Reviewerly/exHarmony huggingface dataset:
Computer-Inspired Concept for High-Dimensional Multipartite Quantum Gates

An open question in quantum optics is how to manipulate and control complex quantum states in an experimentally feasible way. Here we present concepts for transformations of high-dimensional multiphotonic quantum systems. The proposals rely on two new ideas: (i) a novel high-dimensional quantum nondemolition measurement, (ii) the encoding and decoding of the entire quantum transformation in an ancillary state for sharing the necessary quantum information between the involved parties. Many solutions can readily be performed in laboratories around the world and thereby we identify important pathways for experimental research in the near future. The concepts have been found using the computer algorithm melvin for designing computer-inspired quantum experiments. As opposed to the field of machine learning, here the human learns new scientific concepts by interpreting and analyzing the results presented by the machine. This demonstrates that computer algorithms can inspire new ideas in science, which has a widely unexplored potential that goes far beyond experimental quantum information science.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.
CHURP

We introduce CHURP (CHUrn-Robust Proactive secret sharing). CHURP enables secure secret-sharing in dynamic settings, where the committee of nodes storing a secret changes over time. Designed for blockchains, CHURP has lower communication complexity than previous schemes: $O(n)$ on-chain and $O(n^2)$ off-chain in the optimistic case of no node failures. CHURP includes several technical innovations: An efficient new proactivization scheme of independent interest, a technique (using asymmetric bivariate polynomials) for efficiently changing secret-sharing thresholds, and a hedge against setup failures in an efficient polynomial commitment scheme. We also introduce a general new technique for inexpensive off-chain communication across the peer-to-peer networks of permissionless blockchains. We formally prove the security of CHURP, report on an implementation, and present performance measurements.
Multi-Key Homomorphic Encryption from TFHE

In this paper, we propose a Multi-Key Homomorphic Encryption (MKHE) scheme by generalizing the low-latency homomorphic encryption by Chillotti et al. (ASIACRYPT 2016). Our scheme can evaluate a binary gate on ciphertexts encrypted under different keys followed by a bootstrapping. The biggest challenge to meeting the goal is to design a multiplication between a bootstrapping key of a single party and a multi-key RLWE ciphertext. We propose two different algorithms for this hybrid product. Our first method improves the ciphertext extension by Mukherjee and Wichs (EUROCRYPT 2016) to provide better performance. The other one is a whole new approach which has advantages in storage, complexity, and noise growth. Compared to previous work, our construction is more efficient in terms of both asymptotic and concrete complexity. The length of ciphertexts and the computational costs of a binary gate grow linearly and quadratically on the number of parties, respectively. We provide experimental results demonstrating the running time of a homomorphic NAND gate with bootstrapping. To the best of our knowledge, this is the first attempt in the literature to implement an MKHE scheme.
PAGE-Net: Interpretable and Integrative Deep Learning for Survival Analysis Using Histopathological Images and Genomic Data

The integration of multi-modal data, such as histopathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions in cancer study. Histopathology, as a clinical gold-standard tool for diagnosis and prognosis in cancers, allows clinicians to make precise decisions on therapies, whereas high-throughput genomic data have been investigated to dissect the genetic mechanisms of cancers. We propose a biologically interpretable deep learning model (PAGE-Net) that integrates histopathological images and genomic data, not only to improve survival prediction, but also to identify genetic and histopathological patterns that cause different survival rates in patients. PAGE-Net consists of pathology/genome/demography-specific layers, each of which provides comprehensive biological interpretation. In particular, we propose a novel patch-wise texture-based convolutional neural network, with a patch aggregation strategy, to extract global survival-discriminative features, without manual annotation for the pathology-specific layers. We adapted the pathway-based sparse deep neural network, named Cox-PASNet, for the genome-specific layers. The proposed deep learning model was assessed with the histopathological images and the gene expression data of Glioblastoma Multiforme (GBM) at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). PAGE-Net achieved a C-index of 0.702, which is higher than the results achieved with only histopathological images (0.509) and Cox-PASNet (0.640). More importantly, PAGE-Net can simultaneously identify histopathological and genomic prognostic factors associated with patients survivals. The source code of PAGE-Net is publicly available at https://github.com/DataX-JieHao/PAGE-Net.
A Framework for Traceable and Transparent Supply Chain Management for Agri-food Sector in Malaysia using Blockchain Technology

This paper presents a framework for traceable and transparent supply chain management (SCM) system for the agri-food sector using blockchain technology in Malaysia.Numerous researchers believed that the current SCM system consists of several weak points, especially when multiple enterprise resource planning (ERP) system utilizing centralized SCM.Thus, data transparency and traceability are limited.This study hypothesized that if blockchain technology correlates with transparency and traceability of SCM, the above limitation can be minimized, as blockchain technology works in a distributed manner.This research uses "pepper" as an agri-food domain.The research also recommends that permissioned blockchain is a better fit as compared to permissionless blockchain.
XDL

With the rapid growth of data and computing power, deep learning based approaches have become the main solution for many artificial intelligence problems such as image classification, speech recognition and computer vision. Several excellent deep learning (DL) frameworks including Tensorflow, MxNet and PyTorch have been made open-sourced, further accelerating the advance of the community. However, existing DL frameworks are not designed for applications involving high-dimensional sparse data, which exists widely in many successful online businesses such as search engine, recommender systems and online advertising. In these industrial scenarios, deep models are typically trained on large scale datasets with up to billions of sparse features and hundreds of billions of samples, bringing great challenges to DL framework.
A Practical Visual Servo Control for Aerial Manipulation Using a Spherical Projection Model

This article addresses the problem of autonomous servoing control of an unmanned aerial manipulator with the capability of grasping target objects using computer vision. Specifically, a practical visual servo control using a spherical projection model is proposed. The aerial manipulator is an unmanned aerial vehicle equipped with a robotic arm that greatly increases the freedom and operational flexibility of the end-effector. However, it also increases the complexity of the kinematics, dynamics, and control design of the complete system. A novel passivity-like error equation of the image features is established by using the spherical camera geometry dynamics with an eye-in-hand configuration. To further improve the grasping performance, a task-priority control scheme is utilized with one main task and several subtasks, i.e., controlling the gripper position and orientation, vertically aligning the center of gravity, and avoiding the joint limitation. Simulation results are provided to illustrate and assess the performance of the proposed visual servo control. The practicability and effectiveness of autonomous aerial manipulation are well supported by the experimental results acquired through outdoor environments.
An Adaptive Matrix Pencil Algorithm Based-Wavelet Soft-Threshold Denoising for Analysis of Low Frequency Oscillation in Power Systems

One of the main reasons that affecting the stability of power systems is low frequency oscillation (LFO). The existence of noise influences the accuracy of LFO mode identification extracted from wide-area measurement system (WAMS). The wavelet threshold de-noising is widely used in signal processing. In this paper, wavelet soft threshold is illustrated to attenuate the noise of LFO signal, the optimal wavelet basis and decomposition level for de-noising LFO signal with noise are obtained and verified by experiments. Following the signal de-noising, an improved Matrix Pencil (MP) algorithm is used for mode identification of LFO. This improvement particularly lies in the ratio of adjacent singular entropy increment difference (RASEID) designed as an adaptive order determination method in the MP algorithm proposed in the paper. RASEID not only makes the MP algorithm adaptive, but also enhances the stability of the order determination in the mode identification process. The proposed method ensures the accuracy of mode identification with lower sensitivity to noise interference. Finally, the validity of the proposed method is verified by three cases studies. The first study is on the analysis of synthetic signal typically performed in many literatures. The second study is to identify the mode of active power LFO signal which is generated by IEEE four-generator and two-area system given with disturbance on RT-LAB experimental platform. The third study is the oscillation analysis of the actual LFO data in the North American power grid. The results validate the feasibility of the proposed method for mode identification of noisy LFO.
Learning Agent Communication under Limited Bandwidth by Message Pruning

Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical limited bandwidth in multi-agent communication has been largely ignored by the existing DRL methods. Specifically, many methods keep sending messages incessantly, which consumes too much bandwidth. As a result, they are inapplicable to multi-agent systems with limited bandwidth. To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. We evaluate the gating mechanism on several tasks. Experiments demonstrate that it can prune a lot of messages with little impact on performance. In fact, the performance may be greatly improved by pruning redundant messages. Moreover, the proposed gating mechanism is applicable to several previous methods, equipping them the ability to address bandwidth restricted settings.
Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment

The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use synthetic data for training, but deep networks show a critical performance drop when analyzing data with slightly different statistical properties with respect to the training set. In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. An adversarial model, based on the cycle consistency framework, performs the mapping between the synthetic and real domain. The data is then fed to a MobileNet-v2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNet-v2, allows to better align the features of the two domain distributions and to further improve the performance. Finally, the consistency of the semantic maps is exploited. After an initial supervised training on synthetic data, the whole UDA architecture is trained end-to-end considering all its components at once. Experimental results show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios. The usage of the lightweight MobileNet-v2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles.
Scale-Iterative Upscaling Network for Image Deblurring

Machine learning based methods for blind deblurring are efficient to handle real-world blurred images, whose blur may be caused by various combined distortions.However, existing multi-level architectures fail to fit images of various scenarios.In this paper, we propose a scale-iterative upscaling network (SIUN) that restores sharp images in an iterative manner.It is not only able to preserve the advantages of weights sharing across scales but also more flexible when training and predicting with different iterations to fit different images.Specifically, we bring in the super-resolution structure instead of the upsampling layer between two consecutive scales to restore a detailed image.Besides, we explore different curriculum learning strategies for both training and prediction of the network and introduce a widely applicable strategy to make SIUN compatible with different scenarios, including text and face.Experimental results on both benchmark datasets and real blurred images show that our method can produce better results than state-of-the-art methods.Code is available at https://github.com/minyuanye/SIUN.
Formation of Design and Research Competencies in Future Teachers in the Framework of STEAM Education

In today's digital society and economy, professions associated with high technologies are becoming increasingly in demand: programmers, system en-gineers, IT specialists, biotechnologists, project leaders, etc. In this regard, school graduates are required not just good knowledge of school subjects, but also knowledge interdisciplinary, the formation of which is a long pro-cess, starting as early as school, and only traditional methods are indispensa-ble. One of these innovative technologies is STEAM education. To imple-ment the concept of STEAM-education, the system of pedagogical education is tasked with training relevant personnel with the necessary competencies, among which the most relevant are meta-subjects and design and research skills. In our opinion, one of the possible innovations in the preparation of teachers for STEAM education is the inclusion of the “Robotics” discipline in the content of training future teachers of physics, mathematics, technolo-gy, visual arts, and others. The authors conducted a survey among future teachers studying at the Faculty of Mathematics and Natural Sciences and Engineering and Technology Faculties of the Elabuga Institute of Kazan Fed-eral University, showed that the problem of training graduates of general ed-ucational institutions to the challenges of the new time is more relevant than ever. Only 18% of respondents are aware of the existing the world and the country STEM and STEAM education programs, 15% of the students sur-veyed believe that they are ready to implement project activities STI and on-ly 10% of students believe that they are ready to lead project activities. These data indicate that not only the problem of teachers' readiness to im-plement new educational programs, but also the problem of preparing future teachers to implement practice-oriented educational activities based on the formation of design and research competencies, is coming to the fore.
A Formal Verification Framework for Security Issues of Blockchain Smart Contracts

Blockchain technology has attracted more and more attention from academia and industry recently. Ethereum, which uses blockchain technology, is a distributed computing platform and operating system. Smart contracts are small programs deployed to the Ethereum blockchain for execution. Errors in smart contracts will lead to huge losses. Formal verification can provide a reliable guarantee for the security of blockchain smart contracts. In this paper, the formal method is applied to inspect the security issues of smart contracts. We summarize five kinds of security issues in smart contracts and present formal verification methods for these issues, thus establishing a formal verification framework that can effectively verify the security vulnerabilities of smart contracts. Furthermore, we present a complete formal verification of the Binance Coin (BNB) contract. It shows how to formally verify the above security issues based on the formal verification framework in a specific smart contract. All the proofs are checked formally using the Coq proof assistant in which contract model and specification are formalized. The formal work of this paper has a variety of essential applications, such as the verification of blockchain smart contracts, program verification, and the formal establishment of mathematical and computer theoretical foundations.
Managing data locally to answer questions globally: The role of collaborative science in ecology

Abstract Ecologists are increasingly asking large‐scale and/or broad‐scope questions that require vast datasets. In response, various top‐down efforts and incentives have been implemented to encourage data sharing and integration. However, despite general consensus on the critical need for more open ecological data, several roadblocks still discourage compliance and participation in these projects; as a result, ecological data remain largely unavailable. Grassroots initiatives (i.e. efforts initiated and led by cohesive groups of scientists focused on specific goals) have thus far been overlooked as a powerful means to meet these challenges. These bottom‐up collaborative data integration projects can play a crucial role in making high quality datasets available because they tackle the heterogeneity of ecological data at a scale where it is still manageable, all the while offering the support and structure to do so. These initiatives foster best practices in data management and provide tangible rewards to researchers who choose to invest time in sound data stewardship. By maintaining proximity between data generators and data users, grassroots initiatives improve data interpretation and ensure high‐quality data integration while providing fair acknowledgement to data generators. We encourage researchers to formalize existing collaborations and to engage in local activities that improve the availability and distribution of ecological data. By fostering communication and interaction among scientists, we are convinced that grassroots initiatives can significantly support the development of global‐scale data repositories. In doing so, these projects help address important ecological questions and support policy decisions.
A Study of College English Culture Intelligence-Aided Teaching System and Teaching Pattern

College English teaching is supposed to cover both language acquisition and culture learning due to the close relationship between language and culture, taking cultural teaching as an indispensible part of college English courses. With the rapid integration of information technology and English curriculum, artificial intelligence has brought new opportunities to college English teaching, and college English cultural teaching methods are now faced with new innovations. In the age of intelligence, to promote teaching quality and learning effect, artificial intelligence technology can be embedded in English teaching practice, exerting its technical advantages and frontier characteristics. In consideration of integrated developing tendency of college English cultural teaching model and modern information technology, the paper is aimed to design and build up an intelligence-aided system so as to extend the depth and width of the application of modern information technology in college English cultural teaching as well as to exploit the great application potential of modern information technology in college English cultural teaching, thus opening a new way and presenting a direction for college English cultural teaching.
A Compact Brain Storm Algorithm for Matching Ontologies

An ontology can formally present the domain knowledge by specifying the domain concepts and their relationships, which is a kernel technique for addressing the data heterogeneity issue in the semantic web. However, since existing ontologies are developed and maintained independently by different communities, a concept and its relationship with the others could be described in different ways, yielding the ontology heterogeneity problem. To solve this problem, in this work, we formally construct an optimal model for it, and propose a similarity measure for distinguishing identical ontology entities. Since determining the high-quality ontology alignment is a complex process, we propose to utilize a Brain Storm Optimization algorithm (BSO) to optimize the alignment. BSO is a recently developed Swarm Intelligence algorithm (SI), which can effectively solve the complex optimization problem by imitating the human's idea generating process. However, classic BSO needs to cluster various ideas in each generation and carry out the evolving operators on all ideas, which increases the computational complexity. To improve the efficiency of BSO-based ontology matcher, a Compact BSO (CBSO) is further proposed, which can reduce the memory consumption by utilizing the probabilistic representation on the idea cluster, and improve the algorithm's speed through the compact crossover operator and perturbation operator. The experiment uses the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our approach's performance. The comparisons among the state-of-the-art ontology matchers and our proposal show that CBSO-based ontology matcher can efficiently determine high-quality ontology alignments.