Reviewerly/exHarmony huggingface dataset:
Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes

Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted image often contains various semantic information, conventional two-stage approaches utilizing structural information often lead to the problem of unreliable structural prediction and ambiguous image texture generation. In this paper, we propose a Semantic Guidance and Evaluation Network (SGE-Net) to iteratively update the structural priors and the inpainted image in an interplay framework of semantics extraction and image inpainting. It utilizes semantic segmentation map as guidance in each scale of inpainting, under which location-dependent inferences are re-evaluated, and, accordingly, poorly-inferred regions are refined in subsequent scales. Extensive experiments on real-world images of mixed scenes demonstrated the superiority of our proposed method over state-of-the-art approaches, in terms of clear boundaries and photo-realistic textures.
ARTEMIS: A Collaborative Mixed-Reality System for Immersive Surgical Telementoring

Traumatic injuries require timely intervention, but medical expertise is not always available at the patient’s location. Despite recent advances in telecommunications, surgeons still have limited tools to remotely help inexperienced surgeons. Mixed Reality hints at a future where remote collaborators work side-by-side as if co-located; however, we still do not know how current technology can improve remote surgical collaboration. Through role-playing and iterative-prototyping, we identify collaboration practices used by expert surgeons to aid novice surgeons as well as technical requirements to facilitate these practices. We then introduce ARTEMIS, an AR-VR collaboration system that supports these key practices. Through an observational study with two expert surgeons and five novice surgeons operating on cadavers, we find that ARTEMIS supports remote surgical mentoring of novices through synchronous point, draw, and look affordances and asynchronous video clips. Most participants found that ARTEMIS facilitates collaboration despite existing technology limitations explored in this paper.
Multi-Scale Ship Detection From SAR and Optical Imagery Via A More Accurate YOLOv3

Deep learning detection methods use in ship detection remains a challenge, owing to the small scale of the objects and interference from complex sea surfaces. In addition, existing ship detection methods rarely verify the robustness of their algorithms on multisensor images. Thus, we propose a new improvement on the “you only look once” version 3 (YOLOv3) framework for ship detection in marine surveillance, based on synthetic aperture radar (SAR) and optical imagery. First, improved choices are obtained for the anchor boxes by using linear scaling based on the k-means++ algorithm. This addresses the difficulty in reflecting the advantages of YOLOv3's multiscale detection, as the anchor boxes of a single detection target type between different detection scales have small differences. Second, we add uncertainty estimators for the positioning of the bounding boxes by introducing a Gaussian parameter for ship detection into the YOLOv3 framework. Finally, four anchor boxes are allocated to each detection scale in the Gaussian-YOLO layer instead of three as in the default YOLOv3 settings, as there are wide disparities in an object's size and direction in remote sensing images with different resolutions. Applying the proposed strategy to "YOLOv3-spp” and "YOLOv3-tiny,” the results are enhanced by 2%-3%. Compared with other models, the improved-YOLOv3 has the highest average precision on both the optical (93.56%) and SAR (95.52%) datasets. The improved-YOLOv3 is robust, even in the context of a mixed dataset of SAR and optical images comprising images from different satellites and with different scales.
Solar Powered Multi-Controlled Smart Wheelchair for Disabled: Development and Features

As per the report presented by the World Health Organization, it is well aware that 15% of the total world’s population is physically challenged. Accessibility of health care services is limited to people with physical disabilities. The utilization of battery powered wheelchairs with excellent navigational capabilities is one of the extraordinary strides towards the incorporation of severely physically and mentally challenged people. Motion, movement and localization are significant issues for the blind, paraplegic and handicapped people who are accompanied by eminent tiresome work. There exist different systems to override the problems described, allowing the end-user to perform safe movements and complete certain daily life tasks. Considering the said issues as a motivation, this work presents the design and development of Solar Powered Multi-Controller Smart Wheelchair. The developed smart wheelchair uses eye blink sensor to steer the wheelchair for quadriplegia patient along with Joystick and Keypad module for several kinds of disabilities. In addition, more liberty is provided to the disabled person by using additional sensors such as heartbeat sensor and a temperature sensor which continuously monitors the health condition of the patient. Additionally, a urine level indicator is also used to avoid inconvenience to the patient. If the patient falls down along with a wheelchair, a fall detection system in the wheelchair detects the same. All the detail can be shared with hospital staff and the patient’s guardian during a contingency condition, so that the staffs and guardians can take immediate actions. The safety of the patient and the wheelchair with respect to the incorporation of solar power is highly given priority during this system design.
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes

Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models are available at https://github.com/ChiCheng123/PedHunter.
Distributed Ledger Technology for eHealth Identity Privacy: State of The Art and Future Perspective

Electronic healthcare (eHealth) identity management (IdM) is a pivotal feature in the eHealth system. Distributed ledger technology (DLT) is an emerging technology that can achieve agreements of transactional data states in a decentralized way. Building identity management systems using Blockchain can enable patients to fully control their own identity and provide increased confidence in data immutability and availability. This paper presents the state of the art of decentralized identity management using Blockchain and highlights the possible opportunities for adopting the decentralized identity management approaches for future health identity systems. First, we summarize eHealth identity management scenarios. Furthermore, we investigate the existing decentralized identity management solutions and present decentralized identity models. In addition, we discuss the current decentralized identity projects and identify new challenges based on the existing solutions and the limitations when applying it to healthcare as a particular use case.
Human Activity Recognition Based on Improved Bayesian Convolution Network to Analyze Health Care Data Using Wearable IoT Device

In the current scenario, it is significant to design active learning paradigms for analyzing human activities using Wearable Internet of Things (W-IoT) sensors for health parameter analysis. Further, in the healthcare sector, data collection using decision-making tools uses wearable sensors for monitoring using Cloud assisted Internet of Things (IoT). Although several conventional algorithms and deep learning models show promising results in sensor data analysis for recognizing human behaviors, the evaluation of their ambiguity in decision-making is still difficult and several conventional systems are more complex. Due to the restricted computing capacity, low-power W-IoT devices need an optimized network to manage the healthcare data effectively and efficiently for reliable analysis. Hence, a new Human Activity Recognition based on Improved Bayesian Convolution Network (IBCN)has been proposed which allows each smart system to download data via either traditional Radio Frequency (RF) communication or low power back dispersion communications with cloud assistance. In IBCN, A distribution of the model's latent variable is designed and the features are extracted using convolution layers, the performance of the W-IoT has been improved by combining a variable autoencoder with a standard deep net classifier. Furthermore, the Bayesian network helps to address the security issues using Enhanced deep learning (EDL) design with an effective offloading strategy. The experimental results show that the data collected from the wearable IoT sensor is sensitive to various sources of uncertainty, i.e. aleatoric and epistemic, as especially named noise and reliability. Furthermore, lab-scale experimental analysis on patient's health data classification accuracy has been considerably developed using IBCN than conventional design as namedCognitive radio (CR) learning, deep learning-based sensor activity recognition (DL-SAR) and Cloud-assisted Agent-based Smart home Environment (CASE).
Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data, collected from various data providers. Then, ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process. In this study, the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers, where IoT data is encrypted and recorded in a distributed ledger. The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts. To examine the performance of the proposed method, it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set (BCWD) and Heart Disease Data Set (HDD) from UCI AI repository. The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.
Salient Object Detection in the Distributed Cloud-Edge Intelligent Network

Intelligent network is crucial in the building of telecom networks because it utilizes artificial intelligent technologies to improve the performance. Salient object detection has increasingly attracted interest from intelligent network research since estimating human attention to objects is a crucial step in various surveillance applications. However, the computational-consuming and memory-consuming detection model is still less effective when it is deployed only either on the cloud or on the edge. In this article, we propose a specially-designed cloud-edge distributed framework for salient object detection based on the intelligent network. This framework can overcome the difficulty to transmit massive data in the cloud-only deployment scheme, as well as the difficulty to analyze massive data in the edge-only deployment scheme. However, the traditional cloud-edge distributed schemes are unsuitable to salient object detection task because of two challenges: 1) balance between the within-semantic knowledge and cross-semantic knowledge for the model training in different servers; 2) contradiction between extracting the semantic knowledge with global contextual information and local detailed information. To tackle the first challenge, our framework enables a hierarchical information allocation strategy in the cloud. It can prompt the salient object detection model in the edge to learn more from the similar scenes or semantics with where the edge server is located, while preserving the generalization ability of the model in the different scenes. To address the second challenge, our framework proposes a novel pyramidal deep learning model. It can effectively capture the global contextual features of the salient object, while preserving its local detailed features. The extensive experiments performed on six commonly- used public datasets can demonstrate the effectiveness of our framework and its superiority to 11 state-of-the-art approaches.
A Parallel Multi-Verse Optimizer for Application in Multilevel Image Segmentation

Multi-version optimizer (MVO) inspired by the multi-verse theory is a new optimization algorithm for challenging multiple parameter optimization problems in the real world. In this paper, a novel parallel multi-verse optimizer (PMVO) with the communication strategy is proposed. The parallel mechanism is implemented to randomly divide the initial solutions into several groups, and share the information of different groups after each fixed iteration. This can significantly promote the cooperation individual of MVO algorithm, and reduce the deficiencies that the original MVO is premature convergence, search stagnation and easily trap into local optimal search space. To confirm the performance of the proposed scheme, the PMVO algorithm was compared with the other well-known optimization algorithms, such as gray wolf optimizer (GWO), particle swarm optimization (PSO), multi-version optimizer (MVO), and parallel particle swarm optimization (PPSO) under CEC2013 test suite. The experimental results prove that the PMVO is superior to the other compared algorithms. In addition, PMVO is also applied to solve complex multilevel image segmentation problems based on minimum cross entropy thresholding. The application results appear that the proposed PMVO algorithm can achieve higher quality image segmentation compared to other similar algorithms.
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

Incorporating second-order curvature information into machine learning optimization algorithms can be subtle, and doing so naïvely can lead to high per-iteration costs associated with forming the Hessian and performing the associated linear system solve. To address this, we introduce ADAHESSIAN, a new stochastic optimization algorithm. ADAHESSIAN directly incorporates approximate curvature information from the loss function, and it includes several novel performance-improving features, including: (i) a fast Hutchinson based method to approximate the curvature matrix with low computational overhead; (ii) a spatial averaging to reduce the variance of the second derivative; and (iii) a root-mean-square exponential moving average to smooth out variations of the second-derivative across different iterations. We perform extensive tests on NLP, CV, and recommendation system tasks, and ADAHESSIAN achieves state-of-the-art results. In particular, we find that ADAHESSIAN: (i) outperforms AdamW for transformers by0.13/0.33 BLEU score on IWSLT14/WMT14, 2.7/1.0 PPLon PTB/Wikitext-103; (ii) outperforms AdamW for Squeeze-Bert by 0.41 points on GLUE; (iii) achieves 1.45%/5.55%higher accuracy on ResNet32/ResNet18 on Cifar10/ImageNetas compared to Adam; and (iv) achieves 0.032% better score than Adagrad for DLRM on the Criteo Ad Kaggle dataset. The cost per iteration of ADAHESSIANis comparable to first-order methods, and ADAHESSIAN exhibits improved robustness towards variations in hyperparameter values. The code for ADAHESSIAN is open-sourced and publicly-available [1].
Quantum Computing

Quantum computing, and to an even greater extent quantum technology, is changing the world. Quantum computing is not an evolution of classical computer science; it is actually a revolution that completely changes the computing paradigm. Quantum computers are based on the principles of quantum mechanics, such as superposition and entanglement, and they seek to boost computational power exponentially. Many problems that have until now been impossible to solve, in practical terms, might very well be able to be addressed by means of quantum computing. The fact is that at the present time quantum computing is influencing most business sectors and research fields, due to its various promising applications. To make such applications become reality, quantum algorithms must be specially coded for these extremely different computers. Although some well-known quantum algorithms already exist, the need for quantum software will increase dramatically in the next years. In that context, quantum software has to be produced in a more industrial and controlled way, i.e., aspects such as quality, delivery, project management, or evolution of quantum software must be addressed. We are sure that quantum computing will be the main driver for a new software engineering golden age during the present decade of the 2020s.
Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation

3D hand pose estimation is still far from a well-solved problem mainly due to the highly nonlinear dynamics of hand pose and the difficulties of modeling its inherent structural dependencies. To address this issue, we connect this structured output learning problem with the structured modeling framework in sequence transduction field. Standard transduction models like Transformer adopt an autoregressive connection to capture dependencies from previously generated tokens and further correlate this information with the input sequence in order to prioritize the set of relevant input tokens for current token generation. To borrow wisdom from this structured learning framework while avoiding the sequential modeling for hand pose, taking a 3D point set as input, we propose to leverage the Transformer architecture with a novel non-autoregressive structured decoding mechanism. Specifically, instead of using previously generated results, our decoder utilizes a reference hand pose to provide equivalent dependencies among hand joints for each output joint generation. By imposing the reference structural dependencies, we can correlate the information with the input 3D points through a multi-head attention mechanism, aiming to discover informative points from different perspectives, towards each hand joint localization. We demonstrate our model’s effectiveness over multiple challenging hand pose datasets, comparing with several state-of-the-art methods.
ASVspoof 2019: Spoofing Countermeasures for the Detection of Synthesized, Converted and Replayed Speech

The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensemble system submissions from 62 teams, all of which out-perform the two baseline systems, often by a substantial margin. Deeper analyses shows that performance is dominated by specific conditions involving either specific spoofing attacks or specific acoustic environments. While fusion is shown to be particularly effective for the logical access scenario involving speech synthesis and voice conversion attacks, participants largely struggled to apply fusion successfully for the physical access scenario involving simulated replay attacks. This is likely the result of a lack of system complementarity, while oracle fusion experiments show clear potential to improve performance. Furthermore, while results for simulated data are promising, experiments with real replay data show a substantial gap, most likely due to the presence of additive noise in the latter. This finding, among others, leads to a number of ideas for further research and directions for future editions of the ASVspoof challenge.
Automated radiology report generation using conditioned transformers

Radiology report writing in hospitals is a time-consuming task that also requires experience from the involved radiologists. This paper proposes a deep learning model to automatically generate radiology reports given a chest x-ray image from the public IU-Xray dataset. Our work consists of three stages: (1) Fine-tune a pre-trained Chexnet to predict specific tags from the image. (2) Calculate weighted semantic features from the predicted tag's pre-trained embeddings. (3) Condition a pre-trained GPT2 model on the visual and semantic features to generate the full medical reports. We analyze the generated reports using word-overlap metrics while also adding new meaningful semantic-based similarity metrics. The proposed model, which we call CDGPT2, surpassed most non-hierarchical recurrent models and transformer-based models in quantitative metrics while being considerably faster to train. Moreover, the model does not require a specific vocabulary and can be trained on different datasets without changing the architecture. Furthermore, we include a qualitative analysis from a radiologist from Egypt's national institute of cancer which showed that 61.6% of the generated reports on the test set were expertly written, and only 10% contained false information. We represent the first work to condition a pre-trained transformer on visual and semantic features to generate medical reports and to include semantic similarity metrics in the quantitative analysis of the generated reports.
Entanglement Negativity at Measurement-Induced Criticality

Universal multipartite entanglement is studied for a variety of measurement induced phase transitions, exact results are presented in a special case, and remarkable consistency is found in several different models away from this limit.