Reviewerly/exHarmony huggingface dataset:
Nanoscale digital image correlation at elementary fibre/matrix level in polymer–based composites

Multiscale mechanical modelling aims at predicting the failure of composites from the fibre/matrix level up to the component scale. Existing frameworks are limited by the lack of reliable experimental data and by an incomplete understanding of the submicron deformation and failure mechanisms. A novel digital image correlation (DIC) method has been developed for the characterisation of the nanoscale mechanical response in composites, based on latest advances in surface patterning. Indium has been deposited on unidirectional composites leading to a dense, homogeneous speckle with particle diameter around 15 nm. The specimens were subjected to transverse compression in a scanning electron microscope, while minimising distortion effects. Strain concentration areas, like submicron shear bands and fibre–matrix interphases, were successfully captured for two systems: a carbon fibre-reinforced thermoset and a glass fibre-reinforced thermoplastic. DIC results were compared with alternative experimental data, obtained by atomic force microscopy, and with finite element simulations based on a conventional elastoplastic model.
KINet: Incorporating Relevant Facts Into Knowledge-Grounded Dialog Generation

Knowledge-grounded conversation has led to great progress in producing informative dialog responses by leveraging external knowledge. This work focuses on two affiliated knowledge grounded conversation tasks: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledge Selection</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Response Generation</i> . Previous work followed the paradigm of selecting the most optimal knowledge piece to guide the conversation towards generating the proper response. However, some knowledge pieces, which are not recognized as optimal, may still benefit response generation. How to effectively leverage these relevant knowledge pieces for response generation still remain a tricky issue. To address this problem, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>KINet</b></small> , a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ncorporation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Net</b> work, which deals with the problem by boosting both the knowledge selection and the response generation. The proposed model contains a negative enhanced knowledge approximator which improves knowledge selection by enhancing the dense representation of knowledge pieces, and a curriculum knowledge sampler which improves generated responses by incorporating more relevant knowledge pieces in an easy-to-hard manner. We conduct the experiment on two datasets of knowledge-grounded conversations, the results show that the proposed model significantly outperforms state-of-the-art methods in terms of both automatic and human evaluations.
GGCNN: An Efficiency-Maximizing Gated Graph Convolutional Neural Network Architecture for Automatic Modulation Identification

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.
A systematic review of intelligent tutoring systems based on Gross body movement detected using computer vision

The computer vision applications in intelligent tutoring systems (ITS) have enabled its use in various domains such as dance and sports. The adaptation in the tutoring system is based on the analysis of hand gestures, body postures, and movement. All these are termed as gross body movements (GBM). The use of these in the intelligent tutoring systems is termed as GBM-ITS in this article. There is no survey paper that considers the use of GBM-ITS in different domains. A systematic process is followed to address six review questions (RQs) by considering the 33 articles published between 2010 to 2022. A brief discussion regarding the methods adopted for analysis and the performance metrics used for evaluation has been made. The use of devices for the purpose of the study is analyzed with the need for its use. The feedback mechanisms adopted by the studies are reviewed. The information derived from the survey by addressing the RQs has been summarised as observations. This review results indicate the impact of GBM-ITSs in various domains and its potential to reduce human interventions significantly in the near future. Some of the future directions the review results indicated are: 1. the state-of-the-art computer vision methods for detection and tracking along with the temporal aspects are not much explored in GBM-ITS. 2. the current GBM-ITS are designed for beginners, and there is a vast scope to extend it for intermediate and experts. 3. the test users are considerably less, and there is a need for large-scale implementations and testing for generalizability or to check robustness. 4. more importance can be given to privacy and ethics as the GBM-ITS is performing better 5. since most methods use machine/deep learning methods, dataset dissemination will increase the design and development of GBM-ITS. 6. mechanism used in providing feedback needs to be evaluated and optimized.
Joint Privacy Enhancement and Quantization in Federated Learning

Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets, and the communication load due to the repeated exchange of updated models. These challenges are often tackled individually via techniques that induce some distortion on the updated models, e.g., local differential privacy (LDP) mechanisms and lossy compression. In this work we propose a method coined joint privacy enhancement and quantization (JoPEQ), which jointly implements lossy compression and privacy enhancement in FL settings. In particular, JoPEQ utilizes vector quantization based on random lattice, a universal compression technique whose byproduct distortion is statistically equivalent to additive noise. This distortion is leveraged to enhance privacy by augmenting the model updates with dedicated multivariate privacy preserving noise. We show that JoPEQ simultaneously quantizes data according to a required bit-rate while holding a desired privacy level, without notably affecting the utility of the learned model. This is shown via analytical LDP guarantees, distortion and convergence bounds derivation, and numerical studies. Finally, we empirically assert that JoPEQ demolishes common attacks known to exploit privacy leakage.
Innovative Application of Sensor Combined with Speech Recognition Technology in College English Education in the Context of Artificial Intelligence

English listening is an effective way to improve students’ English expression ability and use oral communication. However, from the current situation of English teaching, the current English teaching methods are too single, and teachers do not focus on oral training in the classroom, resulting in low efficiency of classroom teaching. On the basis of following the principles of wholeness, interaction, balance, and sustainable development of educational ecology, by enhancing the synergy of ecological elements of English speaking classroom, promoting interactive dialogue among ecological subjects, and regulating classroom behaviors, it is conducive to giving full play to the advantageous role of information technology on English speaking teaching reform and promoting its sustainable development. This paper addresses the current situation of English listening teaching, especially the problem of reduced recognition rate of spoken language in noisy environment, and the principle of using dual-sensor speech recognition system proposed. We design the speech recognition method based on recurrent neural network by acquiring the weak vibration pressure speech signal of the jaw skin and the speech signal transmitted through the air during the vocalization process through the sensor. Deep machine learning algorithm is used for speech recognition in English teaching. A reasonable frame sampling frequency is set to obtain the English speech signal, then the feature parameters representing this speech signal are obtained by linear prediction coefficients, and the speech feature vector is generated, followed by the recurrent neural network algorithm to train the speech features. In the related experiments, by comparing with the commonly used speech recognition algorithms, it is proved that the proposed algorithm English teaching speech recognition has higher accuracy and faster convergence.
Autism, Attachment, and Alexithymia: Investigating Emoji Comprehension

Emoji are often misinterpreted. This study investigated whether individual differences known to impact facial emotion recognition would also affect emoji recognition. Participants completed an online emoji classification task, and then completed questionnaires assessing their autistic traits, attachment style, and alexithymia score. Results showed that Autism Quotient (AQ) scores influenced classification accuracy, but only when considered in conjunction with alexithymia and attachment anxiety. Accuracy was poorer when AQ scores and alexithymia scores were both high, whereas high attachment anxiety boosted emotion recognition in participants with high AQ scores. Results highlight the importance of studying individual differences factors concomitantly, allowing for more accurate identification of individuals who may be at risk of emotional miscommunication online, and are therefore suitable targets for support or intervention. Furthermore, findings will be informative for designers of digital tools that are used to convey emotion.
A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, PyLDAvis web-based interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. PyLDAvis visualization describes the flexibility of exploring the terms of the topics’ association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability and validity of the model.
Assessing the Presence of Intentional Waveform Structure In Preamble-based SEI

Internet of Things (IoT) deployments continue at an accelerated rate and are projected to reach 75 billion by 2025. Most IoT devices employ weak or no encryption at all due to constrained onboard resources, prohibitive manufacturing costs, and challenges due to implementing and managing encryption at scale. Specific Emitter Identification (SEI) is an approach intended to address the security risks associated with unencrypted or weakly encrypted IoT deployments. SEI passively exploits inherent, unintentional waveform features that are imparted during normal device operations, thus it does not require modification of the device being identified. Recently, Deep Learning (DL)-based SEI has garnered a lot of interest due to its ability to learn discriminatory features directly from an emitter's digitally sampled waveforms, thus eliminating feature engineering commonly associated with SEI. DL-based SEI uses the entirety of the digitally sampled waveform, which includes the unintentional as well as intentional waveform structure. The intentional waveform structure does not convey emitter specific features, thus it is information not used by the DL-based SEI process. In fact, this work shows that the intentional waveform structure acts as a confuser that inhibits the DL network's ability to learn the features needed to discern one emitter from another and its negative impacts worsen as the number of emitters increases. Our work shows that frequency-domain removal of the intentional waveform structure coupled with a Long Short-Term Memory (LSTM) results in superior SEI performance.
Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication

Intent-based networks that integrate sophisticated machine reasoning technologies will be a cornerstone of future wireless 6G systems. Intent-based communication requires the network to consider the semantics (meanings) and effectiveness (at end-user) of the data transmission. This is essential if 6G systems are to communicate reliably with fewer bits while simultaneously providing connectivity to heterogeneous users. In this paper, contrary to state of the art, which lacks explainability of data, the framework of neuro-symbolic artificial intelligence (NeSy AI) is proposed as a pillar for learning causal structure behind the observed data. In particular, the emerging concept of generative flow networks (GFlowNet) is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data. Further, a novel optimization problem for learning the optimal encoding and decoding functions is rigorously formulated with the intent of achieving higher semantic reliability. Novel analytical formulations are developed to define key metrics for semantic message transmission, including semantic distortion, semantic similarity, and semantic reliability. These semantic measure functions rely on the proposed definition of semantic content of the knowledge base and this information measure is reflective of the nodes' reasoning capabilities. Simulation results validate the ability to communicate efficiently (with less bits but same semantics) and significantly better compared to a conventional system which does not exploit the reasoning capabilities.
Enhanced SS-FIM Algorithm For High Utility Uncertain Itemsets

In transactional databases, mining frequently occurring item sets. It’s a brand-new, very accurate single-scan method for mining frequently used item sets. Frequent patterns are a pattern that comes frequently in a dataset that show an important part in mining connections, correlations, and further stimulating data interactions that lead to discovering knowledge and supporting several commercial decision-making processes. In information discovery and decision-making procedures, data mining is a vital operational tool. In data mining method, frequent pattern mining approaches have become important for big datasets. This paper discusses a technique for mining frequent item sets that is both efficient and scalable in transactional dataset over simply one time scanning entire transactional dataset.
TeSP-TMF: A temporal-aware personalized POI recommendation approach based on potential preferences and grey relational analysis

The great advances in mobile devices make it critical to exploit multiple auxiliary information for efficient point-of-interest (POI) recommendations. However, most existing methods can neither effectively address the extreme data sparsity encountered in temporal-aware recommendation nor reasonably deal with implicit feedback. Furthermore, previous methods, including those relying on similarity measurement, cannot capture the intrinsic data correlations exhibited in the sparse data. To remedy these drawbacks, this paper proposes a temporal-aware personalized recommendation framework called Temporal Spatial Popularity and Temporal-aware Matrix Factorization (TeSP-TMF). In TeSP-TMF, we present the potential preferences model and temporal-aware matrix factorization model. Specifically, users' potential preferences that incorporate temporal, spatial, and popularity information are investigated to learn a set of potential POIs for each user, which are filled into the original matrix for further matrix factorization, owing to the sparse nature of the rating matrix. On this basis, we develop a new approach using a combination of grey relational analysis and matrix factorization, which not only relieves data scarcity caused by dividing the matrix with time slots but also strategically mines inherent temporal relationships. Finally, we implement extensive experiments on two public datasets collected from Foursquare and Yelp. Experimental results demonstrate that our proposed method yields competitive quality compared to several state-of-the-art methods. Theoretically, our work contributes promising insights for mitigating data sparsity. In practice, our results can enhance personalized temporal-aware POI recommendation services for location-based social networks.
Handwritten devanagari manuscript characters recognition using capsnet

Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet.
NMNet: Learning Multi-level semantic information from scale extension domain for improved medical image segmentation

Medical image segmentation methods based on encoder-decoder network structure have gained great success. However, these methods inevitably cause feature loss due to the pooling operation on the features during the encoding stage. Furthermore, there exists semantic gaps caused by the difference between low-level features and high-level features in the encoder-decoder network structure. By fusing the contextual features through simple skip-connections, it will limit the segmentation performance. To address these problems, this paper presents a new network for medical image segmentation, termed as NMNet, which mainly consists of a reverse encoder-decoder major structure with new attention modules. Specifically, in this network, we first design an N-shaped reverse encoder-decoder medical image segmentation structure (NNet), which can effectively reduce the impact of feature loss during the encoding process by performing feature representation compensation from the scale extension domain. Then, we build a Multi-scale Cross-attention Mechanism (MSC) in the skip-connections, which can enhance low-level features to bridge the semantic gaps. Extensive experiments on three benchmark datasets show that our NMNet performs favorably against most state-of-the-art methods under different evaluation metrics.
When CNN Meet with ViT: Towards Semi-supervised Learning for Multi-class Medical Image Semantic Segmentation

Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation tasks. In this paper, an advanced consistency-aware pseudo-label-based self-ensembling approach is presented to fully utilize the power of Vision Transformer (ViT) and Convolutional Neural Network (CNN) in semi-supervised learning. Our proposed framework consists of a feature-learning module which is enhanced by ViT and CNN mutually, and a guidance module which is robust for consistency-aware purposes. The pseudo labels are inferred and utilized recurrently and separately by views of CNN and ViT in the feature-learning module to expand the data set and are beneficial to each other. Meanwhile, a perturbation scheme is designed for the feature-learning module, and averaging network weight is utilized to develop the guidance module. By doing so, the framework combines the feature-learning strength of CNN and ViT, strengthens the performance via dual-view co-training, and enables consistency-aware supervision in a semi-supervised manner. A topological exploration of all alternative supervision modes with CNN and ViT are detailed validated, demonstrating the most promising performance and specific setting of our method on semi-supervised medical image segmentation tasks. Experimental results show that the proposed method achieves state-of-the-art performance on a public benchmark data set with a variety of metrics. The code is publicly available ( https://github.com/ziyangwang007/CV-SSL-MIS ).