Reviewerly/exHarmony huggingface dataset:
Biosafety and biosecurity challenges during the COVID-19 pandemic and beyond

As the world continues to battle the SARS-CoV-2 pandemic, it is a stark reminder of the devastation biological threats can cause. In an unprecedented way the global community saw a massive surge in the demand for diagnostic capacities, which had a substantial impact on biosafety and biosecurity. Laboratories had to cope with a surge in laboratory testing capacity, while resources and training possibilities were limited. In addition, the pandemic highlighted the impact biological threats can have, thereby giving rise to new dialogue about biosecurity and new biological threats. This paper aims to highlight some of the most pressing issues regarding biosafety and biosecurity observed during the COVID-19 pandemic with special focus on low and lower middle-income countries. The authors provide lessons learned, tools and recommendations to improve future biosafety and biosecurity and increase preparedness for the next global health crisis.
Identifying Pitfalls and Solutions in Parallelizing Long Short-Term Memory Network on Graphical Processing Unit by Comparing with Tensor Processing Unit Parallelism

In deep learning, an important class of algorithms is recurrent neural networks (RNN). RNN experiences high latency and high computational cost, which makes it difficult. RNN is used mainly for sequential data, but the dependencies are complex, making it difficult to parallelize. Resource utilization is inefficient for RNN in GPU compared to TPU. This analysis of RNN for hate speech application is done in GPU and TPU, and the data reuse is identified. The main reasons for the unsatisfactory utilization of GPU for RNN are synchronization overhead and data reuse. In TPU, the global memory accesses and overhead that occur in synchronization are less than GPU. The main objective of this work is to identify the gaps and find solutions for them; analysis is conducted to answer the questions identified regarding the acceleration of RNN in GPU and TPU and to overcome the pitfalls that occur during the parallelizing of RNN in GPU. For implementing the LSTM, we have chosen the hate speech detection application because it is an application that needs to be parallelized for the timely detection of hate speech. This system works as a closed loop, and the attackers are aware of the detection process, so they try all possible ways to evade detection, so timely detection is needed to avoid the problem. The pitfalls and solutions in parallelizing LSTM on GPU are identified.
Capacitive empirical risk function-based bag-of-words and pattern classification processes

This paper proposes capacitive bag-of-words (Cap BoW) modeling and capacitive pattern classification processes for improved generalization ability. The Cap BoW and capacitive pattern classification are realized by introducing the notion of the capacitive empirical risk function (CERF). The CERF is used as a cost function to build a capacitive pattern classification process. The resulted capacitive pattern classification is used in the classification stage of the bag-of-words process, realizing thereby the Cap BoW process. The use of the CERF in building the capacitive pattern classification and Cap BoW processes is demonstrated to achieve simultaneous reduction to the empirical risk function (ERF) and confidence interval, reducing potential overfitting and enhancing the generalization ability of considered models. To have a thorough evaluation, two groups of experiments are carried out. The first group addresses the capacitive pattern classification process for 4 datasets. The second group addresses a more holistic impact of the CERF by using Cap Bow model to build capacitive dual ergodicity limits-based bag-of-words (Cap DEL-BoW) process, which is applied to 5 image datasets. In both groups of experiments, remarkable enhancement is demonstrated with the use of CERF-based pattern classification and Cap BoW processes. Comparison with corresponding conventional non-capacitive models demonstrates the tangible enhancement with the use of CERF-based models.
Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records

With over 17 million annual deaths, cardiovascular diseases (CVDs) dominate the cause of death statistics. CVDs can deteriorate the quality of life drastically and even cause sudden death, all the while inducing massive healthcare costs. This work studied state-of-the-art deep learning techniques to predict increased risk of death in CVD patients, building on the electronic health records (EHR) of over 23,000 cardiac patients. Taking into account the usefulness of the prediction for chronic disease patients, a prediction period of six months was selected. Two major transformer models that rely on learning bidirectional dependencies in sequential data, BERT and XLNet, were trained and compared. To our knowledge, the presented work is the first to apply XLNet on EHR data to predict mortality. The patient histories were formulated as time series consisting of varying types of clinical events, thus enabling the model to learn increasingly complex temporal dependencies. BERT and XLNet achieved an average area under the receiver operating characteristic curve (AUC) of 75.5% and 76.0%, respectively. XLNet surpassed BERT in recall by 9.8%, suggesting that it captures more positive cases than BERT, which is the main focus of recent research on EHRs and transformers.
ISL recognition system using integrated mobile-net and transfer learning method

A sign language recognition system (SLRS) facilitates the flow of information between the signer and non-signer communities. In recent years, deep learning has become ubiquitous and has also been found in the literature of SLRS, but the system designed for real-time recognition is still lacking. This paper presents a small and efficient architecture of Mobilenetv2 integrated with transfer learning for the recognition of Indian sign language (ISL). For this, firstly pre-trained weight file of mobilenetV2 has been used for the feature extraction, and then this model has a global average pooling layer (GAP), followed by a dense layer with a softmax classifier for the classification of ISL gestures. A self-collected ISL dataset consisting of 28 isolated gestures and a publicly available ASL dataset has been used to evaluate the performance. This model's compact architecture yielded excellent results, with a high classification accuracy of 99.3% and 99.9% for the two datasets, respectively, and low computational complexity. A real-time application prototype using GUI interface has also been designed using the proposed work to translate the gestures of ISL into English. The experimental findings show that the proposed model outperforms the state-of-art method with minimum computational and memory requirements.
Transferable visual pattern memory network for domain adaptation in anomaly detection

Anomaly detection transfer aims to utilize knowledge learned from source anomaly detection task to improve the performance of target anomaly detection task. Conventional methods typically assume that labeled normal or abnormal data are available in the source or target domain. However, many real-world applications do not satisfy this assumption because such labels are hard to collect. This study focuses on the case where anomalous labels are unavailable. More specifically, a rarely studied scenario in which the target domain contains unlabeled normal and abnormal instances, whereas only normal instances are available in the source domain, is addressed. To this end, a transferable visual pattern memory network was designed to transfer knowledge for anomaly detection tasks. The network comprises an adversarial domain adaptation method to extract transferable visual patterns, and a memory module utilized to store these patterns. The model utilizes transferable patterns stored in memory to identify anomalous samples. Moreover, a self-supervised objective is integrated to enhance the discriminability of target abnormal instances, thereby improving the anomaly detection performance. The results of extensive experiments conducted on publicly available anomaly-detection datasets verified the efficacy of the proposed approach.
Adversarial Machine Learning: Bayesian Perspectives

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
FAML-RT: Feature alignment-based multi-level similarity metric learning network for a two-stage robust tracker

Existing multi-stage trackers treat visual object tracking as a multiple feature extraction and similarity metric process. However, the similarity metric methods used in them are typically based on linear cross-correlation, ignoring the matching of detailed information. Moreover, the feature extraction operators (e.g., RoI align) lead to a sub-optimal feature representation for matching. In this paper, we propose a novel similarity metric method called feature alignment-based multi-level similarity metric learning network to address these issues. Technically, we elaborate a feature alignment module to extract the features, suppressing the useless background information that affects the matching. Subsequently, using the aligned features, we design a learnable multi-level similarity metric learning network to implement the matching for detailed information at the channel and spatial levels, which effectively guides an accurate and discriminative similarity score. By integrating the above components as second stage, a two-stage robust tracking method FAML-RT is presented. Extensive experiments on the challenging benchmarks OTB100, LaSOT and VOT2018 show that FAML-RT achieves a competitive performance against state-of-the-art methods, while running at a high speed of 60 fps. Furthermore, a series of ablation studies demonstrate the effectiveness of the proposed feature alignment-based multi-level metric learning network.
Fuzzy clustering with entropy regularization for interval-valued data with an application to scientific journal citations

Abstract In recent years, the research of statistical methods to analyze complex structures of data has increased. In particular, a lot of attention has been focused on the interval-valued data. In a classical cluster analysis framework, an interesting line of research has focused on the clustering of interval-valued data based on fuzzy approaches. Following the partitioning around medoids fuzzy approach research line, a new fuzzy clustering model for interval-valued data is suggested. In particular, we propose a new model based on the use of the entropy as a regularization function in the fuzzy clustering criterion. The model uses a robust weighted dissimilarity measure to smooth noisy data and weigh the center and radius components of the interval-valued data, respectively. To show the good performances of the proposed clustering model, we provide a simulation study and an application to the clustering of scientific journals in research evaluation.
The Perceived Self-Efficacy of Teachers in the Use of Digital Tools during the COVID-19 Pandemic: A Comparative Study between Spain and the United States

This study analyzed the use of fifteen groups of digital tools that 197 active teachers made during the sudden shift to remote instruction due to the COVID-19 quarantine orders placed by various health departments in Spain and the United States. The study also examined the impact that the use of digital tools had on teachers' perceived self-efficacy. A quantitative research design was used, supported by an exploratory descriptive approach that materializes in the application of an online questionnaire during the spring of 2020. The results indicate that perceived self-efficacy differs from country of origin and is conditioned by sociodemographic variables such as the training received and type of center. It is noted that teachers in Spain prefer the use of Moodle or Escholarium over that of teachers in the United States that opted for Google Classroom as a primary platform for teaching online, and the frequency of use of digital tools analyzed does not guarantee that their implementation is effective.
SA-GAN: A Second Order Attention Generator Adversarial Network with Region Aware Strategy for Real Satellite Images Super Resolution Reconstruction

High-resolution (HR) remote sensing images have important applications in many scenarios, and improving the resolution of remote sensing images via algorithms is one of the key research fields. However, current super-resolution (SR) algorithms, which are trained on synthetic datasets, tend to have poor performance in real-world low-resolution (LR) images. Moreover, due to the inherent complexity of real-world remote sensing images, current models are prone to color distortion, blurred edges, and unrealistic artifacts. To address these issues, real-SR datasets using the Gao Fen (GF) satellite images at different spatial resolutions have been established to simulate real degradation situations; moreover, a second-order attention generator adversarial attention network (SA-GAN) model based on real-world remote sensing images is proposed to implement the SR task. In the generator network, a second-order channel attention mechanism and a region-level non-local module are used to fully utilize the a priori information in low-resolution (LR) images, as well as adopting region-aware loss to suppress artifact generation. Experiments on test data demonstrate that the model delivers good performance for quantitative metrics, and the visual quality outperforms that of previous approaches. The Frechet inception distance score (FID) and the learned perceptual image patch similarity (LPIPS) value using the proposed method are improved by 17.67% and 6.61%, respectively. Migration experiments in real scenarios also demonstrate the effectiveness and robustness of the method.
Lawfulness by design – development and evaluation of lawful design patterns to consider legal requirements

New political objectives, emerging regulatory regimes for the digital sphere, and higher penalties for violations have intensified the pressure to develop lawful IT artefacts. As the adaptation of existing IT artefacts to new regulations can be expensive and arduous, a more attractive approach would be to design IT artefacts lawfully from the beginning. A major challenge is that the law is generally technology-neutral, and lawful design requires legal expertise throughout the development, which is costly and time consuming due to communication challenges between legal experts and developers. One possible approach to proactively consider IT regulations in the systems development is design patterns that convey legal design knowledge and support developers in determining the appropriate design options. Consequently, we develop a framework for lawful design patterns and demonstrate their feasibility and advantages using the example of developing AI-based assistants and the regulation of the General Data Protection Regulation (GDPR). Using the design pattern framework, we develop design patterns for lawful AI-based assistants and evaluate them using (a) an experimental approach to show the usefulness of the patterns for developers and (b) rely on a legal simulation study to holistically evaluate how design patterns contribute to lawful IT.
The Perceived Impacts of COVID-19 on Users’ Acceptance of Virtual Reality Hardware: A Digital Divide Perspective

The COVID-19 pandemic has directly or indirectly impacted everyone around the globe. However, the pandemic and its long-term consequences have not been distributed evenly within societies. These disparities have in many cases intensified existing social and economic inequalities such as the uptake of novel digital technologies. This study investigates the influence of the pandemic on the acceptance of virtual reality (VR) hardware within the framework of the technology acceptance model (TAM) and the digital divide/inequality scholarship. A survey was designed to examine the perceived impact of COVID-19 on the potential adoption of VR hardware. Specifically, this study included variables related to perceived ease of using VR hardware (ease of use), perceived usefulness of VR hardware (usefulness), intention to use VR hardware (use intention), and intention to purchase VR hardware (purchase intention). The predictors included two digital divide variables (material access and VR experience) and two other variables (COVID-19 and demographic variables). A total of 298 participants were recruited from Amazon Mechanical Turk. The results suggest that the perceived impacts of COVID-19 positively predicted participants’ perceived usefulness of VR hardware as well as their intention to use and purchase VR hardware.
Lithological discrimination and structural lineaments extraction using Landsat 8 and ASTER data: a case study of Tiwit (Anti-Atlas, Morocco)

The present work reveals the potential of Landsat 8 and ASTER imagery in the lithological discrimination and structural lineaments extraction in the Tiwit region (Jbel Saghro). Various remote sensing and image processing techniques were applied to the Landsat 8 and ASTER scenes: False-color composites (RGB 751 & 531), Principal Component Analysis (PCA 653 & 821), Minimum Noise Fraction (MNF 643 & 541), and Independent Component Analysis (ICA 137 & 235). These techniques discriminate the granitic formations (Isk-n-Alla, Mimasmarane, Ibantarn, and Ikniwn), the rhyolitic and ignimbrite formation (Amtattouch, Ouzarzamand Assaka), and other various rock types (aphanitic basalts, sandstones, conglomerates, etc.). Maximum Likelihood, Spectral Angle Mapper, and Mahalanobis distance classifiers show an overall accuracy of 88%, 56%, and 82.6%, respectively, for Landsat 8. ASTER data show a better result in classification with an overall accuracy of 90.6%, 84%, and 88% for the same classifiers. Structural lineaments are extracted using manual (visual interpretation) and automatique (LINE algorithm) approaches on both datasets. The extracted lineaments show NE-SW, E-W, and ENE-WSW trends for ASTER and Landsat 8 data, these results are consistent with the geologic map of Tiwit. Ultimately, the study demonstrates that ASTER data is more effective for lithological discrimination and structural lineaments mapping.
Handwritten Gujarati Numerals Classification Based on Deep Convolution Neural Networks Using Transfer Learning Scenarios

In recent years, handwritten numeral classification has achieved remarkable attention in the field of computer vision. Handwritten numbers are difficult to recognize due to the different writing styles of individuals. In a multilingual country like India, negligible research attempts have been carried out for handwritten Gujarati numerals recognition using deep learning techniques compared to the other regional scripts. The Gujarati digit dataset is not available publicly and deep learning requires a large amount of labeled data for the training of the models. If the number of annotated data is not sufficient enough to train Convolutional Neural Networks (CNN) from the scratch, transfer learning can be applied. However, the issue arises by using transfer learning is that how deep to fine-tune the pre-trained convolutional neural network while training the target model. In this paper, we addressed these problems using three deep transfer learning scenarios to classify handwritten Gujarati numerals from the images of zero to nine. We presented transfer learning scenarios using ten pre-trained CNN architectures including LeNet, VGG16, InceptionV3, ResNet50, Xception, ResNet101, MobileNet, MobileNetV2, DenseNet169 and EfficientNetV2S to find the best performing model by freezing and fine-tuning the weight parameters. We implemented the pre-trained models using a self-created handwritten Gujarati digit dataset with 8000 images of zero to nine digits with data augmentation. Exhaustive experiments are performed using various performance evaluation matrices. EfficientNetV2S model showed promising results among all the models including three transfer learning scenarios and achieved 98.39% training accuracy, 97.92% testing accuracy, 97.69% f1-score, and 97.15% AUC. Our handwritten Gujarati digit dataset is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Parth-Goel/gujarati-handwritten-digit-dataset/</uri> .
Transformer-Auxiliary Neural Networks for Image Manipulation Localization by Operator Inductions

Image manipulation localization (IML), which seeks to accurately segment tampered regions that are artfully fastened into a normal image, is a fundamental yet challenging computer vision task. Despite that impressive results have been achieved by some progressive deep learning methods, they usually fail in capturing the subtle manipulation artifacts at different object scales, which are not competent to generate a perfect segmentation mask with complete and fine object structures. Besides, the problem of coarse boundaries also occurs frequently. To this end, in this paper, we propose a Transformer-Auxiliary by operator-induced neural Network (TANet) to localize forged regions for IML. Specifically, a stacked multi-scale transformer (SMT) branch is first introduced as a compensation for feature representations of the mainstream convolutional neural network branch. SMT can detect structured abnormalities of the input image at multi-levels by operating on patches of different sizes. Then TANet explicitly exploits an operator induction module (OIM) to excavate valuable and manipulated region-related boundary semantics to guide the representative learning of the mainstream branch. The OIM encourages the network to generate features that highlight object structure, thereby promoting precise boundary localization of forged regions. We conduct extensive experiments on various datasets and settings to validate the effectiveness of TANet. Results show that TANet outperforms the state-of-the-art methods by a large margin under widely-used evaluation metrics.
A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Synthetic biometric samples are created with an ultimate goal of getting around privacy concerns, mitigating biases in biometric datasets, and reducing the sample acquisition effort to enable largescale evaluations.The recent breakthrough in the development of neural generative models shifted the focus from image synthesis by mathematical modeling of biometric modalities to data-driven image generation.This paradigm shift on the one hand greatly improves the realism of synthetic biometric samples and therefore enables new use cases, but on the other hand new challenges and concerns arise.Despite their realism, synthetic samples have to be checked for appropriateness for the tasks they are intended which includes new quality metrics.Here, we highlight the benefits of using synthetic samples, review the use cases, and summarize and categorize the most prominent studies on synthetic biometrics aiming at showing recent progress and the direction of future research.