Reviewerly/exHarmony huggingface dataset:
Improved Deep CNN-based Two Stream Super Resolution and Hybrid Deep Model-based Facial Emotion Recognition

Humans have traditionally found it easy to determine emotions from facial expressions, but doing so by utilizing a computer algorithm is far more challenging. It is now feasible to discern emotions from photographs because of recent advances in computer vision as well as artificial intelligence. The following issues afflict existing face expression detection systems throughout the application process: As the extant shallow feature extraction framework has lost a great deal of effective feature data and also has limited detection performance, the paper proposes a novel technique termed “Improved Deep CNN-based Two Stream Super Resolution and Hybrid Deep Model-based Facial Emotion Detection”, which consists of three working phases: super-resolution, facial emotion recognition, as well as classification. Improved Deep CNN has been used in the super-resolution phase for two streams, structure and texture stream super-resolution, that delivers high pixel density to the pictures with minimal cross-entropy loss. The facial emotion identification phase involves face recognition utilizing the Viola–Jones face detection algorithm and feature extraction using three traditional methods: Texton, Bag of Words (BOW), and GLCM, as well as improved LGXP features. In addition, classification was performed in our work utilizing RNN as well as Bi-GRU neural networks. A voting mechanism known as Score level fusion was used to get precise categorization results. Our unique Deep CNN-based facial emotion recognition system provides 95% accuracy, which is superior to other traditional approaches, according to the results. Positive and negative performance metrics for various databases have also been assessed and compared with our proposed methodology, proving that our novel strategy surpasses conventional approaches.
Hierarchical Item Inconsistency Signal Learning for Sequence Denoising in Sequential Recommendation

Sequential recommender systems aim to recommend the next items in which target users are most interested based on their historical interaction sequences. In practice, historical sequences typically contain some inherent noise (e.g., accidental interactions), which is harmful to learn accurate sequence representations and thus misleads the next-item recommendation. However, the absence of supervised signals (i.e., labels indicating noisy items) makes the problem of sequence denoising rather challenging. To this end, we propose a novel sequence denoising paradigm for sequential recommendation by learning hierarchical item inconsistency signals. More specifically, we design a hierarchical sequence denoising (HSD) model, which first learns two levels of inconsistency signals in input sequences, and then generates noiseless subsequences (i.e., dropping inherent noisy items) for subsequent sequential recommenders. It is noteworthy that HSD is flexible to accommodate supervised item signals, if any, and can be seamlessly integrated with most existing sequential recommendation models to boost their performance. Extensive experiments on five public benchmark datasets demonstrate the superiority of HSD over state-of-the-art denoising methods and its applicability over a wide variety of mainstream sequential recommendation models. The implementation code is available at https://github.com/zc-97/HSD
Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.
Application of Machine Learning Algorithms to Classification of Pb–Zn Deposit Types Using LA–ICP–MS Data of Sphalerite

Pb–Zn deposits supply a significant proportion of critical metals, such as In, Ga, Ge, and Co. Due to the growing demand for critical metals, it is urgent to clarify the different types of Pb–Zn deposits to improve exploration. The trace element concentrations of sphalerite can be used to classify the types of Pb–Zn deposits. However, it is difficult to assess the multivariable system through simple data analysis directly. Here, we collected more than 2200 analyses with 14 elements (Mn, Fe, Co, Ni, Cu, Ga, Ge, Ag, Cd, In, Sn, Sb, Pb, and Bi) from 65 deposits, including 48 analyses from carbonate replacement (CR), 684 analyses from distal magmatic-hydrothermal (DMH), 197 analyses from epithermal, 456 analyses from Mississippi Valley-type (MVT), 199 analyses from sedimentary exhalative (SEDEX), 377 analyses from skarn, and 322 analyses from volcanogenic massive sulfide (VMS) types of Pb–Zn deposits. The critical metals in different types of deposits are summarized. Machine learning algorithms, namely, decision tree (DT), K-nearest neighbors (KNN), naive Bayes (NB), random forest (RF), and support vector machine (SVM), are applied to process and explore the classification. Learning curves show that the DT and RF classifiers are the most suitable for classification. Testing of the DT and RF classifier yielded accuracies of 91.2% and 95.4%, respectively. In the DT classifier, the feature importances of trace elements suggest that Ni (0.22), Mn (0.17), Cd (0.13), Co (0.11), and Fe (0.09) are significant for classification. Furthermore, the visual DT graph shows that the Mn contents of sphalerite allow the division of the seven classes into three groups: (1) depleted in Mn, including MVT and CR types; (2) enriched in Mn, including epithermal, skarn, SEDEX, and VMS deposits; and (3) DMH deposits, which have variable Mn contents. Data mining also reveals that VMS and skarn deposits have distinct Co and Ni contents and that SEDEX and DMH deposits have different Ni and Ge contents. The optimal DT and RF classifiers are deployed at Streamlit cloud workspace. Researchers can select DT or RF classifier and input trace element data of sphalerite to classify the Pb–Zn deposit type.
Parallel and deep reservoir computing using semiconductor lasers with optical feedback

Abstract Photonic reservoir computing has been intensively investigated to solve machine learning tasks effectively. A simple learning procedure of output weights is used for reservoir computing. However, the lack of training of input-node and inter-node connection weights limits the performance of reservoir computing. The use of multiple reservoirs can be a solution to overcome this limitation of reservoir computing. In this study, we investigate parallel and deep configurations of delay-based all-optical reservoir computing using semiconductor lasers with optical feedback by combining multiple reservoirs to improve the performance of reservoir computing. Furthermore, we propose a hybrid configuration to maximize the benefits of parallel and deep reservoirs. We perform the chaotic time-series prediction task, nonlinear channel equalization task, and memory capacity measurement. Then, we compare the performance of single, parallel, deep, and hybrid reservoir configurations. We find that deep reservoirs are suitable for a chaotic time-series prediction task, whereas parallel reservoirs are suitable for a nonlinear channel equalization task. Hybrid reservoirs outperform other configurations for all three tasks. We further optimize the number of reservoirs for each reservoir configuration. Multiple reservoirs show great potential for the improvement of reservoir computing, which in turn can be applied for high-performance edge computing.
Multi-scale deep multi-view subspace clustering with self-weighting fusion and structure preserving

Deep subspace clustering methods for multi-view have achieved impressive clustering performance over other clustering methods. However, the existing methods either cannot integrate the global and local information of multi-view or fail to explore the discriminative contributions among views. In this paper, we propose a novel multi-scale deep multi-view subspace clustering (MDMVSC) method, which unifies the multi-scale learning (ML) module, self-weighting fusion (SF) module and structure preserving (SP) constraint. Specifically, to take advantage of the complementarity and diversity of different views, ML module first learns specific self-representation matrix for each view from the multi-scale low-dimensional latent features with the global and local information. Then, using the SF module, these matrices are fused to obtain the consensus representation of multi-view via attention mechanism guided weights according to their discriminative contributions. Moreover, SP constraint encourages the multi-scale latent features to preserve the consistent structural information of the original multi-view for enhancing representation ability. Extensive experimental results on five datasets demonstrate the superiority of MDMVSC in comparison with several state-of-the-art methods.
Rock type classification based on petrophysical, geochemical, and core imaging data using machine and deep learning techniques

Rock type classification is one of the most crucial steps of geological and geotechnical core logging. In conventional core logging, rock type classification is subjective and time-consuming. This study aims to automate rock type classification using Machine Learning (ML). About 35 m of core samples from five different rock types obtained from an open pit mine were logged using a Multi-Sensor Core Logging (MSCL) system, along with a core scanner that automatically captured geochemical and petrophysical properties of the samples and 360° images of the core circumference. A train/test split strategy (interval split) was introduced, as it produces more realistic predictions than a random shuffle split. The collected logging data were split into train/test subsets based on the core length intervals. For the automated rock type classification, three approaches were implemented. First, different ML algorithms were used to classify rock types based on their petrophysical (P- and S- wave velocities, Leeb hardness) and geochemical properties (collected using a portable X-Ray Fluorescence analyzer (pXRF)). XGBoost outperformed the other models across all rock types. The second approach classified rock types using core images by applying a pre-trained ResNet-50 on ImageNet. Both classical ML and Convolutional Neural Network (CNN) models have higher accuracy for distinct rock samples than transition and interbedding zones. In the third approach, an expert decision procedure was mimicked by concatenating rock properties (first approach) and five features extracted from images (second approach). The concatenation of images and rock properties improved the F1-score of each approach by 10% and 35%, respectively. The core samples had been annotated with a marker in the field, and the effect of removing marked images from the dataset was investigated. The cleaned images improved the rock type prediction by up to 16% (F1-score) using the CNN approach. However, the improvement in the concatenation approach (7%) was not significant enough to justify the labor-intensive cleaning process.
Quantum memory-assisted entropic uncertainty and entanglement dynamics: two qubits coupled with local fields and Ornstein Uhlenbeck noise

Entropic uncertainty and entanglement are two distinct aspects of quantum mechanical procedures. The estimation of the entropic uncertainty is required for the increased accuracy and, hence, for the enhanced efficacy of quantum information processing tasks. In this regard, we analyze the entropic uncertainty, entropic uncertainty lower bound, and concurrence dynamics in two non-interacting qubits. The exposure of two qubits is studied in two different qubit-noise configurations, namely, common qubit-noise and independent qubit-noise interactions. To include the noisy effects of the local external fields, a Gaussian Ornstein–Uhlenbeck process is considered. We show that the increase in entropic uncertainty gives rise to the disentanglement in the two-qubit Werner state. Depending on the parameters adjustment and the number of environments coupled, different classical environments have varying capacities to induce entropic uncertainty and disentanglement in quantum systems. The entanglement remains vulnerable to the external classical fields; however, by employing the ideal parameter ranges we provided, prolonged entanglement retention while preventing entropic uncertainty growth can be achieved. Besides, we have also analyzed the intrinsic behavior of the classical fields towards two-qubit entanglement with no imperfection with respect to different parameters.
Efficient methods for digital image watermarking and information embedding

Now a day to achieve security of digital data, utilizing digital image watermarking scheme is the option. The primary purpose to upgrade image watermarking scheme is to fulfill the need of sufficient imperceptibility, capacity, and robustness of watermark against several attacks like JPEG compression parameter, salt and pepper noise, cropping factor of the image part which contains a watermark and rotating the watermark at one particular angle. During embedding stage in digital image watermarking scheme, a secret message or image introduced into a host image without destroying the quality of original cover image for security and authentication of the data. During extraction stage in the watermarking scheme applying a process to that employed in insertion step but in a reverse way. In this paper different evaluation based on the watermarking methods described. Many projects have been presented such as DCT, DWT, DFT, SVD using FWHT, QIM, BPNN, DFIS, EZW, Huffman coding to provide protection, robustness and high embedding capacity. In the digital image watermarking world, where Peak signal-to-noise ratio (PSNR) is utilized to determine the image quality factor and Normalized correlation (NC) and robustness of the watermark. Using this learning opportunity an environment friendly digital image watermarking scheme can be used with sustainable infrastructure in developing countries.
Model-Based Clustering

Clustering is the task of automatically gathering observations into homogeneous groups, where the number of groups is unknown. Through its basis in a statistical modeling framework, model-based clustering provides a principled and reproducible approach to clustering. In contrast to heuristic approaches, model-based clustering allows for robust approaches to parameter estimation and objective inference on the number of clusters, while providing a clustering solution that accounts for uncertainty in cluster membership. The aim of this article is to provide a review of the theory underpinning model-based clustering, to outline associated inferential approaches, and to highlight recent methodological developments that facilitate the use of model-based clustering for a broad array of data types. Since its emergence six decades ago, the literature on model-based clustering has grown rapidly, and as such, this review provides only a selection of the bibliography in this dynamic and impactful field.
Persisting changes in sales due to global pandemic challenges

AbstractThe global health pandemic triggered many challenges for businesses and society, forcing organizations and salespeople alike to pivot, alter their sales strategies, accelerate their digital transformation, and adjust to a 'new norm' going forward. Since some of the changes wrought by the pandemic are likely to persist into the post pandemic era, we asked the questions, how has personal selling and sales management been transformed? What have we learned? And where do we go from here? We identified trends, which we categorized into six broader themes, including sales strategy, sales force design, technology, leadership, salesperson wellness, and customer engagement. Each broader theme includes multiple future research questions on sub-topics such as internationalization, risk management, sales enablement, artificial intelligence, motivation, ethics, mental health concerns, buyer-seller relationships, and more. We first begin by highlighting current research in the field and end with these future research directions to inspire ongoing investigations that will inform and transform both scholarship and practice.Keywords: Future Research DirectionsPersisting Changes in SellingSales StrategySales Force DesignTechnology (A.I.)LeadershipSalesperson WellnessCustomer Engagement Disclosure statementNo potential conflict of interest was reported by the authors.
Compressed gastric image generation based on soft-label dataset distillation for medical data sharing

Sharing of medical data is required to enable the cross-agency flow of healthcare information and construct high-accuracy computer-aided diagnosis systems. However, the large sizes of medical datasets, the massive amount of memory of saved deep convolutional neural network (DCNN) models, and patients' privacy protection are problems that can lead to inefficient medical data sharing. Therefore, this study proposes a novel soft-label dataset distillation method for medical data sharing.The proposed method distills valid information of medical image data and generates several compressed images with different data distributions for anonymous medical data sharing. Furthermore, our method can extract essential weights of DCNN models to reduce the memory required to save trained models for efficient medical data sharing.The proposed method can compress tens of thousands of images into several soft-label images and reduce the size of a trained model to a few hundredths of its original size. The compressed images obtained after distillation have been visually anonymized; therefore, they do not contain the private information of the patients. Furthermore, we can realize high-detection performance with a small number of compressed images.The experimental results show that the proposed method can improve the efficiency and security of medical data sharing.
A survey on device fingerprinting approach for resource-constraint IoT devices: Comparative study and research challenges

Modernization and technological advancement have made smart and convenient living environments, including smart houses and smart cities, possible, by combining the Internet of Things (IoT), data, and internet-based services over various communication protocols. IoT is the next generation of the Internet. However, commonly resource-constraint IoT devices that are designed to perform a specific purpose, impose new security challenges, including node forgery, unauthorized access of data, and denial of services. They are more susceptible to being compromised by adversaries as opposed to general-purpose computing devices, and are exposed to different kinds of attacks, including spoofing and botnet attacks. Device identification is one of the promising approaches for improving network security. Devices can be identified either using explicit identifiers (internet protocol/media access control addresses) or implicit identifiers (network traffic and radio signal features), with implicit identifiers being more reliable, robust, and secure for device fingerprinting (DFP). In this paper, DFP methods have been studied in detail, with features generated from their communication traffic characteristics, including network traffic traces, IEEE 802.11 MAC frames, and radio signals, discussed. Additionally, key limitations and research challenges have been studied in the context of the IoT paradigm. Research challenges within the context of DFP and the future of IoT technologies are also discussed to shape future directions of work in the area. The key contribution of this study is the identification of different DFP research scopes in the domain of the IoT paradigm, which can be designed and implemented toward the development of IoT network security.
Fusioncount: Efficient Crowd Counting Via Multiscale Feature Fusion

State-of-the-art crowd counting models follow an encoder-decoder approach. Images are first processed by the encoder to extract features. Then, to account for perspective distortion, the highest-level feature map is fed to extra components to extract multiscale features, which are the input to the decoder to generate crowd densities. However, in these methods, features extracted at earlier stages during encoding are underutilised, and the multiscale modules can only capture a limited range of receptive fields, albeit with considerable computational cost. This paper proposes a novel crowd counting architecture (FusionCount), which exploits the adaptive fusion of a large majority of encoded features instead of relying on additional extraction components to obtain multiscale features. Thus, it can cover a more extensive scope of receptive field sizes and lower the computational cost. We also introduce a new channel reduction block, which can extract saliency information during decoding and further enhance the model’s performance. Experiments on two benchmark databases demonstrate that our model achieves state-of-the-art results with reduced computational complexity. PyTorch implementation of the model and weights trained on these two datasets are available at https://github.com/YimingMa/FusionCount.
Single-shot quantum error correction with the three-dimensional subsystem toric code

Fault-tolerant protocols and quantum error correction (QEC) are essential to building reliable quantum computers from imperfect components that are vulnerable to errors. Optimizing the resource and time overheads needed to implement QEC is one of the most pressing challenges. Here, we introduce a new topological quantum error-correcting code, the three-dimensional subsystem toric code (3D STC). The 3D STC can be realized with geometrically-local parity checks of weight at most three on the cubic lattice with open boundary conditions. We prove that one round of parity-check measurements suffices to perform reliable QEC with the 3D STC even in the presence of measurement errors. We also propose an efficient single-shot QEC decoding strategy for the 3D STC and numerically estimate the resulting storage threshold against independent bit-flip, phase-flip and measurement errors to be pSTC ≈ 1.045%. Such a high threshold together with local parity-check measurements make the 3D STC particularly appealing for realizing fault-tolerant quantum computing.