Reviewerly/exHarmony huggingface dataset:
Lower Bounds for Locally Private Estimation via Communication Complexity

We develop lower bounds for estimation under local privacy constraints---including differential privacy and its relaxations to approximate or Renyi differential privacy---by showing an equivalence between private estimation and communication-restricted estimation problems. Our results apply to arbitrarily interactive privacy mechanisms, and they also give sharp lower bounds for all levels of differential privacy protections, that is, privacy mechanisms with privacy levels $\varepsilon \in [0, \infty)$. As a particular consequence of our results, we show that the minimax mean-squared error for estimating the mean of a bounded or Gaussian random vector in $d$ dimensions scales as $\frac{d}{n} \cdot \frac{d}{ \min\{\varepsilon, \varepsilon^2\}}$.
Orientation-point relation based inspection path planning method for 5-axis OMI system

Inspection path planning methods are of great importance to OMI systems. Most researches only optimize the inspection path length. However, the number of probe orientations (NPOs) also has a great influence on inspection efficiency and accuracy, especially for parts with complicated features. To optimize the NPOs and the path length, this paper firstly proposes the concept of orientation-point (O-P) relation schema which includes all valid O-P inspection cases. Then based on the O-P relation schema, two algorithms – the minimum orientation algorithm (MOA) and the shortest path algorithm (SPA), which consider NPOs and path length in different priorities were proposed. MOA was designed to find the minimum orientations that can measure all the inspection points according to the O-P relation schema, then the inspection points were categorized into groups by the selected orientations. The traveling salesman problem (TSP) algorithm was used to plan the inspection path for each group's points, finally the total path was obtained by connecting paths of different groups in sequence. SPA firstly uses the TSP algorithm to obtain the shortest inspection sequence, then minimizes the NPOs by finding orientations that can inspect most consecutive points along the planned inspection sequence according to the O-P relation schema. To avoid possible collisions during the inspection process, a safe box that contains the inspection part was defined before planning the path, which enables the probe to cross obstacles by moving to the safe planes of the safe box. Simulation experiments were conducted on three parts, the results show that both MOA and SPA can obtain a collision-free inspection path. MOA can effectively decrease the NPOs compared with SPA, and the path length can be even shorter than that of SPA in some complicated cases.
PGAS: Privacy-preserving graph encryption for accurate constrained shortest distance queries

The constrained shortest distance (CSD) query is used to determine the shortest distance between two vertices of a graph while ensuring that the total cost remains lower than a given threshold. The virtually unlimited storage and processing capabilities of cloud computing have enabled the graph owners to outsource their graph data to cloud servers. However, it may introduce privacy challenges that are difficult to address. In recent years, some relevant schemes that support the shortest distance query on the encrypted graph have been proposed. Unfortunately, some of them have unacceptable query accuracy, and some of them leak sensitive information that jeopardizes the graph privacy. In this work, we propose Privacy-preserving Graph encryption for Accurate constrained Shortest distance queries, called PGAS. This solution is capable of providing accurate CSD queries and ensures the privacy of the graph data. Besides, we also propose a secure integer comparison protocol and a secure minimum value protocol that realize two kinds of operations on encrypted integers. We provide theoretical security analysis to prove that PGAS achieves CQA-2 Security with less privacy leakage. In addition, the performance analysis and experimental evaluation based on real-world dataset show that PGAS achieves 100% accuracy with acceptable efficiency.
Certifiable Robustness to Graph Perturbations

Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showing that they are extremely vulnerable to adversarial attacks on both the graph structure and the node attributes. We propose the first method for verifying certifiable (non-)robustness to graph perturbations for a general class of models that includes graph neural networks and label/feature propagation. By exploiting connections to PageRank and Markov decision processes our certificates can be efficiently (and under many threat models exactly) computed. Furthermore, we investigate robust training procedures that increase the number of certifiably robust nodes while maintaining or improving the clean predictive accuracy.
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies

State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing full-planning on Markov Decision Processes (MDPs) built by the gathered experience. In this paper, we focus on model-based RL in the finite-state finite-horizon MDP setting and establish that exploring with greedy policies -- act by 1-step planning -- can achieve tight minimax performance in terms of regret, O(\sqrt{HSAT}). Thus, full-planning in model-based RL can be avoided altogether without any performance degradation, and, by doing so, the computational complexity decreases by a factor of S. The results are based on a novel analysis of real-time dynamic programming, then extended to model-based RL. Specifically, we generalize existing algorithms that perform full-planning to such that act by 1-step planning. For these generalizations, we prove regret bounds with the same rate as their full-planning counterparts.
Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

Wide neural networks with random weights and biases are Gaussian processes, as observed by Neal (1995) for shallow networks, and more recently by Lee et al.~(2018) and Matthews et al.~(2018) for deep fully-connected networks, as well as by Novak et al.~(2019) and Garriga-Alonso et al.~(2019) for deep convolutional networks. We show that this Neural Network-Gaussian Process correspondence surprisingly extends to all modern feedforward or recurrent neural networks composed of multilayer perceptron, RNNs (e.g. LSTMs, GRUs), (nD or graph) convolution, pooling, skip connection, attention, batch normalization, and/or layer normalization. More generally, we introduce a language for expressing neural network computations, and our result encompasses all such expressible neural networks. This work serves as a tutorial on the \emph{tensor programs} technique formulated in Yang (2019) and elucidates the Gaussian Process results obtained there. We provide open-source implementations of the Gaussian Process kernels of simple RNN, GRU, transformer, and batchnorm+ReLU network at github.com/thegregyang/GP4A. Please see our arxiv version for the complete and up-to-date version of this paper.
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a novel and theoretically-supported paradigm for large-scale graph analysis. The proposed method is based on the fact that Gromov-Wasserstein discrepancy is a pseudometric on graphs. Given two graphs, the optimal transport associated with their Gromov-Wasserstein discrepancy provides the correspondence between their nodes and achieves graph matching. When one of the graphs is a predefined graph with isolated but self-connected nodes ($i.e.$, disconnected graph), the optimal transport indicates the clustering structure of the other graph and achieves graph partitioning. Further, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs. Our method combines a recursive $K$-partition mechanism with a warm-start proximal gradient algorithm, whose time complexity is $\mathcal{O}(K(E+V)\log_K V)$ for graphs with $V$ nodes and $E$ edges. To our knowledge, our method is the first attempt to make Gromov-Wasserstein discrepancy applicable to large-scale graph analysis and unify graph partitioning and matching into the same framework. It outperforms state-of-the-art graph partitioning and matching methods, achieving a trade-off between accuracy and efficiency.
Code-Switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation

Code-switching is about dealing with alternative languages in speech or text. It is partially speaker-depend and domain-related, so completely explaining the phenomenon by linguistic rules is challenging. Compared to most monolingual tasks, insufficient data is an issue for code-switching. To mitigate the issue without expensive human annotation, we proposed an unsupervised method for code-switching data augmentation. By utilizing a generative adversarial network, we can generate intra-sentential code-switching sentences from monolingual sentences. We applied proposed method on two corpora, and the result shows that the generated code-switching sentences improve the performance of code-switching language models.
Digital Transformation in Higher Education—Buzzword or Opportunity?

We argue that, starting from a sophisticated understanding of "digital," we should develop a corresponding notion of "education" suitable to meet the challenges of the ongoing digital transformation. The central task for higher education institutions is to model the complex networks of digital skills (critical thinking, media literacy, cross-cultural competence, etc.) as a foundation for creating contextualized learning scenarios in the disciplines. The crucial success factor is the reunification of the classroom with the real world.
Dense members of local cores-based density peaks clustering algorithm

An efficient clustering algorithm by fast search and find of density peaks (DP) was proposed and attracted much attention from researchers. It assumes that cluster centers are surrounded by lower density points and have a larger distance from points with higher densities. According to the characteristic of cluster centers, we can easily obtain centers from decision graph. However, DP algorithm fails to cluster manifold data sets, especially when there are a lot of noises in the manifold data sets. In this paper, we propose a dense members of local core-based density peaks clustering algorithm DLORE-DP. First, we find local cores to represent the data set. After that, only dense members of local cores are taken into consideration when computing the graph distance between local cores, avoiding the interference of noises. Then, natural neighbor-based density and the new defined graph distance are used to construct decision graph on local cores and DP algorithm is employed to cluster local cores. Finally, we assign each remaining point to the cluster its representative belongs to. The new defined graph distance helps our algorithm cluster manifold data sets and the elimination of low density points makes it more robust. Moreover, since we only calculate the graph distance between local cores, instead of all pairs of points, it greatly reduces the running time. The experimental results on synthetic and real data sets show that DLORE-DP is more effective, efficient and robust than other algorithms when clustering manifold data sets with noises.
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph

Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge. In one prominent example, a causal health knowledge graph could learn relationships between diseases and symptoms and then serve as a diagnostic tool to be refined with additional clinical input. Prior research has demonstrated the ability to construct such a graph from over 270,000 emergency department patient visits. In this work, we describe methods to evaluate a health knowledge graph for robustness. Moving beyond precision and recall, we analyze for which diseases and for which patients the graph is most accurate. We identify sample size and unmeasured confounders as major sources of error in the health knowledge graph. We introduce a method to leverage non-linear functions in building the causal graph to better understand existing model assumptions. Finally, to assess model generalizability, we extend to a larger set of complete patient visits within a hospital system. We conclude with a discussion on how to robustly extract medical knowledge from EHRs.
The effect of 360-degree rotatable product images on purchase intention

In three experiments, we tested the effectiveness of 360-degree rotatable product images on retail websites. In Study 1, participants reported higher purchase intention in response to a 360-degree rotatable image than a two-dimensional static image. In Study 2, participants who were primed for cognitive busyness by writing about activities that kept them busy (vs. writing about typical daily activities) reported lower purchase intention than in Study 1. In Study 3, we found a similar effect by directly manipulating cognitive busyness: participants memorized long or short number strings while performing a shopping task. Furthermore, sensory vividness fully mediated the effect of 360-degree rotatable images on purchase intention for the less busy participants, not the highly busy participants. Theoretical and practical implications for virtual product presentation are discussed.
COIN Attacks

Intel SGX is a hardware-based trusted execution environment (TEE), which enables an application to compute on confidential data in a secure enclave. SGX assumes a powerful threat model, in which only the CPU itself is trusted; anything else is untrusted, including the memory, firmware, system software, etc. An enclave interacts with its host application through an exposed, enclave-specific, (usually) bi-directional interface. This interface is the main attack surface of the enclave. The attacker can invoke the interface in any order and inputs. It is thus imperative to secure it through careful design and defensive programming.
Efficient Precision-Adjustable Architecture for Softmax Function in Deep Learning

The softmax function has been widely used in deep neural networks (DNNs), and studies on efficient hardware accelerators for DNN have also attracted tremendous attention. However, it is very challenging to design efficient hardware architectures for softmax because of the expensive exponentiation and division calculations in it. In this brief, the softmax function is firstly simplified by exploring algorithmic strength reductions. Afterwards, a hardware-friendly and precision-adjustable calculation method for softmax is proposed, which can meet different precision requirements in various deep learning (DL) tasks. Based on the above innovations, an efficient architecture for softmax is presented. By tuning the parameter P , the system accuracy and complexity can be adjusted dynamically to achieve a good tradeoff between them. The proposed design is coded using hardware description language (HDL) and evaluated on two platforms, Xilinx Zynq-7000 ZC706 development board and TSMC 28-nm CMOS technology, respectively. Hardware implementation results show that our architecture significantly outperforms other works in speed and area, and that by adjusting P , the accuracy can be further increased with little hardware overhead.
Cultural Marxism: far-right conspiracy theory in Australia’s culture wars

As a conspiracy promoted by the far-right, Cultural Marxism has gained ground over the past quarter century. In its dominant iteration, the US-originating conspiracy holds that a small group of Marxist critical theorists have conspired to destroy Western civilisation by taking over key cultural institutions. Yet what does such a conspiracy look like in a transnational context – and how are such conspiracy theories adapted for local use? In this article, we trace Cultural Marxism’s use and function in Australia’s recent culture wars, asking when, where and in what contexts the conspiracy is invoked in the Australian political and media landscape. Our analysis examines the ways in which Cultural Marxism has moved from the ‘fringe’ to the ‘mainstream’, revealing the porous nature of divisions on the contemporary Australian right as well as differences between ‘old’ and ‘new’ right. We pay particular attention to the localised use of the conspiracy in the ‘Safe Schools’ controversy of 2016–2017, whereby Cultural Marxist tropes were imbued with local concerns about sexuality and gender issues. The article provides an important illustration and analysis of the ways in which transnational conspiracy theories and tropes of the far-right can be translated, adapted and used in different national contexts.
A development framework for decision support systems in high-performance sport

Abstract Decision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.
A Hierarchical Multi Blockchain for Fine Grained Access to Medical Data

The health care ecosystem involves various interconnected stakeholders with different, and sometimes conflicting security and privacy needs. Sharing medical data, sometimes generated by remote medical devices, is a challenging task. Although several solutions exist in the literature covering functional requirements such as interoperability and scalability, as well as security & privacy requirements such as fine-grained access control and data privacy, balancing between them is not a trivial task as off-the-shelf solutions do not exist. On one hand, centralized cloud architectures provide scalability and interoperable access, but make strong trust assumptions. On the other, decentralized blockchain based solutions favor data privacy and independent trust management, but typically do not support dynamic changes of the underlying trust domains. To cover this gap, in this paper, we present a novel hierarchical multi expressive blockchain architecture. At the top layer, a proxy blockchain enables independently managed trust authorities to interoperate. End-users from different health care domains, such as hospitals or device manufacturers are able to access and securely exchange medical data, provided that a commonly agreed domain-wise access policy is enforced. At the bottom layer, one or more domain blockchains allow each domain (e.g. a hospital or device manufacturer) to enforce their policy and allow fine-grained access control with attribute-based encryption. This architecture is designed to provide the autonomous management of trusted medical data/devices and the transactions of mutually untrusted stakeholders, as well as an inherent forensics mechanism tailored for granular auditing. Smart contracts are used to enforce decentralized policies. Ciphertext-policy attribute based encryption (CP-ABE) is used to distribute the decryption process among end users and the system, as well as support an efficient credential revocation mechanism. We demonstrate the efficiency of the proposed architecture through a proof of concept implementation. Finally we analyse the major security and performance characteristics.
Rich Visual Knowledge-Based Augmentation Network for Visual Question Answering

Visual question answering (VQA) that involves understanding an image and paired questions develops very quickly with the boost of deep learning in relevant research fields, such as natural language processing and computer vision. Existing works highly rely on the knowledge of the data set. However, some questions require more professional cues other than the data set knowledge to answer questions correctly. To address such an issue, we propose a novel framework named a knowledge-based augmentation network (KAN) for VQA. We introduce object-related open-domain knowledge to assist the question answering. Concretely, we extract more visual information from images and introduce a knowledge graph to provide the necessary common sense or experience for the reasoning process. For these two augmented inputs, we design an attention module that can adjust itself according to the specific questions, such that the importance of external knowledge against detected objects can be balanced adaptively. Extensive experiments show that our KAN achieves state-of-the-art performance on three challenging VQA data sets, i.e., VQA v2, VQA-CP v2, and FVQA. In addition, our open-domain knowledge is also beneficial to VQA baselines. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yyyanglz/KAN</uri> .
Strong spin squeezing induced by weak squeezing of light inside a cavity

Abstract We propose a simple method for generating spin squeezing of atomic ensembles in a Floquet cavity subject to a weak, detuned two-photon driving. We demonstrate that the weak squeezing of light inside the cavity can, counterintuitively, induce strong spin squeezing . This is achieved by exploiting the anti-Stokes scattering process of a photon pair interacting with an atom. Specifically, one photon of the photon pair is scattered into the cavity resonance by absorbing partially the energy of the other photon whose remaining energy excites the atom . The scattering, combined with a Floquet sideband, provides an alternative mechanism to implement Heisenberg-limited spin squeezing. Our proposal does not need multiple classical and cavity-photon drivings applied to atoms in ensembles, and therefore its experimental feasibility is greatly improved compared to other cavity-based schemes. As an example, we demonstrate a possible implementation with a superconducting resonator coupled to a nitrogen-vacancy electronic-spin ensemble.