Reviewerly/exHarmony huggingface dataset:
Efficient verification of bipartite pure states

We propose practical and efficient protocols for verifying bipartite pure states for any finite dimension, which can also be applied to fidelity estimation. Our protocols are based on adaptive local projective measurements with either one-way or two-way communications, which are very easy to implement in practice. They can extract the key information much more efficiently than known protocols based on tomography or direct fidelity estimation, and their efficiencies are comparable to the best protocols based on entangling measurements. These protocols highlight the significance of mutually unbiased bases and complex projective 2-designs in quantum state verification and fidelity estimation. Moreover, our protocols can be applied to the adversarial scenario, in which states are controlled by a malicious adversary. In this case, surprisingly, one of our protocols based on local measurements is even optimal among protocols without locality restriction.
Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing

Summary Cloud computing is significantly contributing to the development of smart Chinese medicine. The diagnosis and treatment of spleen and stomach diseases has been arousing great interest in smart Chinese medicine with cloud computing since many persons are suffering from spleen and stomach diseases. Currently, spleen and stomach diseases present some new characteristics with the dramatic changes in natural climate, social environment, and human living habits. Recently, deep learning, together with cloud computing techniques, has successfully used in medical image analysis and therefore it is the most promising model for diagnosing spleen and stomach disease in smart Chinese medicine. In this paper, we present a survey on deep learning models in medical image analysis for computer‐aided diagnosis in modern medicine. Afterwards, we summarize the syndrome types of spleen and stomach diseases and furthermore analyze the causes and pathogenesis for each syndrome. Finally, we discuss the open challenges and research directions of deep learning models applicable to the computer‐aided diagnosis of spleen and stomach diseases, which is expected to contribute to the development of smart Chinese medicine with cloud computing.
Sample Fusion Network: An End-to-End Data Augmentation Network for Skeleton-Based Human Action Recognition

Data augmentation is a widely used technique for enhancing the generalization ability of deep neural networks for skeleton-based human action recognition (HAR) tasks. Most existing data augmentation methods generate new samples by means of handcrafted transforms. However, these methods often cannot be trained and then are discarded during testing because of the lack of learnable parameters. To solve those problems, a novel type of data augmentation network called a sample fusion network (SFN) is proposed. Instead of using handcrafted transforms, an SFN generates new samples via a long short-term memory (LSTM) autoencoder (AE) network. Therefore, an SFN and HAR network can be cascaded together to form a combined network that can be trained in an end-to-end manner. Moreover, an adaptive weighting strategy is employed to improve the complementarity between a sample and the new sample generated from it by an SFN, thus allowing the SFN to more efficiently improve the performance of the HAR network during testing. The experimental results on various datasets verify that the proposed method outperforms state-of-the-art data augmentation methods. More importantly, the proposed SFN architecture is a general framework that can be integrated with various types of networks for HAR. For example, when a baseline HAR model with three LSTM layers and one fully connected (FC) layer was used, the classification accuracy was increased from 79.53% to 90.75% on the NTU RGB+D dataset using a cross-view protocol, thus outperforming most other methods.
Review and Trend Analysis of Knowledge Graphs for Crop Pest and Diseases

Current techniques of knowledge management have some common defects in efficiency, scalability, and applicability. Knowledge graph provides a new way for knowledge management and is a more flexible knowledge management method. Considering the specific features of crop diseases and pest data, this paper analyzed and classified the key techniques and methods of knowledge graph technology in the field of crop diseases and pest in recent years. We introduced the definition and connotation of the crop diseases and pest knowledge, and the current construction method of it was analyzed in detail from four aspects: knowledge representation, extraction, fusion, and reasoning. Furthermore, the application of crop diseases and pest knowledge graph was introduced in detail in the expert system, search engine, and knowledge question-answering system. At last, this paper summarized the challenges and important problems of diseases and pest knowledge graph and forecasted the prospect of knowledge graph according to the key points and difficulties of current knowledge graph research.
Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration

Auto-encoder network can be used for dimensionality reduction of data and for re-construction of sample population with unknown, complex multivariate probability distribution, where small-probability samples have little contribution to the auto-encoder network, leading to high re-construction error. In this paper, the trained auto-encoder networks were used to detect geochemical anomalies. Compared with deep auto-encoder network, the density-based spatial clustering application with noise (DBSCAN) regards noise samples (e.g., geochemically anomalous samples) that differ from core samples (e.g., geochemically background samples) as anomalies. Therefore, the learned representations from the code layer in the auto-encoder network are clustered by DBSCAN to detect noise samples representing geochemical anomalies. As benchmark for evaluating the performance of auto-encoder network and DBSCAN, and in consideration of the compositional nature of geochemical data, the compositional multivariate outlier detection was also applied. We applied these methods to two forms of the geochemical data, namely (1) without any transformation and (2) with isometric log ratio transformation. The similarities of the resulting anomaly maps in terms of data forms indicate that the auto-encoder network is effective for detecting multivariate geochemical anomalies. Differences between the anomaly maps indicate, however, that the compositional nature of geochemical data affects the performance of multivariate geochemical anomaly detection. Nevertheless, the assessment, by receiver operating characteristics analysis, of the geochemical anomalies derived using the different methodologies described implies that the detected geochemical anomalies are related to Au mineralization. Finally, the Youden index, which measures the relationship between binary anomalies and known deposits, was used for optimal threshold selection to create an optimal mineral potential map from the derived continuous geochemical anomaly data. The spatial distribution of geochemical anomalies at/around faults and magmatic rocks provides insights to where further detailed exploration is warranted in the study area.
An Internet of Things Based Bed-Egress Alerting Paradigm Using Wearable Sensors in Elderly Care Environment

The lack of healthcare staff and increasing proportions of elderly population is alarming. The traditional means to look after elderly has resulted in 255,000 reported falls (only within UK). This not only resulted in extensive aftercare needs and surgeries (summing up to £4.4 billion) but also in added suffering and increased mortality. In such circumstances, the technology can greatly assist by offering automated solutions for the problem at hand. The proposed work offers an Internet of things (IoT) based patient bed-exit monitoring system in clinical settings, capable of generating a timely response to alert the healthcare workers and elderly by analyzing the wireless data streams, acquired through wearable sensors. This work analyzes two different datasets obtained from divergent families of sensing technologies, i.e., smartphone-based accelerometer and radio frequency identification (RFID) based accelerometer. The findings of the proposed system show good efficacy in monitoring the bed-exit and discriminate other ambulating activities. Furthermore, the proposed work manages to keep the average end-to-end system delay (i.e., communications of sensed data to Data Sink (DS)/Control Center (CC) + machine-based feature extraction and class identification + feedback communications to a relevant healthcare worker/elderly) below 1 10 th of a second.
Formation Obstacle Avoidance: A Fluid-Based Solution

To enhance the mission success rate, the multi-agent system (MAS) often adopts a formation form to shuttle over complex terrain. Compared with obstacle avoidance in other multi-agent coordinated missions, multi-agent formation (MAF) obstacle avoidance is more challenging because agents must both avoid obstacles and subsequently return to their intended positions in the original formation at a uniform speed. Aiming at the three-dimensional complex obstacle environment, the fluid methodology is extended to the application of the formation issue for the first time. In this paper, an MAF obstacle avoidance framework built upon model-based interfered fluid dynamical system (MIFDS), which is the most representative algorithm in fluid methodology, is proposed. The proposed MIFDS is modified to be more in accordance with the kinematic performance of agents than traditional IFDS by introducing the kinematic model and constraints. Moreover, on the basis of MIFDS and the virtual leader formation structure, a novel original fluid speed adjustment strategy in MIFDS, which is the core to solve the formation issue, is designed to balance obstacle avoidance and formation reconfiguration. Finally, receding horizon control is introduced to further guarantee the performance of obstacle avoidance and motion safety. Simulations are used to demonstrate the effectiveness of the proposed method.
Adaptive Deep Models for Incremental Learning

Recent years have witnessed growing interests in developing deep models for incremental learning. However, existing approaches often utilize the fixed structure and online backpropagation for deep model optimization, which is difficult to be implemented for incremental data scenarios. Indeed, for streaming data, there are two main challenges for building deep incremental models. First, there is a requirement to develop deep incremental models with Capacity Scalability. In other words, the entire training data are not available before learning the task. It is a challenge to make the deep model structure scaling with streaming data for flexible model evolution and faster convergence. Second, since the stream data distribution usually changes in nature (concept drift), there is a constraint for Capacity Sustainability. That is, how to update the model while preserving previous knowledge for overcoming the catastrophic forgetting. To this end, in this paper, we develop an incremental adaptive deep model (IADM) for dealing with the above two capacity challenges in real-world incremental data scenarios. Specifically, IADM provides an extra attention model for the hidden layers, which aims to learn deep models with adaptive depth from streaming data and enables capacity scalability. Also, we address capacity sustainability by exploiting the attention based fisher information matrix, which can prevent the forgetting in consequence. Finally, we conduct extensive experiments on real-world data and show that IADM outperforms the state-of-the-art methods with a substantial margin. Moreover, we show that IADM has better capacity scalability and sustainability in incremental learning scenarios.
Incorporating Linguistic Constraints into Keyphrase Generation

Keyphrases, that concisely describe the highlevel topics discussed in a document, are very useful for a wide range of natural language processing tasks.Though existing keyphrase generation methods have achieved remarkable performance on this task, they generate many overlapping phrases (including sub-phrases or super-phrases) of keyphrases.In this paper, we propose the parallel Seq2Seq network with the coverage attention to alleviate the overlapping phrase problem.Specifically, we integrate the linguistic constraints of keyphrases into the basic Seq2Seq network on the source side, and employ the multi-task learning framework on the target side.In addition, in order to prevent from generating overlapping phrases with correct syntax, we introduce the coverage vector to keep track of the attention history and to decide whether the parts of source text have been covered by existing generated keyphrases.The experimental results show that our method can outperform the state-of-the-art CopyRNN on scientific datasets, and is also more effective in news domain.
The MetaCoq Project

The MetaCoq project aims to provide a certified meta-programming environment in Coq. It builds on Template-Coq, a plugin for Coq originally implemented by Malecha (Extensible proof engineering in intensional type theory, Harvard University, http://gmalecha.github.io/publication/2015/02/01/extensible-proof-engineering-in-intensional-type-theory.html , 2014), which provided a reifier for Coq terms and global declarations, as represented in the Coq kernel, as well as a denotation command. Recently, it was used in the CertiCoq certified compiler project (Anand et al., in: CoqPL, Paris, France, http://conf.researchr.org/event/CoqPL-2017/main-certicoq-a-verified-compiler-for-coq , 2017), as its front-end language, to derive parametricity properties (Anand and Morrisett, in: CoqPL’18, Los Angeles, CA, USA, 2018). However, the syntax lacked semantics, be it typing semantics or operational semantics, which should reflect, as formal specifications in Coq, the semantics of Coq ’s type theory itself. The tool was also rather bare bones, providing only rudimentary quoting and unquoting commands. We generalize it to handle the entire polymorphic calculus of cumulative inductive constructions, as implemented by Coq, including the kernel’s declaration structures for definitions and inductives, and implement a monad for general manipulation of Coq ’s logical environment. We demonstrate how this setup allows Coq users to define many kinds of general purpose plugins, whose correctness can be readily proved in the system itself, and that can be run efficiently after extraction. We give a few examples of implemented plugins, including a parametricity translation and a certified extraction to call-by-value $$\lambda $$ -calculus. We also advocate the use of MetaCoq as a foundation for higher-level tools.
Park: An Open Platform for Learning-Augmented Computer Systems

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL) for computer systems. Using RL for improving the performance of systems has a lot of potential, but is also in many ways very different from, for example, using RL for games. Thus, in this work we first discuss the unique challenges RL for systems has, and then propose Park an open extensible platform, which makes it easier for ML researchers to work on systems problems. Currently, Park consists of 12 real world system-centric optimization problems with one common easy to use interface. Finally, we present the performance of existing RL approaches over those 12 problems and outline potential areas of future work.
Underwater Image Restoration Based on a Parallel Convolutional Neural Network

Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods.
Influences of Augmented Reality Assistance on Performance and Cognitive Loads in Different Stages of Assembly Task

According to the assembly task model proposed by Stork and Schubö (2010), the assembly task is divided into commissioning and joining subtasks. Each subtask includes two sequential stages, namely, perception and response selection, and action. This division enables a convenient discussion of the influences of Augmented reality (AR) assistance on operators during different stages of an assembly task. Research results can provide a basis for the further analysis of the influence mechanism of AR assistance on an assembly task. This study is composed of three experiments. Experiment 1 explores the influences of AR assistance on the performance of the overall assembly task and the commissioning and joining subtasks. Combining a variation of task complexities, Experiments 2 and 3 discuss the influences of AR assistance on the different stages of the commissioning and joining subtasks. We found that AR assistance can shorten the time of the overall assembly task and subtasks (commissioning and joining) and reduce mistakes during these tasks. Moreover, AR assistance can decrease cognitive load in the commissioning subtask, but it increases cognitive load in the joining task with low complexity. In the perception and response selection stage of the commissioning and joining subtasks, AR assistance can shorten the time for users to recognize the target part and understand the assembly relation. This advantage is extremely significant for the high-complexity task. In the action stage of two subtasks, AR assistance can shorten the time for users to capture parts, but it prolongs the time for users to build parts.
A fast face detection method via convolutional neural network

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection.
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. The zoomed fine-resolution system description, and a probabilistic representation of the uncertainty in sensing and actuation, are used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions. The fine-resolution outcomes of executing these concrete actions are used to infer coarse-resolution outcomes that are added to the coarse-resolution history and used for subsequent coarse-resolution reasoning. The architecture thus combines the complementary strengths of declarative programming and probabilistic graphical models to represent and reason with non-monotonic logic-based and probabilistic descriptions of uncertainty and incomplete domain knowledge. In addition, we describe a general methodology for the design of software components of a robot based on these knowledge representation and reasoning tools, and provide a path for proving the correctness of these components. The architecture is evaluated in simulation and on a mobile robot finding and moving target objects to desired locations in indoor domains, to show that the architecture supports reliable and efficient reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.
Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers

The growing demand for computational power has led to the emergence of large-scale data centers that consume massive amounts of energy, thus resulting in high operating costs and CO2 emission. Furthermore, cloud computing environments are required to provide a high Quality of Service (QoS) to their clients and, therefore, need to handle power shortages. An optimized virtual machine allocation to physical hosts lowers energy consumption and allows for high-quality services. In this study, a novel solution was proposed for the allocation of virtual machines to physical hosts in cloud data centers using the Krill Herd algorithm, which is the fastest collective intelligence algorithm recently introduced. The performance of the proposed method was evaluated using the CloudSim simulator, and the results are suggestive of a 35% reduction in energy consumption.
Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.