Reviewerly/exHarmony huggingface dataset:
Blockchain, health disparities and global health

Health disparities remain vast around the world and are perpetuated by error-prone information technology systems, administrative inefficiencies and wasteful global health spending. Blockchain technology is a novel, distributed peer-to-peer ledger technology that uses unique, immutable and time-stamped blocks of records or sets of data that are linked as chains through cryptography to more reliably and transparently store and transfer data. Various industries have successfully leveraged blockchain technology to disintermediate and reduce costs, but its use in healthcare and global health has remained limited. In this narrative review, we describe blockchain technology and elaborate on the experiences and opportunities for leveraging blockchain within global health in terms of cryptocurrencies and health financing, supply chain management, health records, identification and verification, telehealth and misinformation. We conclude each section with an analysis of the restrictions imposed by the COVID-19 pandemic to highlight blockchain’s unique opportunities for improving healthcare services and access to care during future pandemics or natural disasters.
Differential Morphed Face Detection Using Deep Siamese Networks

Although biometric facial recognition systems are fast becoming part of security applications, these systems are still vulnerable to morphing attacks, in which a facial reference image can be verified as two or more separate identities. In border control scenarios, a successful morphing attack allows two or more people to use the same passport to cross borders. In this paper, we propose a novel differential morph attack detection framework using a deep Siamese network. To the best of our knowledge, this is the first research work that makes use of a Siamese network architecture for morph attack detection. We compare our model with other classical and deep learning models using two distinct morph datasets, VISAPP17 and MorGAN. We explore the embedding space generated by the contrastive loss using three decision making frameworks using Euclidean distance, feature difference and a support vector machine classifier, and feature concatenation and a support vector machine classifier.
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh

A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred. DSLR can easily render this kind of effect naturally. However, due to the limitation of sensors, smartphones cannot capture images with depth-of-field effects directly. In this paper, we propose a novel generator called Glass-Net, which generates bokeh images not relying on complex hardware. Meanwhile, the GAN-based method and perceptual loss are combined for rendering a realistic bokeh effect in the stage of finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in our network, which ensures our tflite model with IN can be accelerated on smartphone GPU. Experiments show that our method is able to render a high-quality bokeh effect and process one $1024 \times 1536$ pixel image in 1.9 seconds on all smartphone chipsets. This approach ranked First in AIM 2020 Rendering Realistic Bokeh Challenge Track 1 \& Track 2.
LBC-IoT: Lightweight Block Cipher for IoT Constraint Devices

With the new era of the Internet of Things (IoT) technology, many devices with limited resources are utilized.Those devices are susceptible to a signi cant number of new malware and other risks emerging rapidly.One of the most appropriate methods for securing those IoT applications is cryptographic algorithms, as cryptography masks information by eliminating the risk of collecting any meaningful information patterns.This ensures that all data communications are private, accurate, authenticated, authorized, or nonrepudiated.Since conventional cryptographic algorithms have been developed speci cally for devices with limited resources; however, it turns out that such algorithms are not ideal for IoT restricted devices with their current con guration.Therefore, lightweight block ciphers are gaining popularity to meet the requirements of low-power and constrained devices.A new ultra-lightweight secret-key block-enciphering algorithm named "LBC-IoT" is proposed in this paper.The proposed block length is 32-bit supporting key lengths of 80-bit, and it is mainly based on the Feistel structure.Energy-ef cient cryptographic features in "LBC-IoT" include the use of simple functions (shift, XOR) and small rigid substitution boxes (4-bit-S-boxes).Besides, it is immune to different types of attacks such as linear, differential, and side-channel as well as exible in terms of implementation.Moreover, LBC-IoT achieves reasonable performance in both hardware and software compared to other recent algorithms.LBC-IoT's hardware implementation results are very promising (smallest ever area "548" GE) and competitive with today's leading lightweight ciphers.LBC-IoT is also ideally suited for ultra-restricted devices such as RFID tags.
Effective and diverse POI recommendations through complementary diversification models

Nowadays, recommender systems play an important role in several Location-Based Social Networks (LBSNs). The current advances have considered the trade-off between accuracy and diversity to help users to discover and explore new points-of-interest (POI). However, differently from traditional recommendation scenarios, other equally relevant dimensions (e.g., social and geographical user information) have to be considered to understand how the characteristics of services offered by each POI fit the user needs. Specifically, this work sheds light upon naive failures introduced by traditional recommendation methods while they handle this trade-off between diversity and accuracy in POI recommendations. We hypothesize that some efforts on POI recommendations somehow are deviating from basic learnings from the area. In this context, this work addresses four characteristics inherent to the POI domain that previous efforts have failed to recognize: (1) POI categories and locations are complementary dimensions of diversification that should be simultaneously addressed; (2) Diversity is a complex concept that should be modeled by distinct and non-orthogonal models; (3) Distinct users have different biases and willingness to move to fulfill their needs; (4) POI recommendation is a multi-objective task. In order to demonstrate the gains of properly addressing these aspects, we also propose DisCovER, a straightforward re-ordering method that linearly combines geographical and categorical diversification. DisCovER results demonstrate that even simple strategies to exploit simultaneously these complementary dimensions can increase diversification while keeping accuracy high. Differently from state-of-the-art diversification methods, DisCovER does not penalize any quality dimension in favor of others. It allows us to discuss future directions towards more robust user modeling and preference elicitation in POI domains.
Hybrid collaborative recommendation of co-embedded item attributes and graph features

In recent decades, personalized recommendation systems have attracted much attention from multiple disciplines for recommending interested products and services to users. Recommendation accentuates both the importance of feature learning tasks and the challenges posed by the sparsity of rating matrix. A common method for addressing the sparsity problem is to extend the feature space by the attributes of users and/or items. However, there are two main drawbacks in most existing recommendation methods. The first is the high computational cost of most existing recommendation models when using additional information from users and/or items to expand the feature space. The second problem is that it is difficult to obtain user additional information due to the high cost of acquiring tag knowledge and the increase in user privacy awareness. In this paper, we propose a novel and simple model to address the abovementioned issues, which employs a semi-autoencoder to co-embed the attributes and the graph features of the items for rating prediction (short for Item-Agrec). More specifially, a semi-autoencoder is introduced to learn the hidden nonlinear features of items for achieving a low computational cost, and thus the proposed Item-Agrec model can flexibly use side information from different sources. Meanwhile, in the case that it is not easy to obtain the user's additional information, we take the item's graph features and attributes into consideration for improving the accuracy of recommendation. Experiments on several real-world datasets demonstrate the effectiveness of the proposed Item-Agrec compared with state-of-the-art attribute-aware and content-aware methods.
Campaigning for the greater good? – How persuasive messages affect the evaluation of contact tracing apps

The underlying scenario for adopting Covid-19 contact tracing apps is complex, given that users face potential surveillance, while expected health benefits are for the greater societal good rather than for themselves. To encourage adoption, many governments employ persuasive messages, highlighting either the app’s potential societal benefits (e.g., protecting the elderly) or high privacy standards. Responding to public media criticism, we compare the impact of two different persuasive messages, which focus on either societal benefits or privacy assurance. Emphasising societal benefits successfully stimulates this decision making without users losing sight of privacy risks. In contrast, emphasising privacy assurance diminishes users’ societal welfare concerns, as potential personal gains and losses largely frame their decision. These results are critical for developing and launching tracing apps, also beyond Covid-19, as well as for other applications of which widespread adoption is imperative to unlock potential societal level benefits, while requiring individual disclosure of sensitive data.
Achieving Efficient and Privacy-Preserving Set Containment Search Over Encrypted Data

Set containment search, which aims to retrieve all set records containing a specific query set, has received considerable attention. Meanwhile, due to the dramatic growth of data, data owners tend to outsource their data to the cloud and deploy the cloud server to offer the set containment search services. However, as the cloud server is not fully trustable and the data may be sensitive, a straightforward strategy for the data owners is to encrypt the data before outsourcing them. Although the encryption technique can preserve data privacy, it inevitably hinders the functionality of set containment search. Many existing studies on the set containment search over outsourced data still suffer from the search efficiency and security issues. In this article, aiming at the above issues, we propose an efficient and privacy-preserving set containment search scheme. Specifically, we first deploy an asymmetric scalar-product-preserving encryption technique to design a set containment/intersection encryption (SCIE-Enc) scheme. Then, we build a radix tree to represent the set records. Based on the radix tree and SCIE-Enc construction, we present our scheme that can achieve efficient set containment search while preserving the privacy of set records, query sets, and query results, as indicated in our security analysis and performance evaluation.
Deep Semisupervised Multitask Learning Model and Its Interpretability for Survival Analysis

Survival analysis is a commonly used method in the medical field to analyze and predict the time of events. In medicine, this approach plays a key role in determining the course of treatment, developing new drugs, and improving hospital procedures. Most of the existing work in this area has addressed the problem by making strong assumptions about the underlying stochastic process. However, these assumptions are usually violated in the real-world data. This paper proposed a semisupervised multitask learning (SSMTL) method based on deep learning for survival analysis with or without competing risks. SSMTL transforms the survival analysis problem into a multitask learning problem that includes semisupervised learning and multipoint survival probability prediction. The distribution of survival times and the relationship between covariates and outcomes were modeled directly without any assumptions. Semisupervised loss and ranking loss are used to deal with censored data and the prior knowledge of the nonincreasing trend of the survival probability. Additionally, the importance of prognostic factors is determined, and the time-dependent and nonlinear effects of these factors on survival outcomes are visualized. The prediction performance of SSMTL is better than that of previous models in settings with or without competing risks, and the effects of predictors are successfully described. This study is of great significance for the exploration and application of deep learning methods involving medical structured data and provides an effective deep-learning-based method for survival analysis with complex-structured clinical data.
Faster Stochastic Quasi-Newton Methods

Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems, such as training neural networks, due to low per-iteration computational complexity. In fact, the Newton or quasi-newton (QN) methods leveraging the second-order information are able to achieve a better solution than the first-order methods. Thus, stochastic QN (SQN) methods have been developed to achieve a better solution efficiently than the stochastic first-order methods by utilizing approximate second-order information. However, the existing SQN methods still do not reach the best known stochastic first-order oracle (SFO) complexity. To fill this gap, we propose a novel faster stochastic QN method (SpiderSQN) based on the variance reduced technique of SIPDER. We prove that our SpiderSQN method reaches the best known SFO complexity of O(n+n1/2ϵ-2) in the finite-sum setting to obtain an ϵ -first-order stationary point. To further improve its practical performance, we incorporate SpiderSQN with different momentum schemes. Moreover, the proposed algorithms are generalized to the online setting, and the corresponding SFO complexity of O(ϵ-3) is developed, which also matches the existing best result. Extensive experiments on benchmark data sets demonstrate that our new algorithms outperform state-of-the-art approaches for nonconvex optimization.
Explaining Image Classifiers by Removing Input Features Using Generative Models

Perturbation-based explanation methods often measure the contribution of an input feature to an image classifier’s outputs by heuristically removing it via e.g. blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Instead, we propose to integrate a generative inpainter into three representative attribution methods to remove an input feature. Our proposed change improved all three methods in (1) generating more plausible counterfactual samples under the true data distribution; (2) being more accurate according to three metrics: object localization, deletion, and saliency metrics; and (3) being more robust to hyperparameter changes. Our findings were consistent across both ImageNet and Places365 datasets and two different pairs of classifiers and inpainters.
Towards Corner Case Detection by Modeling the Uncertainty of Instance Segmentation Networks

State-of-the-art instance segmentation techniques currently provide a bounding box, class, mask, and scores for each instance. What they do not provide is an epistemic uncertainty estimate of these predictions. With our approach, we want to identify corner cases by considering the epistemic uncertainty. Corner cases are data/situations that are underrepresented or not covered in our data set. Our work is based on Mask R-CNN. We estimate the epistemic uncertainty by extending the architecture with Monte-Carlo dropout layers. By repeatedly executing the forward pass, we create a large number of predictions per instance. Afterward, we cluster the predictions of an instance based on the bounding box coordinates. It becomes possible to determine the epistemic position uncertainty for the bounding boxes and the classifier's epistemic class uncertainty. For the epistemic uncertainty regarding the bounding box position and the class assignment, we provide a criterion for detecting corner cases utilizing the model's epistemic uncertainty.
Distributed feedback network for single-image deraining

Recently, deep convolutional neural networks have achieved great success for single-image deraining. However, affected by the intrinsic overlapping between rain streaks and background texture patterns, a majority of these methods tend to almost remove texture details in rain-free regions and lead to over-smoothing effects in the recovered background. To generate reasonable rain streak layers and improve the reconstruction quality of the background, we propose a distributed feedback network (DFN) in recurrent structure. A novel feedback block is designed to implement the feedback mechanism. In each feedback block, the hidden state with high-level information (output) will flow into the next iteration to correct the low-level representations (input). By stacking multiple feedback blocks, the proposed network where the hidden states are distributed can extract powerful high-level representations for rain streak layers. Curriculum learning is employed to connect the loss of each iteration and ensure that hidden states contain the notion of output. In addition, a self-ensemble strategy for rain removal task, which can retain the approximate vertical character of rain streaks, is explored to maximize the potential performance of the deraining model. Extensive experimental results demonstrated the superiority of the proposed method in comparison with other deraining methods.
ABC: An Auction-Based Blockchain Consensus-Incentive Mechanism

The rapid development of blockchain technology and its various applications have attracted huge attention in the last five years. The consensus mechanism and incentive mechanism are the backbone of a blockchain network. The consensus mechanism plays a crucial role in sustaining the network security, integrity, and efficiency. The incentive mechanism motivates the distributed nodes to “mine” so as to participate the consensus mechanism. The existing mechanisms bear the fairness and justice issues. In this paper, from the perspective of mechanism design, we propose a consensus-incentive mechanism through applying continuous double auction theory, which is abbreviated as ABC mechanism. Our mechanism consists of four stages, including initiation stage, auction stage, completion stage, and confirmation stage. The auction model in use is the continuous double auction to ensure the transactions are stored in a real-time manner. Through extensive experimental evaluations, our mechanism is proven to improve the fairness and justice of the blockchain network.
I2Net: Mining intra-video and inter-video attention for temporal action localization

This paper focuses on two challenges for temporal action localization community, i.e., lack of long-term relationship and action pattern uncertainty. The former prevents the cooperation among multiple action instances within a video, while the latter may cause incomplete localizations or false positives. The lack of long-term relationship challenge results from the limited receptive field. Instead of stacking multiple layers or using large convolution kernels, we propose the intra-video attention mechanism to bring global receptive field to each temporal point. As for the action pattern uncertainty challenge, although it is hard to precisely depict the desired action pattern, paired videos that share the same action category can provide complementary information about action pattern. Consequently, we propose an inter-video attention mechanism to assist learning accurate action patterns. Based on the intra-video attention and inter-video attention, we propose a unified framework, namely I2Net, to tackle the challenging temporal action localization task. Given two videos containing sharing action categories, I2Net adopts the widely used one-stage action localization paradigm to dispose of them in parallel. As for two neighboring layers within the same video, the intra-video attention brings global information to each temporal point and helps to learn representative features. As for two parallel layers between two videos, the inter-video attention introduces complementary information to each video and helps to learn accurate action patterns. With the cooperation of intra-video and inter-video attention mechanisms, I2Net shows obvious performance gains over the baseline and builds new state-of-the-art on two widely-used benchmarks, i.e., THUMOS14 and ActivityNet v1.3.
Cloud Load Balancing Algorithm

Cloud computing where consumers can access massive quantities of computer power without worrying the network by charging the money. The key problem with cloud computing processing millions of user requests, and the frequently seen device efficiency deteriorating as cloud infrastructure demands to increase. Load management in cloud setting is the optimal approach. Load management in cloud setting is the optimal approach. Several researchers are investigating the problem in this field and have proposed a number of load balancing algorithms to the solution. There are two types of algorithms for cloud load balancing, one is static, and the other is algorithms for dynamic load balancing. While implementing a load balancing algorithm or planning a new load balancing algorithm, researchers need to consider the different parameters such as response time, efficiency, use of energy, scalability, flexibility. Cloud computing other danger is growing interest in cloud computing for resource sharing. There is a need for a solution to address the different performance associated with cloud computing ecosystem such as resource sharing, load balancing, and trust development in the cloud environment. In order to develop trust between the cloud provider and user, we maintain a table where the information is stored in relation to the user and cloud service provider and on the basis of the table a trust rating is given to the model of resource sharing and load balancing. In this paper we propose a new approach to load balancing in cloud computing [9] and aims of the algorithm are to distribute the same load on each server.
Quantum Algorithms: Overviews, Foundations, and Speedups

This paper discusses quantum computing with a strong focus on quantum software, quantum networks, quantum simulation, and applications. The study on quantum speedups reveals fundamental differences between quantum algorithms and classical algorithms. As a case study, further improvement on Shor's algorithm is presented with experimental results. The study shows that quantum circuits can be generated automatically to further improve the efficiency of quantum algorithms.