Reviewerly/exHarmony huggingface dataset:
Two-stage Attention-based Fusion Neural Network for Image-Text Sentiment Classification

Social networks have gradually become an indispensable part of our daily lives, more and more people share their opinions and feelings through social software. As a result, Sentiment analysis, the core area of social media analysis, has been a hot research topic in recent years. Most previous work on multimodal sentiment analysis does not fully explore the information between text and image data and ignores the importance of analyzing fusion features. This paper presents a novel two-stage attention-based neural network to analyze textual-visual content for sentiment classification. The proposed network includes four sections: feature extraction, inter-modal feature learning, dual attention network, and sentiment classifier. The model first obtains multimodal features from pretrained models. Then in inter-modal feature learning, a cross-attention module and a self-attention module are adopted to correlate textual-visual features as well as explore independent individual information. Next, a dual attention network is proposed to obtain crucial representation from unimodal based bimodal features both in channel and position dimensions. Finally, a multilayer perceptron is applied to fuse multimodal information and predict sentiment deeply. The experimental results on two public multimodal sentiment datasets, MVSA-Single and MVSA-Multiple, show the effectiveness of the proposed model.
Crafting Trajectories of Smart Phone Use at the Opera

Losing Her Voice is a new opera which highlights the challenges of subtly interweaving digital technologies into established cultural forms. Audience members were encouraged to use their own mobile phones to interact with on-stage projections before, during and after the performance. We chart the trajectories of participation that were designed into the premiere performances of this work, and how these unfolded in practice. We identify the strategies that were effective to: encourage the adoption of the mobile app; interweave use with the other elements of the opera performance; make it consistent with the content; and ensure it complemented but was not essential to the show. We highlight the way in which three canonical trajectories must be woven together in this experience: the standard trajectory of “a night at the opera”; the dramatic arc of the specific work, and the audience member's use of their own device.
Exploration into the Explainability of Neural Network Models for Power Side-Channel Analysis

In this work, we present a comprehensive analysis of explainability of Neural Network (NN) models in the context of power Side-Channel Analysis (SCA), to gain insight into which features or Points of Interest (PoI) contribute the most to the classification decision. Although many existing works claim state-of-the-art accuracy in recovering secret key from cryptographic implementations, it remains to be seen whether the models actually learn representations from the leakage points. In this work, we evaluated the reasoning behind the success of a NN model, by validating the relevance scores of features derived from the network to the ones identified by traditional statistical PoI selection methods. Thus, utilizing the explainability techniques as a standard validation technique for NN models is justified.
Privacy Protection of Medical Service Data Based on Blockchain and Artificial Intelligence in the Era of Smart Medical Care

Smart medical care will realize the self-management, selection, and optimization of related things with more thorough induction, more comprehensive interconnection, and more intelligent insight, so that people can get an increasingly personalized medical and health service experience. 5G-enabled Internet of Things and AI (artificial intelligence) will continue to drive innovative applications in the medical industry. Access control and sharing of medical data is of great significance to the development of smart medical care, but the security problems in medical data sharing cannot be ignored. In this paper, a privacy protection scheme of medical service data based on blockchain and AI is proposed. The user chain is constructed as a public chain. In the user chain, the data privacy of users is protected, and users can safely transmit data to doctors and realize the management of session keys. CNN (convolutional neural network) privacy protection protocol based on homomorphic encryption can protect users’ privacy input, server model parameters, and calculated intermediate values. Experimental analysis and comparison with other schemes show that the scheme in this model is safer and more practical.
Quantum coherence dynamics of displaced squeezed thermal state in a non-Markovian environment

The dynamical behavior of quantum coherence of a displaced squeezed thermal state in contact with an external bath is discussed in the present work. We use a Fano-Anderson type of Hamiltonian to model the environment and solve the quantum Langevin equation. From the solution of the quantum Langevin equation we obtain the Green's functions which are used to calculate the expectation value of the quadrature operators which are in turn used to construct the covariance matrix. We use a relative entropy based measure to calculate the quantum coherence of the mode. The single mode squeezed thermal state is studied in the Ohmic, sub-Ohmic and the super-Ohmic limits for different values of the mean photon number. In all these limits, we find that when the coupling between the system and the environment is weak, the coherence decays monotonically and exhibit a Markovian nature. When the system and the environment are strongly coupled, we observe that the evolution is initially Markovian and after some time it becomes non-Markovian. The non-Markovian effect is due to the environmental back action on the system. Finally, we also present the steady state dynamics of the coherence in the long time limit in both low and high temperature regime. We find that the qualitative behavior remains the same in both the low and high temperature limits. But quantitative values differ because the coherence in the system is lower due to thermal decoherence.
A Study on Automatic Detection of Cyberbullying using Machine Learning

With time, use of the internet has become very common among people, and thus the rise of internet usage has given birth to a problem of cyberbullying. Cyberbullying can have a serious impact on the psychological health of the person who is the victim of it. Hence, detection of cyberbullying is required on the internet or social media. Much research has been done in the field of detection of cyberbullying. Machine learning can be the one of the approaches that is used for automatic cyberbullying detection. This paper has studied some of the papers related to cyberbullying. Moreover, some NLP techniques and different models used for cyberbullying detection tasks have been reviewed. The graph, which is based on the papers reviewed, shows that the tf-idf is mostly used either directly or with a combination of other techniques for feature extraction in cyberbullying detection using machine learning.
A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression

Abstract Classifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. In this context, feature selection becomes a crucial step in the classification process such that it reduces model complexity and computational costs, avoids overfitting, and thereby it improves the model performance. In this study, we propose a new ensemble feature selection method by combining a set of base selectors, classifiers, and rank aggregation methods, aiming to determine from any initial set of handcrafted features, a smaller set of relevant color and texture pixel-level features, subsequently used for segmenting HER2 overexpression on a pixel-level, in breast cancer tissue images. We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using $$\chi ^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>χ</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> , Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.
Autoencoder based Architecture for Mitigating phishing URL attack in the Internet of Things (IoT) using Deep Neural Networks

One of the serious emerging threats on the internet is a Phishing URL attack, wherein the attackers try to steal the login credentials like username, passwords, or even bank details of the user by creating fake websites then, get into the actual website then try to corrupt or delete the information. According to a survey conducted in 2016 at least two-thirds of people were exposed to these phishing attacks so, these types of attacks must be taken seriously to avoid losses occurred by these cyber threats. There are some existing defense mechanisms like a firewall, but it is not effective in preventing different forms of attacks, since it works on a set of predefined rules, it identifies and blocks only those predefined attacks. Defending mechanism that itself able to learn the possibility of attacks so that, it not only identifies and blocks the existing forms of attacks but also any other forms of future attacks must be needed. Our proposed method uses Deep Neural Networks (DNN) model combined with autoencoders, to detect and block phishing websites in the network layer. The model is used to feature reduction and trained with the help of a dataset received from autoencoder, which consists of a set of legitimate websites and phishing websites. This architecture shows the accuracy of about 98.18&#x0025; for Multiclassification Dataset. Since the memory, availability in sensor nodes is less only limited processing of data can be done security algorithms are implemented in network layers.
Innovative Research on College English Teaching Mode Based on Strategy Reasoning Mechanism

In the era of artificial intelligence, the traditional English teaching methods cannot meet the increasing demand of course learning at this stage. Therefore, in this paper, a brand-new English teaching model based on strategic reasoning mechanism is designed by analyzing the problems faced by construction of network curriculum in universities. By introducing the idea of artificial intelligence, the student model and teacher model are designed, and the separation of teaching content and teaching strategy is realized. The development of the system adopts B/S structure and relies on lightweight J2EE multitier framework. At the same time, the overall architecture, functions, and processes of the teaching system are analyzed and designed. The system is convenient to transplant and expand, which has certain practical value and can provide reference for the design of various teaching systems and the innovation of teaching modes.
Three-dimensional measurement method based on a three-step phase-shifting fringe and a binary fringe

Gray-code plus phase-shifting is currently a commonly used method for structured light three-dimensional (3D) measurement that is able to measure complex surfaces. However, the Gray-code fringe patterns tend to be complicated, making the measurement process time-consuming. To solve this problem and to obtain faster speed without sacrificing accuracy, a 3D measurement method based on three-step phase-shifting and a binary fringe is proposed; the method contains three phase-shifting fringe patterns and an additional binary fringe pattern. The period of the binary fringe is designed to be the same as the three-step phase-shifting fringe. Because of the specific pattern design strategy, the three-step phase-shifting algorithm is used to obtain the wrapped phase, and the connected region labeling theorem is used to calculate the fringe order. A theoretical analysis, simulation, and experiments validate the efficiency and robustness of the proposed method. It can achieve high-precision 3D measurement, which performs almost the same as the Gray-code plus phase-shifting method. Since only one additional binary fringe pattern is required, it has the potential to achieve higher measurement speed.
Deep and shallow types for gradual languages

Sound gradual types come in many forms and offer varying levels of soundness. Two extremes are deep types and shallow types. Deep types offer compositional guarantees but depend on expensive higher-order contracts. Shallow types enforce only local properties, but can be implemented with first-order checks. This paper presents a language design that supports both deep and shallow types to utilize their complementary strengths.
Monocular depth estimation with spatially coherent sliced network

Monocular depth estimation from a single still image is one of the most challenging fundamental problems in scenes understanding. As a pixel level regression problem, the inherent continuous large range of depth itself causes three main difficulties: (1) The unbalance between large value and small value during regressing; (2) Exploiting multi-scale contextual information; (3) Preserving spatial and semantic structures. To overcome these difficulties, this paper presents a novel spatially coherent sliced network for monocular depth estimation (SCS-Net). It first uses feature pyramids network to form the feature fusions of hierarchical feature maps. Then, the depth is sliced to supervise the estimation in different ranges generated from the feature fusions of multi-scale contexts. The holistic depth is invoked as the supervised signal of aggregated feature maps to ensure the learning of global structure of the scene. The self-spatial-attention mechanism further takes advantage of the semantics of objects and the spatial pixel relations to maintain the coherence of space and semantics in depth sliced estimation. Finally, the regulations of second-order depth information further make the estimated boundaries not too smooth. Numerous ablation experiments and comparisons on three popular indoor and outdoor benchmark datasets indicate the effectiveness and robustness of the proposed approach.
API-based evidence acquisition in the cloud - a survey

<ns7:p>Cloud services and cloud storage solutions are special challenges in digital forensic investigations. Cloud services allow their users, with relatively little technical knowledge, store, manage and share content with others. At the same time investigators are faced with a wide range of technical, legal and organizational issues. Unfortunately, evidence acquisition for such services still follows the traditional way of collecting artefacts on a client device. In this article, first, an overview of the state of research is given. Next, technical and legal challenges related to the forensically sound acquisition of cloud data are presented. Since accessing these data is highly challenging, basic techniques for acquiring data from the cloud are discussed and compared, using the example of 30 cloud storage services. We introduce the concept of an API-based evidence acquisition for cloud services that utilize the officially supported API of the service. We show how well this approach applies to most current cloud drive services in the survey context. We present the first glance of a proof-of-concept acquisition framework called CLOUDxTRACT, which can acquire evidence from selected cloud service providers.</ns7:p>
Specific Emitter Identification Based on Ensemble Neural Network and Signal Graph

Specific emitter identification (SEI) is a technology for extracting fingerprint features from a signal and identifying the emitter. In this paper, the author proposes an SEI method based on ensemble neural networks (ENN) and signal graphs, with the following innovations: First, a signal graph is used to show signal data in a non-Euclidean space. Namely, sequence signal data is constructed into a signal graph to transform the sequence signal from a Euclidian space to a non-Euclidean space. Hence, the graph feature (the feature of the non-Euclidean space) of the signal can be extracted from the signal graph. Second, the ensemble neural network is integrated with a graph feature extractor and a sequence feature extractor, making it available to extract both graph and sequence simultaneously. This ensemble neural network also fuses graph features with sequence features, obtaining an ensemble feature that has both features in Euclidean space and non-Euclidean space. Therefore, the ensemble feature contains more effective information for the identification of the emitter. The study results demonstrate that this SEI method has higher SEI accuracy and robustness than traditional machine learning methods and common deep learning methods.
A Convolutional Neural Network Based on Ultrasound Images of Primary Breast Masses: Prediction of Lymph-Node Metastasis in Collaboration With Classification of Benign and Malignant Tumors

Purpose: A convolutional neural network (CNN) can perform well in either of two independent tasks [classification and axillary lymph-node metastasis (ALNM) prediction] based on breast ultrasound (US) images. This study is aimed to investigate the feasibility of performing the two tasks simultaneously. Methods: We developed a multi-task CNN model based on a self-built dataset containing 5911 breast US images from 2131 patients. A hierarchical loss (HL) function was designed to relate the two tasks. Sensitivity, specificity, accuracy, precision, F1-score, and analyses of receiver operating characteristic (ROC) curves and heatmaps were calculated. A radiomics model was built by the PyRadiomics package. Results: The sensitivity, specificity and area under the ROC curve (AUC) of our CNN model for classification and ALNM tasks were 83.5%, 71.6%, 0.878 and 76.9%, 78.3%, 0.836, respectively. The inconsistency error of ALNM prediction corrected by HL function decreased from 7.5% to 4.2%. Predictive ability of the CNN model for ALNM burden (≥3 or ≥4) was 77.3%, 62.7%, and 0.752, and 66.6%, 76.8%, and 0.768, respectively, for sensitivity, specificity and AUC. Conclusion: The proposed multi-task CNN model highlights its novelty in simultaneously distinguishing breast lesions and indicating nodal burden through US, which is valuable for "personalized" treatment.
Marking Attendance using Modern Face Recognition (FR): Deep Learning using the OpenCV Method

Face Recognition and Detection encompasses an ocean of study and development involving picture analysis and algorithm-based comprehension, sometimes known as computer vision. Attendance is a right that no one can reject, and to support this right, many efforts and studies are being conducted around the world. A Deep Convolutional Neural Network (CNN) using the OpenCV model has been suggested for marking Attendance in this work. Convolutional Neural Network is employed to gain the unique features of the faces based on the distance. A wide variety of parameters influence the training of a Convolutional Neural Network (CNN) based classifier. These aspects include assembling an appropriate dataset, choosing a suitable Convolutional Neural Network (CNN), processing the dataset, and choosing training parameters to get the required classification results. The current publication compiles state-of-the-art research that used dataset preparation and artificial augmentation before training. Accuracy rates are achieved using the proposed model.
Interferometric thickness assessment of optical flat by combining iterative analysis and wavelength tuning

During the thickness assessment of an optical flat with wavelength-tuning interferometry, the second-order harmonics generated by double reflection inside the optical flat and nonlinear wavelength tuning reduce the accuracy of interferometric assessment. In this study, a harmonic phase-iterative analysis was proposed for the thickness assessment with the elimination of the effects of double reflection and nonlinearity. The harmonic phase-iterative analysis was derived by combining the intensity signal, including the harmonic components, with the partial least-squares method. Then, the proposed iterative analysis was integrated with the harmonic convergence condition and the selected pixel technique to improve the computational ability of the phase-iterative analysis. For evaluation, a numerical simulation and experimental thickness assessment using wavelength-tuning Fizeau interferometer were performed employing a five-frame harmonic phase-iterative analysis. The standard deviation of the experimental thickness assessment was 2.561 nm, which was smaller than those by other phase-detection technique. Based on the simulated and experimental results, it can be seen that the proposed harmonic phase-iterative analysis could determine the target phase with enhanced computational ability and compensation for the combined error between the second-order harmonics and the nonlinearity of the wavelength tuning.
Credit Card Fraud Detection with Unbalanced Real and Synthetic dataset using Machine Learning models

As credit card becomes one of the most trusted and popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. It is of utmost importance for financial institutions like banks and credit card companies to find fraudulent credit card transactions in real time so as to withhold any suspicious transaction till they get further confirmation from the customers and ensure customers aren't paying for anything they haven't bought. But the challenge in making such a model that classifies transactions into legitimate and fraudulent transactions is that the legitimate transactions occur much more frequently than the fraud transactions. In this paper we will analyse how the traditional machine learning algorithms handle highly imbalanced data, biased against fraud transactions, and compare it with algorithms that are designed specifically to deal with highly imbalanced data. For fraud detection in mobile money transaction, a few supervised machine learning models (Logistic Regression, Random Forest, Decision Tree, SVM) are tested and compared. All classification models are tested using a synthetic dataset generated by a simulator based on real transaction of a company.
Accelerating physics simulations with tensor processing units: An inundation modeling example

Recent advancements in hardware accelerators such as Tensor Processing Units (TPUs) speed up computation time relative to Central Processing Units (CPUs) not only for machine learning but, as demonstrated here, also for scientific modeling and computer simulations. To study TPU hardware for distributed scientific computing, we solve partial differential equations (PDEs) for the physics simulation of fluids to model riverine floods. We demonstrate that TPUs achieve a two orders of magnitude speedup over CPUs. Running physics simulations on TPUs is publicly accessible via the Google Cloud Platform, and we release a Python interactive notebook version of the simulation.