Reviewerly/exHarmony huggingface dataset:
Predicting amplitude death with machine learning

In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the oscillations of the state variables---the phenomenon of amplitude death. The underlying bifurcation is one at which the system settles into a steady state from chaotic or regular oscillations. As the normal functioning of many physical, biological, and physiological systems hinges on oscillations, amplitude death is undesired. To predict amplitude death in advance of its occurrence based solely on oscillatory time series collected while the system still functions normally is a challenge problem. We exploit machine learning to meet this challenge. In particular, we develop the scheme of ``parameter-aware'' reservoir computing, where training is conducted for a small number of bifurcation parameter values in the oscillatory regime to enable prediction upon a parameter drift into the regime of amplitude death. We demonstrate successful prediction of amplitude death for three prototypical dynamical systems in which the transition to death is preceded by either chaotic or regular oscillations. Because of the completely data-driven nature of the prediction framework, potential applications to real-world systems can be anticipated.
Gamification and Hazard Communication in Virtual Reality: A Qualitative Study

An effective warning attracts attention, elicits knowledge, and enables compliance behavior. Game mechanics, which are directly linked to human desires, stand out as training, evaluation, and improvement tools. Immersive virtual reality (VR) facilitates training without risk to participants, evaluates the impact of an incorrect action/decision, and creates a smart training environment. The present study analyzes the user experience in a gamified virtual environment of risks using the HTC Vive head-mounted display. The game was developed in the Unreal game engine and consisted of a walk-through maze composed of evident dangers and different signaling variables while user action data were recorded. To demonstrate which aspects provide better interaction, experience, perception and memory, three different warning configurations (dynamic, static and smart) and two different levels of danger (low and high) were presented. To properly assess the impact of the experience, we conducted a survey about personality and knowledge before and after using the game. We proceeded with the qualitative approach by using questions in a bipolar laddering assessment that was compared with the recorded data during the game. The findings indicate that when users are engaged in VR, they tend to test the consequences of their actions rather than maintaining safety. The results also reveal that textual signal variables are not accessed when users are faced with the stress factor of time. Progress is needed in implementing new technologies for warnings and advance notifications to improve the evaluation of human behavior in virtual environments of high-risk surroundings.
Nanopore base calling on the edge

Abstract Motivation MinION is a portable nanopore sequencing device that can be easily operated in the field with features including monitoring of run progress and selective sequencing. To fully exploit these features, real-time base calling is required. Up to date, this has only been achieved at the cost of high computing requirements that pose limitations in terms of hardware availability in common laptops and energy consumption. Results We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed new versions of two key components used in convolutional neural networks for speech recognition and base calling. In our components, we propose a new way of factorization of a full convolution into smaller operations, which decreases memory access operations, memory access being a bottleneck on this device. DeepNano-coral achieves real-time base calling during sequencing with the accuracy slightly better than the fast mode of the Guppy base caller and is extremely energy efficient, using only 10 W of power. Availability and implementation https://github.com/fmfi-compbio/coral-basecaller Supplementary information Supplementary data are available at Bioinformatics online.
Electric-Circuit Realization of Fast Quantum Search

Quantum search algorithm, which can search an unsorted database quadratically faster than any known classical algorithms, has become one of the most impressive showcases of quantum computation. It has been implemented using various quantum schemes. Here, we demonstrate both theoretically and experimentally that such a fast search algorithm can also be realized using classical electric circuits. The classical circuit networks to perform such a fast search have been designed. It has been shown that the evolution of electric signals in the circuit networks is analogies of quantum particles randomly walking on graphs described by quantum theory. The searching efficiencies in our designed classical circuits are the same to the quantum schemes. Because classical circuit networks possess good scalability and stability, the present scheme is expected to avoid some problems faced by the quantum schemes. Thus, our findings are advantageous for information processing in the era of big data.
Multiscale permutation entropy for two-dimensional patterns

Complexity measures are important to understand and analyze systems with one dimensional data. However, extension of these methods to images (two dimensional data) are much less usual. Bidimensional multiscale sample entropy (MSE2D) has recently been proposed as a new complexity measure for texture evaluation. However, MSE2D leads to undefined or unreliable values for small-sized textures and requires a long computation time. This is why we herein propose the bidimensional multiscale permutation entropy (MPE2D) to evaluate the complexity of 2D patterns. MPE2D is applied to different synthesized textures, to softwood samples, and to study the texture of breast histopathology images. The results show that MPE2D is a valuable tool for texture analysis and that it is computationally noticeably faster than MSE2D.
Dense Attentive Feature Enhancement for Salient Object Detection

Attention mechanisms have been proven highly effective for salient object detection. Most previous works utilize attention as a self-gated module to reweigh the feature maps at different levels independently. However, they are limited to certain-level guidance and could not satisfy the need of both accurately detecting intact objects and maintaining their detailed boundaries. In this paper, we build dense attention upon features from multiple levels simultaneously and propose a novel Dense Attentive Feature Enhancement (DAFE) module for efficient feature enhancement in saliency detection. DAFE stacks several attentional units and densely connects attentive feature output from current unit to its all subsequent units. This allows feature maps at deep units to absorb attentive information from shallow units, thus more discriminative information can be efficiently selected at the final output. Note that DAFE is plug and play, which can be effortlessly inserted into any saliency or video saliency models for their performance improvements. We further instantiate a highly effective Dense Attentive Feature Enhancement Network (DAFE-Net) for accurate salient object detection. DAFE-Net constructs DAFE over the aggregation feature that contains both semantics and saliency details, the entire salient objects and their boundaries can be well retained through dense attentions. Extensive experiments demonstrate that the proposed DAFE module is highly effective, and the DAFE-Net performs favorably compared with state-of-the-art approaches.
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding

Temporal grounding aims to localize a video moment which is semantically aligned with a given natural language query. Existing methods typically apply a detection or regression pipeline on the fused representation with the research focus on designing complicated prediction heads or fusion strategies. Instead, from a perspective on temporal grounding as a metric-learning problem, we present a Mutual Matching Network (MMN), to directly model the similarity between language queries and video moments in a joint embedding space. This new metric-learning framework enables fully exploiting negative samples from two new aspects: constructing negative cross-modal pairs in a mutual matching scheme and mining negative pairs across different videos. These new negative samples could enhance the joint representation learning of two modalities via cross-modal mutual matching to maximize their mutual information. Experiments show that our MMN achieves highly competitive performance compared with the state-of-the-art methods on four video grounding benchmarks. Based on MMN, we present a winner solution for the HC-STVG challenge of the 3rd PIC workshop. This suggests that metric learning is still a promising method for temporal grounding via capturing the essential cross-modal correlation in a joint embedding space. Code is available at https://github.com/MCG-NJU/MMN.
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

Existing KBQA approaches, despite achieving strong performance on i.i.d.test data, often struggle in generalizing to questions involving unseen KB schema items.Prior rankingbased approaches have shown some success in generalization, but suffer from the coverage issue.We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability.Our approach first uses a contrastive ranker to rank a set of candidate logical forms obtained by searching over the knowledge graph.It then introduces a tailored generation model conditioned on the question and the top-ranked candidates to compose the final logical form.We achieve new state-ofthe-art results on GRAILQA and WEBQSP datasets.In particular, our method surpasses the prior state-of-the-art by a large margin on the GRAILQA leaderboard.In addition, RnG-KBQA outperforms all prior approaches on the popular WEBQSP benchmark, even including the ones that use the oracle entity linking.The experimental results demonstrate the effectiveness of the interplay between ranking and generation, which leads to the superior performance of our proposed approach across all settings with especially strong improvements in zero-shot generalization. 1
Road Anomaly Detection by Partial Image Reconstruction with Segmentation Coupling

We present a novel approach to the detection of unknown objects in the context of autonomous driving. The problem is formulated as anomaly detection, since we assume that the unknown stuff or object appearance cannot be learned. To that end, we propose a reconstruction module that can be used with many existing semantic segmentation networks, and that is trained to recognize and reconstruct road (drivable) surface from a small bottleneck. We postulate that poor reconstruction of the road surface is due to areas that are outside of the training distribution, which is a strong indicator of an anomaly. The road structural similarity error is coupled with the semantic segmentation to incorporate information from known classes and produce final per-pixel anomaly scores. The proposed JSR-Net was evaluated on four datasets, Lost-and-found, Road Anomaly, Road Obstacles, and FishyScapes, achieving state-of-art performance on all, reducing the false positives significantly, while typically having the highest average precision for wide range of operation points.
Quantum Linearization Attacks

Recent works have shown that quantum period-finding can be used to break many popular constructions (some block ciphers such as Even-Mansour, multiple MACs and AEs...) in the superposition query model. So far, all the constructions broken exhibited a strong algebraic structure, which enables to craft a periodic function of a single input block. Recovering the secret period allows to recover a key, distinguish, break the confidentiality or authenticity of these modes.
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.Existing research efforts are majorly tagging-based.Among the methods taking a sequence tagging approach, some fail to capture the strong interdependence between the three opinion factors, whereas others fall short of identifying triplets with overlapping aspect/opinion spans.A recent grid tagging approach on the other hand fails to capture the span-level semantics while predicting the sentiment between an aspect-opinion pair.Different from these, we present a tagging-free solution for the task, while addressing the limitations of the existing works.We adapt an encoder-decoder architecture with a Pointer Network-based decoding framework that generates an entire opinion triplet at each time step thereby making our solution end-to-end.Interactions between the aspects and opinions are effectively captured by the decoder by considering their entire detected spans while predicting their connecting sentiment.Extensive experiments on several benchmark datasets establish the better efficacy of our proposed approach, especially in recall, and in predicting multiple and aspect/opinion-overlapped triplets from the same review sentence.We report our results both with and without BERT and also demonstrate the utility of domainspecific BERT post-training for the task.
Deep learning-based malicious smart contract detection scheme for internet of things environment

Smart contracts are essential in maintaining the trust between the members of the blockchain. Its verification is of utmost importance, as it is unmodifiable once deployed. Moreover, a malicious user can deploy vulnerable smart contracts to breach the blockchain data. To restrict this, we propose a deep learning-based scheme to detect the vulnerabilities and rate them as safe/vulnerable based on probability value <0.5/≥0.5 respectively. An open Google BigQuery dataset with 7000 samples was used to train the classifier. We train artificial neural networks (ANN), long-short term memory (LSTM), and gated recurrent unit models (GRU) and compare their accuracy, precision, recall, and receiver operating characteristic (ROC) curve values. Results show the LSTM model outperforms ANN and GRU. Then, we simulate the LSTM to classify the smart contracts before their deployment in the blockchain. Also, the efficacy of the blockchain is justified with the proposed system’s data storage cost and scalability.
Workshop on Context-Aware Recommender Systems (CARS) 2021

Contextual information has been widely recognized as an important modeling dimension both in social sciences and in computing. In particular, the role of context has been recognized in enhancing recommendation results and retrieval performance. While a substantial amount of existing research has focused on context-aware recommender systems (CARS), many interesting problems remain under-explored. The CARS 2021 workshop provides a venue for presenting and discussing: the important features of the next generation of CARS; and application domains that may require the use of novel types of contextual information and cope with their dynamic properties in group recommendations and in online environments.
Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis

Abstract Objective Early identification of chronic diseases is a pillar of precision medicine as it can lead to improved outcomes, reduction of disease burden, and lower healthcare costs. Predictions of a patient’s health trajectory have been improved through the application of machine learning approaches to electronic health records (EHRs). However, these methods have traditionally relied on “black box” algorithms that can process large amounts of data but are unable to incorporate domain knowledge, thus limiting their predictive and explanatory power. Here, we present a method for incorporating domain knowledge into clinical classifications by embedding individual patient data into a biomedical knowledge graph. Materials and Methods A modified version of the Page rank algorithm was implemented to embed millions of deidentified EHRs into a biomedical knowledge graph (SPOKE). This resulted in high-dimensional, knowledge-guided patient health signatures (ie, SPOKEsigs) that were subsequently used as features in a random forest environment to classify patients at risk of developing a chronic disease. Results Our model predicted disease status of 5752 subjects 3 years before being diagnosed with multiple sclerosis (MS) (AUC = 0.83). SPOKEsigs outperformed predictions using EHRs alone, and the biological drivers of the classifiers provided insight into the underpinnings of prodromal MS. Conclusion Using data from EHR as input, SPOKEsigs describe patients at both the clinical and biological levels. We provide a clinical use case for detecting MS up to 5 years prior to their documented diagnosis in the clinic and illustrate the biological features that distinguish the prodromal MS state.
A novel algorithm for mask detection and recognizing actions of human

Face recognition has become a significant challenge today since an increasing number of individuals wear masks to avoid infection with the novel coronavirus or Covid-19. Due to its rapid proliferation, it has garnered growing attention. The technique proposed in this chapter seeks to produce unconstrained generic actions in the video. Conventional anomaly detection is difficult because computationally expensive characteristics cannot be employed directly, owing to the necessity for real-time processing. Even before activities are completely seen, they must be located and classified. This paper proposes an expanded Mask R-CNN (Ex-Mask R-CNN) architecture that overcomes these issues. High accuracy is achieved by using robust convolutional neural network (CNN)-based features. The technique consists of two steps. First, a video surveillance algorithm is employed to determine whether or not a human is wearing a mask. Second, Multi-CNN forecasts the frame's suspicious conventional abnormality of people. Experiments on tough datasets indicate that our approach outperforms state-of-the-art online traditional detection of anomaly systems while maintaining the real-time efficiency of existing classifiers.
Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Social media networking is a prominent topic in real life, particularly at the current moment. The impact of comments has been investigated in several studies. Twitter, Facebook, and Instagram are just a few of the social media networks that are used to broadcast different news worldwide. In this paper, a comprehensive AI-based study is presented to automatically detect the Arabic text misogyny and sarcasm in binary and multiclass scenarios. The key of the proposed AI approach is to distinguish various topics of misogyny and sarcasm from Arabic tweets in social media networks. A comprehensive study is achieved for detecting both misogyny and sarcasm via adopting seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, and KNNC. To fine tune, validate, and evaluate all of these techniques, two Arabic tweets datasets (i.e., misogyny and Abu Farah datasets) are used. For the experimental study, two scenarios are proposed for each case study (misogyny or sarcasm): binary and multiclass problems. For misogyny detection, the best accuracy is achieved using the AraBERT classifier with 91.0% for binary classification scenario and 89.0% for the multiclass scenario. For sarcasm detection, the best accuracy is achieved using the AraBERT as well with 88% for binary classification scenario and 77.0% for the multiclass scenario. The proposed method appears to be effective in detecting misogyny and sarcasm in social media platforms with suggesting AraBERT as a superior state-of-the-art deep learning classifier.
A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4

As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, the optimal selection of detection methods, and the objective limitations of detection tasks. For the purpose of overcoming these difficulties, this paper proposes a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods. The framework optimizes the latency concern by reducing the computational overhead of the network, and facilitates information transfer and sharing at diverse levels. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in objective environments, such as scale and illumination changes. The proposed model is tested and evaluated on real road scene datasets and compared with the current mainstream advanced detection models to verify its effectiveness. In addition, this paper successfully finds a reasonable balance between detection performance and deployment difficulty by effectively reducing the computational cost, which provides a possibility for realistic deployment on edge devices with limited hardware conditions, such as mobile devices and embedded devices. More importantly, the related theories have certain application potential in technology industries such as artificial intelligence or autonomous driving.
Vision-Based Finger Tapping Test in Patients With Parkinson’s Disease via Spatial-Temporal 3D Hand Pose Estimation

Finger tapping test is crucial for diagnosing Parkinson's Disease (PD), but manual visual evaluations can result in score discrepancy due to clinicians' subjectivity. Moreover, applying wearable sensors requires making physical contact and may hinder PD patient's raw movement patterns. Accordingly, a novel computer-vision approach is proposed using depth camera and spatial-temporal 3D hand pose estimation to capture and evaluate PD patients' 3D hand movement. Within this approach, a temporal encoding module is leveraged to extend A2J's deep learning framework to counter the pose jittering problem, and a pose refinement process is utilized to alleviate dependency on massive data. Additionally, the first vision-based 3D PD hand dataset of 112 hand samples from 48 PD patients and 11 control subjects is constructed, fully annotated by qualified physicians under clinical settings. Testing on this real-world data, this new model achieves 81.2% classification accuracy, even surpassing that of individual clinicians in comparison, fully demonstrating this proposition's effectiveness. The demo video can be accessed at https://github.com/ZhilinGuo/ST-A2J.