Reviewerly/exHarmony huggingface dataset:
GSM-HM: Generation of Saliency Maps for Black-Box Object Detection Model Based on Hierarchical Masking

Interpretability of DNN-based object detection has been a rising concern for the research community.The first step towards this goal is a saliency map that visualizes the importance (saliency) of pixels in an image for the object detected by a specific model.Black-box based methods generate a saliency map without the need to look into the internals of a model, thus applicable to all models without the need of adaptation.In addition, they provide more reliable evaluation on the saliency of pixels than white-box methods by means of the absence of these pixels from the image.However, with current black-box methods, the absence of pixels is produced by random image masks.Despite the need of a great number of random masks for sufficient coverage, the quality of the pixel saliency is not assured to be satisfactory.In this work, we propose a more effective black-box framework with hierarchical masking.In this framework, called GSM-HM, pixel saliency is evaluated at multiple levels, with each lower level performing a refinement on the saliency information of the upper level.This hierarchical framework significantly reduces the masking efforts on less valuable pixels, thus it can produce saliency maps with higher qualities.In our experiments, the quality of a generated saliency map is evaluated with four different metrics: deletion, insertion, convergence and RAM (the ratio of average to maximum).Compared with D-RISE, a recent black-block method, GSM-HM generates more accurate saliency maps evaluated by these metrics.
Infrared-visible cross-modal person re-identification via dual-attention collaborative learning

Person re-identification is regarded as a retrieval task for searching the same person in different cameras, within which infrared-visible cross-modal re-identification (VI-ReID) is challenging because the inter-class distance is larger than the intra-class distance. In this paper, a dual-attention collaborative(DAC) learning method is proposed, which unites channel and spatial attentive deep features to obtain supplementary information for multiple classifiers via a cross-modal consistency constraint. A channel attention and part-wise spatial pooling are adopted for discriminative feature learning. A multiple-classifier strategy with a cross-modal consistency constraints is presented for the cross-modal identification. In this way complementary information among modality-sharable classifier and modality-specific classifier can be better utilized. The experimental results show that the proposed method distinctly outperforms the baseline method by a margin of 9.83% Rank-1 and 6.84% mAP on SYSU-MM01.
Exploring Practical Deep Learning Approaches for English-to-Hindi Image Caption Translation Using Transformers and Object Detectors

Most of the captions available for images are only present in a few languages prominent on the internet. The task of machine translation of image captions aims to democratize this information for other low resource languages through automatic translation. Compared to regular machine translation, image information can also be utilized to improve translated caption quality. The proposed work aims to demonstrate various deep learning techniques and approaches that can be used for optimal and efficient translation of captions from English to Hindi. Results show that transformer-based approaches outperform sequence to sequence approaches across all metrics (around 5–20% higher accuracy scores). Further, pre-trained transformer-based approaches are also able to resolve ambiguity very easily. Results also show that for such low resource scenarios, text only approaches are sufficient enough while multimodal approaches are unable to improve the translation quality. So, text only pre-trained transformers are recommended for most English-to-Hindi image caption translation applications.
The connection of open science practices and the methodological approach of researchers

Abstract The Open Science movement is gaining tremendous popularity and tries to initiate changes in science, for example the sharing and reuse of data. The new requirements that come with Open Science poses researchers with several challenges. While most of these challenges have already been addressed in several studies, little attention has been paid so far to the underlying Open Science practices (OSP). An exploratory study was conducted focusing on the OSP relating to sharing and using data. 13 researchers from the Weizenbaum Institute were interviewed. The Weizenbaum Institute is an interdisciplinary research institute in Germany that was founded in 2017. To reconstruct OSP a grounded theory methodology (Strauss in Qualitative Analysis for Social Scientists, Cambridge University Press, Cambridge, 1987) was used and classified OSP into open production, open distribution and open consumption (Smith in Openness as social praxis. First Monday, 2017). The research shows that apart from the disciplinary background and research environment, the methodological approach and the type of research data play a major role in the context of OSP. The interviewees’ self-attributions related to the types of data they work with: qualitative, quantitative, social media and source code. With regard to the methodological approach and type of data, it was uncovered that uncertainties and missing knowledge, data protection, competitive disadvantages, vulnerability and costs are the main reasons for the lack of openness. The analyses further revealed that knowledge and established data infrastructures as well as competitive advantages act as drivers for openness. Because of the link between research data and OSP, the authors of this paper argue that in order to promote OSP, the methodological approach and the type of research data must also be considered.
Scale-Space Feature Recalibration Network for Single Image Deraining

Computer vision technology is increasingly being used in areas such as intelligent security and autonomous driving. Users need accurate and reliable visual information, but the images obtained under severe weather conditions are often disturbed by rainy weather, causing image scenes to look blurry. Many current single image deraining algorithms achieve good performance but have limitations in retaining detailed image information. In this paper, we design a Scale-space Feature Recalibration Network (SFR-Net) for single image deraining. The proposed network improves the image feature extraction and characterization capability of a Multi-scale Extraction Recalibration Block (MERB) using dilated convolution with different convolution kernel sizes, which results in rich multi-scale rain streaks features. In addition, we develop a Subspace Coordinated Attention Mechanism (SCAM) and embed it into MERB, which combines coordinated attention recalibration and a subspace attention mechanism to recalibrate the rain streaks feature information learned from the feature extraction phase and eliminate redundant feature information to enhance the transfer of important feature information. Meanwhile, the overall SFR-Net structure uses dense connection and cross-layer feature fusion to repeatedly utilize the feature maps, thus enhancing the understanding of the network and avoiding gradient disappearance. Through extensive experiments on synthetic and real datasets, the proposed method outperforms the recent state-of-the-art deraining algorithms in terms of both the rain removal effect and the preservation of image detail information.
FluentSigners-50: A signer independent benchmark dataset for sign language processing

This paper presents a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) for the purposes of Sign Language Processing. We envision it to serve as a new benchmark dataset for performance evaluations of Continuous Sign Language Recognition (CSLR) and Translation (CSLT) tasks. The proposed FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers resulting in 43,250 video samples. Dataset contributors recorded videos in real-life settings on a wide variety of backgrounds using various devices such as smartphones and web cameras. Therefore, distance to the camera, camera angles and aspect ratio, video quality, and frame rates varied for each dataset contributor. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life sign language. FluentSigners-50 baseline is established using two state-of-the-art methods, Stochastic CSLR and TSPNet. To this end, we carefully prepared three benchmark train-test splits for models' evaluations in terms of: signer independence, age independence, and unseen sentences. FluentSigners-50 is publicly available at https://krslproject.github.io/FluentSigners-50/.
Leveraging Natural Learning Processing to Uncover Themes in Clinical Notes of Patients Admitted for Heart Failure

Heart failure occurs when the heart is not able to pump blood and oxygen to support other organs in the body as it should. Treatments include medications and sometimes hospitalization. Patients with heart failure can have both cardiovascular as well as non-cardiovascular comorbidities. Clinical notes of patients with heart failure can be analyzed to gain insight into the topics discussed in these notes and the major comorbidities in these patients. In this regard, we apply machine learning techniques, such as topic modeling, to identify the major themes found in the clinical notes specific to the procedures performed on 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling revealed five hidden themes in these clinical notes, including one related to heart disease comorbidities.
A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images

Accurate segmentation of nuclei is an essential step in analysis of digital histology images for diagnostic and prognostic applications. Despite recent advances in automated frameworks for nuclei segmentation, this task is still challenging. Specifically, detecting small nuclei in large-scale histology images and delineating the border of touching nuclei accurately is a complicated task even for advanced deep neural networks. In this study, a cascaded deep learning framework is proposed to segment nuclei accurately in digitized microscopy images of histology slides. A U-Net based model with customized pixel-wised weighted loss function is adapted in the proposed framework, followed by a U-Net based model with VGG16 backbone and a soft Dice loss function. The model was pretrained on the Post-NAT-BRCA public dataset before training and independent evaluation on the MoNuSeg dataset. The cascaded model could outperform the other state-of-the-art models with an AJI of 0.72 and a F1-score of 0.83 on the MoNuSeg test set.
“From Kilobytes to Kilodaltons”: A Novel Algorithm for Medical Image Encryption based on the Central Dogma of Molecular Biology

With the continued integration of technology in medicine, large amounts of patient data are often vulnerable to cyber-attacks. Medical data must be secured, however traditional cryptographic algorithms are inapplicable to medical images due to factors such as bulk data capacity, strong correlation among adjacent pixels, and high redundancy. To address the need for new medical image encryption algorithms, a novel approach based on the central dogma of molecular biology is proposed. The resulting algorithm has a linear runtime complexity, and is resistant to brute force, differential and statistical attacks. The algorithm advances the state-of-the-art in DNA-based image encryption and surpasses recent approaches in medical image encryption in its defence against cyber-attacks. Clinical Relevance- Secure data transmission and storage is critical for patient privacy. This algorithm increases the security of patient imaging when compared to image encryption algorithms in literature.
Fall Prediction in People with Parkinson's Disease

A preliminary study result predicting fall events in patients with Parkinson's disease (PD) by using a simple motion sensor is described in this paper. Causes of falls in people with PD can be postural instability, freezing of gait, festinating gait, dyskinesias, visuospatial dysfunction, orthostatic hypotension, and posture problems. This study uses only one motion sensor in collecting data. Thus, only fall events caused by festinating gait factors, which are moments when the patient suddenly moves faster with smaller steps, can be performed and tested. In this preliminary study, fall event scenarios of simulated test cases are performed by five healthy young subjects aged 20 to 28 years old. The acceleration mode in the motion sensor provides information that can detect how fast the subjects move. Data collected by the sensor will be analyzed by simple analysis methods and machine learning techniques classification. The proposed study achieved an accuracy of 70.3% for the 10-class model, while for binary classification, the accuracy was 99%. Clinical Relevance-This study focuses on predicting falls by analyzing the gaits prior to an actual so that fall prediction can be possible. If falls can be predicted, researchers can develop other protective gear to prevent fall-related injuries not only for PD patients but also for the elderly.
Capped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si367.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>-norm linear discriminant analysis for robust projections learning

Linear Discriminant Analysis (LDA) is one of the most representative supervised robust dimensionality reduction methods for handling high-dimensional data. High-dimensional datasets tend to contain more outliers and other sorts of noise, whereas most of the existing LDA models incorrectly consider the arithmetic mean of samples as the optimal mean, leading to the deviation of the data mean and thus reduce the robustness of LDA. In this paper, we propose a novel robust trace ratio objective in which the calculation of the difference between sample and class mean is converted to the calculation of the difference between each pair of samples. Besides, the within-class scatter and the total scatter are measured by capped ℓp-norm. As a result, this novel reformulation can automatically avoid mean calculation and meanwhile mitigate the negative effect of outliers on the objective function. Furthermore, an iterative optimization algorithm is derived to obtain the solution of the model. Extensive experimental results on several benchmark datasets show the superior performance of the proposed method.
On the role of alkanethiol Au complex in the formation of gold deposits; an in-silico approach

The association of organic matter with ore minerals has been observed for many years. Over decades, many speculations have been proposed to define the possible roles that organic matter might have played in the ore genesis. In this paper, we performed a series of electronic calculations, and molecular dynamic simulations to investigate the behaviour of organic matter in ore-forming environments. Ab initio calculations revealed that variation in the length of alkane tails has a limited impact on the geometry of the Au-ligand, and the strength of AuS bonds. MD and metadynamics simulations proved that the organic Au-ligand could be dissolved in the organic phase but it mostly pose at the interface of the organic-inorganic phase, in a way that its ionic head place in the aqueous solution and its alkane tails dissolved in organic matter. These observations suggest the organic-gold ions (and organic ligands of some other metals) are preferentially accumulated in the narrow interface of the aqueous/petroleum phase. Overall, although experimental results suggest that organic matter might have a role in gold transport, this study proves that organic gold ligand is concentrated only at the interface between the organic and aqueous phases and therefor, the organic phase alone cannot carry gold. This finding is also consistent with field observations, which suggest that gold concentrations in oil are relatively low.
Differences in mobile-assisted acquisition of receptive and productive vocabulary knowledge: a case study using <i>Mondly</i>

The number of learners seeking to acquire an L2 by means of apps is ever-increasing. This study examined the effectiveness of using the app Mondly for the acquisition of L2 English vocabulary while accounting for differences in the acquisition of receptive and productive aspects of word knowledge. Twelve Danish young learners (aged 10–11) used Mondly, installed on their personal devices, as their sole English curriculum for 4 weeks. The results showed a significant increase in vocabulary knowledge. These gains were particularly notable in regards to productive knowledge of the form-meaning connection and areas of word knowledge relating to use. While these learning gains correlated positively with the frequency of appearance of each target word in Mondly's lexical inventory, they correlated negatively with the participants' use of the app. We hypothesise that this was because additional use of the app with inadequate repetitions of target words leads to the creation of weak synaptic pathways which interfere with recollection of receptive knowledge of the form-meaning connection. The study indicates that Mondly may be effective as a tool for acquiring L2 vocabulary. However, explicit attention should be given to the number of repetitions of each target word in order to ensure adequate entrenchment.
Deep Shape-from-Template: Single-image quasi-isometric deformable registration and reconstruction

Shape-from-Template (SfT) solves 3D vision from a single image and a deformable 3D object model, called a template. Concretely, SfT computes registration (the correspondence between the template and the image) and reconstruction (the depth in camera frame). It constrains the object deformation to quasi-isometry. Real-time and automatic SfT represents an open problem for complex objects and imaging conditions. We present four contributions to address core unmet challenges to realise SfT with a Deep Neural Network (DNN). First, we propose a novel DNN called DeepSfT, which encodes the template in its weights and hence copes with highly complex templates. Second, we propose a semi-supervised training procedure to exploit real data. This is a practical solution to overcome the render gap that occurs when training only with simulated data. Third, we propose a geometry adaptation module to deal with different cameras at training and inference. Fourth, we combine statistical learning with physics-based reasoning. DeepSfT runs automatically and in real-time and we show with numerous experiments and an ablation study that it consistently achieves a lower 3D error than previous work. It outperforms in generalisation and achieves great performance in terms of reconstruction and registration error with wide-baseline, occlusions, illumination changes, weak texture and blur.
Multispectral Image Compression Using Adaptive Thresholding in Wavelet Domain with Binary Plane Techniques

Multispectral image acquirement systems create multilayer images in which each layer having the different pixel values which are not negative in general. The compression of these images aims to transform the image into more solid form that is convenient for pressuring, transmission, processing, and recovery. In this paper, band decomposition and discarding approach are proposed with wavelet and correlation coefficients. The resultant spectral image is subjected for spatial binary plane method for based compression algorithm. The approach is operated in lossless mode and differentiating against traditional JPEG-LS with multiple metrics. Experiments were conducted on several regular multispectral images that are available for research and observed that the proposed method provides an average compression ratio of 8.34 which is 1.86 times more than earlier method.
Dark Side of Digitalisation: Discussion on Digital Assets Leakage and Its Protection Mechanisms in Operations and Supply Chain Research

The value and intensified prevalence of digital assets have been well recognised in today’s digital age. Despite the benefits of digitalisation to firms, little is known regarding its potential dark side, that is, the concern on the digital assets leakage. For example, firms are now using on-demand IT services based on cloud technologies, which poses the risk of losing the right to control their information security. This chapter will discuss the research discourse on digital assets leakage and suggest protective mechanisms to counter back this problem, thus helping firms to maximise the value of digital assets in their digitalisation trajectories.
CDFM: A cross‐domain few‐shot model for marine plankton classification

Marine plankton classification is important for monitoring marine biological populations and understanding marine ecosystems. However, it is difficult to obtain abundant annotated image data of marine plankton to train classification models of high quality. To classify marine plankton with a small number of image samples, a cross-domain few-shot learning model (CDFM) is proposed. First, CDFM learns knowledge from existing labelled images and then transfers the knowledge to new images. In this process, there is an issue of domain differences between the new images and the existing datasets due to differences in data acquisition time and locations. To address this issue, the authors pre-train a model in the source domain and then use fine-tuning to adapt it to the target domain. Second, the graph neural network is used as a meta-learning module to learn a feature distance metric for marine plankton classification with limited samples. Extensive experiments on four marine plankton image datasets are conducted, including Kaggle Plankton, miniPPlankton, ZooScan and WHOI, and CDFM outperforms existing methods for marine plankton classification.
The Rise and Fall of the HyFlex Approach in Mexico

During the Covid-19 pandemic, the world turned to online tools as a means of ensuring continued access to education, highlighting possibilities for innovation, particularly in contexts like Mexico, where the use of edtech was not previously prevalent. When educational institutions reopened their physical campuses, many schools and universities considered a hybrid, flexible (HyFlex) approach as a way of catering for different needs. While research findings on students' perspectives tend to be positive, the implementation of HyFlex courses has shown some challenges. Issues reported by teachers relate to poor technical infrastructure, work overload and lack of guidance. The initial excitement for the HyFlex approach has subdued. However, an openness to innovation is still there, ready to be exploited.