Reviewerly/exHarmony huggingface dataset:
Efficient Medical Big Data Management With Keyword-Searchable Encryption in Healthchain

Along with applying blockchain technology in the health care service system (HSS), many distributed solutions have been proposed to solve the centralized, data island problems. Although blockchain-enabled HSS has significantly changed the traditional management mode of medical Big Data [e.g., electronic medical records (EMRs)], some new problems such as ledger corpulence, data searchable security, and quantum attack threat are exposed. In this article, we first introduce an efficient EMRs management model called on-chain ledger and off-chain storage (OLOS), in which the indexes of EMRs are uploaded on public blockchain and the real EMRs data are stored in native server. This model can relieve the public ledger and save the public cloud space. Meanwhile, it can protect data security by keeping off the direct operation on real EMRs data. Then, to protect the cross-institutional EMRs sharing security and improve the quantum-resist ability, we proposed a secure keyword-searchable attribute-based encryption (KS-ABE) scheme based on lattice cryptography. Moreover, the scheme can prove to be secure against adaptive chosen-keyword attack and adaptive chosen-policy attack in the random oracle model. Besides, comparison analysis and experimental results show that our KS-ABE scheme has fewer communication costs and smaller key sizes than similar literary works.
A Comprehensive Introduction

This article gives a comprehensive introduction into one of the main branches of evolutionary computation - the evolution strategies (ES) the history of which dates back to the 1960s in Germany. Starting from a survey of history the philosophical background is explained in order to make understandable why ES are realized in the way they are. Basic ES algorithms and design principles for variation and selection operators as well as theoretical issues are presented, and future branches of ES research are discussed.
Charge-Based Prison Term Prediction with Deep Gating Network

Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction.While existing work merely predicts the total prison term, in reality a defendant is often charged with multiple crimes.In this paper, we argue that charge-based prison term prediction (CPTP) not only better fits realistic needs, but also makes the total prison term prediction more accurate and interpretable.We collect the first large-scale structured data for CPTP and evaluate several competitive baselines.Based on the observation that fine-grained feature selection is the key to achieving good performance, we propose the Deep Gating Network (DGN) for charge-specific feature selection and aggregation.Experiments show that DGN achieves the state-of-the-art performance.
MyRocks

Facebook uses MySQL to manage tens of petabytes of data in its main database named the User Database (UDB). UDB serves social activities such as likes, comments, and shares. In the past, Facebook used InnoDB, a B+Tree based storage engine as the backend. The challenge was to find an index structure using less space and write amplification [1]. LSM-tree [2] has the potential to greatly improve these two bottlenecks. RocksDB, an LSM tree-based key/value store was already widely used in variety of applications but had a very low-level key-value interface. To overcome these limitations, MyRocks, a new MySQL storage engine, was built on top of RocksDB by adding relational capabilities. With MyRocks, using the RocksDB API, significant efficiency gains were achieved while still benefiting from all the MySQL features and tools. The transition was mostly transparent to client applications. Facebook completed the UDB migration from InnoDB to MyRocks in 2017. Since then, ongoing improvements in production operations, and additional enhancements to MySQL, MyRocks, and RocksDB, provided even greater efficiency wins. MyRocks also reduced the instance size by 62.3% for UDB data sets and performed fewer I/O operations than InnoDB. Finally, MyRocks consumed less CPU time for serving the same production traffic workload. These gains enabled us to reduce the number of database servers in UDB to less than half, saving significant resources. In this paper, we describe our journey to build and run an OLTP LSM-tree SQL database at scale. We also discuss the features we implemented to keep pace with UDB workloads, what made migrations easier, and what operational and software development challenges we faced during the two years of running MyRocks in production. Among the new features we introduced in RocksDB were transactional support, bulk loading, and prefix bloom filters, all are available for the benefit of all RocksDB users.
Cooperative Path Planning of UAVs &amp; UGVs for a Persistent Surveillance Task in Urban Environments

There have been many applications of drones in urban environments, such as delivery, rescue, and surveillance. In a persistent surveillance task, the drones sometimes cannot complete it independently when some regions are required to be covered on the ground. For this purpose, unmanned aerial vehicles and unmanned ground vehicles (UAVs & UGVs) system is introduced to perform such a task in this article, and the goal is to generate the circular paths for the drones and the UGVs, respectively, to minimize their travel time of realizing a complete coverage. First, the cooperative path planning problem of UAVs & UGVs is formulated into a large-scale 0-1 optimization problem, in which the on-off states of the discrete points are to be optimized. Second, a hybrid algorithm integrating the estimation of distribution algorithm (EDA) and the genetic algorithm (GA) algorithm is proposed to solve the problem. The advantages of EDA and GA in the global and local search are fully taken considering the demands in different phases of the iterative process. A simple sweep-based approach is employed to determine the optimal sequence of passing the open points. Then, an online local adjustment strategy is also applied to address the changes of the requirements on covering the ground area. Simulation results demonstrate that the UAVs & UGVs system can enhance the efficiency of the task. The hybrid EDA-GA algorithm can greatly improve the performance of EDA and GA in terms of the quality and the stability of solutions. The online adjustment strategy is effective to maintain a complete coverage while minimizing the impact on the circular paths.
Blockchain-based Education Project

Blockchain is a jointly distributed and decentralized ledger, in which nodes are aiming to record transaction history with very different networks. This technology is quite practical if used in the field of higher education to carry out digital certification, record keeping, etc. From several papers published on this, no single paper covering educational projects based on the blockchain can find it. So there are the latest opportunities for higher education trends. Projects in the field of higher education that are blockchain-based are aimed at solving problems that occur in educators today. On that basis, we conclude that there is a need for a systematic literature review. Therefore, based on the education project, this study will review the artistic gap between the two. This paper will focus on the protocol used in this project and explore several blockchain-based higher education projects that will be the aim of this paper. As well as analyzing the blockchain features currently in use today and the services that will be offered by existing educational projects, to improve the implementation of technology in the field of education by using the blockchain feature.
Live speech portraits

To the best of our knowledge, we first present a live system that generates personalized photorealistic talking-head animation only driven by audio signals at over 30 fps. Our system contains three stages. The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space. In the second stage, we learn facial dynamics and motions from the projected audio features. The predicted motions include head poses and upper body motions, where the former is generated by an autoregressive probabilistic model which models the head pose distribution of the target person. Upper body motions are deduced from head poses. In the final stage, we generate conditional feature maps from previous predictions and send them with a candidate image set to an image-to-image translation network to synthesize photorealistic renderings. Our method generalizes well to wild audio and successfully synthesizes high-fidelity personalized facial details, e.g., wrinkles, teeth. Our method also allows explicit control of head poses. Extensive qualitative and quantitative evaluations, along with user studies, demonstrate the superiority of our method over state-of-the-art techniques.
Quantum secret sharing with polarization-entangled photon pairs

We describe and experimentally demonstrate a three-party quantum secret sharing protocol using polarization-entangled photon pairs. The source itself serves as an active participant and can switch between the required photon states by modulating the pump beam only, thereby making the protocol less susceptible to loss and amenable to fast switching. Compared to three-photon protocols, the practical efficiency is dramatically improved as there is no need to generate, transmit, or detect a third photon.
Efficient algorithms and lower bounds for robust linear regression

We study the prototypical problem of high-dimensional linear regression in a robust model where an e-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution N(0, Σ) on Rd. We give nearly tight upper bounds and computational lower bounds for this problem. Specifically our main contributions are as follows:• For the case that the covariance matrix is known to be the identity we give a sample near-optimal and computationally efficient algorithm that draws O(d/e2) labeled examples and outputs a candidate hypothesis vector [MATH HERE] that approximates the unknown regression vector β within e2-norm O(e log(1/e)σ), where σ is the standard deviation of the random observation noise. An error of Ω(eσ) is information-theoretically necessary even with infinite sample size. Hence, the error guarantee of our algorithm is optimal, up to a logarithmic factor in 1/e. Prior work gave an algorithm for this problem with sample complexity [MATH HERE] whose error guarantee scales with the e2-norm of β.• For the case of unknown covariance Σ, we show that we can efficiently achieve the same error guarantee of O(e log(1/e)σ), as in the known covariance case, using an additional O(d2 / e2) unlabeled examples. On the other hand, an error of O(eσ) can be information-theoretically attained with O(d/e2) samples. We prove a Statistical Query (SQ) lower bound providing evidence that this quadratic tradeoff in the sample size is inherent. More specifically, we show that any polynomial time SQ learning algorithm for robust linear regression (in Huber's contamination model) with estimation complexity O(d2−c), where c > 0 is an arbitrarily small constant, must incur an error of [MATH HERE].
Quantitative program reasoning with graded modal types

In programming, some data acts as a resource (e.g., file handles, channels) subject to usage constraints. This poses a challenge to software correctness as most languages are agnostic to constraints on data. The approach of linear types provides a partial remedy, delineating data into resources to be used but never copied or discarded, and unconstrained values. Bounded Linear Logic provides a more fine-grained approach, quantifying non-linear use via an indexed-family of modalities. Recent work on coeffect types generalises this idea to graded comonads, providing type systems which can capture various program properties. Here, we propose the umbrella notion of graded modal types, encompassing coeffect types and dual notions of type-based effect reasoning via graded monads. In combination with linear and indexed types, we show that graded modal types provide an expressive type theory for quantitative program reasoning, advancing the reach of type systems to capture and verify a broader set of program properties. We demonstrate this approach via a type system embodied in a fully-fledged functional language called Granule, exploring various examples.
Emergent Tool Use From Multi-Agent Autocurricula

Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clear evidence of six emergent phases in agent strategy in our environment, each of which creates a new pressure for the opposing team to adapt; for instance, agents learn to build multi-object shelters using moveable boxes which in turn leads to agents discovering that they can overcome obstacles using ramps. We further provide evidence that multi-agent competition may scale better with increasing environment complexity and leads to behavior that centers around far more human-relevant skills than other self-supervised reinforcement learning methods such as intrinsic motivation. Finally, we propose transfer and fine-tuning as a way to quantitatively evaluate targeted capabilities, and we compare hide-and-seek agents to both intrinsic motivation and random initialization baselines in a suite of domain-specific intelligence tests.
WeightNet: Revisiting the Design Space of Weight Networks

We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective SENet and CondConv into the same framework on weight space. The method, called WeightNet, generalizes the two methods by simply adding one more grouped fully-connected layer to the attention activation layer. We use the WeightNet, composed entirely of (grouped) fully-connected layers, to directly output the convolutional weight. WeightNet is easy and memory-conserving to train, on the kernel space instead of the feature space. Because of the flexibility, our method outperforms existing approaches on both ImageNet and COCO detection tasks, achieving better Accuracy-FLOPs and Accuracy-Parameter trade-offs. The framework on the flexible weight space has the potential to further improve the performance. Code is available at https://github.com/megvii-model/WeightNet .
A survey on artificial intelligence assurance

Abstract Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide (and growing) library of algorithms that could be applied for different problems. One important notion for the adoption of AI algorithms into operational decision processes is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 and 2021, and aims to provide a structured alternative to the landscape. A new AI assurance definition is adopted and presented, and assurance methods are contrasted and tabulated. Additionally, a ten-metric scoring system is developed and introduced to evaluate and compare existing methods. Lastly, in this manuscript, we provide foundational insights, discussions, future directions, a roadmap, and applicable recommendations for the development and deployment of AI assurance.
Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.
Graph Wavelet Neural Network.

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures

Thiago Castro Ferreira, Chris van der Lee, Emiel van Miltenburg, Emiel Krahmer. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
You CAN Teach an Old Dog New Tricks! On Training Knowledge Graph Embeddings

Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations in a knowledge graph. A vast number of KGE techniques for multi-relational link prediction has been proposed in the recent literature, often with state-of-the-art performance. These approaches differ along a number of dimensions, including different model architectures, different training strategies, and different approaches to hyperparameter optimization. In this paper, we take a step back and aim to summarize and quantify empirically the impact of each of these dimensions on model performance. We report on the results of an extensive experimental study with popular model architectures and training strategies across a wide range of hyperparameter settings. We found that when trained appropriately, the relative performance differences between various model architectures often shrinks and sometimes even reverses when compared to prior results. For example, RESCAL, one of the first KGE models, showed strong performance when trained with state-of-the-art techniques; it was competitive to or outperformed more recent architectures. We also found that good (and often superior to prior studies) model configurations can be found by exploring relatively few random samples from a large hyperparameter space. Our results suggest that many of the more advanced architectures and techniques proposed in the literature should be revisited to reassess their individual benefits.
Topological Quantum Compiling with Reinforcement Learning

Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum computing. We introduce an efficient algorithm based on deep reinforcement learning that compiles an arbitrary single-qubit gate into a sequence of elementary gates from a finite universal set. It generates near-optimal gate sequences with given accuracy and is generally applicable to various scenarios, independent of the hardware-feasible universal set and free from using ancillary qubits. For concreteness, we apply this algorithm to the case of topological compiling of Fibonacci anyons and obtain near-optimal braiding sequences for arbitrary single-qubit unitaries. Our algorithm may carry over to other challenging quantum discrete problems, thus opening up a new avenue for intriguing applications of deep learning in quantum physics.
DeepSonar: Towards Effective and Robust Detection of AI-Synthesized Fake Voices

With the recent advances in voice synthesis, AI-synthesized fake voices are indistinguishable to human ears and widely are applied to produce realistic and natural DeepFakes, exhibiting real threats to our society. However, effective and robust detectors for synthesized fake voices are still in their infancy and are not ready to fully tackle this emerging threat. In this paper, we devise a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition (SR) system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices. Layer-wise neuron behaviors provide an important insight to meticulously catch the differences among inputs, which are widely employed for building safety, robust, and interpretable DNNs. In this work, we leverage the power of layer-wise neuron activation patterns with a conjecture that they can capture the subtle differences between real and AI-synthesized fake voices, in providing a cleaner signal to classifiers than raw inputs. Experiments are conducted on three datasets (including commercial products from Google, Baidu, etc) containing both English and Chinese languages to corroborate the high detection rates (98.1% average accuracy) and low false alarm rates (about 2% error rate) of DeepSonar in discerning fake voices. Furthermore, extensive experimental results also demonstrate its robustness against manipulation attacks (e.g., voice conversion and additive real-world noises). Our work further poses a new insight into adopting neuron behaviors for effective and robust AI aided multimedia fakes forensics as an inside-out approach instead of being motivated and swayed by various artifacts introduced in synthesizing fakes.
DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem

The class imbalance problem occurs when one class far outnumbers the other classes, causing most traditional classifiers perform poorly on the minority classes. To tackle this problem, a plethora of techniques have been proposed, especially centered around resampling methods. This paper introduces a two-stage method that combines the DBSCAN clustering algorithm to filter noisy majority class instances with a graph-based procedure to overcome the class imbalance. We then experimentally evaluate the behavior of the proposed method on a collection of two-class imbalanced data sets. The experimental results show an improvement in the classification performance measured by the geometric mean of the accuracy on each class and also a higher reduction in the imbalance ratio when compared to several state-of-the-art under-sampling techniques.