Reviewerly/exHarmony huggingface dataset:
Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data

To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data.Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data.Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively.Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89.Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.
Incorporating entity-level knowledge in pretrained language model for biomedical dense retrieval

In recent years, pre-trained language models (PLMs) have dominated natural language processing (NLP) and achieved outstanding performance in various NLP tasks, including dense retrieval based on PLMs. However, in the biomedical domain, the effectiveness of dense retrieval models based on PLMs still needs to be improved due to the diversity and ambiguity of entity expressions caused by the enrichment of biomedical entities. To alleviate the semantic gap, in this paper, we propose a method that incorporates external knowledge at the entity level into a dense retrieval model to enrich the dense representations of queries and documents. Specifically, we first add additional self-attention and information interaction modules in the Transformer layer of the BERT architecture to perform fusion and interaction between query/document text and entity embeddings from knowledge graphs. We then propose an entity similarity loss to constrain the model to better learn external knowledge from entity embeddings, and further propose a weighted entity concatenation mechanism to balance the impact of entity representations when matching queries and documents. Experiments on two publicly available biomedical retrieval datasets show that our proposed method outperforms state-of-the-art dense retrieval methods. In term of NDCG metrics, the proposed method (called ELK) improves the ranking performance of coCondenser by at least 5% on both two datasets, and also obtains further performance gain over state-of-the-art EVA methods. Though having a more sophisticated architecture, the average query latency of ELK is still within the same order of magnitude as that of other efficient methods.
Underwater computational imaging: a survey

Abstract With the increasingly urgent demand for marine research and development, optical imaging technology remains crucial for underwater close-range information detection. However, the inherent obstacles of light transmission in strongly attenuating environments constitute a bottleneck that restricts the development of traditional optical imaging technology. Underwater computational imaging has emerged gradually, leveraging its cross-disciplinary advantages. It deeply couples optical system design with signal calculation and processing and has a high utilization rate of focusing information. It can achieve qualitative breakthroughs in imaging resolution, scale, dimension, and hardware convenience. However, existing work is mostly limited to the extension of free-space computational imaging techniques to underwater environments, lacking systematic research on common needs and key technologies. Therefore, it is essential to refine the connotation and advantages of underwater computational imaging technology, especially in combination with highly complex and nonlinear application scenarios, and to identify potential development space and breakthroughs.
Super-resolution biomedical imaging via reference-free statistical implicit neural representation

Objective.Supervised deep learning for image super-resolution (SR) has limitations in biomedical imaging due to the lack of large amounts of low- and high-resolution image pairs for model training. In this work, we propose a reference-free statistical implicit neural representation (INR) framework, which needs only a single or a few observed low-resolution (LR) image(s), to generate high-quality SR images.Approach.The framework models the statistics of the observed LR images via maximum likelihood estimation and trains the INR network to represent the latent high-resolution (HR) image as a continuous function in the spatial domain. The INR network is constructed as a coordinate-based multi-layer perceptron, whose inputs are image spatial coordinates and outputs are corresponding pixel intensities. The trained INR not only constrains functional smoothness but also allows an arbitrary scale in SR imaging.Main results.We demonstrate the efficacy of the proposed framework on various biomedical images, including computed tomography (CT), magnetic resonance imaging (MRI), fluorescence microscopy, and ultrasound images, across different SR magnification scales of 2×, 4×, and 8×. A limited number of LR images were used for each of the SR imaging tasks to show the potential of the proposed statistical INR framework.Significance.The proposed method provides an urgently needed unsupervised deep learning framework for numerous biomedical SR applications that lack HR reference images.
AB-GRU: An attention-based bidirectional GRU model for multimodal sentiment fusion and analysis

Multimodal sentiment analysis is an important area of artificial intelligence. It integrates multiple modalities such as text, audio, video and image into a compact multimodal representation and obtains sentiment information from them. In this paper, we improve two modules, i.e., feature extraction and feature fusion, to enhance multimodal sentiment analysis and finally propose an attention-based two-layer bidirectional GRU (AB-GRU, gated recurrent unit) multimodal sentiment analysis method. For the feature extraction module, we use a two-layer bidirectional GRU network and connect two layers of attention mechanisms to enhance the extraction of important information. The feature fusion part uses low-rank multimodal fusion, which can reduce the multimodal data dimensionality and improve the computational rate and accuracy. The experimental results demonstrate that the AB-GRU model can achieve 80.9% accuracy on the CMU-MOSI dataset, which exceeds the same model type by at least 2.5%. The AB-GRU model also possesses a strong generalization capability and solid robustness.
Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction

Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both "spoken language" (e.g., human textual annotations) and "unspoken language" (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information modalities from the real-world HMI scenarios. We aim to extract the important but possibly implicit HMI concepts (as the named entities) from the textual annotations (provided by human annotators) through a novel human language and sensor data co-learning design. To this end, we propose CG-HMI, a novel Cross-modality Graph fusion approach for extracting important Human-Mobility Interaction concepts from co-learning of textual annotations as well as the visual and behavioral sensor data. In order to fuse both unspoken and spoken "languages", we have designed a unified representation called the human--mobility interaction graph (HMIG) for each modality related to the HMI scenes, i.e., textual annotations, visual video frames, and behavioral sensor time-series (e.g., from the on-board or smartphone inertial measurement units). The nodes of the HMIG in these modalities correspond to the textual words (tokenized for ease of processing) related to HMI concepts, the detected traffic participant/environment categories, and the vehicle maneuver behavior types determined from the behavioral sensor time-series. To extract the inter- and intra-modality semantic correspondences and interactions in the HMIG, we have designed a novel graph interaction fusion approach with differentiable pooling-based graph attention. The resulting graph embeddings are then processed to identify and retrieve the HMI concepts within the annotations, which can benefit the downstream human-computer interaction and ubiquitous computing applications. We have developed and implemented CG-HMI into a system prototype, and performed extensive studies upon three real-world HMI datasets (two on car driving and the third one on e-scooter riding). We have corroborated the excellent performance (on average 13.11% higher accuracy than the other baselines in terms of precision, recall, and F1 measure) and effectiveness of CG-HMI in recognizing and extracting the important HMI concepts through cross-modality learning. Our CG-HMI studies also provide real-world implications (e.g., road safety and driving behaviors) about the interactions between the drivers and other traffic participants.
Headar

Nod and shake of one's head are intuitive and universal gestures in communication. As smartwatches become increasingly intelligent through advances in user activity sensing technologies, many use scenarios of smartwatches demand quick responses from users in confirmation dialogs, to accept or dismiss proposed actions. Such proposed actions include making emergency calls, taking service recommendations, and starting or stopping exercise timers. Head gestures in these scenarios could be preferable to touch interactions for being hands-free and easy to perform. We propose Headar to recognize these gestures on smartwatches using wearable millimeter wave sensing. We first surveyed head gestures to understand how they are performed in conversational settings. We then investigated positions and orientations to which users raise their smartwatches. Insights from these studies guided the implementation of Headar. Additionally, we conducted modeling and simulation to verify our sensing principle. We developed a real-time sensing and inference pipeline using contemporary deep learning techniques, and proved the feasibility of our proposed approach with a user study (n=15) and a live test (n=8). Our evaluation yielded an average accuracy of 84.0% in the user study across 9 classes including nod and shake as well as seven other signals -- still, speech, touch interaction, and four non-gestural head motions (i.e., head up, left, right, and down). Furthermore, we obtained an accuracy of 72.6% in the live test which reveals rich insights into the performance of our approach in various realistic conditions.
MMTSA

Multimodal sensors provide complementary information to develop accurate machine-learning methods for human activity recognition (HAR), but introduce significantly higher computational load, which reduces efficiency. This paper proposes an efficient multimodal neural architecture for HAR using an RGB camera and inertial measurement units (IMUs) called Multimodal Temporal Segment Attention Network (MMTSA). MMTSA first transforms IMU sensor data into a temporal and structure-preserving gray-scale image using the Gramian Angular Field (GAF), representing the inherent properties of human activities. MMTSA then applies a multimodal sparse sampling method to reduce data redundancy. Lastly, MMTSA adopts an inter-segment attention module for efficient multimodal fusion. Using three well-established public datasets, we evaluated MMTSA's effectiveness and efficiency in HAR. Results show that our method achieves superior performance improvements (11.13% of cross-subject F1-score on the MMAct dataset) than the previous state-of-the-art (SOTA) methods. The ablation study and analysis suggest that MMTSA's effectiveness in fusing multimodal data for accurate HAR. The efficiency evaluation on an edge device showed that MMTSA achieved significantly better accuracy, lower computational load, and lower inference latency than SOTA methods.
LapTouch

Use of virtual reality while seated is common, but studies on seated interaction beyond the use of controllers or hand gestures have been sparse. This work present LapTouch, which makes use of the lap as a touch interface and includes two user studies to inform the design of direct and indirect touch interaction using the lap with visual feedback that guides the user touch, as well as eye-free interaction in which users are not provided with such visual feedback. The first study suggests that direct interaction can provide effective layouts with 95% accuracy with up to a 4×4 layout and a shorter completion time, while indirect interaction can provide effective layouts with up to a 4×5 layout but a longer completion time. Considering user experience, which revealed that 4-row and 5-column layouts are not preferred, it is recommended to use both direct and indirect interaction with a maximum of a 3×4 layout. According to the second study, increasing the eye-free interaction with support vector machine (SVM) allows for a 2×2 layout with a generalized model and 2×2, 2×3 and 3×2 layouts with personalized models.
Enhancing PKI Security in Hyperledger Fabric with an Indigenous Certificate Authority

Public Key Infrastructure (PKI) is utilised in Hyperledger Fabric (HLF) to safeguard the network and transactions. It accomplishes this by managing the public-private key pairs and digital certificates that are essential for encryption and authentication. A conventional certificate authority (CA) called Fabric CA comes with HLF and offers certificate management services for the network. However, Fabric CA has certain limitations, such as limited scalability, the inability to handle identities and certificates outside of the network, and limited support for certificate revocation lists. To address these drawbacks, an indigenous CA has been developed and integrated into HLF by replacing fabric CA. The proposed research work also explains how successful integration offers enhanced security, scalability, and flexibility in managing certificates and identities. This article also outlines the steps required to replace the Fabric CA in HLF and explains how the proposed CA was developed. Finally, the effectiveness of the CA is evaluated using metrics such as throughput, time per signature, and issue time. The performance evaluations demonstrate that the developed CA surpasses Fabric CA and is suitable for production environments.
Review of diffractive deep neural networks

In 2018, a UCLA research group published an important paper on optical neural network (ONN) research in the journal Science . It developed the world’s first all-optical diffraction deep neural network (DNN) system, which can perform MNIST dataset classification tasks at near-light-speed. To be specific, the UCLA research group adopted a terahertz light source as the input, established the all-optical diffractive DNN (D 2 NN) model using the Rayleigh-Sommerfeld diffraction theory, optimized the model parameters using the stochastic gradient descent algorithm, and then used 3D printing technology to make the diffraction grating and built the D 2 NN system. This research opened a new ONN research direction. Here, we first review and analyze the development history and basic theory of artificial neural networks (ANNs) and ONNs. Second, we elaborate D 2 NN as holographic optical elements (HOEs) interconnected by free space light and describe the theory of D 2 NN. Then we cover the nonlinear research and application scenarios for D 2 NN. Finally, the future directions and challenges of D 2 NN are briefly discussed. Hopefully, our work can provide support and help to researchers who study the theory and application of D 2 NN in the future.
Deep photonic reservoir computer based on frequency multiplexing with fully analog connection between layers

Reservoir computers (RCs) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RCs), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any digital processing. We find that the deep-RC outperforms a traditional RC by up to two orders of magnitude on two benchmark tasks. This work paves the way towards using fully analog photonic neuromorphic computing for complex processing of time series, while avoiding costly analog-to-digital and digital-to-analog conversions.
Path Planning Algorithm of Indoor Mobile Robot Based on ROS System

With the rapid development of global science and technology, robots have been widely used in industry, agriculture, service industry and other fields, among which the demand for mobile robots is rising. This paper takes indoor mobile robot as the research object, starts from the perspective of global and local planning respectively, and optimizes the design principle and design theory of the path planning algorithm in the robot navigation system, and proposes the global path planning algorithm based on the improved A* algorithm and the local path planning algorithm based on the improved artificial potential field algorithm. On this basis, in the case that any single planning algorithm cannot meet the requirements of operation in complex indoor environment, a hybrid path planning algorithm based on static global optimization and dynamic real-time local optimization is proposed, and the two improved algorithms are configured on the actual robot platform to achieve accurate obstacle avoidance and fast path finding theoretical verification of indoor mobile robots. The hybrid planning algorithm was verified by simulation experiment with MATLAB tool, and the effectiveness of the improved algorithm was verified. The indoor mobile robot ROS experiment platform was built to verify the hybrid optimization of the two improved algorithms, which has certain effectiveness and advantages for the dynamic real-time path planning of the actual robot indoor complex environment.
Trusted Virtual Reality Environment for Training Security Officers

Virtual Reality (VR) applications are increasingly used to support ergonomic and safe training activities, including serious games for training security officers and other security related professionals. Nevertheless, they do not exploit opportunities for trusted and secure management of digital assets, which are typically offered by the blockchain infrastructures of emerging metaverse environments. This paper introduces a novel interactive and realistic VR-based serious game for training law enforcement officers in the analysis and understanding of terroristic activities. The game is driven by pragmatic models of terroristic actions and teaches its users how to predict and anticipate indicators of terroristic attacks. It also provides the means for generating datasets to train Artificial Intelligence (AI) modules that could help analyzing and predict potentially terroristic activities. Moreover, the paper provides an outlook for the evolution of the game in metaverse environments, where blockchain infrastructures can be used to both boost the cyber-resilience of the game and to safeguard the trustworthiness of the data generation process.
Federated Learning for Image Semantic Communication System Based on CNN and Transformer

The letter presents a semantic communication approach that combines Convolutional Neural Network (CNN) and Transformer models. By using CNN as a feature extractor to convert image features into feature sequences, which are then processed by the Transformer model, this method captures spatial relationships and contextual information in images, enabling better image reconstruction in image transmission tasks. Furthermore, the letter applies federated learning to the proposed model. Client devices train the model locally and then send the updated model parameters to a central server for aggregation, achieving global model training without centrally storing the original dataset on the server. Federated learning not only addresses data privacy protection concerns but also reduces the training time for each user. Experimental results demonstrate the effectiveness of combining CNN and Transformer models on different datasets, while federated learning significantly improves the model's performance and privacy protection capabilities. Therefore, the proposed method in the letter can serve as an effective image modality semantic communication encoder.
Hidden Backdoor Attack: A New Threat to Learning-Aided Physical Layer Authentication

Radio frequency (RF) fingerprinting techniques have been used as an extra method in physical layer authentication for wireless devices. Unique fingerprints are used to identify wireless devices in order to avoid spoofing or impersonating attacks. With the development of deep learning (DL), many techniques based on DL are used for RF fingerprint identification. However, due to the openness of wireless channel and unexplainability of DL, it is vulnerable to adversarial attacks. In this paper, we investigate hidden backdoor attack to deep learning-aided physical layer authentication, where the adversary puts elaborately designed poisoned samples on the basis of IQ sequences into training dataset. And poisoned samples are same to samples with triggers which are patched samples in feature space. We show that hidden backdoor attack can reduce the accuracy of RF fingerprint identification significantly with patched samples.