Reviewerly/exHarmony huggingface dataset:
An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.
Multi-level multi-secret sharing scheme for decentralized e-voting in cloud computing

The cryptosystem-based data privacy preserving methods employ high computing power of cloud servers, where the main feature is to allow resource sharing and provide multi-user independent services. Therefore, to achieve the rapid allocation and release of resource sharing in cloud computing, decentralized cryptographic protocols need to be proposed for multi-user consensus systems. In this work, we first present a multi-secret sharing scheme with multi-level access structure, where the secret reconstruction algorithm satisfies the additive homomorphism. The secret sharing scheme needs no trusted third parties and any user can play the role of dealer. In the designing, multiple target secrets are independently shared, where each subset of users forms a sub-access structure and shares one target secret only with a short secret share. This scheme is efficient and unconditionally secure. Furthermore, based on the multi-level access structures, a decentralized multi-role e-voting protocol is designed using Chinese Remainder Theorem, where each role's election is associated with one sub-access structure. The voters employ a shared parameter to blind the sum of ballot values. Meanwhile, the e-voting scheme supports a public verification for the final election results. Compared with the existing e-voting protocols, our e-voting system does not require any authority center and the cloud server runs vote counting. And our e-voting scheme does not need any high-complexity computational cost operation such as module exponential operation, etc. Finally, the common feature of Blockchain and Ad Hoc networks is decentralized. Thus the main idea of this protocol without a trusted third party can be used to achieve a secure consensus among multiple nodes in Blockchain and Ad Hoc network, meanwhile, the consensus results can be verified.
Local differential privacy for social network publishing

Social networks often contain sensitive information. Releasing social network data could possibly seriously jeopardize individual privacy. Therefore, we need to protect privacy when publish social network data. However, the current differential privacy for social network data publishing seriously influences the structure of the social network. We propose a local differential privacy model for social network publishing that preserves community structure information. The model generates the synthetic social network data as published versions under the structural constraints of the edge probability reconstruction. We theoretically prove that the local differential privacy model satisfies the definition of differential privacy. We evaluate the efficacy of the proposed method using three real-life social network datasets and show that our method effectively preserves network structural properties, while ensuring a strong degree of privacy.
Unsupervised Entity Alignment Using Attribute Triples and Relation Triples

Entity alignment aims to find entities referring to the same real-world object across different knowledge graphs (KGs). Most existing works utilize the relations between entities contained in the relation triples with embedding-based approaches, but require a large number of training data. Some recent attempt works on using types of their attributes in attribute triples for measuring the similarity between entities across KGs. However, due to diverse expressions of attribute names and non-standard attribute values across different KGs, the information contained in attribute triples can not be fully used. To tackle the drawbacks of the existing efforts, we novelly propose an unsupervised entity alignment approach using both attribute triples and relation triples of KGs. Initially, we propose an interactive model to use attribute triples by performing entity alignment and attribute alignment alternately, which will generate a lot of high-quality aligned entity pairs. We then use these aligned entity pairs to train a relation embedding model such that we could use relation triples to further align the remaining entities. Lastly, we utilize a bivariate regression model to learn the respective weights of similarities measuring from the two aspects for a result combination. Our empirical study performed on several real-world datasets shows that our proposed method achieves significant improvements on entity alignment compared with state-of-the-art methods.
A Multi-Convolutional Autoencoder Approach to Multivariate Geochemical Anomaly Recognition

The spatial structural patterns of geochemical backgrounds are often ignored in geochemical anomaly recognition, leading to the ineffective recognition of valuable anomalies in geochemical prospecting. In this contribution, a multi-convolutional autoencoder (MCAE) approach is proposed to deal with this issue, which includes three unique steps: (1) a whitening process is used to minimize the correlations among geochemical elements, avoiding the diluting of effective background information embedded in redundant data; (2) the Global Moran’s I index is used to determine the recognition domain of the background spatial structure for each element, and then the domain is used for convolution window size setting in MCAE; and (3) a multi-convolutional autoencoder framework is designed to learn the spatial structural pattern and reconstruct the geochemical background of each element. Finally, the anomaly score at each sampling location is calculated as the difference between the whitened geochemical features and the reconstructed features. This method was applied to the southwestern Fujian Province metalorganic belt in China, using the concentrations of Cu, Mn, Pb, Zn, and Fe2O3 measured from stream sediment samples. The results showed that the recognition domain determination greatly improved the quality of anomaly recognition, and MCAE outperformed several existing methods in all aspects. In particular, the anomalies from MCAE were the most consistent with the known Fe deposits in the area, achieving an area under the curve (AUC) of 0.89 and a forecast area of 17%.
An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes.
Detecting Fraud in Online Surveys by Tracing, Scoring, and Visualizing IP Addresses

Amazon's Mechanical Turk (MTurk) and other online convenience samples are used in thousands of published social science studies every year.One survey estimated that, in 2015 alone, over 1,200 published studies used MTurk (Bohannon, 2016).Another found that over 40% of studies in two top psychology journals in 2015 included at least one MTurk experiment (Zhou & Fishbach, 2016).Many recent studies have validated the use of MTurk to address substantive questions of interest in the social sciences, e.g., (Clifford, Jewell, & Waggoner, 2015), (Huff & Tingley, 2015), (Casler, Bickel, & Hackett, 2013), (Buhrmester, Kwang, & Gosling, 2011).Because of this, recent reports of widespread fraudulent responses on MTurk, up to 25% of respondents in some studies, set off a panic in academia (Dreyfuss, 2018), (Ahler, Roush, & Sood, 2018).The problem has been traced to the use of Virtual Private Servers (VPS) to answer U.S. surveys from abroad (Dennis, Goodson, & Pearson, 2018), (TurkPrime, 2018), and may have affected studies as far back as 2015 (Kennedy, Clifford, Burleigh, Jewell, & Waggoner, 2018).Yet, the tools available to social scientists to check their surveys for VPS use and non-U.S.respndents are not easily usable for most researchers; some are outdated and involve Python programming (Ahler et al., 2018), while other require researchers to paste IP Addresses in one at a time (Dennis et al., 2018).As more research moves online using services like MTurk, CrowdFlower, and Luc.id, there is a need for tools to check IP addresses that fit into standard social science research flow.
Energy and cost aware scheduling with batch processing for instance-intensive IoT workflows in clouds

Cloud computing is a suitable platform to execute various applications. At the same time, it should not only provide QoS such as high throughput but also achieve relevant criteria such as efficient power consumption and appropriate execution cost. To address this challenge, we present an energy and cost aware algorithm for scheduling instance-intensive IoT workflows with batch processing in clouds, which is named ECIB and aimed to improve energy efficiency and reduce execution cost while meeting the deadline requirements. Specifically, we propose a prediction based strategy to guide the management of resources by the historical data and CPU usage prediction results of physical machines. Then, we propose two strategies to scale up or scale down the virtual machine resources to optimize the energy consumption for the cloud data centers. In addition, we adopt a batch processing strategy to merge some activity instances of the same type so as to reduce execution cost for the cloud users and improve resource utilization for the cloud data centers. The effectiveness of the ECIB algorithm is evaluated by extensive experiments using CloudSim and four types of instance-intensive IoT workflow applications.
Online sequential echo state network with sparse RLS algorithm for time series prediction

Recently, the echo state networks (ESNs) have been widely used for time series prediction. To meet the demand of actual applications and avoid the overfitting issue, the online sequential ESN with sparse recursive least squares (OSESN-SRLS) algorithm is proposed. Firstly, the ℓ0 and ℓ1 norm sparsity penalty constraints of output weights are separately employed to control the network size. Secondly, the sparse recursive least squares (SRLS) algorithm and the subgradients technique are combined to estimate the output weight matrix. Thirdly, an adaptive selection mechanism for the ℓ0 or ℓ1 norm regularization parameter is designed. With the selected regularization parameter, it is proved that the developed SRLS shows comparable or better performance than the regular RLS. Furthermore, the convergence of OSESN-SRLS is theoretically analyzed to guarantee its effectiveness. Simulation results illustrate that the proposed OSESN-SRLS always outperforms other existing ESNs in terms of estimation accuracy and network compactness.
Complex Word Identification as a Sequence Labelling Task

Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline.It has been shown that reliable CWI systems considerably improve text simplification.However, most CWI systems to date address the task on a word-by-word basis, not taking the context into account.In this paper, we present a novel approach to CWI based on sequence modelling.Our system is capable of performing CWI in context, does not require extensive feature engineering and outperforms state-of-the-art systems on this task.
Learning to Abstract for Memory-augmented Conversational Response Generation

Neural generative models for open-domain chit-chat conversations have become an active area of research in recent years.A critical issue with most existing generative models is that the generated responses lack informativeness and diversity.A few researchers attempt to leverage the results of retrieval models to strengthen the generative models, but these models are limited by the quality of the retrieval results.In this work, we propose a memory-augmented generative model, which learns to abstract from the training corpus and saves the useful information to the memory to assist the response generation.Our model clusters query-response samples, extracts characteristics of each cluster, and learns to utilize these characteristics for response generation.Experimental results show that our model outperforms other competitive baselines.
Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM

Product reviews are usually determined by sentiment of customers; however sentiment analysis based on aspects still need further research.A hotel commonly has five aspects, which are location, meal, service, comfort and cleanliness.This research proposes methods to determine review sentiment according to the hotel aspects.A hotel reviews are preprocessed into a term list.Firstly, Latent Dirichelet Allocation (LDA) determines the hidden topics of a term list; then Semantic Similarity categorizes the term list based on the topic resulted by Latent Dirichelet Allocation (LDA) into the five aspects of a hotel.Then in calculating similarity measurement, the term list is expanded by using the Term Frequency-Inverse Cluster Frequency (TF-ICF) method.Finally, a classification of customer sentiment (satisfied or dissatisfied) is conducted by using the combination of Word Embedding and Long-short Term Memory (LSTM).The results show that the proposed method can classify the reviews into the five hotel aspects.The highest aspect categorization performance is obtained by using LDA + TF-ICF 100% + Semantic Similarity which reaches 85%; the performance sentiment classification for the highest aspect-based sentiment analysis is obtained by using Word Embedding + LSTM which reaches 93%; and the comfort aspect receives more negative sentiments compared to the sentiments of other aspects.Also the results show that a sentiment is influenced by an aspect.
Skeleton-Based Gesture Recognition Using Several Fully Connected Layers with Path Signature Features and Temporal Transformer Module

The skeleton based gesture recognition is gaining more popularity due to its wide possible applications. The key issues are how to extract discriminative features and how to design the classification model. In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i.e., spatial PS (S PS), temporal PS (T PS) and temporal spatial PS (T S PS). Considering the significance of fine hand movements in the gesture, we propose an ”attention on hand” (AOH) principle to define joint pairs for the S PS and select single joint for the T PS. In addition, the dyadic method is employed to extract the T PS and T S PS features that encode global and local temporal dynamics in the motion. Secondly, without the recurrent strategy, the classification model still faces challenges on temporal variation among different sequences. We propose a new temporal transformer module (TTM) that can match the sequence key frames by learning the temporal shifting parameter for each input. This is a learning-based module that can be included into standard neural network architecture. Finally, we design a multi-stream fully connected layer based network to treat spatial and temporal features separately and fused them together for the final result. We have tested our method on three benchmark gesture datasets, i.e., ChaLearn 2016, ChaLearn 2013 and MSRC-12. Experimental results demonstrate that we achieve the state-of-the-art performance on skeleton-based gesture recognition with high computational efficiency.
Outlier Detection for Robust Multi-Dimensional Scaling

Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization. State-of-the-art MDS algorithms are not robust to outliers, yielding significant errors in the embedding even when only a handful of outliers are present. In this paper, we introduce a technique to detect and filter outliers based on geometric reasoning. We test the validity of triangles formed by three points, and mark a triangle as broken if its triangle inequality does not hold. The premise of our work is that unlike inliers, outlier distances tend to break many triangles. Our method is tested and its performance is evaluated on various datasets and distributions of outliers. We demonstrate that for a reasonable amount of outliers, e.g., under 20 percent, our method is effective, and leads to a high embedding quality.
Unsupervised Domain Adaptation Based on Source-Guided Discrepancy

Unsupervised domain adaptation is the problem setting where data generating distributions in the source and target domains are different and labels in the target domain are unavailable. An important question in unsupervised domain adaptation is how to measure the difference between the source and target domains. Existing discrepancy measures for unsupervised domain adaptation either require high computation costs or have no theoretical guarantee. To mitigate these problems, this paper proposes a novel discrepancy measure called source-guided discrepancy (S-disc), which exploits labels in the source domain unlike the existing ones. As a consequence, S-disc can be computed efficiently with a finitesample convergence guarantee. In addition, it is shown that S-disc can provide a tighter generalization error bound than the one based on an existing discrepancy measure. Finally, experimental results demonstrate the advantages of S-disc over the existing discrepancy measures.
Learning Exploration Policies for Navigation

Numerous past works have tackled the problem of task-driven navigation. But, how to effectively explore a new environment to enable a variety of down-stream tasks has received much less attention. In this work, we study how agents can autonomously explore realistic and complex 3D environments without the context of task-rewards. We propose a learning-based approach and investigate different policy architectures, reward functions, and training paradigms. We find that the use of policies with spatial memory that are bootstrapped with imitation learning and finally finetuned with coverage rewards derived purely from on-board sensors can be effective at exploring novel environments. We show that our learned exploration policies can explore better than classical approaches based on geometry alone and generic learning-based exploration techniques. Finally, we also show how such task-agnostic exploration can be used for down-stream tasks. Code and Videos are available at: this https URL.
Fused GRU with semantic-temporal attention for video captioning

The encoder-decoder framework has been widely used for video captioning to achieve promising results, and various attention mechanisms are proposed to further improve the performance. While temporal attention determines where to look, semantic decides the context. However, the combination of semantic and temporal attention has never be exploited for video captioning. To tackle this issue, we propose an end-to-end pipeline named Fused GRU with Semantic-Temporal Attention (STA-FG), which can explicitly incorporate the high-level visual concepts to the generation of semantic-temporal attention for video captioning. The encoder network aims to extract visual features from the videos and predict their semantic concepts, while the decoder network is focusing on efficiently generating coherent sentences using both visual features and semantic concepts. Specifically, the decoder combines both visual and semantic representation, and incorporates a semantic and temporal attention mechanism in a fused GRU network to accurately learn the sentences for video captioning. We experimentally evaluate our approach on the two prevalent datasets MSVD and MSR-VTT, and the results show that our STA-FG achieves the currently best performance on both BLEU and METEOR.
Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning

Beneﬁting from the excellent ability of neural networks on learning semantic representations, existing studies for entity linking (EL) have resorted to neural networks to exploit both the local mention-to-entity compatibility and the global interdependence between different EL decisions for target entity disambiguation. However, most neural collective EL methods depend entirely upon neural networks to automatically model the semantic dependencies between different EL decisions, which lack of the guidance from external knowledge. In this paper, we propose a novel end-to-end neural network with recurrent random-walk layers for collective EL, which introduces external knowledge to model the semantic interdependence between different EL decisions. Speciﬁcally, we ﬁrst establish a model based on local context features, and then stack random-walk layers to reinforce the evidence for related EL decisions into high-probability decisions, where the semantic interdependence between candidate entities is mainly induced from an external knowledge base. Finally, a semantic regularizer that preserves the collective EL decisions consistency is incorporated into the conventional objective function, so that the external knowledge base can be fully exploited in collective EL decisions. Experimental results and in-depth analysis on various datasets show that our model achieves better performance than other state-of-the-art models. Our code and data are released at https://github.com/DeepLearnXMU/RRWEL.