Reviewerly/exHarmony huggingface dataset:
Task-Adaptive Feature Fusion for Generalized Few-Shot Relation Classification in an Open World Environment

Relation Classification (RC) is an important task in information extraction. In most real-world scenarios, the frequency of relations often follows a long-tailed and open-ended distribution. However, current efforts mainly focus on the partial frequency distribution of relations, which is limited in real-world applications. Meanwhile, prototypical network achieves remarkable performance among fields of deep supervised learning, few-shot learning and open set learning. Nevertheless, in the open world environment, it still suffers from the incompatible feature embedding problem as the novel and unknown relations come in. To address these problems, we propose an Open Generalized Prototypical Network with task-adaptive feature fusion for the open generalized few-shot relation classification. Extensive experiments are conducted on public large-scale datasets and our proposed model obtains the better performances.
Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks

Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">what</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how</i> recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation. The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. A representative dataset from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deception island volcano</i> (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.
Pretrained transformer framework on pediatric claims data for population specific tasks

The adoption of electronic health records (EHR) has become universal during the past decade, which has afforded in-depth data-based research. By learning from the large amount of healthcare data, various data-driven models have been built to predict future events for different medical tasks, such as auto diagnosis and heart-attack prediction. Although EHR is abundant, the population that satisfies specific criteria for learning population-specific tasks is scarce, making it challenging to train data-hungry deep learning models. This study presents the Claim Pre-Training (Claim-PT) framework, a generic pre-training model that first trains on the entire pediatric claims dataset, followed by a discriminative fine-tuning on each population-specific task. The semantic meaning of medical events can be captured in the pre-training stage, and the effective knowledge transfer is completed through the task-aware fine-tuning stage. The fine-tuning process requires minimal parameter modification without changing the model architecture, which mitigates the data scarcity issue and helps train the deep learning model adequately on small patient cohorts. We conducted experiments on a real-world pediatric dataset with more than one million patient records. Experimental results on two downstream tasks demonstrated the effectiveness of our method: our general task-agnostic pre-training framework outperformed tailored task-specific models, achieving more than 10% higher in model performance as compared to baselines. In addition, our framework showed a potential to transfer learned knowledge from one institution to another, which may pave the way for future healthcare model pre-training across institutions.
An Efficient Workflow Scheduling Algorithm in Cloud Computing Using Cuckoo Search and PSO Algorithms

Recent developments in cloud computing are a great advantage to the IT enterprises as most of the IT infrastructure used by IT companies are managed by cloud vendors. This led to the situation all the services, i.e., compute, storage and network were given to the users as services with the cloud paradigm. Many of the users around the globe access the cloud for different kind of applications and to provision the resources to users in a seamless fashion with variable requests in terms of size and variety is a huge challenge in cloud computing. It all depends on how the workflows can be assigned to the appropriate resources for a specified amount of time. Scheduling workflows is a great challenge in cloud computing as each of a task in a workflow depends on another task. An appropriate scheduler is necessary to effectively map these variable tasks onto VMs by minimizing makespan and energy consumption as these are the important parameters in the view of cloud vendors. In this paper, we are scheduling tasks, which are in the workflow to a suitable VM by considering task priority. This algorithm is modeled by combining both PSO and CS algorithms. It is simulated by using cloudsim simulator, it is evaluated against the existing algorithms PSO and CS algorithms, and our proposed approach is outperformed over existing algorithms in terms of makespan and energy consumption.
An Analysis on Fraud Detection in Credit Card Transactions using Machine Learning Techniques

Today, digitization is turning popular because of the ease and convenience of utilizing e-commerce. People are selecting on-line payments and electronic shopping due to the convenience of time, the convenience of transportation, and so forth. As a result of the high level of e-commerce usage, fraud of credit card is increasing rapidly. Credit card transactions are very common nowadays and so is the fraud related to it. One of the most common fraud interface processes is to illegally collect the cards, user data and use the collected data for tele-ordering. Once enough info is collected and made available, it becomes challenging for an individual or any company to track down such fraud records among thousands of standard transactions. The fraud detection in credit card transactions is essential with enhanced performance measures. A methodology for effectual classification of fraudulent transactions is proposed in this paper. Also, Machine Learning (ML) algorithms like Decision tree, Random Forest, Logistic Regression and KNN are applied for fraud detections in credit card dataset. Random Forest and Decision Tree methods have shown highest accuracy with adequate F-score. The fused feature selection process is required in future to identify the significant features of the data to enhance the performance of the classifier models.
A Graph Convolution Network Based on Improved Density Clustering for Recommendation System

Recommendation systems have been widely used in various applications to solve information overload andimprove user experience. Traditional recommendation algorithms mainly used Euclidean data for calculationand abandoned the graph structure features in user and item data. Aiming at the problems in the current recommendation algorithms, this paper proposes an improved user density clustering method and extracts userfeatures through optimized graph neural network. Firstly, the improved density clustering method is used toform the clustering subgraph of users based on the influence value of users. Secondly, the user data and itemdata features of cluster subgraph are extracted by graph convolution network. Finally, the features of clustersubgraphs are processed by global graph convolution network and the recommendation results are generatedaccording to the global graph features. This model not only improves the efficiency of decomposing large graphinto small graph through the improved user density clustering algorithm, but also extracts the features of usergroups through graph convolution neural network to improve the recommendation effect. The experiment alsoproves the validity of this model.
An extension of Synthetic Minority Oversampling Technique based on Kalman filter for imbalanced datasets

More often than not, data collected in real-time tends to be imbalanced i.e., the samples belonging to a particular class are significantly more than the others. This degrades the performance of the predictor. One of the most notable algorithms to handle such an imbalance in the dataset by fabricating synthetic data, is the “Synthetic Minority Oversampling Technique (SMOTE)”. However, data imbalance is not solely responsible for the poor performance of the classifier. Certain research works have demonstrated that noisy samples can have a significant role in misclassifying the dataset. Also, handling large data is computationally expensive. Hence, data reduction is imperative. In this work, we put forth a novel extension of SMOTE by integrating it with the Kalman filter. The proposed method, Kalman-SMOTE (KSMOTE), filters out the noisy samples in the final dataset after SMOTE, which includes both the raw data and the synthetically generated samples, thereby reducing the size of the dataset. Our model is validated with a wide range of datasets. An experimental analysis of the results shows that our model outperforms the presently available techniques.
Analyzing the Sociodemographic Factors Impacting the Use of Virtual Reality for Controlling Obesity

Obesity is one of the most pressing issues in society today. Virtual reality has been used in the design of tools that promotes obesity control. However, the design of current VR tools lacks the involvement of prospective users and health practitioners. Such engagement is crucial in gathering semantic information that identifies stakeholders’ needs and ensures that all aspects of health are considered. Therefore, this paper aims to study the sociodemographic factors and individual-level characteristics and preferences that make the design of any obesity-control VR tool effective and satisfactory for a wide range of users. The paper also aims to solicit opinions of health practitioners to identify best health aspects that should be available in the design of any VR tool for obesity control. Organizations, businesses, and people will be able to readily augment such VR technologies on the semantic web, as well as on personal and mobile devices.
VAE-KRnet and Its Applications to Variational Bayes

In this work, we have proposed a generative model, called VAE-KRnet, for density estimation or approximation, which combines the canonical variational autoencoder (VAE) with our recently developed flow-based generative model, called KRnet. VAE is used as a dimension reduction technique to capture the latent space, and KRnet is used to model the distribution of the latent variable. Using a linear model between the data and the latent variable, we show that VAE-KRnet can be more effective and robust than the canonical VAE. VAE-KRnet can be used as a density model to approximate either data distribution or an arbitrary probability density function (PDF) known up to a constant. VAE-KRnet is flexible in terms of dimensionality. When the number of dimensions is relatively small, KRnet can effectively approximate the distribution in terms of the original random variable. For high-dimensional cases, we may use VAE-KRnet to incorporate dimension reduction. One important application of VAE-KRnet is the variational Bayes for the approximation of the posterior distribution. The variational Bayes approaches are usually based on the minimization of the Kullback-Leibler (KL) divergence between the model and the posterior. For high-dimensional distributions, it is very challenging to construct an accurate density model due to the curse of dimensionality, where extra assumptions are often introduced for efficiency. For instance, the classical mean-field approach assumes mutual independence between dimensions, which often yields an underestimated variance due to oversimplification. To alleviate this issue, we include into the loss the maximization of the mutual information between the latent random variable and the original random variable, which helps keep more information from the region of low density such that the estimation of variance is improved.
Provable Secure Software Masking in the Real-World

We evaluate eight implementations of provable secure side-channel masking schemes that were published in top-tier academic venues such as Eurocrypt, Asiacrypt, CHES and SAC. Specifically, we evaluate the side-channel attack resistance of eight open-source and first-order side-channel protected AES-128 software implementations on the Cortex-M4 platform. Using a T-test based leakage assessment we demonstrate that all implementations produce first-order leakage with as little as 10,000 traces. Additionally, we demonstrate that all except for two Inner Product Masking based implementations are vulnerable to a straightforward correlation power analysis attack. We provide an assembly level analysis showing potential sources of leakage for two implementations. Some of the studied implementations were provided for benchmarking purposes. We demonstrate several flaws in the benchmarking procedures and question the usefulness of the reported performance numbers in the face of the implementations’ poor side-channel resistance. This work serves as a reminder that practical evaluations cannot be omitted in the context of side-channel analysis.
Real-time monitoring of social distancing with person marking and tracking system using YOLO V3 model

The global economy has been affected enormously due to the spread of coronavirus (COVID-19). Even though, there is the availability of vaccines, social distancing in public places is one of the viable solutions to reduce the spreading of COVID-19 suggested by the World Health Organization (WHO) for fighting against the pandemic. This paper presents a YOLO v3 object detection model to automate the monitoring of social distancing among persons through a CCTV surveillance camera. Furthermore, this research work used to detect and track the person, measure the inter-person distance in the crowd under a challenging environment which includes partial visibility, lighting variations, and person occlusion. Moreover, the YOLO V3 model experiments with Darknet53 and ShuffleNetV2 backbone architecture. Compared with Darknet53 architecture, ShuffleNetV2 achieves better detection accuracy tested on Custom Video Footage Dataset (CVFD), Oxford Town Centre Dataset (OTCD), and Custom Personal Image Dataset (CPID) datasets.
HyText – A Scene-Text Extraction Method for Video Retrieval

Scene-text has been shown to be an effective query target for video retrieval applications in a known-item search context. While much progress has been made in scene-text extraction from individual pictures, the special case of video has so far received less attention. This paper introduces HyText, a scene-text extraction method for video with a focus on retrieval applications. HyText uses intermittent scene-text detection in combination with bi-directional tracking in order to increase throughput without reducing detection accuracy.
Feature discrimination and diversification for image emotion recognition

Emotion is strongly subjective, and different parts of the image may have a different degree of impact on emotion. The key for solving image emotion recognition is to effectively mine different discriminative local regions. We present a deep architecture to guide the network to extract discriminative and diverse affective semantic information. First, training a full convolutional network with a cross-channel max pooling strategy (CCMP) to extract discriminative feature maps. Second, to ensure that most of the discriminative sentiment regions are located accurately, we add a module consisting of the convolution layer and the CCMP. After obtaining the discriminative regions of the first module, the feature elements corresponding to the discriminant regions are erased, and then the erased features are fed into the second module. Such adversarial erasure operation can force the network to discover different sentiment discriminative regions. Third, an adaptive feature fusion mechanism is proposed to better integrate discriminative and diverse sentiment representations. Sufficient experiments are conducted on the benchmark datasets FI, EmotionROI, Instagram, and Twitter1 to achieve 72.17%, 61.13%, 81.97%, and 85.44% recognition accuracies, respectively. The results of the experiment demonstrate that the proposed network outperforms the state-of-the-art results.
A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of ‘unsharpness’ in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level.
Low-Depth Unitary Coupled Cluster Theory for Quantum Computation

The unitary coupled cluster (UCC) approximation is one of the more promising wave function ansätzes for electronic structure calculations on quantum computers via the variational quantum eigensolver algorithm. However, for large systems with many orbitals, the required number of UCC factors still leads to very deep quantum circuits, which can be challenging to implement. Based on the observation that most UCC amplitudes are small for both weakly correlated and strongly correlated molecules, we devise an algorithm that employs a Taylor expansion in the small amplitudes, trading off circuit depth for extra measurements. Strong correlations can be taken into account by performing the expansion about a small set of UCC factors, which are treated exactly. Near equilibrium, the Taylor series expansion often works well without the need to include any exact factors; as the molecule is stretched and correlations increase, we find only a small number of factors need to be treated exactly.
Systematic Study of Decryption and Re-encryption Leakage: The Case of Kyber

The side-channel cryptanalysis of Post-Quantum (PQ) key encapsulation schemes has been a topic of intense activity over the last years. Many attacks have been put forward: Simple Power Analysis (SPAs) against the re-encryption of schemes using the Fujisaki-Okamoto (FO) transform are known to be very powerful; Differential Power Analysis (DPAs) against the decryption are also possible. Yet, to the best of our knowledge, a systematic and quantitative investigation of their impact for designers is still missing. In this paper, we propose to capture these attacks with shortcut formulas in order to compare their respective strength in function of the noise level. Taking the case of Kyber for illustration, we then evaluate the (high) cost of preventing them with masking and the extent to which different parts of an implementation could benefit from varying security levels. We finally discuss tweaks to improve the situation and enable a better leveling of the countermeasures. Our conclusions confirm that current solutions for side-channel secure PQ key encapsulation schemes like Kyber are unlikely to be efficient in low-noise settings without (design or countermeasures) improvements.
Automatic facial skin feature detection for everyone

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skincare product recommendation, etc. Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges.The key to guaranteeing an accurate assessment is accurate detection of different skin features.We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild.To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions.The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation.We employ Unet++ as the network architecture for feature detection.This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.
Experimental Investigation of Quantum Uncertainty Relations With Classical Shadows

The quantum component in uncertainty relation can be naturally characterized by the quantum coherence of a quantum state, which is of paramount importance in quantum information science. Here, we experimentally investigate quantum uncertainty relations construed with relative entropy of coherence, $l_1$ norm of coherence and coherence of formation. In stead of quantum state tomographic technology, we employ the classical shadow algorithm for the detection of lower bounds in quantum uncertainty relations. With an all optical setup, we prepare a family of quantum states whose purity can be fully controlled. We experimentally explore the tightness of various lower bounds in different reference bases on the prepared states. Our results indicate the tightness of quantum coherence lower bounds dependents on the reference bases as well as the purity of quantum state.