Reviewerly/exHarmony huggingface dataset:
Data Imbalance in Autism Pre-Diagnosis Classification Systems: An Experimental Study

Machine learning (ML) is a branch of computer science that is rapidly gaining popularity within the healthcare arena due to its ability to explore large datasets to discover useful patterns that can be interepreted for decision-making and prediction. ML techniques are used for the analysis of clinical parameters and their combinations for prognosis, therapy planning and support and patient management and wellbeing. In this research, we investigate a crucial problem associated with medical applications such as autism spectrum disorder (ASD) data imbalances in which cases are far more than just controls in the dataset. In autism diagnosis data, the number of possible instances is linked with one class, i.e. the no ASD is larger than the ASD, and this may cause performance issues such as models favouring the majority class and undermining the minority class. This research experimentally measures the impact of class imbalance issue on the performance of different classifiers on real autism datasets when various data imbalance approaches are utilised in the pre-processing phase. We employ oversampling techniques, such as Synthetic Minority Oversampling (SMOTE), and undersampling with different classifiers including Naive Bayes, RIPPER, C4.5 and Random Forest to measure the impact of these on the performance of the models derived in terms of area under curve and other metrics. Results pinpoint that oversampling techniques are superior to undersampling techniques, at least for the toddlers’ autism dataset that we consider, and suggest that further work should look at incorporating sampling techniques with feature selection to generate models that do not overfit the dataset.
Chaotic Color Image Encryption Scheme Using Deoxyribonucleic Acid (DNA) Coding Calculations and Arithmetic over the Galois Field

This paper proposes a chaotic color image encryption scheme based on DNA-coding calculations and arithmetic over the Galois field. Firstly, three modified one-dimensional (1D) chaotic maps with larger key space and better chaotic characteristics are presented. The experimental results show that their chaotic intervals are not only expanded to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mfenced open="(" close="]" separators="|"><mml:mrow><mml:mn>0,15</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:math>, but their average largest Lyapunov Exponent reaches 10. They are utilized as initial keys. Secondly, DNA coding and calculations are applied in order to add more permutation of the cryptosystem. Ultimately, the numeration over the Galois field ensures the effect for the diffusion of pixels. The simulation analysis shows that the encryption scheme proposed in this paper has good encryption effect, and the numerical results verify that it has higher security than some of the latest cryptosystems.
A Robust <i>k</i>-Means Clustering Algorithm Based on Observation Point Mechanism

The k -means algorithm is sensitive to the outliers. In this paper, we propose a robust two-stage k -means clustering algorithm based on the observation point mechanism, which can accurately discover the cluster centers without the disturbance of outliers. In the first stage, a small subset of the original data set is selected based on a set of nondegenerate observation points. The subset is a good representation of the original data set because it only contains all those points that have a higher density of the original data set and does not include the outliers. In the second stage, we use the k -means clustering algorithm to cluster the selected subset and find the proper cluster centers as the true cluster centers of the original data set. Based on these cluster centers, the rest data points of the original data set are assigned to the clusters whose centers are the closest to the data points. The theoretical analysis and experimental results show that the proposed clustering algorithm has the lower computational complexity and better robustness in comparison with k -means clustering algorithm, thus demonstrating the feasibility and effectiveness of our proposed clustering algorithm.
High-Level Synthesis in Implementing and Benchmarking Number Theoretic Transform in Lattice-Based Post-Quantum Cryptography Using Software/Hardware Codesign

Compared to traditional hardware development methodologies, High-Level Synthesis (HLS) offers a faster time-to-market and lower design cost at the expense of implementation efficiency. Although Software/Hardware Codesign has been used in many areas, its usability for benchmarking of candidates in cryptographic competitions has been largely unexplored. This paper provides a comparison of the HLS- and RTL-based design methodologies when applied to the hardware design of the Number Theoretic Transform (NTT) – a core arithmetic function of lattice-based Post-Quantum Cryptography (PQC). As a next step, we apply Software/Hardware Codesign approach to the implementation of three PQC schemes based on NTT. Then, we integrate our HLS implementation into the Xilinx SDSoC environment. We demonstrate that an overhead of SDSoC compared to traditional Bare Metal approach is acceptable. This paper also shows that an HLS implementation obtained by modeling a block diagram is typically much better than an implementation obtained by using design space exploration. We conclude that the HLS/SDSoC and RTL/Bare Metal approaches generate comparable results.
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks

This study proposes an anomaly detection method for operational data of industrial control systems (ICSs). Sequence-to-sequence neural networks were applied to train and predict ICS operational data and interpret their time-series characteristic. The proposed method requires only a normal dataset to understand ICS’s normal state and detect outliers. This method was evaluated with SWaT (secure water treatment) dataset, and 29 out of 36 attacks were detected. The reported method also detects the attack points, and 25 out of 53 points were detected. This study provides a detailed analysis of false positives and false negatives of the experimental results.
Context-aware social media user sentiment analysis

The user-generated social media messages usually contain considerable multimodal content. Such messages are usually short and lack explicit sentiment words. However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance. Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model. In this study, we propose a novel model for performing context-aware user sentiment analysis. This model involves the semantic correlation of different modalities and the effects of tweet context information. Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
Semi-Supervised Semantic Segmentation via Dynamic Self-Training and Class-Balanced Curriculum

In this work, we propose a novel and concise approach for semi-supervised semantic segmentation. The major challenge of this task lies in how to exploit unlabeled data efficiently and thoroughly. Previous state-of-the-art methods utilize unlabeled data by GAN-based self-training or consistency regularization. However, these methods either suffer from noisy self-supervision and class-imbalance, resulting in a low unlabeled data utilization rate, or do not consider the apparent link between self-training and consistency regularization. Our method, Dynamic Self-Training and Class-Balanced Curriculum (DST-CBC), exploits inter-model disagreement by prediction confidence to construct a dynamic loss robust against pseudo label noise, enabling it to extend pseudo labeling to a class-balanced curriculum learning process. While we further show that our method implicitly includes consistency regularization. Thus, DST-CBC not only exploits unlabeled data efficiently, but also thoroughly utilizes $all$ unlabeled data. Without using adversarial training or any kind of modification to the network architecture, DST-CBC outperforms existing methods on different datasets across all labeled ratios, bringing semi-supervised learning yet another step closer to match the performance of fully-supervised learning for semantic segmentation. Our code and data splits are available at: this https URL .
Face Recognition using Artificial Neural Network and Feature Extraction

Face Recognition is one of the major research areas in Computer Vision. Researchers have applied many image processing techniques and neural networks for the problem but still not able to achieve the desired accuracy for all kinds of data. This work presents a hybrid approach by combining output of two different artificial neural networks PCA-ANN and LDA-ANN. For any given face image, feature extraction techniques have been applied to obtain a representation of the image, using interest point and edge detectors, namely, Harris, SIFT, Canny and Laplacian of Gaussian. Principal Component Analysis and Linear Disciminant Analysis have been actively used for dimensionality reduction of the extracted feature vector. Considering two such different representations, we have trained using an artificial neural network and finally combined the result using a logical OR operation. On Faces94, the proposed approach achieves 98.5% accuracy outshines DeepID and Light CNN-9 approach and fairs significantly better than most state-of-the-art deep learning works.
Problems of Teaching Mathematics in Modernization

This article is devoted to the problem of teaching mathematics in the context of modernization, where the problems and obstacles that hinder the quality and effectiveness of the teaching of mathematical sciences in secondary schools are currently sufficiently explained.
SdBAN: Salient Object Detection Using Bilateral Attention Network With Dice Coefficient Loss

Visual attention plays an important role in saliency detection by highlighting meaningful context regions. In this paper, we present a novel saliency detection method using a bilateral attention network. The proposed network consists of two branches: i) a spatial path using an encoder-decoder structure to learn spatial cues and ii) a context path using an attention mechanism to learn contextual cues. The feature aggregation module is finally used to predict salient objects by concatenating the cues. To optimize the weights of the network in the sense of minimizing the class imbalance problem, we minimize the dice coefficient loss together with the classical cross-entropy loss. The proposed network can predict salient regions in an end-to-end manner without post-processing. Experimental results show that the proposed network achieved better performance than existing state-of-the-art methods in most cases. Furthermore, the proposed network takes only 0.03 seconds to process a 224 × 224 image. The code for the proposed method can be found at the following URL: https://github.com/tiruss/SdBAN.
Towards Accurate and Robust Domain Adaptation under Noisy Environments

In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation works have achieved great success in theory and practice. However, they always lose robustness in noisy environments where the labels and features of examples from the source domain become corrupted. In this paper, we report our attempt towards achieving accurate noise-robust domain adaptation. We first give a theoretical analysis that reveals how harmful noises influence unsupervised domain adaptation. To eliminate the effect of label noise, we propose an offline curriculum learning for minimizing a newly-defined empirical source risk. To reduce the impact of feature noise, we propose a proxy distribution based margin discrepancy. We seamlessly transform our methods into an adversarial network that performs efficient joint optimization for them, successfully mitigating the negative influence from both data corruption and distribution shift. A series of empirical studies show that our algorithm remarkably outperforms state of the art, over 10% accuracy improvements in some domain adaptation tasks under noisy environments.
Chasing Play Potentials in Food Culture

In this pictorial, we turn to culture and traditions to present an annotated portfolio of play-food potentials, i.e. interesting design qualities and/or interaction mechanisms that could help promote playful and social engagement in food practices. Our portfolio emerged from a one-day workshop where we played with and analyzed a collection of 27 food traditions from diverse cultural backgrounds and historical periods. We highlight play forms and experiential textures that are underexplored in Human-Food Interaction (HFI) research. Our contribution is intended to inspire designers to broaden the palette of play experiences and emotions embraced in HFI.
Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

Background . Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases. Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation. In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers. Method . RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time. RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task. Results . In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously. We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis. We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views. Our study shows that both classification and detection effects are noteworthy. The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively. The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively.
Liquid information flow control

We present Lifty, a domain-specific language for data-centric applications that manipulate sensitive data. A Lifty programmer annotates the sources of sensitive data with declarative security policies, and the language statically and automatically verifies that the application handles the data according to the policies. Moreover, if verification fails, Lifty suggests a provably correct repair, thereby easing the programmer burden of implementing policy enforcing code throughout the application. The main insight behind Lifty is to encode information flow control using liquid types, an expressive yet decidable type system. Liquid types enable fully automatic checking of complex, data dependent policies, and power our repair mechanism via type-driven error localization and patch synthesis. Our experience using Lifty to implement three case studies from the literature shows that (1) the Lifty policy language is sufficiently expressive to specify many real-world policies, (2) the Lifty type checker is able to verify secure programs and find leaks in insecure programs quickly, and (3) even if the programmer leaves out all policy enforcing code, the Lifty repair engine is able to patch all leaks automatically within a reasonable time.
Evidence-Efficient Affinity Propagation Scheme for Virtual Machine Placement in Data Center

In cloud data center, without efficient virtual machine placement, the overload of any types of resources on physical machines (PM) can easily cause the waste of other types of resources, and frequent costly virtual machine (VM) migration, which further negatively affects quality of service (QoS). To address this problem, in this paper we propose an evidence-efficient affinity propagation scheme for VM placement (EEAP-VMP), which is capable of balancing the workload across various types of resources on the running PMs. Our approach models the problem of searching the desirable destination hosts for the live VM migration as the propagation of responsibility and availability. The sum of responsibility and availability represent the accumulated evidence for the selection of candidate destination hosts for the VMs to be migrated. Further, in combination with the presented selection criteria for destination hosts. Extensive experiments are conducted to compare our EEAP-VMP method with the previous VM placement methods. The experimental results demonstrate that the EEAP-VMP method is highly effective on reducing VM migrations and energy consumption of data centers and in balancing the workload of PMs.
ESTATE: A Lightweight and Low Energy Authenticated Encryption Mode

NIST has recently initiated a standardization project for efficient lightweight authenticated encryption schemes. SUNDAE, a candidate in this project, achieves optimal state size which results in low circuit overhead on top of the underlying block cipher. In addition, SUNDAE provides security in nonce-misuse scenario as well. However, in addition to the block cipher circuit, SUNDAE also requires some additional circuitry for multiplication by a primitive element. Further, it requires an additional block cipher invocation to create the starting state. In this paper, we propose a new lightweight and low energy authenticated encryption family, called ESTATE, that significantly improves the design of SUNDAE in terms of implementation costs (both hardware area and energy) and efficient processing of short messages. In particular, ESTATE does not require an additional multiplication circuit, and it reduces the number of block cipher calls by one. Moreover, it provides integrity security even under the release of unverified plaintext (or RUP) model. ESTATE is based on short-tweak tweakable block ciphers (or tBC, small ’t’ denotes short tweaks) and we instantiate it with two recently designed tBCs: TweAES and TweGIFT. We also propose a low latency variant of ESTATE, called sESTATE, that uses a round-reduced (6 rounds) variant of TweAES called TweAES-6. We provide comprehensive FPGA based hardware implementation for all the three instances. The implementation results depict that ESTATE_TweGIFT-128 (681 LUTs, 263 slices) consumes much lesser area as compared to SUNDAE_GIFT-128 (931 LUTs, 310 slices). When we moved to the AES variants, along with the area-efficiency (ESTATE_TweAES consumes 1901 LUTs, 602 slices while SUNDAE_AES-128 needs 1922 LUTs, 614 slices), we also achieve higher throughput for short messages (For 16-byte message, a throughput of 1251.10 and 945.36 Mbps for ESTATE_TweAES and SUNDAE_AES-128 respectively).
Denoising Relation Extraction from Document-level Distant Supervision

Distant supervision (DS) has been widely used to generate auto-labeled data for sentencelevel relation extraction (RE), which improves RE performance.However, the existing success of DS cannot be directly transferred to the more challenging document-level relation extraction (DocRE), since the inherent noise in DS may be even multiplied in document level and significantly harm the performance of RE.To address this challenge, we propose a novel pre-trained model for DocRE, which denoises the document-level DS data via multiple pre-training tasks.Experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy DS data and achieve promising results.
A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis

(T)ACSA tasks, including aspect-category sentiment analysis (ACSA) and targeted aspect-category sentiment analysis (TACSA), aims at identifying sentiment polarity on predefined categories. Incremental learning on new categories is necessary for (T)ACSA real applications. Though current multi-task learning models achieve good performance in (T)ACSA tasks, they suffer from catastrophic forgetting problems in (T)ACSA incremental learning tasks. In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net). We set both encoder and decoder shared among all categories to weaken the catastrophic forgetting problem. Besides the origin input sentence, we applied another input feature, i.e., category name, for task discrimination. Our model achieved state-of-the-art on two (T)ACSA benchmark datasets. Furthermore, we proposed a dataset for (T)ACSA incremental learning and achieved the best performance compared with other strong baselines.
The Intention to Study Using Zoom During the SARS-CoV-2 Pandemic

This study uses the Theory of Reasoned Action (TRA) model combined with risk perception variables to examine the intention to study using Zoom during the SARS-CoV-2 pandemic. All assumptions are confirmed by the TRA model 's conventional, independent variables affecting the intention to test using Zoom. However, the results of this study differ from expectations. The security risk perception has the lowest impact on the intention to adopt Zoom. The two key determinants of using Zoom for teaching and studying are attitude and subjective norms. Moreover, COVID-19 risk perception has a remarkable influence on the target variables. Thus, the combination of the TRA model and risk perception factors is a very good choice to assess the factors affecting the intention to adopt Zoom as an educational learning tool.
Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

Classification of malignancy for breast cancer and other cancer types is usually tackled as an object detection problem: Individual lesions are first localized and then classified with respect to malignancy. However, the drawback of this approach is that abstract features incorporating several lesions and areas that are not labelled as a lesion but contain global medically relevant information are thus disregarded: especially for dynamic contrast-enhanced breast MRI, criteria such as background parenchymal enhancement and location within the breast are important for diagnosis and cannot be captured by object detection approaches properly. In this work, we propose a 3D CNN and a multi scale curriculum learning strategy to classify malignancy globally based on an MRI of the whole breast. Thus, the global context of the whole breast rather than individual lesions is taken into account. Our proposed approach does not rely on lesion segmentations, which renders the annotation of training data much more effective than in current object detection approaches. Achieving an AUROC of 0.89, we compare the performance of our approach to Mask R-CNN and Retina U-Net as well as a radiologist. Our performance is on par with approaches that, in contrast to our method, rely on pixelwise segmentations of lesions.