Reviewerly/exHarmony huggingface dataset:
Blockchain-based approach to create a model of trust in open and ubiquitous higher education

Currently, the training of the future work force presents challenging problems to higher education. This training, in the form of practical and theoretical knowledge can come from multiple platforms, channels and means, both formal and informal. In addition, it is quite difficult to assess the knowledge skill level that a student has acquired to optimize their chances for future employability. This, together with the need to still manage academic curricula on paper, the problems of confidence when validating these documents and contrasting them with real knowledge, etc., means that management in higher education requires revolutionary new tools. This work evaluates the benefits of the blockchain (or distributed ledger) technology and advocates a decentralised model of confidence for transactions based on an academic crypto currency. In this approach blockchain is used to manage transactions of content, teaching and competencies, assessed by consensus by students, trainers and employers, to eliminate once and for all the “gap” between the academic world and the working world. This paper aims to address the current challenges of an increasingly dispersed, open and ubiquitous higher education. The proposed model can be implemented in any training institution to adapt its teaching to the specific needs of professional profiles validated by employers in the sector. This model has been validated by means of a prototype with more than acceptable results.
A Systematic Review of Attitudes, Anxiety, Acceptance, and Trust Towards Social Robots

Abstract As social robots become more common, there is a need to understand how people perceive and interact with such technology. This systematic review seeks to estimate people’s attitudes toward, trust in, anxiety associated with, and acceptance of social robots; as well as factors that are associated with these beliefs. Ninety-seven studies were identified with a combined sample of over 13,000 participants and a standardized score was computed for each in order to represent the valence (positive, negative, or neutral) and magnitude (on a scale from 1 to − 1) of people’s beliefs about robots. Potential moderating factors such as the robots’ domain of application and design, the type of exposure to the robot, and the characteristics of potential users were also investigated. The findings suggest that people generally have positive attitudes towards social robots and are willing to interact with them. This finding may challenge some of the existing doubt surrounding the adoption of robotics in social domains of application but more research is needed to fully understand the factors that influence attitudes.
Simple security proof of twin-field type quantum key distribution protocol

Abstract Twin-field (TF) quantum key distribution (QKD) was conjectured to beat the private capacity of a point-to-point QKD link by using single-photon interference in a central measuring station. This remarkable conjecture has recently triggered an intense research activity to prove its security. Here, we introduce a TF-type QKD protocol which is conceptually simpler than the original proposal. It relies on the pre-selection of a global phase, instead of the post-selection of a global phase, which significantly simplifies its security analysis and is arguably less demanding experimentally. We demonstrate that the secure key rate of our protocol has a square-root improvement over the point-to-point private capacity, as conjectured by the original TF QKD.
Distributed Differential Privacy via Shuffling

We consider the problem of designing scalable, robust protocols for computing statistics about sensitive data. Specifically, we look at how best to design differentially private protocols in a distributed setting, where each user holds a private datum. The literature has mostly considered two models: the "central" model, in which a trusted server collects users' data in the clear, which allows greater accuracy; and the "local" model, in which users individually randomize their data, and need not trust the server, but accuracy is limited. Attempts to achieve the accuracy of the central model without a trusted server have so far focused on variants of cryptographic MPC, which limits scalability. In this paper, we initiate the analytic study of a shuffled model for distributed differentially private algorithms, which lies between the local and central models. This simple-to-implement model, a special case of the ESA framework of [Bittau et al., '17], augments the local model with an anonymous channel that randomly permutes a set of user-supplied messages. For sum queries, we show that this model provides the power of the central model while avoiding the need to trust a central server and the complexity of cryptographic secure function evaluation. More generally, we give evidence that the power of the shuffled model lies strictly between those of the central and local models: for a natural restriction of the model, we show that shuffled protocols for a widely studied selection problem require exponentially higher sample complexity than do central-model protocols.
Single image portrait relighting

Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph. Professional photographers will carefully control the lighting in their studio to manipulate the appearance of their subject, while consumer photographers are usually constrained to the illumination of their environment. Though prior works have explored techniques for relighting an image, their utility is usually limited due to requirements of specialized hardware, multiple images of the subject under controlled or known illuminations, or accurate models of geometry and reflectance. To this end, we present a system for portrait relighting: a neural network that takes as input a single RGB image of a portrait taken with a standard cellphone camera in an unconstrained environment, and from that image produces a relit image of that subject as though it were illuminated according to any provided environment map. Our method is trained on a small database of 18 individuals captured under different directional light sources in a controlled light stage setup consisting of a densely sampled sphere of lights. Our proposed technique produces quantitatively superior results on our dataset's validation set compared to prior works, and produces convincing qualitative relighting results on a dataset of hundreds of real-world cellphone portraits. Because our technique can produce a 640 $\times$ 640 image in only 160 milliseconds, it may enable interactive user-facing photographic applications in the future.
An image encryption method based on chaos system and AES algorithm

In this paper, a novel image encryption algorithm is proposed based on the combination of the chaos sequence and the modified AES algorithm. In this method, the encryption key is generated by Arnold chaos sequence. Then, the original image is encrypted using the modified AES algorithm and by implementing the round keys produced by the chaos system. The proposed approach not only reduces the time complexity of the algorithm but also adds the diffusion ability to the proposed algorithm, which make the encrypted images by the proposed algorithm resistant to the differential attacks. The key space of the proposed method is large enough to resist the brute-force attacks. This method is so sensitive to the initial values and input image so that the small changes in these values can lead to significant changes in the encrypted image. Using statistical analyses, we show that this approach can protect the image against the statistical attacks. The entropy test results illustrate that the entropy values are close to the ideal, and hence, the proposed algorithm is secure against the entropy attacks. The simulation results clarify that the small changes in the original image and key result in the significant changes in the encrypted image and the original image cannot be accessed.
Generating Radiology Reports via Memory-driven Transformer

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment.Writing imaging reports is time-consuming and can be error-prone for inexperienced radiologists.Therefore, automatically generating radiology reports is highly desired to lighten the workload of radiologists and accordingly promote clinical automation, which is an essential task to apply artificial intelligence to the medical domain.In this paper, we propose to generate radiology reports with memorydriven Transformer, where a relational memory is designed to record key information of the generation process and a memory-driven conditional layer normalization is applied to incorporating the memory into the decoder of Transformer.Experimental results on two prevailing radiology report datasets, IU X-Ray and MIMIC-CXR, show that our proposed approach outperforms previous models with respect to both language generation metrics and clinical evaluations.Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge.Further analyses also demonstrate that our approach is able to generate long reports with necessary medical terms as well as meaningful image-text attention mappings. 1† Corresponding author. 1 Our code and the best performing models are released at https://github.com/cuhksz-nlp/R2Gen.Findings The lungs are clear bilaterally.Specifically, no evidence of focal consolidation, or pleural effusion.Minimal right basilar subsegmental atelectasis noted.Cardio mediastinal silhouette is unremarkable.Tortuosity of the thoracic aorta noted.Impression No acute cardiopulmonary abnormality.
Scaling static analyses at Facebook

Key lessons for designing static analyses tools deployed to find bugs in hundreds of millions of lines of code.
The Progress of Human Pose Estimation: A Survey and Taxonomy of Models Applied in 2D Human Pose Estimation

Human pose estimation localizes body keypoints to accurately recognizing the postures of individuals given an image. This step is a crucial prerequisite to multiple tasks of computer vision which include human action recognition, human tracking, human-computer interaction, gaming, sign languages, and video surveillance. Therefore, we present this survey article to fill the knowledge gap and shed light on the researches of 2D human pose estimation. A brief introduction is followed by classifying it as a single or multi-person pose estimation based on the number of people needed to be tracked. Then gradually the approaches used in human pose estimation are described before listing some applications and also flaws facing in pose estimation. Following that, a center of attention is given on briefly discussing researches with a significant effect on human pose estimation and examine the novelty, motivation, architecture, the procedures (working principles) of each model together with its practical application and drawbacks, datasets implemented, as well as the evaluation metrics used to evaluate the model. This review is presented as a baseline for newcomers and guides researchers to discover new models by observing the procedure and architecture flaws of existing researches.
Accuracy vs. Efficiency

A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific data set? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. However, most of them take a long time to find the optimal architecture due to the huge search space and the lengthy training process needed to evaluate each candidate. In addition, most of them aim at accuracy only and do not take into consideration the hardware that will be used to implement the architecture. This will potentially lead to excessive latencies beyond specifications, rendering the resulting architectures useless. To address both issues, in this paper we use Field Programmable Gate Arrays (FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely FNAS, which will provide an optimal neural architecture with latency guaranteed to meet the specification. In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency. Experimental results on common data set such as ImageNet show that in the cases where the state-of-the-art generates architectures with latencies 7.81× longer than the specification, those from FNAS can meet the specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to 11.13× speedup for the search process. To the best of the authors' knowledge, this is the very first hardware aware NAS.
Use of apps in the COVID-19 response and the loss of privacy protection

Mobile apps provide a convenient source of tracking and data collection to fight against the spread of COVID-19. We report our analysis of 50 COVID-19-related apps, including their use and their access to personally identifiable information, to ensure that the right to privacy and civil liberties are protected.
Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening.We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening.Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.
CLUE: A Chinese Language Understanding Evaluation Benchmark

The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP).The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages.To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark.CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text.To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total).We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU.
An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism

Abstract The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the “survival of the fittest” (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The differential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the fitness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst fitness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with differential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artificial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy.
Smote-variants: A python implementation of 85 minority oversampling techniques

Imbalanced classification problems are definitely around He and Gracia (2009), and a successful approach to avoid the overfitting of majority classes is the synthetic generation of minority training samples Fernandez et al. (2018). Despite the large number of minority oversampling algorithms proposed, open source implementations are available for only a handful of techniques. The package smote-variants provides a Python implementation of 85 oversampling techniques to boost the applications and development in the field of imbalanced learning. The source code, documentation and examples are available in the GitHub repository http://github.com/gykovacs/smote_variants/.
A Decoupled Blockchain Approach for Edge-Envisioned IoT-Based Healthcare Monitoring

The in-house health monitoring sensors form a large network of Internet of things (IoT) that continuously monitors and sends the data to the nearby devices or server. However, the connectivity of these IoT-based sensors with different entities leads to security loopholes wherein the adversary can exploit the vulnerabilities due to the openness of the data. This is a major concern especially in the healthcare sector where the change in data values from sensors can change the course of diagnosis which can cause severe health issues. Therefore, in order to prevent the data tempering and preserve the privacy of patients, we present a decoupled blockchain-based approach in the edge-envisioned ecosystem. This approach leverages the nearby edge devices to create the decoupled blocks in blockchain so as to securely transmit the healthcare data from sensors to the edge nodes. The edge nodes then transmit and store the data at the cloud using the incremental tensor-based scheme. This helps to reduce the data duplication of the huge amount of data transmitted in the large IoT healthcare network. The results show the effectiveness of the proposed approach in terms of the block preparation time, header generation time, tensor reduction ratio, and approximation error.
Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies

Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing.This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution.The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA's distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification.A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing.DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event.Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks.In this paper, we propose TEXT2EVENT, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner.Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning.Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.