Reviewerly/exHarmony huggingface dataset:
Complete and faithful hyperentangled-Bell-state analysis of photon systems using a failure-heralded and fidelity-robust quantum gate

Hyperentangled-Bell-state analysis (HBSA) represents a key step in many quantum information processing schemes that utilize hyperentangled states. In this paper, we present a complete and faithful HBSA scheme for two-photon quantum systems hyperentangled in both the polarization and spatial-mode degrees of freedom, using a failure-heralded and fidelity-robust quantum swap gate for the polarization states of two photons (P-SWAP gate), constructed with a singly charged semiconductor quantum dot (QD) in a double-sided optical microcavity (double-sided QD-cavity system) and some linear-optical elements. Compared with the previously proposed complete HBSA schemes using different auxiliary tools such as parity-check quantum nondemonlition detectors or additional entangled states, our scheme significantly simplifies the analysis process and saves the quantum resource. Unlike the previous schemes based on the ideal optical giant circular birefringence induced by a single-electron spin in a double-sided QD-cavity system, our scheme guarantees the robust fidelity and relaxes the requirement on the QD-cavity parameters. These features indicate that our scheme may be more feasible and useful in practical applications based on the photonic hyperentanglement.
Multi-modal Attention Network for Handwritten Mathematical Expression Recognition

In this paper, we propose a novel multi-modal attention network (MAN), which is based on encoder-decoder framework, for handwritten mathematical expression recognition (HMER). Here, multi-modal means two specific modalities: online and offline, where online modality employs dynamic trajectories as input and offline modality employs static images as input. More specifically, the proposed method first feeds dynamic trajectories and static images into online and offline channels of the multi-modal encoder respectively. The output of the encoder is then transferred to the multi-modal decoder to generate a LaTeX sequence as the mathematical expression recognition result. To make full use of the complementary information that comes from the two modalities, we propose a re-attention mechanism as an enhanced version of the multi-modal attention mechanism which can further improve the recognition performance. Evaluated on a benchmark published by CROHME competition, the proposed approach achieves an expression recognition accuracy of 54.05% on CROHME 2014 and 50.56% on CROHME 2016 which substantially outperforms the state-of-the-arts using the single online or offline modality.
Alcohol Craving in Heavy and Occasional Alcohol Drinkers After Cue Exposure in a Virtual Environment: The Role of the Sense of Presence

The development of new technologies, and more specifically the opportunity to immerse participants in virtual controlled environments, provides a new ecological framework for researchers to study complex behaviors. The aim of this experiment was to compare post-immersion craving in occasional and heavy alcohol drinkers. Twenty-two occasional drinkers and eighteen heavy drinkers were recruited and immersed in a virtual bar, including alcoholic beverages. After the exposure, heavy drinkers reported a significantly higher craving than occasional drinkers. Post-immersion alcohol craving was significantly related to the levels of perceived ecological validity of the virtual environment. Finally, a moderation analysis suggested that the levels of craving more strongly increased with perceived ecological validity in heavy drinkers than in occasional drinkers. Therefore, the perceived ecological validity was an important experimental parameter to study craving in a virtual environment. These results further suggested that virtual reality might be a useful tool for both the scientific study of alcohol addiction and the treatment of alcohol dependence and relapse.
Efficient Pairing-Free Certificateless Signature Scheme for Secure Communication in Resource-Constrained Devices

With the development of wireless communication technology, many network systems are interconnected with large number of smart devices and internet to gather and share electronic data. Due to its open nature, the data transmitted over public networks. Thus ensuring privacy and data security are of great importance. Also computing power, storage and bandwidth requirements are the main constraints in the development of many applications. To resolve these security and efficiency issues, this letter presents an efficient pairing free certificateless signature scheme. This scheme is proven secure and unforgeable. Finally, the comparative analysis shows the efficiency of our scheme.
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90.62% F1 on entity recognition and 72.97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by more than 1.9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.
User Modeling with Click Preference and Reading Satisfaction for News Recommendation

Modeling user interest is critical for accurate news recommendation. Existing news recommendation methods usually infer user interest from click behaviors on news. However, users may click a news article because attracted by its title shown on the news website homepage, but may not be satisfied with its content after reading. In many cases users close the news page quickly after click. In this paper we propose to model user interest from both click behaviors on news titles and reading behaviors on news content for news recommendation. More specifically, we propose a personalized reading speed metric to measure users’ satisfaction with news content. We learn embeddings of users from the news content they have read and their satisfaction with these news to model their interest in news content. In addition, we also learn another user embedding from the news titles they have clicked to model their preference in news titles. We combine both kinds of user embeddings into a unified user representation for news recommendation. We train the user representation model using two supervised learning tasks built from user behaviors, i.e., news title based click prediction and news content based satisfaction prediction, to encourage our model to recommend the news articles which not only are likely to be clicked but also have the content satisfied by the user. Experiments on real-world dataset show our method can effectively boost the performance of user modeling for news recommendation.
GTI: Learning to Generalize across Long-Horizon Tasks from Human Demonstrations

Imitation learning is an effective and safe technique to train robot policies in the real world because it does not depend on an expensive random exploration process.However, due to the lack of exploration, learning policies that generalize beyond the demonstrated behaviors is still an open challenge.We present a novel imitation learning framework to enable robots to 1) learn complex real world manipulation tasks efficiently from a small number of human demonstrations, and 2) synthesize new behaviors not contained in the collected demonstrations.Our key insight is that multi-task domains often present a latent structure, where demonstrated trajectories for different tasks intersect at common regions of the state space.We present Generalization Through Imitation (GTI), a two-stage offline imitation learning algorithm that exploits this intersecting structure to train goaldirected policies that generalize to unseen start and goal state combinations.In the first stage of GTI, we train a stochastic policy that leverages trajectory intersections to have the capacity to compose behaviors from different demonstration trajectories together.In the second stage of GTI, we collect a small set of rollouts from the unconditioned stochastic policy of the first stage, and train a goal-directed agent to generalize to novel start and goal configurations.We validate GTI in both simulated domains and a challenging long-horizon robotic manipulation domain in real world.Additional results and videos are available at https: //sites.google.
Context-Aware Block Net for Small Object Detection

State-of-the-art object detectors usually progressively downsample the input image until it is represented by small feature maps, which loses the spatial information and compromises the representation of small objects. In this article, we propose a context-aware block net (CAB Net) to improve small object detection by building high-resolution and strong semantic feature maps. To internally enhance the representation capacity of feature maps with high spatial resolution, we delicately design the context-aware block (CAB). CAB exploits pyramidal dilated convolutions to incorporate multilevel contextual information without losing the original resolution of feature maps. Then, we assemble CAB to the end of the truncated backbone network (e.g., VGG16) with a relatively small downsampling factor (e.g., 8) and cast off all following layers. CAB Net can capture both basic visual patterns as well as semantical information of small objects, thus improving the performance of small object detection. Experiments conducted on the benchmark Tsinghua-Tencent 100K and the Airport dataset show that CAB Net outperforms other top-performing detectors by a large margin while keeping real-time speed, which demonstrates the effectiveness of CAB Net for small object detection.
Controllable Neural Text-to-Speech Synthesis Using Intuitive Prosodic Features

Modern neural text-to-speech (TTS) synthesis can generate speech that is indistinguishable from natural speech. However, the prosody of generated utterances often represents the average prosodic style of the database instead of having wide prosodic variation. Moreover, the generated prosody is solely defined by the input text, which does not allow for different styles for the same sentence. In this work, we train a sequence-to-sequence neural network conditioned on acoustic speech features to learn a latent prosody space with intuitive and meaningful dimensions. Experiments show that a model conditioned on sentence-wise pitch, pitch range, phone duration, energy, and spectral tilt can effectively control each prosodic dimension and generate a wide variety of speaking styles, while maintaining similar mean opinion score (4.23) to our Tacotron baseline (4.26).
A Survey on Privacy-Preserving Blockchain Systems (PPBS) and a Novel PPBS-Based Framework for Smart Agriculture

Blockchain and smart contracts have seen significant application over the last decade, revolutionising many industries, including cryptocurrency, finance and banking, and supply chain management. In many cases, however, the transparency provided potentially comes at the cost of privacy. Blockchain does have potential uses to increase privacy-preservation. This paper outlines the current state of privacy preservation utilising Blockchain and Smart Contracts, as applied to a number of fields and problem domains. It provides a background of blockchain, outlines the challenges in blockchain as they relate to privacy, and then classifies into areas in which this paradigm can be applied to increase or protect privacy. These areas are cryptocurrency, data management and storage, e-voting, the Internet of Things, and smart agriculture. This work then proposes PPSAF, a new privacy-preserving framework designed explicitly for the issues that are present in smart agriculture. Finally, this work outlines future directions of research in areas combining future technologies, privacy-preservation and blockchain.
FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.
Geochemical signatures in native gold derived from Au-bearing ore deposits

Native gold is a significant indicator mineral for Au-bearing deposits. The chemical composition of gold in diverse geological settings is a function of fluid composition under various physicochemical conditions. Native gold grains from various Au-bearing deposits and districts, including orogenic, volcanogenic massive sulfide (VMS), porphyry Cu-Au deposits, epithermal Au-Ag, and reduced intrusion-related gold (RIRG) deposits, were investigated by electron probe microanalyzer (EPMA) and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to establish discriminant chemical signatures to apply indicator mineral surveys for exploration for gold-bearing deposits. Detailed textural observation of gold identifies compositional heterogeneities in gold grains from epithermal and VMS deposits. Partial least square-discriminant analysis (PLS-DA) identifies that Pd, Ag, Sb, Pb, Cu, Hg, and Te contents in gold grains have a high potential to discriminate gold from different deposit types. Comparison of gold compositional data from different deposit types using Random Forests classification models can be used as a predictive modeling tool to provide an accurate indication of gold-bearing deposit types.
Structural Model to Determine the Factors That Affect the Quality of Emergency Teaching, According to the Perception of the Student of the First University Courses

A quantitative study was conducted in order to know, from the perspective of university students, the relationship between the quality perceived (QP) during the period of confinement derived from the SARS-CoV-2 virus, with the variables teaching plan (PL), material resources (MR), interaction processes (IN), and the affective–emotional component (EM). An online questionnaire was designed, directed to students from 20 universities in Spain, with a total participation of 893 individuals. The results indicate that the perception of the students on the quality of online teaching is directly associated with the material resources provided by the professors and the professor–student interactions. However, this perception does not have any direct effect on the planning or the emotional state or affectation created by the unprecedented situation of confinement. Among the conclusions, we highlight the need for the universities to apply models of support and tutoring, especially for students in their first years at university, to develop competences such as autonomy, digital competence, and self-regulation, and the need for a change of approach of the students and the professors based on the new normality we are currently experiencing.
Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs

Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural information of the AST. To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Block to capture different dependencies in the AST graph for representation learning in code completion. The sub-tasks of code completion are optimized via multi-task learning in CCAG, and the task balance is automatically achieved using uncertainty without the need to tune task weights. The experimental results show that CCAG has superior performance than state-of-the-art approaches and it is able to provide intelligent code completion.
Sky Image Prediction Model Based on Convolutional Auto-Encoder for Minutely Solar PV Power Forecasting

The precise minute time scale forecasting of an individual PV power station output relies on accurate prediction of cloud distribution, which can lead to dramatic fluctuation of PV power generation. Precise cloud distribution information is mainly achieved by ground-based total sky imager, then the future cloud distribution can also be achieved by sky image prediction. In previous studies, traditional digital image processing technology (DIPT) has been widely used in predicting sky images. However, DIPT has two deficiencies: relatively limited input spatiotemporal information and linear extrapolation of images. The first deficiency makes the input spatiotemporal information not rich enough, while the second creates the prediction error from the beginning. To avoid these two deficiencies, convolutional autoencoder (CAE) based sky image prediction models are proposed due to the spatiotemporal feature extraction ability of two-dimensional (2-D) CAEs and 3-D CAEs. For 2-D CAEs and 3-D CAEs, four architectures are given respectively. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry and Fourier phase correlation theory are introduced to build the benchmark models. Besides, five different scenarios are also set and the results show that the proposed models outperform the benchmark models in all scenarios.
k-Nearest Neighbor Learning with Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.
Ring Toss Game-Based Optimization Algorithm for Solving Various Optimization Problems

There are many optimization problems in different scientific disciplines that should be solved and optimized using appropriate techniques.Population-based optimization algorithms are one of the most widely used techniques to solve optimization problems.This paper is focused on presenting a new population-based optimization approach called Ring Toss Game-Based Optimization (RTGBO) algorithm.The main idea of RTGBO is to simulate the behaviour of players and rules of the ring toss game in the design of the proposed algorithm.The main feature of the proposed RTGBO algorithm is the lack of control parameters.Steps of implementing RTGBO are described in detail and the proposed algorithm is mathematically modeled.The ability of RTGBO to solve optimization problems is evaluated on a set of twenty-three standard objective functions.These functions are selected from three different groups including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal.The performance of RTGBO is also compared with eight other well-known optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Emperor Penguin Optimizer (EPO), Hide Objects Game Optimization (HOGO), and Shell Game Optimization (SGO).The results of optimization of objective functions of unimodal type indicate the high exploitation ability of RTGBO in solving optimization problems.On the other hand, the results of optimizing the multi-model type objective functions indicate the acceptable exploration ability of RTGBO.The results also confirm the superiority of the proposed RTGBO algorithm over mentioned optimization techniques.
Detection of spam reviews through a hierarchical attention architecture with N-gram CNN and Bi-LSTM

Spam reviews misguide decision makings of consumers and may seriously affect fair trading in the online markets. Existing methods for detecting spam reviews mainly focus on feature designs from linguistic and psychological clues, but they hardly reveal the potential semantics. Recent research works apply deep learning to capture semantics features, while these models fail to extract multi-granularity information of the text structures nor consider the mutual influence among the sentences. We propose a hierarchical attention network in which distinct attentions are purposely used at the two layers to capture important, comprehensive, and multi-granularity semantic information. At the first layer, we especially use an N-gram CNN to extract the multi-granularity semantics of the sentences. We then use a combination of convolution structure and Bi-LSTM to extract important and comprehensive semantics in a document at the second layer. Extensive experiments on public datasets demonstrate that our model has superior detection performance over the state-of-the-art baselines, improving F1 score in the mixed-domain to 89.3% (with 4.8 points absolute improvement), F1 score in the Doctor domain to 92.8% (with 9.9 points absolute improvement), F1 score in the Hotel domain to 86.1% (with 2.4 points absolute improvement) and F1 score in the cross-domain to 84.7% (with 10.4 points absolute improvement).