Reviewerly/exHarmony huggingface dataset:
Multi-task support vector machine with pinball loss

With the boom in machine learning, support vector machine (SVM) is widely employed in pattern recognition. However, most of SVM models concentrate on single-task learning, multi-task learning has been largely neglected. Compared with single-task learning, multi-task learning can achieve a good performance for each task by mining the shared information among tasks. In addition, loss function also plays an important role in the accuracy of SVM. Inspired by multi-task learning and the SVM with pinball loss (pin-SVM), we propose two novel multi-task support vector machines with pinball loss for binary classification, named as MTL-pin-SVM I and MTL-pin-SVM II. Both methods maximize the quantile distance for each task, which realizes less sensitive to noise and more stable for re-sampling. Moreover, MTL-pin-SVM II can use different combinations of kernel functions for different tasks, which can get better performance than other multi-task models by choosing the suitable combinations of kernel functions for different tasks. And they include the multi-task SVM with hinge loss as their special cases, which are denoted as MTL-C-SVM I and MTL-C-SVM II. The extensive experiments on multi-task datasets fully validate the validity of the proposed models.
Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data. Recently, mainstream approaches perform this task through adversarial learning, yet still suffer from two limitations. First, they mainly align marginal distribution by unsupervised cross-domain feature matching, and ignore each feature&#x0027;s categorical and positional information that can be exploited for conditional alignment; Second, they treat all classes as equally important for transferring cross-domain knowledge and ignore that different classes usually have different transferability. In this article, we propose a joint adaptive detection framework (JADF) to address the above challenges. First, an end-to-end joint adversarial adaptation framework for object detection is proposed, which aligns both marginal and conditional distributions between domains without introducing any extra hyper-parameter. Next, to consider the transferability of each object class, a metric for class-wise transferability assessment is proposed, which is incorporated into the JADF objective for domain adaptation. Further, an extended study from unsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation (UFDA) is conducted, where only a few unlabeled training images are available in unlabeled target domain. Extensive experiments validate that JADF is effective in both the UDA and UFDA settings, achieving significant performance gains over existing state-of-the-art cross-domain detection methods.
Mobile Extended Reality for the Enhancement of an Underground Oil Mill: A Preliminary Discussion

The enhancement of the cultural heritage and the promotion of the territory, with a peculiar attention to alternative routes and decentralized places of interest, is receiving more and more attention from the ICT world. In particular, Extended Reality (XR) technology (Virtual, Augmented and Mixed Reality) is becoming quite pivotal in this process. In this context, two main critical issues are experienced: (a) places accessibility, in particular for sites that are often difficult to visit and (b) valorisation of itineraries and rural heritage, for those points-of-interest that are particularly important for the narration of the territory, even though outside the mainstream circuit. This is a perspective paper to preliminarily discuss the feasibility of two different widely used mobile applications, aimed at exploiting virtual and mixed reality technologies to promote an underground oil mill in Salento. The underlying problems and the steps that the authors intend to follow to develop the system will be briefly treated.
Medical education in Russia: vectors of reboot in pandemic conditions

The pandemic of the new coronavirus infection, determined, the need for accelerated transformations in the fi of national health, as well as a change in the usual principles and approaches in the system of higher medical education. The article, using the example of the Sechenov University case as the flagship of higher medical education in the country, analyzes the experience of operational restructuring of the organization of the university’s activities and highlights the key vectors of the educational process reorganization and training of medical personnel in the context of the spread of COVID-19. Sechenov University, based on the existing digital content modified the lecture materials into a digital format, adapted individual distance learning algorithms to the educational process of a medical university. The result is the preservation of the quality of education, the adaptation of educational programs, taking into account new formats and technologies for the delivery of knowledge, a flexible response to the demands of consumers of educational services of the University.
Two-qubit Heisenberg XYZ dynamics of local quantum Fisher information, skew information coherence: Dyzaloshinskii–Moriya interaction and decoherence

We theoretically demonstrate how to achieve quantum entanglement control of a dissipative anisotropic two-qubit Heisenberg XYZ model. New dynamical features of local quantum coherence, local quantum Fisher information and Bures distance entanglement are achieved by controlling the initial state setting, DM interaction and decoherence. The proposed scheme allows for the generation of sudden changes in the entanglement. We illustrate the control by manipulating the local quantum coherence, Fisher information dynamics, and the sudden appearance of the disentanglement intervals. We further consider simultaneous changes in these behaviors for different values of the system parameters. These results provide helpful significance for understanding the dynamics of the dissipative anisotropic two-qubit Heisenberg XYZ model.
Transforming Fake News: Robust Generalisable News Classification Using Transformers

As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which transformers have achieved state-of-the-art results. Using the publicly available ISOT and Combined Corpus datasets, this study explores transformers’ abilities to identify fake news, with particular attention given to investigating generalisation to unseen datasets with varying styles, topics and class distributions. Moreover, we explore the idea that opinion-based news articles cannot be classified as real or fake due to their subjective nature and often sensationalised language, and propose a novel two-step classification pipeline to remove such articles from both model training and the final deployed inference system. Experiments over the ISOT and Combined Corpus datasets show that transformers achieve an increase in F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores of up to 4.9% for out of distribution generalisation compared to baseline approaches, with a further increase of 10.1% following the implementation of our two-step classification pipeline. To the best of our knowledge, this study is the first to investigate generalisation of transformers in this context.
Analyzing the Data of Software Security Life-Span: Quantum Computing Era

Software or web application security is the main objective in the era of Information Technology (IT) and Artificial Intelligence (AI). Distinguishing proof of security at the initial stage produces significant results to comprehend the administration of security relics for best potential outcomes. A security alternative gives several methods and algorithms to ensure the software security. Security estimation is the vital factor in assessing, administrating, controlling security to improve the nature of security. It is to be realized that assessment of security at early stage of development helps in identifying distinctive worms, dangers, weaknesses and threats. This paper will talk about the definition and characterization of quantum computing in software security. For software security, we use different cryptography (methods or algorithms to secure our financial organizations, medical devices, military weapons, planes, ships, automobiles, navigators, etc. However, many cryptosystems are likely to collapse when the large quantum computer is developed. Recently, Google developed the Sycamore Processor 53 qubits. Such innovations indicate the advent of large quantum computer in future. Since cryptographic algorithm can be solved by the quantum computers, the present cryptosystem would be rendered obsolete. Hence, it is imperative to focus more on intensive research in the context of the present quantum cyber security. The main challenges in quantum era would be cryptography methods that fulfill the demands of security usability and flexibility without sacrificing the users’ confidence. This research study, in particular, focuses on ‘Software durability’ which is a quality of security that alludes to the capacity to execution of an item on schedule. In the context of software and web application, a thorough assessment of security factors will significantly influence the software’s durability in the era of quantum computing.
A Multi-Image Encryption Based on Sinusoidal Coding Frequency Multiplexing and Deep Learning

Multi-image encryption technology is a vital branch of optical encryption technology. The traditional encryption method can only encrypt a small number of images, which greatly restricts its application in practice. In this paper, a new multi-image encryption method based on sinusoidal stripe coding frequency multiplexing and deep learning is proposed to realize the encryption of a greater number of images. In the process of encryption, several images are grouped, and each image in each group is first encoded with a random matrix and then modulated with a specific sinusoidal stripe; therefore, the dominant frequency of each group of images can be separated in the Fourier frequency domain. Each group is superimposed and scrambled to generate the final ciphertext. In the process of decryption, deep learning is used to improve the quality of decrypted image and the decryption speed. Specifically, the obtained ciphertext can be sent into the trained neural network and then the plaintext image can be reconstructed directly. Experimental analysis shows that when 32 images are encrypted, the CC of the decrypted result can reach more than 0.99. The efficiency of the proposed encryption method is proved in terms of histogram analysis, adjacent pixels correlation analysis, anti-noise attack analysis and resistance to occlusion attacks analysis. The encryption method has the advantages of large amount of information, good robustness and fast decryption speed.
Task-adaptive Relation Dependent Network for Few-shot Learning

In solving learning problems with limited training data, few-shot learning is proposed to remember some common knowledge by leveraging a large number of similar few-shot tasks and learning how to adapt a base-learner to a new task for which only a few labeled samples are available. Among different few-shot learning algorithms, metric-based methods, which focus on how to acquire a robust feature embedding space or distance metric for classification, are most studied and believed to be effective for classification tasks. Despite the success of the metric-based methods, the distribution of training data and test data in the same few shot classification task is inconsistent with a distribution bias between them, which affects the quality of feature embeddings. In addition, the way of directly comparing the original features as a whole is deficient. It doesn't emphasize the features with high correlation between samples and thus can't distinguish the differences adequately. This manuscript proposes a novel metric-based few-shot algorithm called Task-adaptive Relation Dependent Network. The method reduces the distribution bias by shifting the dataset and adopting a more detailed comparison of features to capture their intrinsic correspondence, improving the measurements of the similarity between the support set and the query set samples. An experiment is conducted and the results demonstrate that the proposed method has a significant improvement over the baseline on the standard few-shot image classification benchmark datasets.
Unsupervised Network Learning for Cell Segmentation

Cell segmentation is a fundamental and critical step in numerous biomedical image studies. For the fully-supervised cell segmentation algorithms, although highly effective, a large quantity of high-quality training data is required, which is usually labor-intensive to produce. In this work, we formulate the unsupervised cell segmentation as a slightly under-constrained problem, and present the Unsupervised Segmentation network learning by Adversarial Reconstruction (USAR), a novel model able to train cell segmentation networks without any annotation. The key idea is to leverage adversarial learning paradigm to train the segmentation network by adversarially reconstructing the input images based on their segmentation results generated by the segmentation network. The USAR model demonstrates its promising application on training segmentation networks in an unsupervised manner, on two benchmark datasets. The implementation of this project can be found at https://github.com/LiangHann/USAR.
Differentially private SGD with non-smooth losses

In this paper, we are concerned with differentially private stochastic gradient descent (SGD) algorithms in the setting of stochastic convex optimization (SCO). Most of the existing work requires the loss to be Lipschitz continuous and strongly smooth, and the model parameter to be uniformly bounded. However, these assumptions are restrictive as many popular losses violate these conditions including the hinge loss for SVM, the absolute loss in robust regression, and even the least square loss in an unbounded domain. We significantly relax these restrictive assumptions and establish privacy and generalization (utility) guarantees for private SGD algorithms using output and gradient perturbations associated with non-smooth convex losses. Specifically, the loss function is relaxed to have an α-Hölder continuous gradient (referred to as α-Hölder smoothness) which instantiates the Lipschitz continuity (α=0) and the strong smoothness (α=1). We prove that noisy SGD with α-Hölder smooth losses using gradient perturbation can guarantee (ϵ,δ)-differential privacy (DP) and attain optimal excess population risk O(dlog⁡(1/δ)nϵ+1n), up to logarithmic terms, with the gradient complexity O(n2−α1+α+n). This shows an important trade-off between α-Hölder smoothness of the loss and the computational complexity for private SGD with statistically optimal performance. In particular, our results indicate that α-Hölder smoothness with α≥1/2 is sufficient to guarantee (ϵ,δ)-DP of noisy SGD algorithms while achieving optimal excess risk with a linear gradient complexity O(n).
Attention-Based Deep Recurrent Model for Survival Prediction

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.
A Q-learning approach for the autoscaling of scientific workflows in the Cloud

Autoscaling strategies aim to exploit the elasticity, resource heterogeneity and varied prices options of a Cloud infrastructure to improve efficiency in the execution of resource-hungry applications such as scientific workflows. Scientific workflows represent a special type of Cloud application with task dependencies, high-performance computational requirements and fluctuating workloads. Hence, the amount and type of resources needed during workflow execution changes dynamically over time. The well-known autoscaling problem comprises (i) scaling decisions, for adjusting the computing capacity of a virtualized infrastructure to meet the current demand of the application and (ii) task scheduling decisions, for assigning tasks to specific acquired Cloud resources for execution. Both are highly complex sub-problems, even more because of the uncertainty inherent to the Cloud. Reinforcement Learning (RL) provides a solid framework for decision-making problems in stochastic environments. Therefore, RL offers a promising perspective for designing Cloud autoscaling strategies based on an online learning process. In this work, we propose a novel formulation for the problem of infrastructure scaling in the Cloud as a Markov Decision Process, and we use the Q-learning algorithm for learning scaling policies, while demonstrating that considering the specific characteristics of workflow applications when taking autoscaling decisions can lead to more efficient workflow executions. Thus, our RL-based scaling strategy exploits the information available about workflow dependency structures. Simulations performed on four well-known workflows demonstrate significant gains (25%–55%) of our proposal in comparison with a similar state-of-the-art proposal.
ICT enabled Almajiri education in Nigeria: Challenges and prospects

The Almajiri children in Nigeria are deserving of special interventions to reduce the life-long divide in educational achievement, social status, and economic empowerment. One way of speedily achieving this is through the use of Information and Communication Technologies (ICTs). This study examined the prospects and challenges of using ICT in the teaching and learning processes of Almajiri children's education in Nigeria. A descriptive research methodology was adopted and data were collected from the Almajiri children, their teachers, and parents from communities with a high concentration of Almajiri schools. The results showed that while the official language of instruction in schools in Nigeria is English, the language often spoken by children and teachers was Hausa. Also, while teachers and parents were quite familiar with ICTs such as mobile phones, computers, and television, the Almajiri children were more familiar with television and mobile phones, and very few had used a computer. Some strategies proposed for successful adoption of ICT in Almajiri education include the use of indigenous languages in courseware development and the use of one-to-many ICT facilities, such as multimedia teaching aids, projectors, and smart televisions.
A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images

Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues. This paper proposes a new method for segmenting the Gleason tissues (patch-wise) in order to grade PCa from the whole slide images (WSI). Also, the proposed approach encompasses two main contributions: 1) A synergy of hybrid dilation factors and hierarchical decomposition of latent space representation for effective Gleason tissues extraction, and 2) A three-tiered loss function which can penalize different semantic segmentation models for accurately extracting the highly correlated patterns. In addition to this, the proposed framework has been extensively evaluated on a large-scale PCa dataset containing 10,516 whole slide scans (with around 71.7M patches), where it outperforms state-of-the-art schemes by 3.22% (in terms of mean intersection-over-union) for extracting the Gleason tissues and 6.91 % (in terms of F1 score) for grading the progression of PCa.
Radial Basis Function Network with Differential Privacy

Differential privacy (DP) remains a potent solution to what is arguably the defining issue in machine learning: balancing user privacy with an ever-increasing need for data. Practitioners must respect privacy, especially in sensitive healthcare domains. DP strives towards this aim by adding noise to training data to occlude its origin and nature, and is ideal for multiple Neural Network (NN) types. This includes deep varieties that utilise multiple hidden layers, and shallow ones with single hidden layers such as the Radial Base Function Network (RBFN). The work herein explores DP within this context by devising a model that leverages Gaussian RBF parameters to add privacy during training. Our model's efficacy is examined against two real and three synthetic datasets, with results showing reasonable trade-offs between accuracy and privacy. With high intra-class variation, we retained 100% accuracy for two synthetic datasets and a drop of only 1.72% for another. If privacy is prioritised with low intra-class variation, we achieved accuracy drops of 8%–23% with an inherited epsilon that never exceeds one, indicating a good privacy guarantee. We also show that timely training is achievable on a high-dimensional dataset consisting of 2M records and 170 features.
Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations

Accurate traffic flow prediction is an important tool to allow for more efficient use of traffic networks. Current traffic flow prediction algorithms such as LSTM RNNs can be very successful in predicting regular traffic flows, but often fail to accurately predict the more interesting irregularities in the traffic flows. We propose OE-LSTM (Outlier-Enriched LSTM), a novel framework for traffic flow prediction that focuses mainly on these irregular traffic flows. We consider the irregularities as outliers within each traffic flow stream and assume that for these traffic outliers to occur, a certain set of circumstances is present to cause these deviations from the regular traffic flow pattern. After detecting these outliers in traffic flow data, we measure the relation between them by performing a spatiotemporal correlation analysis. We use these correlations to trace the context of the outliers and determine as such which elements are interesting to include in our OE-LSTM prediction model. In addition to this, we also draw the foundations of a reference Cloud-based architecture for supporting big traffic data prediction based on OE-LSTM. The experimental results prove the effectiveness of the OE-LSTM framework in a real world traffic flow dataset. Both the dataset and the implementation of our framework are provided.