Reviewerly/exHarmony huggingface dataset:
Compressing deep graph convolution network with multi-staged knowledge distillation

Given a trained deep graph convolution network (GCN), how can we effectively compress it into a compact network without significant loss of accuracy? Compressing a trained deep GCN into a compact GCN is of great importance for implementing the model to environments such as mobile or embedded systems, which have limited computing resources. However, previous works for compressing deep GCNs do not consider the multi-hop aggregation of the deep GCNs, though it is the main purpose for their multiple GCN layers. In this work, we propose MustaD (Multi-staged knowledge Distillation), a novel approach for compressing deep GCNs to single-layered GCNs through multi-staged knowledge distillation (KD). MustaD distills the knowledge of 1) the aggregation from multiple GCN layers as well as 2) task prediction while preserving the multi-hop feature aggregation of deep GCNs by a single effective layer. Extensive experiments on four real-world datasets show that MustaD provides the state-of-the-art performance compared to other KD based methods. Specifically, MustaD presents up to 4.21%p improvement of accuracy compared to the second-best KD models.
AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful mean for Computer Aided Diagnosis (CADx). This approach requires building a population graph to aggregate structural information, where the graph adjacency matrix represents the relationship between nodes. Until now, this adjacency matrix is usually defined manually based on phenotypic information. In this paper, we propose an encoder that automatically selects the appropriate phenotypic measures according to their spatial distribution, and uses the text similarity awareness mechanism to calculate the edge weights between nodes. The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information. In addition, a novel graph convolution network architecture using multi-layer aggregation mechanism is proposed. The structure can obtain deep structure information while suppressing over-smooth, and increase the similarity between the same type of nodes. Experimental results on two databases show that our method can significantly improve the diagnostic accuracy for Autism spectrum disorder and breast cancer, indicating its universality in leveraging multimodal data for disease prediction.
Smart Cyber Victimization Discovery on Twitter

The advancement of technologies, the promotion of smartphones, and social networking have led to a high tendency among users to spend more time online interacting with each other via the available technologies. This is because they help overcome physical limitations and save time and energy by doing everything online. The rapid growth in this tendency has created the need for extra protection, by creating new rules and policies. However, sometimes users interrupt these rules and policies through unethical behavior. For example, bullying on social media platforms is a type of cyber victimization that can cause serious harm to individuals, leading to suicide. A firm step towards protecting the cyber society from victimization is to detect the topics that trigger the feeling of being a victim. In this paper, the focus is on Twitter, but it can be expanded to other platforms. The proposed method discovers cyber victimization by detecting the type of behavior leading to them being a victim. It consists of a text classification model, that is trained with a collected dataset of the official news since 2000, about suicide, self-harm, and cyberbullying. Results show that LinearSVC performs slightly better with an accuracy of 96%.
A Novel Robust Blind AES/LWT+DCT+SVD-Based Crypto-Watermarking schema for DICOM Images Security

In this paper, we propose a novel robust blind crypto-watermarking method for medical images security based on hiding of DICOM (Digital Imaging and Communications in Medicine) patient information (patient name, patient ID, patient old…) in the medical imaging. The DICOM patient information are encrypted using the AES (Advanced Encryption Standard) standard algorithm before its insertion in the medical imaging. The medical imaging is divided in blocks of 8x8, in each we insert 1-bit of the encrypted watermark in the hybrid transform domain by applying respectively the 2D-LWT (Lifting wavelet transforms), the 2D-DCT (discrete cosine transforms), and the SVD (singular value decomposition). The scheme is tested by applying various attacks such as noise, filtering and compression. Experimental results show that the watermark (patient information) is imperceptible in the imaging and the test against attack shows the good robustness of the proposed algorithm.
SpatialRugs: A compact visualization of space and time for analyzing collective movement data

Compact visualization techniques such as dense pixel displays find application in displaying spatio-temporal datasets in a space-efficient way. While mostly focusing on feature development, the depiction of spatial distributions of the movers in these techniques is often traded against better scalability towards the number of moving objects. We propose SpatialRugs, a technique that can be applied to reintroduce spatial positions in such approaches by applying 2D colormaps to determine object locations and which enables users to follow spatio-temporal developments even in non-spatial representations. Geared towards collective movement datasets, we evaluate the applicability of several color maps and discuss limitations. To mitigate perceptional artifacts, we also present and evaluate a custom, time-aware color smoothing method.
VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluation

Most existing video-and-language (VidL) research focuses on a single dataset, or multiple datasets of a single task. In reality, a truly useful VidL system is expected to be easily generalizable to diverse tasks, domains, and datasets. To facilitate the evaluation of such systems, we introduce Video-And-Language Understanding Evaluation (VALUE) benchmark, an assemblage of 11 VidL datasets over 3 popular tasks: (i) text-to-video retrieval; (ii) video question answering; and (iii) video captioning. VALUE benchmark aims to cover a broad range of video genres, video lengths, data volumes, and task difficulty levels. Rather than focusing on single-channel videos with visual information only, VALUE promotes models that leverage information from both video frames and their associated subtitles, as well as models that share knowledge across multiple tasks. We evaluate various baseline methods with and without large-scale VidL pre-training, and systematically investigate the impact of video input channels, fusion methods, and different video representations. We also study the transferability between tasks, and conduct multi-task learning under different settings. The significant gap between our best model and human performance calls for future study for advanced VidL models. VALUE is available at this https URL.
Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Detection of the facial skin condition enables people to better understand skin problems and helps them select appropriate treatment methods and maintenance products.We collected facial images of different skin problems by using an image optical sensor.To overcome the problem of insufficient training data, the collected dataset was expanded through data augmentation.In the field of computer vision, deep learning is often used for solving image recognition problems with high accuracy.Therefore, we used a convolutional neural network (CNN) to detect facial images of different skin problems.To overcome the difficulty of parameter selection and increase the recognition rate and robustness of the CNN, the parameters of the CNN were optimized using the Taguchi method.Eight control factors in the convolutional layer and the L36 orthogonal array (OA) were used in experiments.Analysis of variance was used for statistical analysis in the design of the experiments to obtain the optimal parameter combination for the developed CNNs.The experimental results indicate that the CNN optimized using the Taguchi method had an accuracy of 86.95%.The accuracy of the optimized CNN was 7.24% higher than that of the original CNN.The experimental results prove that the proposed hyperparameter optimization method can effectively improve the accuracy of network detection.
Devising an image processing method for transport infrastructure monitoring systems

Identifying and categorizing contours in images is important in many areas of computer vision. Examples include such operational tasks solved by using unmanned aerial vehicles as dynamic monitoring of the condition of transport infrastructure, in particular road markings. This study has established that current methods of image contour analysis do not produce clear and reliable results when solving the task of monitoring the state of road markings. Therefore, it is a relevant scientific and applied task to improve the methods and models of filtration, processing of binary images, and qualitative and meaningful separation of the boundaries of objects of interest. To solve the task of highlighting road marking contours on images acquired from an unmanned aerial vehicle, a method has been devised that includes an operational tool for image preprocessing – a combined filter. The method has several advantages and eliminates the limitations of known methods in determining the boundaries of the location of the object of interest, by highlighting the contours of a cluster of points using histograms. The method and procedures reported here make it possible to successfully solve problems that are largely similar to those that an expert person can face when solving intelligent tasks of processing and filtering information. The proposed method was verified at an enterprise producing the Ukrainian unmanned aerial vehicle "Spectator" during tests of information technology of dynamic monitoring of the state of transport infrastructure. The results could be implemented in promising intelligent control systems in the field of modeling human conscious behavior when sorting data required for the perception of environmental features
Phoneme Recognition Through Fine Tuning of Phonetic Representations: A Case Study on Luhya Language Varieties

Models pre-trained on multiple languages have shown significant promise for improving speech recognition, particularly for low-resource languages. In this work, we focus on phoneme recognition using Allosaurus, a method for multilingual recognition based on phonetic annotation, which incorporates phonological knowledge through a language-dependent allophone layer that associates a universal narrow phone-set with the phonemes that appear in each language. To evaluate in a challenging real-world scenario, we curate phone recognition datasets for Bukusu and Saamia, two varieties of the Luhya language cluster of western Kenya and eastern Uganda. To our knowledge, these datasets are the first of their kind. We carry out similar experiments on the dataset of an endangered Tangkhulic language, East Tusom, a Tibeto-Burman language variety spoken mostly in India. We explore both zero-shot and few-shot recognition by fine-tuning using datasets of varying sizes (10 to 1000 utterances). We find that fine-tuning of Allosaurus, even with just 100 utterances, leads to significant improvements in phone error rates.
Aligning Cross-lingual Sentence Representations with Dual Momentum Contrast

In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product.Pre-trained language models are finetuned with the translation ranking task.Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives.We adapt MoCo (He et al., 2020) to further improve the quality of alignment.As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe and Schwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
Assessing the Reliability of Word Embedding Gender Bias Measures

Various measures have been proposed to quantify human-like social biases in word embeddings.However, bias scores based on these measures can suffer from measurement error.One indication of measurement quality is reliability, concerning the extent to which a measure produces consistent results.In this paper, we assess three types of reliability of word embedding gender bias measures, namely testretest reliability, inter-rater consistency and internal consistency.Specifically, we investigate the consistency of bias scores across different choices of random seeds, scoring rules and words.Furthermore, we analyse the effects of various factors on these measures' reliability scores.Our findings inform better design of word embedding gender bias measures.Moreover, we urge researchers to be more critical about the application of such measures. 1
Cerebral Microbleed Detection via Convolutional Neural Network and Extreme Learning Machine

Aim: Cerebral microbleeds (CMBs) are small round dots distributed over the brain which contribute to stroke, dementia, and death. The early diagnosis is significant for the treatment. Method: In this paper, a new CMB detection approach was put forward for brain magnetic resonance images. We leveraged a sliding window to obtain training and testing samples from input brain images. Then, a 13-layer convolutional neural network (CNN) was designed and trained. Finally, we proposed to utilize an extreme learning machine (ELM) to substitute the last several layers in the CNN for detection. We carried out an experiment to decide the optimal number of layers to be substituted. The parameters in ELM were optimized by a heuristic algorithm named bat algorithm. The evaluation of our approach was based on hold-out validation, and the final predictions were generated by averaging the performance of five runs. Results: Through the experiments, we found replacing the last five layers with ELM can get the optimal results. Conclusion: We offered a comparison with state-of-the-art algorithms, and it can be revealed that our method was accurate in CMB detection.
Navigating a Diverse Paradigm: A Conceptual Framework for Experimental Framing Effects Research

This review introduces a conceptual framework with three elements to highlight the richness of the framing effects literature, while providing structure to address its fragmented nature. Our first element identifies and discusses the Enduring Issues that confront framing effects researchers. Second, we introduce the Semantic Architecture Model (SAM), which builds on the premise that meaning can be framed at different textual units within a text, which can form the basis of frame manipulations in framing effects experiments. Third, we provide an Inventory of Framing Effects Research Components used in framing effects research illustrated with salient examples from the framing effects literature. By offering this conceptual framework, we make the case for revitalizing framing effects research.
Optimizing Trajectories with Closed-Loop Dynamic SQP

Indirect trajectory optimization methods such as Differential Dynamic Programming (DDP) have found considerable success when only planning under dynamic feasibility constraints. Meanwhile, nonlinear programming (NLP) has been the state-of-the-art approach when faced with additional constraints (e.g., control bounds, obstacle avoidance). However, a naïve implementation of NLP algorithms, e.g., shooting-based sequential quadratic programming (SQP), may suffer from slow convergence – caused from natural instabilities of the underlying system manifesting as poor numerical stability within the optimization. Re-interpreting the DDP closed-loop rollout policy as a sensitivity-based correction to a second-order search direction, we demonstrate how to compute analogous closedloop policies (i.e., feedback gains) for constrained problems. Our key theoretical result introduces a novel dynamic programmingbased constraint-set recursion that augments the canonical “cost-to-go” backward pass. On the algorithmic front, we develop a hybrid-SQP algorithm incorporating DDP-style closedloop rollouts, enabled via efficient parallelized computation of the feedback gains. Finally, we validate our theoretical and algorithmic contributions on a set of increasingly challenging benchmarks, demonstrating significant improvements in convergence speed over standard open-loop SQP.
The effect of display brightness and viewing distance: a dataset for visually lossless image compression

Fast track article for IS&amp;T International Symposium on Electronic Imaging 2021: Human Vision and Electronic Imaging 2021 proceedings.
A Hybrid Human–Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy

Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system.To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies.We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface.Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review.This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.
Will You Cross the Threshold for Me?

In this work, we propose generic and novel side-channel assisted chosenciphertext attacks on NTRU-based key encapsulation mechanisms (KEMs). These KEMs are IND-CCA secure, that is, they are secure in the chosen-ciphertext model. Our attacks involve the construction of malformed ciphertexts. When decapsulated by the target device, these ciphertexts ensure that a targeted intermediate variable becomes very closely related to the secret key. An attacker, who can obtain information about the secret-dependent variable through side-channels, can subsequently recover the full secret key. We propose several novel CCAs which can be carried through by using side-channel leakage from the decapsulation procedure. The attacks instantiate three different types of oracles, namely a plaintext-checking oracle, a decryptionfailure oracle, and a full-decryption oracle, and are applicable to two NTRU-based schemes, which are NTRU and NTRU Prime. The two schemes are candidates in the ongoing NIST standardization process for post-quantum cryptography. We perform experimental validation of the attacks on optimized and unprotected implementations of NTRU-based schemes, taken from the open-source pqm4 library, using the EM-based side-channel on the 32-bit ARM Cortex-M4 microcontroller. All of our proposed attacks are capable of recovering the full secret key in only a few thousand chosen ciphertext queries on all parameter sets of NTRU and NTRU Prime. Our attacks, therefore, stress on the need for concrete side-channel protection strategies for NTRUbased KEMs.
More Instructions Make Fewer Subtractions

OPINION article Front. Psychol., 28 September 2021 | https://doi.org/10.3389/fpsyg.2021.720616
An Attention Module for Convolutional Neural Networks

Attention mechanism has been regarded as an advanced technique to capture long-range feature interactions and to boost the representation capability for convolutional neural networks. However, we found two ignored problems in current attentional activations-based models: the approximation problem and the insufficient capacity problem of the attention maps. To solve the two problems together, we initially propose an attention module for convolutional neural networks by developing an AW-convolution, where the shape of attention maps matches that of the weights rather than the activations. Our proposed attention module is a complementary method to previous attention-based schemes, such as those that apply the attention mechanism to explore the relationship between channel-wise and spatial features. Experiments on several datasets for image classification and object detection tasks show the effectiveness of our proposed attention module. In particular, our proposed attention module achieves \(1.00\%\) Top-1 accuracy improvement on ImageNet classification over a ResNet101 baseline and 0.63 COCO-style Average Precision improvement on the COCO object detection on top of a Faster R-CNN baseline with the backbone of ResNet101-FPN. When integrating with the previous attentional activations-based models, our proposed attention module can further increase their Top-1 accuracy on ImageNet classification by up to \(0.57\%\) and COCO-style Average Precision on the COCO object detection by up to 0.45. Code and pre-trained models will be publicly available.
Sensors and Sensor’s Fusion in Autonomous Vehicles

Autonomous vehicle navigation has been at the center of several major developments, both in civilian and defense applications [...].
EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.