Reviewerly/exHarmony huggingface dataset:
An Effective Language Convention Model Based on Deep Structured Learning and Natural Language Processing for Higher Education

Language translation is highly needed in the increasingly networked world. The writing of English essays is an essential competency for higher education students. For non-English students in China, this is especially important. They often represent large numbers of Chinese English as second language students. Therefore this paper introduced Deep Structured Learning with Natural language processing (DSL-NLP) has been proposed to convert the Chinese language to the English language for student learning. An automatic input tool for writing assistance will be beneficial in building the proposed DSL-NLP method. The relationship between textual attributes and input from human teachers and provide characteristics feedback system. This study explored natural language processing (NLP) to determine the interpretative actions required for a successful understanding of the literary text. NLP attempts to assist in achieving consistency in applied linguistics, improving classroom communication, optimizing learner attitudes and motivation, raising personality, facilitating personal development, and even changing students' perspectives toward life. Thus the experimental results show the DSL-NLP improves student learning in the English language, enhances student performance in education, and effectively increases the student career
A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction

Image abstraction simplifies complex images, highlights specific features, and preserves different levels of structures to achieve a desired style. This paper presents a constructive and adjustable data hiding algorithm to convey various secret messages and resist modern steganalytic attacks. Our scheme produces an abstracted stego image, while synthesizing an original image during the image abstraction process. Our algorithm is flexible, applicable to two types of images: high-dynamic-range images and ordinary color images, aka low-dynamic-range images. Additionally, we introduce a novel image encryption scheme suitable for the above two types of images, which incorporates a two-dimensional logistic tent modular map and a bit-level random permutation technique, thereby further protecting the content of the stego image and the carried secret messages. Compared with the current state-of-the-art methods, our algorithm provides a 14% to 33% larger embedding rate, while lowering the distortion of the abstracted stego image. A comprehensive security analysis confirmed that our algorithm provides high security to resist statistical, differential, brute force, chosen-plaintext, and chosen key attacks.
3D shape reconstruction with a multiple-constraint estimation approach

In this study, a multiple-constraint estimation algorithm is presented to estimate the 3D shape of a 2D image sequence. Given the training data, a sparse representation model with an elastic net, i.e., l1-norm and l2-norm constraints, is devised to extract the shape bases. In the sparse model, the l1-norm and l2-norm constraints are enforced to regulate the sparsity and scale of coefficients, respectively. After obtaining the shape bases, a penalized least-square model is formulated to estimate 3D shape and motion, by considering the orthogonal constraint of the transformation matrix, and the similarity constraint between the 2D observations and the shape bases. Moreover, an Augmented Lagrange Multipliers (ALM) iterative algorithm is adopted to solve the optimization of the proposed approach. Experimental results on the well-known CMU image sequences demonstrate the effectiveness and feasibility of the proposed model.
Efficient Convolutional Neural Network-Based Keystroke Dynamics for Boosting User Authentication

The safeguarding of online services and prevention of unauthorized access by hackers rely heavily on user authentication, which is considered a crucial aspect of security. Currently, multi-factor authentication is used by enterprises to enhance security by integrating multiple verification methods rather than relying on a single method of authentication, which is considered less secure. Keystroke dynamics is a behavioral characteristic used to evaluate an individual's typing patterns to verify their legitimacy. This technique is preferred because the acquisition of such data is a simple process that does not require any additional user effort or equipment during the authentication process. This study proposes an optimized convolutional neural network that is designed to extract improved features by utilizing data synthesization and quantile transformation to maximize results. Additionally, an ensemble learning technique is used as the main algorithm for the training and testing phases. A publicly available benchmark dataset from Carnegie Mellon University (CMU) was utilized to evaluate the proposed method, achieving an average accuracy of 99.95%, an average equal error rate (EER) of 0.65%, and an average area under the curve (AUC) of 99.99%, surpassing recent advancements made on the CMU dataset.
Toward Question-Answering with Multi-Hop Reasoning and Calculation over Knowledge Using a Neural Network Model with External Memories

The differentiable neural computer (DNC) is a neural network model with an addressable external memory that can solve algorithmic and question-answering tasks. Improved versions of the DNC have been proposed, including the robust and scalable DNC (rsDNC) and DNC-deallocation-masking-sharpness (DNC-DMS). However, integrating structured knowledge and calculations into these DNC models remains a challenging research question. In this study, we incorporate an architecture for knowledge and calculations into the DNC, rsDNC, and DNC-DMS to improve their abilities to generate correct answers for questions with multi-hop reasoning and provide calculations over structured knowledge. Our improved rsDNC model achieves the best performance for the mean top-1 accuracy, and our improved DNC-DMS model scores the highest for the top-10 accuracy in the GEO dataset. In addition, our improved rsDNC model outperforms other models in regards to the mean top-1 accuracy and mean top-10 accuracy in the augmented GEO dataset.
Optimal strategy of the simultaneous dice game Pig for multiplayers: when reinforcement learning meets game theory

In this work, we focus on using reinforcement learning and game theory to solve for the optimal strategies for the dice game Pig, in a novel simultaneous playing setting. First, we derived analytically the optimal strategy for the 2-player simultaneous game using dynamic programming, mixed-strategy Nash equilibrium. At the same time, we proposed a new Stackelberg value iteration framework to approximate the near-optimal pure strategy. Next, we developed the corresponding optimal strategy for the multiplayer independent strategy game numerically. Finally, we presented the Nash equilibrium for simultaneous Pig game with infinite number of players. To help promote the learning of and interest in reinforcement learning, game theory and statistics, we have further implemented a website where users can play both the sequential and simultaneous Pig game against the optimal strategies derived in this work.
The Italian debate on the digital COVID certificate: co-producing epistemic and normative rationalities

Italy's digital Covid certificate, known nationally as the ‘Green Pass,’ was enforced through unusual restrictions for a liberal democracy, as part of the government's effort to bolster the Covid-19 vaccination campaign. Since July 2021, the Green Pass provided the main authorizing tool for the public to access a wide spectrum of social spaces and activities, from leisure to public transport and from education to workplaces. The Green Pass therefore served as a normative technology, and triggered intense political controversy and heated debates in the Italian public discourse. In constructing claims about the Green Pass, advocates and critics alike co-produced normative arguments with understandings of scientific evidence. Notably, they articulated competing framings around: conceptions of freedom during a pandemic; what should be considered as ‘evidence that matters’ regarding the effectiveness of Covid-19 vaccines; value-laden projections of vaccination as either a solidaristic practice or an act of self-protection; the proper relationship between the state and its citizens; and the most appropriate modes of public health intervention. Accordingly, Italy's Green Pass offers a revealing case study for probing the implications of a normative technology with respect to public health effectiveness and the safeguarding of individual and social rights. It also provides an opportunity for scrutinizing the (re-)structuring of scientific and public health governance in a major Western democracy during a public health crisis.
Waveforms Eavesdropping Prevention Framework: The Case of Classification of EPG Waveforms of Aphid Utilizing Wavelet Kernel Extreme Learning Machine

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A space and time efficient convolutional neural network for age group estimation from facial images

Background Age estimation has a wide range of applications, including security and surveillance, human-computer interaction, and biometrics. Facial aging is a stochastic process affected by various factors, such as lifestyle, habits, genetics, and the environment. Extracting age-related facial features to predict ages or age groups is a challenging problem that has attracted the attention of researchers in recent years. Various methods have been developed to solve the problem, including classification, regression-based methods, and soft computing approaches. Among these, the most successful results have been obtained by using neural network based artificial intelligence (AI) techniques such as convolutional neural networks (CNN). In particular, deep learning approaches have achieved improved accuracies by automatically extracting features from images of the human face. However, more improvements are still needed to achieve faster and more accurate results. Methods To address the aforementioned issues, this article proposes a space and time-efficient CNN method to extract distinct facial features from face images and classify them according to age group. The performance loss associated with using a small number of parameters to extract high-level features is compensated for by including a sufficient number of convolution layers. Additionally, we design and test suitable CNN structures that can handle smaller image sizes to assess the impact of size reduction on performance. Results To validate the proposed CNN method, we conducted experiments on the UTKFace and Facial-age datasets. The results demonstrated that the model outperformed recent studies in terms of classification accuracy and achieved an overall weighted F1-score of 87.84% for age-group classification problem.
A Hybrid Watermarking Approach for DICOM Images Security

A hybrid watermarking approach for DICOM medical image protection is proposed in this paper. The medical image is first separated into two parts: the region used for the diagnosis, called Region Of Interest (ROI), and the remaining, called Region Of Non-Interest (RONI). The patient’s name is then extracted from the header of the DICOM image and pertinent features from the ROI. These data are therefore used to construct a watermark based on a Jacobian model. On the one hand, this watermark is used for the zero watermarking of the ROI. On the other hand, it is divided into blocks that are embedded in the RONI using a linear interpolation technique. Experiment results show a good performance of the proposed approach. The average values of SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054, and the watermark embedding and extraction times do not exceed 12 s.
Photonic crystal fiber based automated system to diagnose silent killer

Pancreatic cancer (PC) is a lethal disease that is difficult to diagnose in its early stages. This is the reason it is deadly known as “The silent killer”. Traditional diagnostic methods are often invasive and can lead to misdiagnosis. The purpose of this manuscript is to propose photonic crystal fibers (PCFs) based artificial intelligence (AI) systems to materialize it as a promising tool for diagnosing pancreatic cancer. PCFs are optical fibers (OFs) that allow for the detection of light at high resolution and used to analyze the biochemical composition of tissues samples and feed the resulting data into an AI algorithm. The proposed system has the potential to significantly improve the early detection and diagnosis of pancreatic cancer, which lead to better outcomes. The Decision Tree (DT) model achieved an accuracy of 86.8%, a sensitivity of 81.6%, and a specificity of 90.3%. The Support Vector Machine (SVM) model achieved an accuracy of 90.9%, a sensitivity of 95.7%, and a specificity of 86.0%. The K-nearest neighbor (KNN) model achieved an accuracy of 90.8%, a sensitivity of 91.7%, and a specificity of 89.1%.
Swahili Speech Dataset Development and Improved Pre-training Method for Spoken Digit Recognition

Speech dataset is an essential component in building commercial speech applications. However, low-resource languages such as Swahili lack such a resource that is vital for spoken digit recognition. For languages where such resources exist, they are usually insufficient. Thus, pre-training methods have been used with external resources to improve continuous speech recognition. However, to the best of our knowledge, no study has investigated the effect of pre-training methods specifically for spoken digit recognition. This study aimed at addressing these problems. First, we developed a Swahili spoken digit dataset for Swahili spoken digit recognition. Then, we investigated the effect of cross-lingual and multi-lingual pre-training methods on spoken digit recognition. Finally, we proposed an effective language-independent pre-training method for spoken digit recognition. The proposed method has the advantage of incorporating target language data during the pre-training stage that leads to an optimal solution when using less training data. Experiments on Swahili (being developed), English, and Gujarati datasets show that our method achieves better performance compared with all the baselines listed in this study.
An efficient combination strategy for hybrid quantum ensemble classifier

Quantum machine learning has shown advantages in many ways compared to classical machine learning. In machine learning, a difficult problem is how to learn a model with high robustness and strong generalization ability from a limited feature space. Combining multiple models as base learners, ensemble learning (EL) can effectively improve the accuracy, generalization ability and robustness of the final model. The key to EL lies in two aspects, the performance of base learners and the choice of the combination strategy. Recently, quantum EL (QEL) has been studied. However, existing combination strategies in QEL are inadequate in considering the accuracy and variance among base learners. This paper presents a hybrid EL framework that combines quantum and classical advantages. More importantly, we propose an efficient combination strategy for improving the accuracy of classification in the framework. We verify the feasibility and efficiency of our framework and strategy by using the MNIST dataset. Simulation results show that the hybrid EL framework with our combination strategy not only has a higher accuracy and lower variance than the single model without the ensemble, but also has a better accuracy than the majority voting and the weighted voting strategies in most cases.
Design of intuitive user interfaces for virtual assistants in university education

Designing an intuitive user interface for virtual assistants in higher education presents several challenges, chief among them being natural language understanding, virtual assistant customization, user-centered design, integration with existing technology, and consideration of the educational context. The success of virtual assistants in higher education depends on the ability of designers and developers to understand the needs and preferences of users and their ability to design intuitive and effective user interfaces that enhance the learning and teaching experience. To address the challenges in designing intuitive user interfaces for virtual assistants, various methods can be used, such as creating a natural language-based user interface, including visual elements, and designing a custom user interface. and adaptable.
Uniform misclassification loss for unbiased model prediction

Deep learning algorithms have achieved tremendous success over the past few years. However, the biased behavior of deep models, where the models favor/disfavor certain demographic subgroups, is a major concern in the deep learning community. Several adverse consequences of biased predictions have been observed in the past. One solution to alleviate the problem is to train deep models for fair outcomes. Therefore, in this research, we propose a novel loss function, termed as Uniform Misclassification Loss (UML) to train deep models for unbiased outcomes. The proposed UML function penalizes the model for the worst-performing subgroup for mitigating bias and enhancing the overall model performance. The proposed loss function is also effective while training with imbalanced data as well. Further, a metric, Joint Performance Disparity Measure (JPD) is introduced to jointly measure the overall model performance and the bias in model prediction. Multiple experiments have been performed on four publicly available datasets for facial attribute prediction and comparisons are performed with existing bias mitigation algorithms. Experimental results are reported using performance and bias evaluation metrics. The proposed loss function outperforms existing bias mitigation algorithms that showcase its effectiveness in obtaining unbiased outcomes and improved performance.
Multi-scale foreground-background separation for light field depth estimation with deep convolutional networks

There are three viewpoints to divide the depth from light field (DFLF) methods: light field (LF) representation, signal processing, and learning viewpoints. In the signal processing viewpoint in details, the DFLF methods can be divided into three groups: cost-based, foreground-background separation (FBS)-based, and depth model-based methods. Although the deep learning techniques are widely implemented in the cost-based and depth model-based methods for the DFLF, they are not implemented in the FBS-based method, yet. In this paper, we propose a DFLF method based on the FBS with the deep learning techniques. Based on an observation that the angular aliasing artifacts are reduced in the resulting disparity maps obtained from spatially low-scale LF images, multi-scale (MS) FBS score maps are used in the proposed methods. To combine the MS FBS score maps into a single FBS score maps, four directional LF gradients, features from the 4-D LF, and features from the center view images are utilized. Based on the sensitivity and specificity loss, the gradient matching loss between the predicted and ground truth disparity maps is used to improve the performance around the occlusion boundary in the resulting disparity maps. The experimental results show that the proposed method performs reasonably in both the synthetic and real LF datasets. Especially, in the texture-less region in the real dataset, the resulting disparity maps obtained by the proposed method show that the artifacts are reduced compared to those by the existing DFLF methods.
Credit assignment with predictive contribution measurement in multi-agent reinforcement learning

Credit assignment is a crucial issue in multi-agent tasks employing a centralized training and decentralized execution paradigm. While value decomposition has demonstrated strong performance in Q-learning-based approaches and certain Actor–Critic variants, it remains challenging to achieve efficient credit assignment in multi-agent tasks using policy gradient methods due to decomposable value limitations. This paper introduces Predictive Contribution Measurement, an explicit credit assignment method that compares prediction errors among agents and allocates surrogate rewards based on their relevance to global state transitions, with a theoretical guarantee. With multi-agent proximal policy optimization (MAPPO) as a training backend, we propose Predictive Contribution MAPPO (PC-MAPPO). Our experiments demonstrate that PC-MAPPO, with a 10% warm-up phase, outperforms MAPPO, QMIX, and Weighted QMIX on StarCraft multi-agent challenge tasks, particularly in maps requiring heightened cooperation to defeat enemies, such as the map corridor. Employing a pre-trained predictor, PC-MAPPO achieves significantly improved performance on all tested super-hard maps. In parallel training scenarios, PC-MAPPO exhibits superior data efficiency and achieves state-of-the-art performance compared to other methods.
JPEG 2000 Extensions for Scalable Coding of Discontinuous Media

In this paper we propose novel extensions to JPEG 2000 for the coding of discontinuous media which includes piecewise smooth imagery such as depth maps and optical flows. These extensions use breakpoints to model discontinuity boundary geometry and apply a breakpoint dependent Discrete Wavelet Transform (BP-DWT) to the input imagery. The highly scalable and accessible coding features provided by the JPEG 2000 compression framework are preserved by our proposed extensions, with the breakpoint and transform components encoded as independent bit streams that can be progressively decoded. Comparative rate-distortion results are provided along with corresponding visual examples which highlight the advantages of using breakpoint representations with accompanying BD-DWT and embedded bit-plane coding. Recently our proposed extensions have been adopted and are in the process of being published as a new Part 17 to the JPEG 2000 family of coding standards.
Implementation of Online Mess Recommender System using Sentiment Analysis

Abstract: In this research paper we proposed mess recommender system using sentiment analysis. We proposed Wordnet based sentiment analysis. Users will submit ratings and reviews for mess as per their experience; our model will analyse the submitted reviews and find out the polarity of the review. Based on these reviews and ratings, user profile like city, area, likings etc. our proposed system will recommend appropriate messes to the user.
Comparative Study of Target Image Detection using Deep Learning

The object detection and deep learning technology has certainly proven effective in surveillance systems, automated driving, and facial recognition. Today, computer vision has given an entirely new perspective. However, when it comes to targeting a particular object within a complex image or video footage, it may seem to be a major challenge. By the rapid developments in the area of computer vision, the detectors have certainly improved greatly. This study presents a comprehensive literature review of various object detection algorithms, and their challenges, including one-stage and two-stage detectors. Finally, based on the current development of target image detection, the future prospects of research have been stated.