirement in Algeria, particularly in the agricultural field. Modern agriculture is mainly based on new communication technologies such as IoT, 5G and Cloud/Edge computing, where data can be shared quickly and transparently. Ensuring security to access data in this system is a challenge that has always aroused researchers because of the importance of data reliability to make the right decisions for the advancement and development of any sector. Blockchain's various qualities, including smart contracts, decentralisation, transparency, data immutability, data privacy, and a consensus process, make it suitable for securing data and achieving its integrity from alteration and tampering. This paper proposes a Blockchain strategy to improve agriculture data security, integrity, management and sharing performance. The proposed approach works in three steps, collecting raw agricultural data from greenhouses and storing it in Edge. This data is encrypted and uploaded to InterPlanetary File Systems (IPFS). Then, a transaction is generated by the smart contract and stored in the Blockchain network. The approved transaction cannot be modified. Therefore, our system is guaranteed in terms of security and integrity.
Exploring the Quality of Dynamic Open Government Data for Developing Data Intelligence Applications: The Case of Attica Traffic Data

Dynamic data (including environmental, traffic, and sensor generated data) were, recently, recognised as an important part of the Open Government Data (OGD) movement. These data are of vital importance in the development of data intelligence applications. For example, various business applications exploit traffic data to predict, e.g., traffic demand and an estimated time of arrival. However, this type of data is inherently vulnerable to data quality errors produced by, e.g., failures of sensors and network faults. The objective of this paper is to explore the quality of Dynamic Open Government Data for the development of data intelligence applications. Towards this end, we study a single case about the traffic data provided by the official Greek OGD portal. The portal involves the use of an Application Programming Interface (API), which is essential for the effective dissemination of dynamic data. Our research approach involves the exploration and the evaluation of the provided data with regards to missing values and anomalies. We anticipate that this paper will contribute to the identification of organisational and technical challenges that hamper the effective dissemination of dynamic OGD.
Augmented Reality in Primary Education: Adopting the new normal in learning by easily using AR-based Android applications

Augmented Reality technologies can be considered to be a fascinating choice for educators seeking resources and methods to stimulate their students about the topic they teach. In recent years, the increasing number of relevant educational applications indicates that this new technology has the potential of becoming a leading educational method in schools and universities. The current work overviews the importance and impact of this technology in widening primary students' knowledge and how it can be used by teachers worldwide to motivate and broaden educational horizons. Furthermore, this work examines a wide number of augmented reality applications for the Android mobile operating system, available online for educational purposes and analyzes the ways in which they can be helpful for schoolteachers, lecturers, and parents.
Deep Multi-Task Learning Based Fast Intra-Mode Decision for Versatile Video Coding

The latest Versatile Video Coding (VVC) standard has significantly coding efficiency improvement compared with its ancestor High Efficiency Video Coding (HEVC) standard, but at the expense of over-high complexity. As measured by the VVC test model (VTM), the intra-mode comparison and selection in the rate-distortion optimization (RDO) search consume most of the encoding time. In this paper, we propose a deep multi-task learning based fast intra-mode decision approach via adaptively pruning off most redundant modes. First, we create a large-scale intra-mode database for VVC, including both normal angular modes and the newly introduced tools, i.e., intra sub-partition (ISP) and matrix-based intra prediction (MIP). Next, we propose a multi-task intra-mode decision network (MID-Net) model to effectively predict the most probable angular modes and whether to skip ISP and MIP modes. Then, a fast intra-coding workflow is designed accordingly, involving rough mode decision (RMD) acceleration and candidate mode list (CML) pruning. For the workflow output, the learning-oriented probability and the statistics-oriented probability are synthesized together to further improve the prediction accuracy, ensuring that only unnecessary intra-modes are skipped. Finally, experimental results show that our approach can significantly reduce 40.48% of encoding time of VVC intra-coding with negligible rate-distortion degradation, outperforming other state-of-the-art approaches.
Integrating Spatial Details With Long-Range Contexts for Semantic Segmentation of Very High-Resolution Remote-Sensing Images

This letter presents a cross-learning network (i.e., CLCFormer) integrating fine-grained spatial details within long-range global contexts based upon convolutional neural networks (CNNs) and transformer, for semantic segmentation of very high-resolution (VHR) remote-sensing images. More specifically, CLCFormer comprises two parallel encoders, derived from the CNN and transformer, and a CNN decoder. The encoders are backboned on SwinV2 and EfficientNet-B3, from which the extracted semantic features are aggregated at multiple levels using a bilateral feature fusion module (BiFFM). First, we used attention gate (ATG) modules to enhance feature representation, improving segmentation results for objects with various shapes and sizes. Second, we used an attention residual (ATR) module to refine spatial features’s learning, alleviating boundary blurring of occluded objects. Finally, we developed a new strategy, called auxiliary supervise strategy (ASS), for model optimization to further improve segmentation performance. Our method was tested on the WHU, Inria, and Potsdam datasets, and compared with CNN-based and transformer-based methods. Results showed that our method achieved state-of-the-art performance on the WHU building dataset (92.31% IoU), Inria building dataset (83.71% IoU), and Potsdam dataset (80.27% MIoU). We concluded that CLCFormer is a flexible, robust, and effective method for the semantic segmentation of VHR images. The codes of the proposed model are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/long123524/CLCFormer</uri> .
Bio-medical Image Denoising using Autoencoders

Image denoising, in which the original image is reconstructed by eliminating noise from a noise-corrupted version of the original image, is one of the greatest requirements in the image processing field. The sources of noise in image may be in-trinsic (such as sensors) and extrinsic (such as environment) that are often practically unavoidable. One of the crucial initial steps in biomedical image processing and analysis is image denoising as improved visual quality of images may increase the accuracy of medical diagnosis and image preprocessing is required at the input for analytical methods like segmentation and content recognition. Autoencoder is an artificial neural network that is trained in the unsupervised manner to reconstruct its input at the output. This work aims at the development of a denoising convolutional autoencoder which will first generate the encoded lower-dimensional representation of the image and then decodes the image back from the lower-dimensional representation. The denoising autoencoders build corrupted copies of the input images by adding random noise and then attempt to eliminate the noise from the noisy input to reconstruct the output that is very similar to the original input. Medical images like X-Ray, Magnetic Resonance Imaging(MRI) etc. which are corrupted by various types of noise like Gaussian noise, Rician noise etc. are denoised using the convolutional denoising autoencoder and their denoising efficiency is measured using the Structural Similarity Index Measure(SSIM) parameter of both the noisy image and the denoised image compared to the original noise free image and the Peak Signal to Noise Ratio(PSNR) of the denoised image with the noise free image as reference.
An extensive study of sentiment analysis techniques: A survey

Text mining is one of Artificial Intelligent (AI) technologies that uses Natural Language Processing (NLP) for extracting data from structured text. Sentiment analysis (also known as opinion mining) is one of text mining techniques, which is the computational study of personals' behaviours and opinions. The importance of sentiment analysis increases extensively with the enormous growth of the internet sites such as e-commerce, blogs, social media sites, etc. The aim of the survey is to give full view about the most important sentiment analysis techniques. The paper also represents a major contribution through categorizing the SA techniques in an easy and helpful manner for any researcher through classifying SA based on three essential approaches; machine learning-based, lexicon-based, and hybrid approach. Each approach may contain sub-divisions which in turn represent the techniques of SA classification.
Secure Replication-Based Outsourced Computation Using Smart Contracts

The replication-Based Outsourced Computation (RBOC) mechanism allows a client to outsource the same computing job to multiple contractors and the honest contractors will get paid in the incentivized system based on the fact that a majority of contractors will honestly perform the computation. As self-executing contracts, smart contracts are utilized in the decentralized blockchain networks to execute coded programs automatically transparently, and publicly. It is natural to apply smart contracts to RBOC to improve performance by setting smart contracts as the converter between the client and contractors to reduce the load on the client. However, it is infeasible to directly combine these two blocks together because the data including returned computing results from contractors in the decentralized blockchain are in the form of plaintexts such that some lazy contractors could copy others’ results as their own and still get paid, which will compromise the security of RBOC. The existing public-key encryption with equality test (PKEET) is a promising candidate solution to stop the above lazy contractors, where the results are encrypted by PKEET and then transferred without hindering smart contracts to compare the equality of underlying results. Unfortunately, we found that the advanced lazy contractors can still compromise security by forging ciphertexts to pass the equality test only with the encrypted results of other contractors. In this paper, to achieve security against lazy contractors, we introduce the notion of PKEET against lazy encryptors (PKEET-LE). Besides the fundamental property of PKEET that performs equality test on ciphertexts without decryption, PKEET-LE additionally realizes the security against the lazy encryptors who aim to forge a ciphertext for a given one to pass the equality test between them without the knowledge of the underlying plaintext. We further propose a concrete and practical PKEET-LE construction along with formal security proof. Finally, we conduct a performance evaluation to demonstrate that our PKEET-LE scheme is efficient and practical in the RBOC system using smart contracts.