Reviewerly/exHarmony huggingface dataset:
Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation

Knowledge graph (KG) contains well-structured external information and has shown to be effective for high-quality recommendation. However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG. While for model learning, these methods mainly rely on Negative Sampling (NS) to optimize the models for both KG embedding task and recommendation task. Since NS is not robust (e.g., sampling a small fraction of negative instances may lose lots of useful information), it is reasonable to argue that these methods are insufficient to capture collaborative information among users, items, and entities.
Unintended by Design: On the Political Uses of “Unintended Consequences”

This paper revisits the term “unintended consequences,” drawing upon an illustrative vignette to show how it is used to dismiss vital ethical and political concerns. Tracing the term to its original introduction by Robert Merton and building on feminist technoscience analyses, we uncover and rethink its widespread usage in popular and scholarly discourses and practices of technology design.
Entanglement-assisted quantum error-correcting codes over arbitrary finite fields

Abstract We prove that the known formulae for computing the optimal number of maximally entangled pairs required for entanglement-assisted quantum error-correcting codes (EAQECCs) over the binary field hold for codes over arbitrary finite fields as well. We also give a Gilbert–Varshamov bound for EAQECCs and constructions of EAQECCs coming from punctured self-orthogonal linear codes which are valid for any finite field.
Practical Route to Entanglement-Assisted Communication Over Noisy Bosonic Channels

Entanglement offers substantial advantages in quantum information processing, but loss and noise hinder its application in practical scenarios. Although it has been well known for decades that the classical communication capacity of lossy and noisy bosonic channels can be significantly enhanced by entanglement, no practical encoding and decoding schemes are available to realize any entanglement-enabled advantage. Here, we report structured encoding and decoding schemes for such an entanglement-assisted communication scenario. Specifically, we show that phase encoding on an entangled two-mode squeezed vacuum state saturates the entanglement-assisted classical communication capacity of a very noisy channel and overcomes the fundamental limit on covert communication that exists without the assistance of entanglement. We then construct receivers for optimum hypothesis-testing protocols with discrete phase modulation and for optimum noisy phase-estimation protocols with continuous phase modulation. Our results pave the way for entanglement-assisted communication and sensing in the radio-frequency and microwave spectral ranges.
Secure quantum remote state preparation of squeezed microwave states

Abstract Quantum communication protocols based on nonclassical correlations can be more efficient than known classical methods and offer intrinsic security over direct state transfer. In particular, remote state preparation aims at the creation of a desired and known quantum state at a remote location using classical communication and quantum entanglement. We present an experimental realization of deterministic continuous-variable remote state preparation in the microwave regime over a distance of 35 cm. By employing propagating two-mode squeezed microwave states and feedforward, we achieve the remote preparation of squeezed states with up to 1.6 dB of squeezing below the vacuum level. Finally, security of remote state preparation is investigated by using the concept of the one-time pad and measuring the von Neumann entropies. We find nearly identical values for the entropy of the remotely prepared state and the respective conditional entropy given the classically communicated information and, thus, demonstrate close-to-perfect security.
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks

Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolution neural networks (GCN) to achieve state-of-the-art performance.However, the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, potentially leaving irrelevant information for the trigger candidate for event prediction.In addition, the current models for ED fail to exploit the overall contextual importance scores of the words, which can be obtained via the dependency tree, to boost the performance.In this study, we propose a novel gating mechanism to filter noisy information in the hidden vectors of the GCN models for ED based on the information from the trigger candidate.We also introduce novel mechanisms to achieve the contextual diversity for the gates and the importance score consistency for the graphs and models in ED.The experiments show that the proposed model achieves state-of-the-art performance on two ED datasets.
Precise measurement of quantum observables with neural-network estimators

This work introduces a technique to lower the overhead in measurement statistics to achieve accurate estimations of observables relevant in quantum simulations. The approach consists of integrating artificial neural networks with the measurement apparatus of a quantum simulator. By training the neural networks with unsupervised learning of single-qubit measurements, the authors demonstrate a significant decrease in the number of measurements required to reach precise estimates in quantum chemistry applications
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The difficulty of simulating quantum dynamics depends on the norm of the Hamiltonian. When the Hamiltonian varies with time, the simulation complexity should only depend on this quantity instantaneously. We develop quantum simulation algorithms that exploit this intuition. For sparse Hamiltonian simulation, the gate complexity scales with the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>L</mml:mi><mml:mn>1</mml:mn></mml:msup></mml:math>norm<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mo>∫</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>0</mml:mn></mml:mrow><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>t</mml:mi></mml:mrow></mml:msubsup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">d</mml:mi></mml:mrow><mml:mi>τ</mml:mi><mml:mo fence="false" stretchy="false">‖</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>H</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>τ</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:msub><mml:mo fence="false" stretchy="false">‖</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo movablelimits="true" form="prefix">max</mml:mo></mml:mrow></mml:msub></mml:math>, whereas the best previous results scale with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi><mml:munder><mml:mo movablelimits="true" form="prefix">max</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>τ</mml:mi><mml:mo>∈</mml:mo><mml:mo stretchy="false">[</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">]</mml:mo></mml:mrow></mml:munder><mml:mo fence="false" stretchy="false">‖</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>H</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>τ</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:msub><mml:mo fence="false" stretchy="false">‖</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo movablelimits="true" form="prefix">max</mml:mo></mml:mrow></mml:msub></mml:math>. We also show analogous results for Hamiltonians that are linear combinations of unitaries. Our approaches thus provide an improvement over previous simulation algorithms that can be substantial when the Hamiltonian varies significantly. We introduce two new techniques: a classical sampler of time-dependent Hamiltonians and a rescaling principle for the Schrödinger equation. The rescaled Dyson-series algorithm is nearly optimal with respect to all parameters of interest, whereas the sampling-based approach is easier to realize for near-term simulation. These algorithms could potentially be applied to semi-classical simulations of scattering processes in quantum chemistry.
HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots

Path planning and navigation is a very important problem in robotics, especially for mobile robots operating in complex environments.Sampling based planners such as the probabilistic roadmaps (PRM) have been widely used for different robot applications.However, due to the random sampling of nodes in PRM, it suffers from narrow passage problem that generates unconnected graph.The problem is addressed by increasing the number of nodes but at higher computation cost affecting real-time performance.To address this issue, in this paper, we propose an improved sampling-based path planning method for mobile robot navigation.The proposed method uses a layered hybrid Probabilistic Roadmap (PRM) and the Artificial Potential Field (APF) method for global planning.We used a decomposition method for node distribution that uses map segmentation to produce regions of high and low potential, and propose a method of reducing the dispersion of sample set during the roadmap construction.Our method produces better goal planning queries with a smaller graph and is computationally efficient than the traditional PRM.The proposed planner called the Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method with respect to success rate and calculation cost.Furthermore, we present a method for reactive local motion planning in the presence of static and dynamic obstacles in the environment.The advantage of the proposed method is that it can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods.We show the validity of our method with experiments in simulation and real environments for both local and global planning.The results indicate that the proposed HPPRM is effective for autonomous mobile robot navigation in complex environments.The success rate of the proposed method is higher than 95% both for local and global planning.
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect.Based on it, we propose a novel graph-aware model with Interactive Graph Convolutional Networks (InterGCN) for aspect sentiment analysis.Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree.Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph.Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words.Besides, to interactively extract the dependencies between the aspect words and other aspects, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph.Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect.Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.
Learning character-agnostic motion for motion retargeting in 2D

Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer animation, is the retargeting of motion from one performer to another. While humans move in three dimensions, the vast majority of human motions are captured using video, requiring 2D-to-3D pose and camera recovery, before existing retargeting approaches may be applied. In this paper, we present a new method for retargeting video-captured motion between different human performers, without the need to explicitly reconstruct 3D poses and/or camera parameters. In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view. Our key idea is to train a deep neural network to decompose temporal sequences of 2D poses into three components: motion, skeleton, and camera view-angle. Having extracted such a representation, we are able to re-combine motion with novel skeletons and camera views, and decode a retargeted temporal sequence, which we compare to a ground truth from a synthetic dataset. We demonstrate that our framework can be used to robustly extract human motion from videos, bypassing 3D reconstruction, and outperforming existing retargeting methods, when applied to videos in-the-wild. It also enables additional applications, such as performance cloning, video-driven cartoons, and motion retrieval.
Identity-aware Graph Neural Networks

Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes’ identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Alto- gether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks.
Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach

The operation and maintenance of buildings has seen several advances in recent years. Multiple information and communication technology (ICT) solutions have been introduced to better manage building maintenance. However, maintenance practices in buildings remain less efficient and lead to significant energy waste. In this paper, a predictive maintenance framework based on machine learning techniques is proposed. This framework aims to provide guidelines to implement predictive maintenance for building installations. The framework is organised into five steps: data collection, data processing, model development, fault notification and model improvement. A sport facility was selected as a case study in this work to demonstrate the framework. Data were collected from different heating ventilation and air conditioning (HVAC) installations using Internet of Things (IoT) devices and a building automation system (BAS). Then, a deep learning model was used to predict failures. The case study showed the potential of this framework to predict failures. However, multiple obstacles and barriers were observed related to data availability and feedback collection. The overall results of this paper can help to provide guidelines for scientists and practitioners to implement predictive maintenance approaches in buildings.
Deep Model Poisoning Attack on Federated Learning

Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.
Saving superconducting quantum processors from decay and correlated errors generated by gamma and cosmic rays

Abstract Error-corrected quantum computers can only work if errors are small and uncorrelated. Here, I show how cosmic rays or stray background radiation affects superconducting qubits by modeling the phonon to electron/quasiparticle down-conversion physics. For present designs, the model predicts about 57% of the radiation energy breaks Cooper pairs into quasiparticles, which then vigorously suppress the qubit energy relaxation time ( T 1 ~ 600 ns) over a large area (cm) and for a long time (ms). Such large and correlated decay kills error correction. Using this quantitative model, I show how this energy can be channeled away from the qubit so that this error mechanism can be reduced by many orders of magnitude. I also comment on how this affects other solid-state qubits.
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance.We study a set of nine typologically diverse languages with readily available pretrained monolingual models on a set of five diverse monolingual downstream tasks.We first aim to establish, via fair and controlled comparisons, if a gap between the multilingual and the corresponding monolingual representation model of that language exists, and subsequently investigate the reason for any performance difference.To disentangle conflating factors, we train new monolingual models on the same data, with monolingually and multilingually trained tokenizers.We find that while the pretraining data size is an important factor, a designated monolingual tokenizer plays an equally important role in the downstream performance.Our results show that languages that are adequately represented in the multilingual model's vocabulary exhibit negligible performance decreases over their monolingual counterparts.We further find that replacing the original multilingual tokenizer with the specialized monolingual tokenizer improves the downstream performance of the multilingual model for almost every task and language.
An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA

Knowledge-based visual question answering (VQA) involves answering questions that require external knowledge not present in the image. Existing methods first retrieve knowledge from external resources, then reason over the selected knowledge, the input image, and question for answer prediction. However, this two-step approach could lead to mismatches that potentially limit the VQA performance. For example, the retrieved knowledge might be noisy and irrelevant to the question, and the re-embedded knowledge features during reasoning might deviate from their original meanings in the knowledge base (KB). To address this challenge, we propose PICa, a simple yet effective method that Prompts GPT3 via the use of Image Captions, for knowledge-based VQA. Inspired by GPT-3’s power in knowledge retrieval and question answering, instead of using structured KBs as in previous work, we treat GPT-3 as an implicit and unstructured KB that can jointly acquire and process relevant knowledge. Specifically, we first convert the image into captions (or tags) that GPT-3 can understand, then adapt GPT-3 to solve the VQA task in a few-shot manner by just providing a few in-context VQA examples. We further boost performance by carefully investigating: (i) what text formats best describe the image content, and (ii) how in-context examples can be better selected and used. PICa unlocks the first use of GPT-3 for multimodal tasks. By using only 16 examples, PICa surpasses the supervised state of the art by an absolute +8.6 points on the OK-VQA dataset. We also benchmark PICa on VQAv2, where PICa also shows a decent few-shot performance.
Improving IoT Privacy, Data Protection and Security Concerns

IoT has continued to evolve over the years with a promise to provide the users with effective means to interact, communicate, transact and create strong relationship. The invention and the development of IoT have created benefits for many businesses and individuals. However, as the IoT continues to evolve and develop, it has been subjected to certain threats and vulnerabilities. The common vulnerabilities notable in IoT include the security, privacy and data protection concerns. These issues have not been addressed by many scholars thus necessitated the need for this research study. Therefor the research study was concerned with the developed of a new IoT model that can enhance the security and privacy of the users of the IoT. The results indicate that the new model can be effective in addressing the needs of the IoT users. However, it noted that future research studies are still needed to improve the performance of the IoT security models.
Human Activity Recognition Based on Residual Network and BiLSTM

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.