Reviewerly/exHarmony huggingface dataset:
Poisson Noise Reduction with Nonlocal-PCA Hybrid Model in Medical X-ray Images

The presence of Poisson noise in medical X-ray images leads to degradation of the image quality. The obscured information is required for accurate diagnosis. During X-ray image acquisition process, weak light results into limited number of available photons, which leads into the Poisson noise commonly known as X-ray noise. Currently, the available X-ray noise removal methods have not yet obtained satisfying total denoising results to remove noise from the medical X-ray images. The available techniques tend to show good performance when the image model corresponds to the algorithm’s assumptions used but in general, the denoising algorithms fail to do complete denoise. X-ray image quality could be improved by increasing the X-ray dose value (beyond the maximum medically permissible dose) but the process could be lethal to patients’ health since higher X-ray energy may kill cells due to the effects of higher dose values. In this study, the hybrid model that combines the Poisson Principal Component Analysis (Poisson PCA) with the nonlocal (NL) means denoising algorithm is developed to reduce noise in images. This hybrid model for X-ray noise removal and the contrast enhancement improves the quality of X-ray images and can, thus, be used for medical diagnosis. The performance of the proposed hybrid model was observed by using the standard data and was compared with the standard Poisson algorithms.
Socially intelligent machines that learn from humans and help humans learn

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Peano: learning formal mathematical reasoning

General mathematical reasoning is computationally undecidable, but humans routinely solve new problems. Moreover, discoveries developed over centuries are taught to subsequent generations quickly. What structure enables this, and how might that inform automated mathematical reasoning? We posit that central to both puzzles is the structure of procedural abstractions underlying mathematics. We explore this idea in a case study on five sections of beginning algebra on the Khan Academy platform. To define a computational foundation, we introduce Peano, a theorem-proving environment where the set of valid actions at any point is finite. We use Peano to formalize introductory algebra problems and axioms, obtaining well-defined search problems. We observe existing reinforcement learning methods for symbolic reasoning to be insufficient to solve harder problems. Adding the ability to induce reusable abstractions ('tactics') from its own solutions allows an agent to make steady progress, solving all problems. Furthermore, these abstractions induce an order to the problems, seen at random during training. The recovered order has significant agreement with the expert-designed Khan Academy curriculum, and second-generation agents trained on the recovered curriculum learn significantly faster. These results illustrate the synergistic role of abstractions and curricula in the cultural transmission of mathematics. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.
Explainable global error weighted on feature importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation

Evaluating data imputation and augmentation performance is a critical issue in data science. In statistics, methods like Kolmogorov-Smirnov K-S test, Cramér-von Mises $$W^2$$ , Anderson-Darling $$A^2$$ , Pearson’s $$\chi ^2$$ and Watson’s $$U^2$$ exists for decades to compare the distribution of two datasets. In the context of data generation, typical evaluation metrics have the same flaw: They calculate the feature’s error and the global error on the generated data without weighting the error with the feature’s importance. In most cases, the importance of the features is imbalanced, and it can induce a bias on the features and global errors. This paper proposes a novel metric named “Explainable Global Error Weighted on Feature Importance” (xGEWFI). This new metric is tested in a whole preprocessing method that 1. Process the outliers, 2. impute the missing data, and 3. augments the data. At the end of the process, the xGEWFI error is calculated. The distribution error between the original and generated data is calculated using a Kolmogorov-Smirnov test (K-S test) for each feature. Those results are multiplied by the importance of the respective features and calculated using a Random Forest (RF) algorithm. The metric result is expressed in an explainable format, aiming for an ethical AI. This novel method provides a more precise evaluation of a data generation process than if only a K-S test were used.
A visual-semantic approach for building content-based recommender systems

The topic of recommending items based on multimodal content has been addressed to a limited extent, and yet this could be a potential solution to the data bottleneck problem. Content-based semantics-driven recommender systems are often applied in the small-scale news recommendation domain, founded on the TF-IDF measure but also taking into account domain semantics through semantic lexicons or ontologies. In this work, we explore the application of content-based semantics-driven recommender systems to large-scale recommendations and focus on using both textual information and visual information to recommend items that have multimodal content. We propose methods to extract semantic features from various item descriptions, including digital images. In particular, we use computer vision to extract visual-semantic features from images and use these for movie recommendation together with various features extracted from textual information. The visual-semantic approach is scaled up with pre-computation of the cosine similarities and gradient learning of the model. The results of the study on a large-scale MovieLens dataset of user ratings demonstrate that semantics-driven recommenders can be extended to visual-semantic recommenders suitable for more complex domains than news recommendation, and which outperform TF-IDF-based recommenders on ROC, PR, F1, and Kappa metrics.
Accountable multi-authority attribute-based data access control in smart grids

The developments of AI, information security and network technology have improved the security, efficiency of the smart grid system. However real-time data and consume data sharing make privacy leakage, malicious entities and users be uncontrollable factors in the grid. Accountability can solve them partly and give an enhanced security guarantee to smart grid. The existing accountability system only focuses on the device level, and does not consider actual security. In this paper, an accountable multi-authority access control framework based attribute-based encryption (ABE) on for smart grid was proposed to solve the above problems. The proposed scheme not only solves the single-point failure problem in the smart grid, but also supports securely fine-grained access control to sharing data in real time. Furthermore, this scheme uses a protocol to limit the control of private keys by malicious entities, and can trace the responsibilities of malicious entities to ensure data security and privacy. Finally, theoretical analyses and performance evaluations show the proposal is more efficient and secure than the available.
Joint-optimized feature selection and classifier hyperparameters by salp swarm algorithm in piano score difficulty measurement problem

This study proposes the semantic-explicit features that characterize difficulty, and jointly optimizes feature selection and classifier hyperparameters by the salp swarm algorithm (SSA) to deal with the corresponding mixed-integer programming problem with constructing large-scale piano score difficulty level datasets. The difficulty level of piano scores is essential for piano learners to choose the appropriate piece, especially for beginners and amateurs. However, the previous studies lack an open-access baseline dataset and sufficient difficulty-related features, as well as the separate optimization of and feature and model hyperparameter. To address such problems, this study constructs large-scale difficulty-level datasets, proposes novel difficulty-related features, and jointly optimizes feature selection and classifier hyperparameters due to the coupled effect of feature selection and model optimization. The search space of the joint optimization is complex due to the strong mutual constraint relationship of difficulty levels in the piano-score difficulty measurement (PSDM) problem. SSA is adapted to the joint optimization scheme of the PSDM problem with the advantages of only one main controlling parameter and less computation complexity involving the gradual SSA movement approach to balance global exploration and local exploitation in an unknown and complex search space. The joint-optimization mechanism by SSA achieves an overall accuracy of 78.80% and 60.68% on two datasets of 677 and 2040 piano pieces with difficulty levels of four and nine, respectively. The results of recognition accuracy obviously validate the distinguished performance of our joint-optimization scheme compared to the successive optimization and joint optimization by other seven optimization algorithms in terms of the PSDM problem.
Wavelet-FCWAN: Fast and Covert Watermarking Attack Network in Wavelet Domain

The protection of digital image content has become an important topic of scientific research with the continuous development and updates of Internet and multimedia technology. In the past few years, various watermarking algorithms with good robustness and imperceptibility have been proposed, but the development of watermarking attack techniques has stagnated. In this paper, we have attempted to use deep learning to develop a new watermarking attack scheme and present the Fast and Covert Watermarking Attack Network in Wavelet Domain (Wavelet-FCWAN). The watermarking attack scheme employs noise filling as a preprocessing step for the watermarked image, performs wavelet transform operation on the preprocessed watermarked image, and inputs the wavelet transformed sub-image into Wavelet-FCWAN along with the noise level map in parallel. The network can be trained quickly and produces a better watermarking attack effect while ensuring the visual quality and detail retention of the attacked image. The experiments show that Wavelet-FCWAN demonstrates the superiority of its watermarking attack effect by comparing different attack strategies, and it can produce varying degrees of attack effects on various image watermarking algorithms with high levels of universality and imperceptibility.
Reversible data hiding for JPEG images based on block difference model and Laplacian distribution estimation

Reversible data hiding (RDH) for JPEG images has attracted extensive attentions in recent years. As far, a number of related schemes have been proposed, which enhance the embedding performance by improving the strategies such as frequency selection and block sorting to choose appropriate embedding coefficients with the histogram shifting mechanism. Nevertheless, the existing methods do not fully consider the characteristics of DCT coefficients and lack accurate measurement on the embedding distortion. To address the above problems, we propose an improved general framework of RDH for JPEG images based on the Laplacian distribution model. The block difference model is first established for the alternating current (AC) coefficients fitting. Subsequently, the scale parameter is calculated to estimate the embedding efficiency of each AC coefficient, and the coefficients with high embedding efficiency are preferentially selected for embedding. Furthermore, the proposed framework can be combined with other JPEG RDH methods based on quantized DCT coefficient modification. Experimental results demonstrate that better performance in terms of both visual quality and file size preservation can be achieved with the proposed scheme compared with the state-of-the-art RDH schemes for JPEG images.
Sentiment Analysis of Persian Political Tweets Using ParsBERT Embedding Model with Convolutional Neural Network

Recently, social networks have experienced an exponential growth, providing online users with a venue for expressing and sharing their views in a variety of areas. A very popular social media platform for getting user feedback and collecting data is Twitter. Sentiment analysis consists of extracting and analyzing opinions of people and can be used to predict tweet polarities. In this paper, we present a sentiment analysis methodology for Persian political tweets for the first time in order to assist Iranian politicians. We used two datasets of Persian political tweets with three and seven classes which are labeled according their content. This is the first study of this particular subfield of Persian tweets, so we are using a variety of encoding methods, including Bag-of-words, Word Embeddings, as well as neural methods such as Word2Vec, FastText, and ParsBERT Embeddings. We intend to use these techniques to implement Sentiment Analysis in closed domain political tweets using Machine Learning techniques such as Random Forests, Support Vector Machines, and Neural Networks. As a result of our comparisons, we found that CNN+BiLSTM using ParsBERT embeddings had higher robustness than other networks, scoring 0.89 on Dataset 1 and 0.71 on Dataset 2.
Efficient Still Image Action Recognition by the Combination of Ensemble Learning and Knowledge Distillation

Deep neural networks have demonstrated outstanding performance in various image classification tasks, yet action recognition from still images is challenging due to lack of temporal information, scarcity of data, large intra-class variations, and high similarity between different actions. To overcome these problems, ensemble learning appears to be one of the most straightforward approaches. However, ensemble models are computationally inefficient, especially when they are based on a combination of deep neural networks. In order to deal with this issue, we first construct an ensemble model by combining three separately trained deep convolutional neural networks. Then by utilizing this ensemble model as a teacher we train a light-weight student network based on the new knowledge distillation framework. This light-weight model achieves 94.32% MAP on the Stanford40 dataset, demonstrating its superiority over many existing methods despite its high computational efficiency.
An Ensemble Learning Approach for SMS Spam Detection

One of the most accessible ways to communicate via text is through a short message service. In recent years, profit-seeking people have taken advantage of the good features of this service to send large numbers of spam messages to random people for malicious purposes. In this respect, detecting spam messages is an important task. The unbalanced proportion of the spam and ham data and the extraction of efficient features from short messages have been the main challenges in the SMS spam detection problem. So far, various methods have been proposed to filter spam messages, whose accuracy still needs to be improved. In this study, we propose an ensemble learning method based on random forest and logistic regression algorithms to increase the accuracy of SMS spam detection. The proposed approach has been tested on two real datasets. The experimental evaluation based on accuracy and AUC shows the effectiveness of the proposed ensemble learning algorithm.
Reviewer Assignment Decision Support in an Academic Journal based on Multicriteria Assessment and Text Mining

Reviewer assignment to papers is one of the most significant stages of the scientific publishing process. It determines whether high-quality scientific papers are accepted for publication, while low-quality papers are rejected or sent to be improved to the required level. This in turn affects the status of academic journals and the level of scientific papers in general. A large number of researchers deals with issues of improving and speeding up of the reviewer assignment procedure, frequently using intellectual methods. However, insufficient attention is given to comprehensive reviewer assessment in their assignment to papers. A method of reviewer assignment decision support in an academic journal based on a joint use of multicriteria assessment and text mining is proposed in the paper. Calculation of an integral indicator with the use of additive folding of weighted reviewer’s indicators is at the core of the method. Text mining of manuscripts and reviewer’s papers is utilized to determine value of one of significant indicators. The proposed method allows to assess reviewers not only by authority and expertise, but also allows to take into account their work in the role of a reviewer, deciding how good they are in this role, with the use of previously collected statistical information refers to the carried out reviewing. The method has been successfully tested on data of peer-reviewed academic journal "Modeling, Optimization and Information Technology".
Multi-task fine-tuning for generating keyphrases in a scientific domain

Automatic selection of keyphrases (keywords) is a major challenge to finding and systematizing scholarly documents. This paper investigates the efficiency of using titles of scientific papers as additional information for keyphrase generation. We propose an approach to multi-task fine-tuning the BART model using control codes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . It is shown that the suggested approach can improve the performance of BART for the task of keyphrase generation. In some cases, the presented model outperforms state-of-the-art models for keyphrase extraction. Moreover, the results have demonstrated that multitask fine-tuning also increases the performance of title generation.
Slicing4Meta: An Intelligent Integration Architecture with Multi-Dimensional Network Resources for Metaverse-as-a-Service in Web 3.0

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of immersive and interactive experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential applications to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services (e.g., augmented/virtual reality and digital twin) in the three-dimensional virtual world. Nevertheless, there are still no unified solutions for the Metaverse services in terms of orchestration and management. Therefore, continuous and seamless upgrading of network systems is essential yet challenging to support Metaverse services. In this article, to provide scalable solutions for tailoring Metaverse services, we propose a new concept, named Metaverse-as-a-Service (MaaS), where users only need to purchase the MaaS models that are needed (e.g., computing, communications, and data resources) to achieve their required Metaverse services. Furthermore, to unify the orchestration and management of MaaS models, we propose a novel architecture, called Slicing4Meta, to customize Metaverse services by integrating MaaS models and the associated multi-dimensional resources. Additionally, we propose the classification for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulfill the QoE requirements under the Slicing4Meta architecture. We then illustrate a virtual travel study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges.
RFID-Based Human Action Recognition Through Spatiotemporal Graph Convolutional Neural Network

Traditional solutions for human action recognition usually rely on sensor or video methods. However, these methods have some limitations, such as inconvenient portability, light intensity influence, privacy protection, etc. In this article, an RFID-based nonwearable human action recognition scheme is proposed. In order to reduce the occlusion effect of the human body on the signal and increase the diversity of the reflected signal, a tags array is constructed. The data of phase and RSSI are fused as feature data to enhance the diversity of data. Furthermore, a combined processing method is proposed to eliminate thermal noise generated by the equipment and reduce the interference caused by the environment. Then, an action segmentation algorithm is designed to align the RF signals of human action. Finally, an efficient human action signal classification model is constructed using the spatiotemporal graph convolutional neural network (STGCN). Extensive experiments demonstrate that the overall accuracy rate of the system for human action recognition is 92.8%. Compared with the comparative mainstream recognition algorithms, STGCN shows better classification performance in terms of identification precision. In addition, multimodal RFID data fusion also improves the accuracy of identification.