Reviewerly/exHarmony huggingface dataset:
A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions

The extensive growth of data in the health domain has increased the utility of Deep Learning in health. Deep learning is a highly advanced successor of artificial neural networks, having powerful computing ability. Due to the availability of fast data storage and hardware parallelism its popularity grows in the last five years. This in article presents a comprehensive literature review of research deploying deep learning medical imaging and medical NLP including tasks, pipelines, and challenges. In this work, we have presented an extensive survey of deep learning architecture deployed in the fields of medical imaging and medical natural language processing. This paper helps in identifying suitable combination of Deep learning, Natural language processing and medical imaging to enhance diagnosis. We have highlighted the major challenges in deploying deep learning in medical imaging and medical natural language processing. All the results are presented in pictorial form. This survey is very helpful for novices working in the area of health informatics.
648 Hilbert-space dimensionality in a biphoton frequency comb: entanglement of formation and Schmidt mode decomposition

Qubit entanglement is a valuable resource for quantum information processing, where increasing its dimensionality provides a pathway towards higher capacity and increased error resilience in quantum communications, cluster computation and quantum phase measurements. Time-frequency entanglement, a continuous variable subspace, enables the high-dimensional encoding of multiple qubits per particle, bounded only by the spectral correlation bandwidth and readout timing jitter. Extending from a dimensionality of two in discrete polarization variables, here we demonstrate a hyperentangled, mode-locked, biphoton frequency comb with a time-frequency Hilbert space dimensionality of at least 648. Hong-Ou-Mandel revivals of the biphoton qubits are observed with 61 time-bin recurrences, biphoton joint spectral correlations over 19 frequency-bins, and an overall interference visibility of the high-dimensional qubits up to 98.4%. We describe the Schmidt mode decomposition analysis of the high-dimensional entanglement, in both time- and frequency-bin subspaces, not only verifying the entanglement dimensionality but also examining the time-frequency scaling. We observe a Bell violation of the high-dimensional qubits up to 18.5 standard deviations, with recurrent correlation-fringe Clauser-Horne-Shimony-Holt S-parameter up to 2.771. Our biphoton frequency comb serves as a platform for dense quantum information processing and high-dimensional quantum key distribution.
An improved Tiny YOLOv3 for real-time object detection

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Quantum Ranging with Gaussian Entanglement

It is well known that entanglement can benefit quantum information processing tasks. Quantum illumination, when first proposed, is surprising as entanglement's benefit survives entanglement-breaking noise. Since then, many efforts have been devoted to study quantum sensing in noisy scenarios. The applicability of such schemes, however, is limited to a binary quantum hypothesis testing scenario. In terms of target detection, such schemes interrogate a single polarization-azimuth-elevation-range-Doppler resolution bin at a time, limiting the impact to radar detection. We resolve this binary-hypothesis limitation by proposing a quantum ranging protocol enhanced by entanglement. By formulating a ranging task as a multiary hypothesis testing problem, we show that entanglement enables a 6-dB advantage in the error exponent against the optimal classical scheme. Moreover, the proposed ranging protocol can also be utilized to implement a pulse-position modulated entanglement-assisted communication protocol. Our ranging protocol reveals entanglement's potential in general quantum hypothesis testing tasks and paves the way towards a quantum-ranging radar with a provable quantum advantage.
Sampo-UI: A full stack JavaScript framework for developing semantic portal user interfaces

This paper presents a new software framework, Sampo-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the Sampo-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. Sampo-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web.
Continual learning in sensor-based human activity recognition: An empirical benchmark analysis

Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an important question arises: how can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed. This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain. To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets. Then, we present a comprehensive empirical analysis of their computational cost and of their effectiveness of tackling HAR specific challenges (i.e., sensor noise and labels' scarcity). The presented results uncover useful insights on their applicability and suggest future research directions for HAR systems.
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?

Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks.The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT (Alsentzer et al., 2019).While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, nondeidentified EHR with which they might train a BERT model (or similar).Would it be safe to release the weights of such a model if they did?In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT.Specifically, we attempt to recover patient names and conditions with which they are associated.We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR.However, more sophisticated "attacks" may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available. 1
Import test questions into Moodle LMS

The purpose of the study is to highlight the theoretical and methodological aspects of preparing the test questions of the most common types in the form of text files for further import into learning management system (LMS) Moodle. The subject of the research is the automated filling of the Moodle LMS test database.The objectives of the study: to analyze the import files of test questions, their advantages and disadvantages; to develop guidelines for the preparation of test questions of common types in the form of text files for further import into Moodle LMS.The action algorithms for importing questions and instructions for submitting question files in such formats as Aiken, GIFT, Moodle XML, “True/False” questions, “Multiple Choice” (one of many and many of many), “Matching”, with an open answer – “Numerical” or “Short answer” and “Essay” are offered in this article. The formats for submitting questions, examples of its designing and developed questions were demonstrated in view mode in Moodle LMS.
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL methods perform best on iconic images, and struggle on complex scene images with many objects. Analyzing contrastive SSL methods shows that they have poor visual grounding and receive poor supervisory signal when trained on scene images. We propose Contrastive Attention-Supervised Tuning (CAST) to overcome these limitations. CAST uses unsupervised saliency maps to intelligently sample crops, and to provide grounding supervision via a Grad-CAM attention loss. Experiments on COCO show that CAST significantly improves the features learned by SSL methods on scene images, and further experiments show that CAST-trained models are more robust to changes in backgrounds. Our code is available at https://github.com/salesforce/CAST/.
Interval-valued belief entropies for Dempster–Shafer structures

In practical application problems, the uncertainty of an unknown object is often very difficult to accurately determine, so Yager proposed the interval-valued entropies for Dempster-Shafer structures, which is based on Dempster-Shafer structures and classic Shannon entropy and is an interval entropy model. Based on Dempster-Shafer structures and classic Shannon entropy, the interval uncertainty of an unknown object is determined, which provides reference for theoretical research and provides help for industrial applications. Although the interval-valued entropies for Dempster-Shafer structures can solve the uncertainty interval of an object very efficiently, its application scope is only a traditional probability space. How to extend it to the evidential environment is still an open issue. This paper proposes interval-valued belief entropies for Dempster-Shafer structures, which is an extension of the interval-valued entropies for Dempster-Shafer structures. When the belief entropy degenerates to the classic Shannon entropy, the interval-valued belief entropies for Dempster-Shafer structures will degenerate into the interval-valued entropies for Dempster-Shafer structures. Numerical examples are applied to verify the validity of the interval-valued belief entropies for Dempster-Shafer structures. The experimental results demonstrate that the proposed entropy can obtain the interval uncertainty value of a given uncertain object successfully and make decision effectively.
BLOSOM: BLOckchain technology for Security Of Medical records

Today, information security world is witnessing frequent attacks on electronic records by the naive and professional hackers and crackers. Security of Patient’s Electronic Medical Record (EMR) in Hospital Management System is of paramount importance and so warrants immediate attention. In this research work, BlockChain containers running on multiple ports are proposed to be used for holding patient’s medical records. To perform this task effectively, a BlockChain framework named as “Medichain” was developed from scratch that contains all basic functionalities of a BlockChain to secure patient’s data. Each block of the BlockChain keeps a list of records of patient details. Users would be provided with an option of uploading the file containing the list of records as a JSON file, in a decisive, distributed and decentralized network. The proposed BlockChain Algorithm, consisting of cryptographic hash of the records, proof of work and a Merkle tree formulation, were simulated in Python. Results have been positive and encouraging.
Policy gradient assisted MAP-Elites

Quality-Diversity optimization algorithms such as MAP-Elites, aim to generate collections of both diverse and high-performing solutions to an optimization problem. MAP-Elites has shown promising results in a variety of applications. In particular in evolutionary robotics tasks targeting the generation of behavioral repertoires that highlight the versatility of robots. However, for most robotics applications MAP-Elites is limited to using simple open-loop or low-dimensional controllers. Here we present Policy Gradient Assisted MAP-Elites (PGA-MAP-Elites), a novel algorithm that enables MAP-Elites to efficiently evolve large neural network controllers by introducing a gradient-based variation operator inspired by Deep Reinforcement Learning. This operator leverages gradient estimates obtained from a critic neural network to rapidly find higher-performing solutions and is paired with a traditional genetic variation to maintain a divergent search behavior. The synergy of these operators makes PGA-MAP-Elites an efficient yet powerful algorithm for finding diverse and high-performing behaviors. We evaluate our method on four different tasks for building behavioral repertoires that use deep neural network controllers. The results show that PGA-MAP-Elites significantly improves the quality of the generated repertoires compared to existing methods.
Speech Recognition using Machine Learning

Speech recognition is one of the fastest-growing engineering technologies. It has several applications in different areas, and provides many potential benefits. A lot of people are unable to communicate due to language barriers. We aim to reduce this barrier via our project, which was designed and developed to achieve systems in particular cases to provide significant help so people can share information by operating a computer using voice input. This project keeps that factor in mind, and an effort is made to ensure our project is able to recognize speech and convert input audio into text; it also enables a user to perform file operations like Save, Open, or Exit from voice-only input. We design a system that can recognize the human voice as well as audio clips, and translate between English and Hindi. The output is in text form, and we provide options to convert audio from one language to the other. Going forward, we expect to add functionality that provides dictionary meanings for Hindi and English words. Neural machine translation is the primary algorithm used in the industry to perform machine translation. Two recurrent neural networks used in tandem to construct an encoder-decoder structure are the architecture behind neural machine translation. This work on speech recognition starts with an introduction to the technology and the applications used in different sectors. Part of the report is based on software developments in speech recognition.
Augmented reality head-mounted display–based incision planning in cranial neurosurgery: a prospective pilot study

Monitor and wand-based neuronavigation stations (MWBNSs) for frameless intraoperative neuronavigation are routinely used in cranial neurosurgery. However, they are temporally and spatially cumbersome; the OR must be arranged around the MWBNS, at least one hand must be used to manipulate the MWBNS wand (interrupting a bimanual surgical technique), and the surgical workflow is interrupted as the surgeon stops to "check the navigation" on a remote monitor. Thus, there is need for continuous, real-time, hands-free, neuronavigation solutions. Augmented reality (AR) is poised to streamline these issues. The authors present the first reported prospective pilot study investigating the feasibility of using the OpenSight application with an AR head-mounted display to map out the borders of tumors in patients undergoing elective craniotomy for tumor resection, and to compare the degree of correspondence with MWBNS tracing.Eleven consecutive patients undergoing elective craniotomy for brain tumor resection were prospectively identified and underwent circumferential tumor border tracing at the time of incision planning by a surgeon wearing HoloLens AR glasses running the commercially available OpenSight application registered to the patient and preoperative MRI. Then, the same patient underwent circumferential tumor border tracing using the StealthStation S8 MWBNS. Postoperatively, both tumor border tracings were compared by two blinded board-certified neurosurgeons and rated as having an excellent, adequate, or poor correspondence degree based on a subjective sense of the overlap. Objective overlap area measurements were also determined.Eleven patients undergoing craniotomy were included in the study. Five patient procedures were rated as having an excellent correspondence degree, 5 had an adequate correspondence degree, and 1 had poor correspondence. Both raters agreed on the rating in all cases. AR tracing was possible in all cases.In this small pilot study, the authors found that AR was implementable in the workflow of a neurosurgery OR, and was a feasible method of preoperative tumor border identification for incision planning. Future studies are needed to identify strategies to improve and optimize AR accuracy.
An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.
WINNet: Wavelet-Inspired Invertible Network for Image Denoising

Image denoising aims to restore a clean image from an observed noisy one. Model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learning-based approaches. The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network. The network architecture of LINNs is inspired by the lifting scheme in wavelets. LINNs are used to learn a non-linear redundant transform with perfect reconstruction property to facilitate noise removal. The denoising network implements a sparse coding process for denoising. The noise estimation network estimates the noise level from the input image which will be used to adaptively adjust the soft-thresholds in LINNs. The forward transform of LINNs produces a redundant multi-scale representation for denoising. The denoised image is reconstructed using the inverse transform of LINNs with the denoised detail channels and the original coarse channel. The simulation results show that the proposed WINNet method is highly interpretable and has strong generalization ability to unseen noise levels. It also achieves competitive results in the non-blind/blind image denoising and in image deblurring.
Intelligent Edge-Based Recommender System for Internet of Energy Applications

Preserving energy in households and office buildings is a significant challenge, mainly due to the recent shortage of energy resources, the uprising of the current environmental problems, and the global lack of utilizing energy-saving technologies. Not to mention, within some regions, COVID-19 social distancing measures have led to a temporary transfer of energy demand from commercial and urban centers to residential areas, causing an increased use and higher charges, and in turn, creating economic impacts on customers. Therefore, the marketplace could benefit from developing an internet of things (IoT) ecosystem that monitors energy consumption habits and promptly recommends action to facilitate energy efficiency. This paper aims to present the full integration of a proposed energy efficiency framework into the Home-Assistant platform using an edge-based architecture. End-users can visualize their consumption patterns as well as ambient environmental data using the Home-Assistant user interface. More notably, explainable energy-saving recommendations are delivered to end-users in the form of notifications via the mobile application to facilitate habit change. In this context, to the best of the authors' knowledge, this is the first attempt to develop and implement an energy-saving recommender system on edge devices. Thus, ensuring better privacy preservation since data are processed locally on the edge, without the need to transmit them to remote servers, as is the case with cloudlet platforms.