Reviewerly/exHarmony huggingface dataset:
A probabilistic deep motion model for unsupervised cardiac shape anomaly assessment

Automatic shape anomaly detection in large-scale imaging data can be useful for screening suboptimal segmentations and pathologies altering the cardiac morphology without intensive manual labour. We propose a deep probabilistic model for local anomaly detection in sequences of heart shapes, modelled as point sets, in a cardiac cycle. A deep recurrent encoder-decoder network captures the spatio-temporal dependencies to predict the next shape in the cycle and thus derive the outlier points that are attributed to excessive deviations from the network prediction. A predictive mixture distribution models the inlier and outlier classes via Gaussian and uniform distributions, respectively. A Gibbs sampling Expectation-Maximisation (EM) algorithm computes soft anomaly scores of the points via the posterior probabilities of each class in the E-step and estimates the parameters of the network and the predictive distribution in the M-step. We demonstrate the versatility of the method using two shape datasets derived from: (i) one million biventricular CMR images from 20,000 participants in the UK Biobank (UKB), and (ii) routine diagnostic imaging from Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image (M&Ms). Experiments show that the detected shape anomalies in the UKB dataset are mostly associated with poor segmentation quality, and the predicted shape sequences show significant improvement over the input sequences. Furthermore, evaluations on U-Net based shapes from the M&Ms dataset reveals that the anomalies are attributable to the underlying pathologies that affect the ventricles. The proposed model can therefore be used as an effective mechanism to sift shape anomalies in large-scale cardiac imaging pipelines for further analysis.
Self-attentive 3D human pose and shape estimation from videos

We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applied to each image, thereby often leading to inconsistent predictions. In this work, we present a video-based learning algorithm for 3D human pose and shape estimation. The key insights of our method are two-fold. First, to address the inconsistent temporal prediction issue, we exploit temporal information in videos and propose a self-attention module that jointly considers short-range and long-range dependencies across frames, resulting in temporally coherent estimations. Second, we model human motion with a forecasting module that allows the transition between adjacent frames to be smooth. We evaluate our method on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets. Extensive experimental results show that our algorithm performs favorably against the state-of-the-art methods.
Exploring Zero-shot Cross-lingual Aspect-based Sentiment Analysis using Pre-trained Multilingual Language Models

Aspect-based sentiment analysis (ABSA) has received much attention in the Natural Language Processing research community. Most of the proposed methods are conducted exclusively in English and high-resources languages. Leveraging resources available from English and transferring to low-resources languages seems to be an immediate solution. In this paper, we investigate the performance of zero-shot cross-lingual transfer learning based on pre-trained multilingual models (mBERT and XLM-R) for two main sub-tasks in the ABSA problem: Aspect Category Detection and Opinion Target Expression. We experiment on the benchmark data sets of six languages as English, Russian, Dutch, Spanish, Turkish, and French. The experimental results demonstrated that using the XLM-R model can yield relatively acceptable results for the zero-shot cross-lingual scenario.
Design and Analysis of a Collaborative Story Generation Game for Social Robots

Storytelling plays a central role in human socializing and entertainment, and research on conducting storytelling with robots is gaining interest. However, much of this research assumes that story content is curated. In this paper, we introduce the task of collaborative story generation , where an artificial intelligence agent, or a robot, and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative story generation system which works with a human storyteller to create a story by generating new utterances based on the story so far. Our collaborative story generation system consists of a publicly-available large scale language model that was tuned on a dataset of writing prompts and short stories, and a ranker that samples from the language model and chooses the best possible output. We improve storytelling quality by optimizing the ranker’s sample size to strike a balance between quality and computational cost. Since latency can be detrimental to human-robot interaction, we examine the performance-latency trade-offs of our approach and find the optimal ranker sample size that strikes the best balance between quality and computational cost. We evaluate our system by having human participants play the collaborative story generation game and comparing the stories they create with our system to a naive baseline. Next, we conduct a detailed elicitation survey that sheds light on issues to consider when adapting our collaborative story generation system to a social robot. Finally, in a first step towards allowing human players to control the genre or mood of stories generated, we present preliminary work on steering story generation sentiment polarity with a sentiment analysis model. We find that our proposed method achieves a good balance of steering capability and text coherence. Our evaluation shows that participants have a positive view of collaborative story generation with a social robot and consider rich, emotive capabilities to be key to an enjoyable experience.
Advancing the Adoption of Virtual Reality and Neurotechnology to Improve Flight Training

Virtual reality (VR) has been used for training purposes in a wide range of industries, including education, healthcare, and defense. VR allows users to train in a safe and controlled digital environment while being immersed and highly engaged in a realistic task. One of its advantages is that VR can be combined with multiple wearable sensing technologies, allowing researchers to study (neuro)physiological and cognitive processes elicited by dynamic environments and adapt these simulations based on such processes. However, the potential of VR combined with neurotechnology to facilitate effective and efficient aviation training has not yet been fully explored. For instance, despite the growing interest in including VR as part of the training programs for military and commercial airlines pilots, it is still unclear what the effectiveness of VR is in short- and long-term training of pilots. This paper provides an overview of the state-of-the-art research in VR applications for aviation training and identifies challenges and future opportunities. We particularly discuss the potential of neurotechnology in objective measurement of training progress and providing real-time feedback during VR flight tasks. Overall, VR combined with neurotechnology for flight training holds promise to optimize individual learning progress.
Sensor based posture detection system

Posture identification system has been extensively useful in areas such as physical activities, ecological awareness events, man machine interfacing applications, control room panel monitoring systems and in autonomous old people caring systems. This paper describes a sensor based posture detection and guidance system while doing yoga exercise. In our day-to-day life, doing yoga exercise has become a well-known discipline, which helps people to be physically fit and have good mental health. Practicing yoga exercise is a recognized system for the human beings to improve their physical and mental activities. This practice helps in regulatory activities in an individual’s mind, body and soul. As a result of this, flexibility, muscle power and body image will increase. The existing method has shown the recognition of posture analysis through virtual reality and exergame. We proposed a suit that is designed and implemented through a virtual master. The suit is integrated with various types of accelerometer sensors which are fixed on the flexible bands, that are light in weight and are used indirectly to measure the orientation of the limbs. Also the sensor integrated suit is very helpful to measure the direction of the tilt moment. Predefined calibrated values are loaded on to the microcontroller, which are compared with the measured values and when there is a match the information is sent to a mobile application. The mobile app identifies whether the user is doing the posture correctly or not based on the information given to practice the yoga exercise.
Using Knowledge Concept Aggregation towards Accurate Cognitive Diagnosis

Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which is aimed to mine and analyze the level of knowledge for a student in his or her learning process periodically. While a number of approaches and tools have been developed to diagnose the learning states of students, they do not fully learn the relationship between students, exercises and knowledge concepts in the learning system, or do not consider the traits that it is easier to complete diagnosis when focusing on a small part of knowledge concepts rather than all knowledge concepts. To address these limitations, we develop CDGK, a model based artificial neural network to deal with cognitive diagnosis. Our method not only captures non-linear interactions between exercise features, student scores, and their mastery on each knowledge concept, but also performs an aggregation of the knowledge concepts via converting them into graph structure, and only considering the leaf node in the knowledge concept tree, which can reduce the dimension of the model without accuracy loss. In our evaluation on two real-world datasets, CDGK outperforms the state-of-the-art related approaches in terms of accuracy, reasonableness and interpretability.
TREAD: Privacy Preserving Incentivized Connected Vehicle Mobility Data Storage on InterPlanetary-File-System-Enabled Blockchain

Trajectory data from connected vehicles (CVs) and other micromobility sources such as e-scooters, bikes, and pedestrians is important for researchers, policy makers, and other stakeholders for leveraging the location, speed, and heading, along with other mobility data, to improve safety and bolster technology development toward innovative location-based applications for citizens. Such raw data needs to be stored and accessed from a non-proprietary database while the obfuscation and encryption techniques on current cloud-based proprietary solutions incur data losses that are deemed inefficient for accurate usage, particularly in time-sensitive real-time operations. In this paper, we target the problem of scalably storing and retrieving potentially sensitive data generated by vehicles and propose TREAD, a blockchain-based system comprising smart contracts to store this mobility data on a distributed ledger such that multiple peers can access and utilize it in different location-based applications while not revealing users’ sensitive personal information. It is, however, challenging to scalably store large amounts of constantly generated trajectories, and to achieve scalability we leverage InterPlanetary File System (IPFS), a scalable distributed peer-to-peer data storage system. To avoid users injecting malicious/fake trajectories into the ledger, we develop efficient consensus algorithms for the stakeholders to validate the storage and retrieval process in a distributed manner. We implemented TREAD on the open-source Hyperledger Fabric blockchain platform using trajectory data generated for 700 vehicles in a simulation environment well calibrated with vehicle trajectories from a real-world test-bed in New York City. Results show that TREAD scalably stores trajectory data with lower delay and overhead.
A Henkin-Style Completeness Proof for the Modal Logic S5

This paper presents a recent formalization of a Henkin-style completeness proof for the propositional modal logic S5 using the Lean theorem prover. The proof formalized is close to that of Hughes and Cresswell, but the system, based on a different choice of axioms, is better described as a Mendelson system augmented with axiom schemes for K, T, S4, and B, and the necessitation rule as a rule of inference. The language has the false and implication as the only primitive logical connectives and necessity as the only primitive modal operator. The full source code is available online at https://github.com/bbentzen/mpl/ and has been typechecked with Lean 3.4.2.
Unified Transformer Multi-Task Learning for Intent Classification With Entity Recognition

Intent classification (IC) and Named Entity Recognition (NER) are arguably the two main components needed to build a Natural Language Understanding (NLU) engine, which is a main component of conversational agents. The IC and NER components are closely intertwined and the entities are often connected to the underlying intent. Current research has primarily focused to model IC and NER as two separate units, which results in error propagation, and thus, sub-optimal performance. In this paper, we propose a simple yet effective novel framework for NLU where the parameters of the IC and the NER models are jointly trained in a consolidated parameter space. Text semantic representations are obtained from popular pre-trained contextual language models, which are fine-tuned for our task, and these parameters are propagated to other deep neural layers in our framework leading to a faithful unified modelling of the IC and NER parameters. The overall framework results in a faithful parameter sharing when the training is underway, leading to a more coherent learning. Experiments on two public datasets, ATIS and SNIPS, show that our model outperforms other methods by a noticeable margin. On the SNIPS dataset, we obtain a 1.42% improvement in NER in terms of the F1 score, and 1% improvement in intent accuracy score. On ATIS, we achieve 1.54% improvement in intent accuracy score. We also present qualitative results to showcase the effectiveness of our model.
Music Education to Rescue Psychological Stress in Social Crisis Based on Fuzzy Prediction Algorithm

In order to alleviate human drowsiness in social crisis, it is necessary to predict the psychological rescue function of music education in social crisis and improve the accurate monitoring and analysis ability of the psychological rescue function of music education in social crisis; this paper puts forward a prediction model of psychological rescue function of music education in social crisis based on quantitative statistical analysis. The prediction and control model of the psychological rescue function of music education in social crisis is constructed, the fuzzy prediction algorithm is combined, the psychological rescue function characteristics of music education in social crisis are analyzed, and the descriptive statistical analysis model of the psychological rescue function of music education in social crisis is established. Through the fuzzy feature extraction method, the big data feature detection of the psychological rescue function of music education in social crisis is carried out, the statistical analysis model of the psychological rescue function of music education in social crisis is established, the dynamic analysis and prediction of the psychological rescue function of music education in social crisis are carried out by combining fuzzy information mining and adaptive learning methods, and the dynamic feature mining of the psychological rescue function of music education in social crisis is carried out by adopting the quantitative statistical feature analysis method. Statistical characteristics of the psychological rescue function of music education in social crisis are established, dynamic monitoring and feature prediction according to the analysis of the psychological rescue function of music education in social crisis are carried out, the ambiguity prediction and feature optimization judgment ability of the psychological rescue function of music education in social crisis are improved, and accurate prediction of the psychological rescue function of music education in social crisis based on the optimization statistical analysis results is carried out. The simulation results show that the statistical analysis ability and fuzzy judgment ability of using this method to predict the psychological rescue function of music education in social crisis are better, which improves the pertinence and effectiveness of music education.
Multiscale face recognition in cluttered backgrounds based on visual attention

The human vision system can efficiently recognize multiscale objects in cluttered backgrounds. The scheme can be achieved with a visual attention mechanism by concentrating visual resources to the saliency area while ignoring other task-irrelevant areas. However, in the computer vision community, when recognizing multiscale faces in cluttered backgrounds, object detection modules are necessary to locate face regions and reduce the influence of complex backgrounds on the recognition model, which inevitably increases the computational complexity. Motivated by the human vision system, this study proposes the attention developmental network to recognize multiscale faces without using face detectors. A top-down attention mechanism is used to teach the network to focus on the face areas and ignore the backgrounds. An attention-based synapse maintenance mechanism is also introduced to further suppress the background pixels and improve the accuracy of face recognition. Comparative experiments show that our method can attain at least 13% of accuracy improvement over bionic neural networks and ResNet-based recognition networks on the same model scale with less training epochs.
Deep Neural Network Enhanced Sampling-Based Path Planning in 3D Space

Robot path planning in 3D space is a challenging problem for its complex configuration. Sampling-based algorithms have gained great success in solving path planning problems in 3D space, but the quality of the initial path is not guaranteed and the convergence to the optimal solution is slow. To address these problems, in this article, we present a novel sampling-based path planning framework enhanced by the deep neural network (DNN) with applications to 3D space. In the proposed framework, we first train the DNN with a number of successful path planning cases in 3D space. Then the DNN is utilized to predict the promising region where the feasible path probably exists for a given path planning problem. This predicted promising region serves as a nonuniform sampling heuristic to bias the sampling process of the path planner. In this way, the path planner can focus on the promising region in the exploration and exploitation process so that the path planning speed gets accelerated. We conduct numerical simulations to evaluate the performance of the proposed algorithm and the results show that it can perform much better than conventional path planning algorithms. Furthermore, we also investigate the performance of different DNN architectures for path planning in 3D space. Note to Practitioners—In this work, we aim to provide an efficient learning-based method to accelerate the robot path planning process in 3D space. Conventional path planning algorithms need to perceive the environment first, and then implement a series of calculations such as collision checking and data storing to generate a feasible path. When facing complex and high-dimensional environments, they do not perform well. But the proposed neural network method in this article can predict the promising region where the feasible path exists for any given environment. This prediction result is used to guide the path planning process so that the algorithm performance can get significantly improved. Apart from sampling-based algorithms, the proposed neural network model can also be extended to other types of path planning algorithms.
Augmented Reality: Reflections at Thirty Years

Three decades ago in a small lab at Wright Patterson Air Force Base, the first functional augmented reality (AR) system was formally tested on groups of human subjects, each of them experiencing for the first time, virtual objects that were seamlessly merged into their perception of the real world. The system, known as the Virtual Fixtures platform, produced virtual overlays that felt physically real and were spatially registered with such precision, they could be used to significantly boost human performance in dexterous real-world tasks. Now three decades later, the principal investigator of that early effort reflects on the initial lessons learned, the unexpected delays in achieving widespread adoption of AR, and future prospects for the field as it goes mainstream.
AxRLWE: A Multilevel Approximate Ring-LWE Co-Processor for Lightweight IoT Applications

This work presents a multilevel approximation exploration undertaken on the Ring-Learning-with-Errors (R-LWE)-based public-key cryptographic (PKC) schemes that belong to quantum-resilient cryptography algorithms. Among the various quantum-resilient cryptography schemes proposed in the currently running NIST’s post-quantum cryptography (PQC) standardization plan, the lattice-based learning-with-error (LWE) schemes have emerged as the most viable and preferred class for the Internet of Things (IoT) applications due to their compact area and memory footprint compared to other alternatives. However, compared to the classical schemes used today, R-LWE is much harder a challenge to fit on embedded IoT (end-node) devices, due to their stricter resource constraints (lower area, memory, and energy budgets) as well as their limited computational capabilities. To the best of our knowledge, this is the first endeavor exploring the inherent approximate nature of the LWE problem to undertake a multilevel approximate R-LWE (AxRLWE) architecture with respective security estimates opt for lightweight IoT devices. Undertaking AxRLWE on field-programmable gate arrays (FPGAs), we benchmarked a 64% area reduction cost compared to earlier accurate R-LWE designs at the cost of reduced quantum security. For the application-specific integrated circuits (ASICs) with 45-nm CMOS technology, AxRLWE was benchmarked to fit well within the same area budget of a lightweight ECC processor and consume a third of energy compared to special class of R-Binary LWE (R-BLWE) designs being proposed for an IoT, with a better security level.