Reviewerly/exHarmony huggingface dataset:
Non-interactive Zero-Knowledge in Pairing-Free Groups from Weaker Assumptions

We provide new constructions of non-interactive zero-knowledge arguments ($$\textsf {NIZKs}$$) for NP from discrete-logarithm-style assumptions over cyclic groups, without relying on pairings. A previous construction from (Canetti et al., Eurocrypt’18) achieves such $$\textsf {NIZKs}$$ under the assumption that no efficient adversary can break the key-dependent message (KDM) security of (additive) ElGamal with respect to all (even inefficient) functions over groups of size $$2^\lambda $$, with probability better than $$\mathsf {poly} (\lambda )/2^{\lambda }$$. This is an extremely strong, non-falsifiable assumption. In particular, even mild (polynomial) improvements over the current best known attacks on the discrete logarithm problem would already contradict this assumption. (Canetti et al. STOC’19) describe how to improve the assumption to rely only on KDM security with respect to all efficient functions, therefore obtaining an assumption that is (in spirit) falsifiable. Our first construction improves this state of affairs. We provide a construction of $$\textsf {NIZKs}$$ for NP under the CDH assumption together with the assumption that no efficient adversary can break the key-dependent message one-wayness of ElGamal with respect to efficient functions over groups of size $$2^\lambda $$, with probability better than $$\mathsf {poly} (\lambda )/2^{c\lambda }$$ (denoted $$2^{-c\lambda }$$-$$\mathsf {OW\text {-}KDM}$$), for a constant $$c = 3/4$$. Unlike the previous assumption, our assumption leaves an exponential gap between the best known attack and the required security guarantee. We also analyse whether we could build $$\textsf {NIZKs}$$ when CDH does not hold. As a second contribution, we construct an infinitely often $$\textsf {NIZK}$$ argument system for NP (where soundness and zero-knowledge are only guaranteed to hold for infinitely many security parameters), under the $$2^{-c\lambda }$$-$$\mathsf {OW\text {-}KDM}$$ security of ElGamal with $$c = 28/29+o(1)$$, together with the existence of low-depth pseudorandom generators.
Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System

In recent years, 360° videos have gained the attention of researchers due to their versatility and applications in real-world problems. Also, easy access to different visual sensor kits and easily deployable image acquisition devices have played a vital role in the growth of interest in this area by the research community. Recently, several 360° panorama generation systems have demonstrated reasonable quality generated panoramas. However, these systems are equipped with expensive image sensor networks where multiple cameras are mounted in a circular rig with specific overlapping gaps. In this paper, we propose an economical 360° panorama generation system that generates both mono and stereo panoramas. For mono panorama generation, we present a drone-mounted image acquisition sensor kit that consists of six cameras placed in a circular fashion with optimal overlapping gap. The hardware of our proposed image acquisition system is configured in such way that no user input is required to stitch multiple images. For stereo panorama generation, we propose a lightweight, cost-effective visual sensor kit that uses only three cameras to cover 360° of the surroundings. We also developed stitching software that generates both mono and stereo panoramas using a single image stitching pipeline where the panorama generated by our proposed system is automatically straightened without visible seams. Furthermore, we compared our proposed system with existing mono and stereo contents generation systems in both qualitative and quantitative perspectives, and the comparative measurements obtained verified the effectiveness of our system compared to existing mono and stereo generation systems.
Fast graph-cut based optimization for practical dense deformable registration of volume images

Deformable image registration is a fundamental problem in medical image analysis, with applications such as longitudinal studies, population modeling, and atlas-based image segmentation. Registration is often phrased as an optimization problem, i.e., finding a deformation field that is optimal according to a given objective function. Discrete, combinatorial, optimization techniques have successfully been employed to solve the resulting optimization problem. Specifically, optimization based on α-expansion with minimal graph cuts has been proposed as a powerful tool for image registration. The high computational cost of the graph-cut based optimization approach, however, limits the utility of this approach for registration of large volume images. Here, we propose to accelerate graph-cut based deformable registration by dividing the image into overlapping sub-regions and restricting the α-expansion moves to a single sub-region at a time. We demonstrate empirically that this approach can achieve a large reduction in computation time – from days to minutes – with only a small penalty in terms of solution quality. The reduction in computation time provided by the proposed method makes graph-cut based deformable registration viable for large volume images. Graph-cut based image registration has previously been shown to produce excellent results, but the high computational cost has hindered the adoption of the method for registration of large medical volume images. Our proposed method lifts this restriction, requiring only a small fraction of the computational cost to produce results of comparable quality.
Machine Learning Techniques for Identity Document Verification in Uncontrolled Environments: A Case Study

Distributed (i.e. mobile) enrollment to services such as banking is gaining popularity. In such processes, users are often asked to provide proof of identity by taking a picture of an ID. For this to work securely, it is critical to automatically check basic document features, perform text recognition, among others. Furthermore, challenging contexts might arise, such as various backgrounds, diverse light quality, angles, perspectives, etc. In this paper we present a machine-learning based pipeline to process pictures of documents in such scenarios, that relies on various analysis modules and visual features for verification of document type and legitimacy. We evaluate our approach using identity documents from the Republic of Colombia. As a result, our machine learning background detection method achieved an accuracy of 98.4%, and our authenticity classifier an accuracy of 97.7% and an F1-score of 0.974.
RelatIF: Identifying Explanatory Training Samples via Relative Influence

In this work, we focus on the use of influence functions to identify relevant training examples that one might hope explain the predictions of a machine learning model. One shortcoming of influence functions is that the training examples deemed most influential are often outliers or mislabelled, making them poor choices for explanation. In order to address this shortcoming, we separate the role of global versus local influence. We introduce RelatIF, a new class of criteria for choosing relevant training examples by way of an optimization objective that places a constraint on global influence. RelatIF considers the local influence that an explanatory example has on a prediction relative to its global effects on the model. In empirical evaluations, we find that the examples returned by RelatIF are more intuitive when compared to those found using influence functions.
End-to-End Formal Verification of Ethereum 2.0 Deposit Smart Contract

We report our experience in the formal verification of the deposit smart contract, whose correctness is critical for the security of Ethereum 2.0, a new Proof-of-Stake protocol for the Ethereum blockchain. The deposit contract implements an incremental Merkle tree algorithm whose correctness is highly nontrivial, and had not been proved before. We have verified the correctness of the compiled bytecode of the deposit contract to avoid the need to trust the underlying compiler. We found several critical issues of the deposit contract during the verification process, some of which were due to subtle hidden bugs of the compiler.
Compiling effect handlers in capability-passing style

Effect handlers encourage programmers to abstract over repeated patterns of complex control flow. As of today, this abstraction comes at a significant price in performance. In this paper, we aim to achieve abstraction without regret for effect handlers. We present a language for effect handlers in _capability-passing style_ (λ Cap ) and an implementation of this language as a translation to simply-typed lambda calculus in _iterated continuation-passing style_. A suite of benchmarks indicates that the novel combination of capability-passing style and iterated CPS enables significant speedups over existing languages with effect handlers or control operators. Our implementation technique is general and allows us to generate code in any language that supports first-class functions. We then identify a subset of programs for which we can further improve the performance and guarantee full elimination of the effect handler abstraction. To formally capture this subset, we refine λ Cap to λ λ Cap with a more restrictive type system. We present a type-directed translation for λ λ Cap that inserts staging annotations and prove that no abstractions or applications related to effect handlers occur in the translated program. Using this second translation we observe additional speedups in some of the benchmarks.
Social optimization algorithm with application to economic dispatch problem

In this paper, a new population-based meta-heuristic algorithm called Social Optimization Algorithm (SOA) is proposed. This algorithm has been inspired by the social behavior of human-beings, and mimics an ideal justly-established society where the two principles of justice, that is, equality of opportunity and community principles are meticulously followed. Reaching goals in such a society is optimally achieved. Although a real fully-just society does not exist in the real world, we implement the principles in such ideal societies through simulations to introduce our new optimization algorithm. The advantage of the proposed method is that it does not have any algorithm-specified parameters to be set. This is a great merit compared to similar meta-heuristics. The algorithm has been simulated on 23 well-known test functions, and the results have been compared with those produced by Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Differential Evolution (DE). In 57% of the 23 cases SOA yields superior results. The algorithm has also been tested on five other benchmark functions to be compared with Teaching-Learning-Based Optimization (TLBO) and Artificial Bee Colony (ABC).The results provide evidence for efficient performance with superior solution quality of the proposed algorithm in solving benchmark problems. To further illustrate the effectiveness of the proposed algorithm, it has been tested on the Economic Dispatch problem as a real-world constrained optimization problem. The results have been compared with Biogeography-Based Optimizer (BBO), Harmony Search (HS) and PSO. The results show that the SOA is able to produce very competitive results compared to other well-known meta-heuristic algorithms. Meta-heuristic, SOA, Economic Dispatch.
Synthea™ Novel coronavirus (COVID-19) model and synthetic data set

March through May 2020, a model of novel coronavirus (COVID-19) disease progression and treatment was constructed for the open-source Synthea patient simulation. The model was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this simulation, we generated 124,150 synthetic patients, with 88,166 infections and 18,177 hospitalized patients. Patient symptoms, disease severity, and morbidity outcomes were calibrated using clinical data from the peer-reviewed publications. 4.1% of all simulated infected patients died and 20.6% were hospitalized. At peak observation, 548 dialysis machines and 209 mechanical ventilators were needed. This simulation and the resulting data have been used for the development of algorithms and prototypes designed to address the current or future pandemics, and the model can continue to be refined to incorporate emerging COVID-19 knowledge, variations in patterns of care, and improvement in clinical outcomes. The resulting model, data, and analysis are available as open-source code on GitHub and an open-access data set is available for download.
Roles in the Discussion

Social computing provides a variety of challenges and opportunities for people who are experiencing cognitive decline. Following a recent diagnosis of dementia, older adults sometimes engage in online communities designated for people with dementia. In this work, we analyzed all original posts from a seven-year period on a forum for persons with dementia to understand 1) who posts in this forum and 2) what kinds of social support posters seek to gain from this engagement. Our analyses indicate that, in addition to people with dementia, a variety of dementia-related stakeholders create original posts in this forum. Our results suggest that seeking and offering social support are key activities for all forum users, not just those with dementia for whom the forum was designated. Given these findings, we offer design implications for online spaces for vulnerable communities, with a focus on design that allows for external stakeholder participation while still maintaining the privacy and safety of vulnerable members of the community.
Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation

Humans accumulate knowledge in a lifelong fashion. Modern deep neural networks, on the other hand, are susceptible to catastrophic forgetting: when adapted to perform new tasks, they often fail to preserve their performance on previously learned tasks. Given a sequence of tasks, a naive approach addressing catastrophic forgetting is to train a separate standalone model for each task, which scales the total number of parameters drastically without efficiently utilizing previous models. In contrast, we propose a parameter efficient framework, Piggyback GAN, which learns the current task by building a set of convolutional and deconvolutional filters that are factorized into filters of the models trained on previous tasks. For the current task, our model achieves high generation quality on par with a standalone model at a lower number of parameters. For previous tasks, our model can also preserve generation quality since the filters for previous tasks are not altered. We validate Piggyback GAN on various image-conditioned generation tasks across different domains, and provide qualitative and quantitative results to show that the proposed approach can address catastrophic forgetting effectively and efficiently.
Supervised Edge Attention Network for Accurate Image Instance Segmentation

Effectively keeping boundary of the mask complete is important in instance segmentation. In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask. To circumvent this issue, we propose a fully convolutional box head and a supervised edge attention module in mask head. The box head contains one new IoU prediction branch. It learns association between object features and detected bounding boxes to provide more accurate bounding boxes for segmentation. The edge attention module utilizes attention mechanism to highlight object and suppress background noise, and a supervised branch is devised to guide the network to focus on the edge of instances precisely. To evaluate the effectiveness, we conduct experiments on COCO dataset. Without bells and whistles, our approach achieves impressive and robust improvement compared to baseline models. Code is at https://github.com//IPIU-detection/SEANet.
Multi-objective optimization of container-based microservice scheduling in edge computing

Edge computing provides physical resources closer to end users, becoming a good complement to cloud computing. With the rapid development of container technology and microservice architecture, container orchestration has become a hot issue. However, the container-based microservice scheduling problem in edge computing is still urgent to be solved. In this paper, we first formulate the container-based microservice scheduling as a multi-objective optimization problem, aiming to optimize network latency among microservices, reliability of microservice applications and load balancing of the cluster. We further propose a latency, reliability and load balancing aware scheduling (LRLBAS) algorithm to determine the container-based microservice deployment in edge computing. Our proposed algorithm is based on particle swarm optimization (PSO). In addition, we give a handling strategy to separate the fitness function from constraints, so that each particle has two fitness values. In the proposed algorithm, a new particle comparison criterion is introduced and a certain proportion of infeasible particles are reserved adaptively. Extensive simulation experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm compared with other related algorithms.
Multimodal Sentence Summarization via Multimodal Selective Encoding

This paper studies the problem of generating a summary for a given sentence-image pair.Existing multimodal sequence-to-sequence approaches mainly focus on enhancing the decoder by visual signals, while ignoring that the image can improve the ability of the encoder to identify highlights of a news event or a document.Thus, we propose a multimodal selective gate network that considers reciprocal relationships between textual and multi-level visual features, including global image descriptor, activation grids, and object proposals, to select highlights of the event when encoding the source sentence.In addition, we introduce a modality regularization to encourage the summary to capture the highlights embedded in the image more accurately.To verify the generalization of our model, we adopt the multimodal selective gate to the text-based decoder and multimodal-based decoder.Experimental results on a public multimodal sentence summarization dataset demonstrate the advantage of our models over baselines.Further analysis suggests that our proposed multimodal selective gate network can effectively select important information in the input sentence.
A Combination Interval Prediction Model Based on Biased Convex Cost Function and Auto-Encoder in Solar Power Prediction

Due to the intermittent and stochastic nature of solar power, solar power interval prediction is of great importance for grid management and power dispatching. A combination interval prediction model based on the lower and upper bound estimation (LUBE) is proposed to efficiently quantify the solar power prediction uncertainty. In the proposed model, the upper and lower bounds are separately predicted by two prediction engines. The extreme learning machine (ELM) is selected as the basic prediction engine. The auto-encoder technique is used to initialize the input weight matrix of ELM for efficient feature learning. A novel biased convex cost function is developed for ELM to predict the interval boundary. The output weight matrix of ELM can be solved via the convex optimization technique instead of the conventional heuristic algorithm. The proposed interval prediction model can be formulated as a bi-level optimization problem. In the lower-level problem, the lower and upper ELMs are trained under different candidate hyper-parameters of the biased cost function. In the upper-level problem, the optimal combination of the lower and upper prediction engines is determined by evaluating the interval prediction performance. Comprehensive experiments based on public data set are conducted to validate the superiority of the proposed interval prediction model.
A bibliometric analysis of m-learning from topic inception to 2015

Mobile learning is a promising and widely adopted mode of education nowadays. However, to the best of our knowledge, there have been no studies that have investigated publications on the use of mobile learning in the Thomson Reuters Web of Science (WoS) through bibliometric analysis. This study aims to provide readers with statistical information to obtain a deep understanding of this domain. The results show that 3087 mobile learning publications have been published since 1982. As a result, the study has compared and determined the possible ways for researchers and authors to improve the citation rate of their publications on m-learning and to gain a deeper understanding of the field of mobile learning by studying the number of publications and country, the number of authors, the keywords, the number of references, the number of pages, the journal, the authors' publications, and the number of citations. Additionally, other bibliometric results are discussed.
Boundary-induced and scene-aggregated network for monocular depth prediction

Monocular depth prediction is an important task in scene understanding. It aims to predict the dense depth of a single RGB image. With the development of deep learning, the performance of this task has made great improvements. However, two issues remain unresolved: (1) The deep feature encodes the wrong farthest region in a scene, which leads to a distorted 3D structure of the predicted depth; (2) The low-level features are insufficient utilized, which makes it even harder to estimate the depth near the edge with sudden depth change. To tackle these two issues, we propose the Boundary-induced and Scene-aggregated network (BS-Net). In this network, the Depth Correlation Encoder (DCE) is first designed to obtain the contextual correlations between the regions in an image, and perceive the farthest region by considering the correlations. Meanwhile, the Bottom-Up Boundary Fusion (BUBF) module is designed to extract accurate boundary that indicates depth change. Finally, the Stripe Refinement module (SRM) is designed to refine the dense depth induced by the boundary cue, which improves the boundary accuracy of the predicted depth. Several experimental results on the NYUD v2 dataset and the iBims-1 dataset illustrate the state-of-the-art performance of the proposed approach. And the SUN-RGBD dataset is employed to evaluate the generalization of our method. Code is available at https://github.com/XuefengBUPT/BS-Net.