Reviewerly/exHarmony huggingface dataset:
Robust Deep Co-Saliency Detection With Group Semantic and Pyramid Attention

High-level semantic knowledge in addition to low-level visual cues is essentially crucial for co-saliency detection. This article proposes a novel end-to-end deep learning approach for robust co-saliency detection by simultaneously learning high-level groupwise semantic representation as well as deep visual features of a given image group. The interimage interaction at the semantic level and the complementarity between the group semantics and visual features are exploited to boost the inferring capability of co-salient regions. Specifically, the proposed approach consists of a co-category learning branch and a co-saliency detection branch. While the former is proposed to learn a groupwise semantic vector using co-category association of an image group as supervision, the latter is to infer precise co-salient maps based on the ensemble of group-semantic knowledge and deep visual cues. The group-semantic vector is used to augment visual features at multiple scales and acts as a top-down semantic guidance for boosting the bottom-up inference of co-saliency. Moreover, we develop a pyramidal attention (PA) module that endows the network with the capability of concentrating on important image patches and suppressing distractions. The co-category learning and co-saliency detection branches are jointly optimized in a multitask learning manner, further improving the robustness of the approach. We construct a new large-scale co-saliency data set COCO-SEG to facilitate research of the co-saliency detection. Extensive experimental results on COCO-SEG and a widely used benchmark Cosal2015 have demonstrated the superiority of the proposed approach compared with state-of-the-art methods.
Integrating recurrent neural networks and reinforcement learning for dynamic service composition

In the service oriented architecture (SOA), software and systems are abstracted as web services to be invoked by other systems. Service composition is a technology, which builds a complex system by combining existing simple services. With the development of SOA and web service technology, massive web services with the same function begin to spring up. These services are maintained by different organizations and have different QoS (Quality of Service). Thus, how to choose the appropriate service to make the whole system to deliver the best overall QoS has become a key problem in service composition research. Furthermore, because of the complexity and dynamics of the network environment, QoS may change over time. Therefore, how to adjust the composition system dynamically to adapt to the changing environment and ensure the quality of the composed service also poses challenges. To address the above challenges, we propose a service composition approach based on QoS prediction and reinforcement learning. Specifically, we use a recurrent neural network to predict the QoS, and then make dynamic service selection through reinforcement learning. This approach can be well adapted to a dynamic network environment. We carry out a series of experiments to verify the effectiveness of our approach.
Lightweight Blockchain Consensus Protocols for Vehicular Social Networks

This paper presents lightweight blockchain consensus protocols that are specifically designed for vehicular social networks, or other networks that resemble them. They are lightweight in the sense that no puzzles are required to be solved and no cryptocurrency systems are required to be built beforehand. Various scenarios are considered, including whether the communication is reliable and whether the vehicle grouping is static. Although we break our solution of the consensus problem into private chain and public chain, we provide a scheme to smoothly bridge between them. Theoretical analysis is provided to guarantee the algorithms to succeed with high probability, before experiments are conducted to verify the algorithms and compare their theoretical performance versus simulation performance, namely worst-case performance versus average performance.
Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems

Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learning in symbolic domains. Deep learning architectures that employ parameter sharing over graphs can produce models which can be trained on complex properties of relational data. These include highly relevant NP-Complete problems, such as SAT and TSP. In this work, we showcase how Graph Neural Networks (GNN) can be engineered - with a very simple architecture - to solve the fundamental combinatorial problem of graph colouring. Our results show that the model, which achieves high accuracy upon training on random instances, is able to generalise to graph distributions different from those seen at training time. Further, it performs better than the Neurosat, Tabucol and greedy baselines for some distributions. In addition, we show how vertex embeddings can be clustered in multidimensional spaces to yield constructive solutions even though our model is only trained as a binary classifier. In summary, our results contribute to shorten the gap in our understanding of the algorithms learned by GNNs, as well as hoarding empirical evidence for their capability on hard combinatorial problems. Our results thus contribute to the standing challenge of integrating robust learning and symbolic reasoning in Deep Learning systems.
Distributed filtering for a class of discrete-time systems over wireless sensor networks

This paper addresses the distributed filter design problem for a class of dynamic systems over wireless sensor networks. The missing measurements and the correlation among state noises and measurement noises are considered, where a set of mutually uncorrelated random variables is employed to describe the missing phenomena. Firstly, the construction of the designed filter is proposed and the prediction of the state at each node is given. Then, the filtering error covariance is presented and the filter parameters are determined to minimize the trace of such a covariance, where the network topology data are used to simplified the singular matrix. Subsequently, the relationship between the filter performance and missing probability of the measurement is discussed. Finally, a numerical simulation is presented to illustrate the effectiveness and capability of the proposed distributed filters.
Analysis of Twitter Specific Preprocessing Technique for Tweets

Social media plays an important role in capturing the thoughts of people in their own representation of sentences. Twitter, the popular microblog, traps abundant rich information in it, in terms of short texts posted by users. Processing of such colloquial and informal sentences require a specific preprocessing technique that can alleviate its understanding and analysis. Understanding these natural language sentences to produce a desired result is a challenging task. The paper aims at analyzing, identifying and explaining the advantages of twitter specific preprocessing technique and find out if it performs equally well with the established baseline preprocessing method. Experiment is carried out for classifying tweets to assess the performance of preprocessing techniques. The results indicate the effectiveness of twitter specific preprocessing technique and its competence with the baseline text preprocessing technique.
KNN-Based Approximate Outlier Detection Algorithm Over IoT Streaming Data

KNN-Based outlier detection over IoT streaming data is a fundamental problem, which has many applications.However, due to its computational complexity, existing efforts cannot efficiently work in the IoT streaming data.In this paper, we propose a novel framework named GAAOD(Grid-based Approximate Average Outlier Detection) to support KNN-Based outlier detection over IoT streaming data.Firstly, GAAOD introduces a grid-based index to manage summary information of streaming data.It can self-adaptively adjust the resolution of cells, and achieve the goal of efficiently filtering objects that almost cannot become outliers.Secondly, GAAOD uses a min-heap-based algorithm to compute the distance upper-/lower-bound between objects and their k-th nearest neighbors respectively.Thirdly, GAAOD utilizes a k-skyband based algorithm to maintain outliers and candidate outliers.Theoretical analysis and experimental results verify the efficiency and accuracy of GAAOD.
Multi attribute auction based incentivized solution against DDoS attacks

Complexity and severity of DDoS attacks is increasing day by day. Internet has highly inconsistent structure in terms of resource distribution. Numerous technical solutions are present in this domain but solutions considering economic aspects have not been given attention. Therefore, in this paper, a multi attribute based auction mechanism to mitigate DDoS attacks has been proposed. A reputation based detection mechanism has been proposed where reputation of a user is assessed through his marginal utility. Along with detection mechanism, two payment mechanisms have been proposed for legitimate and malicious users separately. A greedy resource allocation is devised to allocate resources fairly among the legitimate users. Malicious users who manipulate their bid to acquire maximum share of limited resources are charged with penalty according to differential payment scheme. Since, this is a generalized concept to mitigate DDoS attacks on any platform, we have taken our case study on cloud computing. So, simulations have been carried out on CloudSim. Results obtained from simulations clearly showed that proposed approach performs better than existing DDoS attack mitigation techniques.
An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning

The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit ‘multiplication and division of time expressions’ was taught to 134 fifth-grade students in three types of instruction conditions: the intelligent tutoring system (ITS), conventional teacher instruction and material reading. The results show that student performance was comparable between the proposed mathematical ITS and conventional instruction conditions but was significantly poorer when no teaching method was used. The questionnaire survey showed that the ITS method not only improved maths learning, but also increased motivation among the fifth graders. The effectiveness of ITS remedial instruction in mathematics is discussed.
On the Study of Generative Adversarial Networks for Cross-Lingual Voice Conversion

Cross-lingual voice conversion (VC) aims to convert the source speaker's voice to sound like that of the target speaker, when the source and target speakers speak different languages. In this paper, we propose to use Generative Adversarial Networks (GANs) for cross-lingual voice-conversion. We further the studies on Variational Autoencoding Wasserstein GAN (VAW-GAN) and cycle-consistent adversarial network (CycleGAN), that are known to be effective for mono-lingual voice conversion. As cross-lingual voice conversion needs to converts the voice across different phonetic system, it is more challenging than mono-lingual voice conversion. By using VAW-GAN and CycleGAN, we successfully convert the speaker identity while carrying over the source speaker's linguistic content. The proposed idea is unique in the sense that it neither relies on bilingual data and their alignment, nor any external process, such as ASR. Moreover, it works with limited amount of training data of any two languages. To our best knowledge, this is the first comprehensive study of Generative Adversarial Networks in cross-lingual voice conversion. In the experiments, we achieve high-quality converted voice, that performs equally well or better than mono-lingual voice conversion.
A novel improved symbiotic organisms search algorithm

Abstract For last two decades, nature‐inspired metaheuristic algorithms together with their modified, improved, and hybrid versions have been gaining huge popularity in the field of optimization in solving continuous and complex real‐life optimization problems. In this work, a novel improved symbiosis organism search (SOS) algorithm, called self‐adaptive beneficial factor‐based improved SOS (SaISOS, in short) is suggested. The self‐adaptive benefit factors and a modified mutualism phase (called “Three‐way mutualism phase”) have been introduced here to upgrade the performance of SOS algorithm. A random weighted reflection coefficient and a new control operator have also been introduced. To validate the proposed algorithm and to compare its performance with other state‐of‐the‐art algorithms, 15 IEEE‐CEC 2015 functions have been employed and the experimental results confirm that SaISOS provides competitive results on most occasions. Also, the proposed algorithm is used to solve five real‐world optimization problems. Considering the average output, it is observed that the proposed method performs significantly better in solving the real‐world problems compared to the alternative state‐of‐the art techniques considered in this work.
Multi-category news classification using Support Vector Machine based classifiers

Support Vector Machine (SVM) and its variants are gaining momentum among the Machine Learning community. In this paper, we present a quantitative analysis between the established SVM based classifiers on multi-category text classification problem. Here, we are particularly interested in studying the behaviour of Least-squares Support Vector Machines, Twin Support Vector Machines and Least-squares Twin Support Vector Machines (LS-TWSVM) classifiers on News data. Since, all these are binary classifiers, they are extended using One-Against-All approach to handle multi-category data. The dataset is first converted into required format by performing preprocessing activities which involve tokenization and removing irrelevant data. The feature set is constructed as Term Frequency-Inverse Document Frequency matrix, so that representative vectors could be obtained for each document. Experimentally, we have compared the performance of each classification algorithm by performing simulations on benchmark UCI News datasets: Reuters and 20 Newsgroups. This paper shows that LS-TWSVM proves to be the best of all three, both in terms of accuracy and time complexity (training and testing).
Mixed Road User Trajectory Extraction From Moving Aerial Videos Based on Convolution Neural Network Detection

Vehicle trajectory data under mixed traffic conditions provides critical information for urban traffic flow modeling and analysis.Recently, the application of unmanned aerial vehicles (UAV) creates a potential of reducing traffic video collection cost and enhances flexibility at the spatial-temporal coverage, supporting trajectory extraction in diverse environments.However, accurate vehicle detection is a challenge due to facts such as small vehicle size and inconspicuous object features in UAV videos.In addition, camera motion in UAV videos hardens the trajectory construction procedure.This research aims at proposing a novel framework for accurate vehicle trajectory construction from UAV videos under mixed traffic conditions.Firstly, a Convolution Neural Network (CNN)-based detection algorithm, named You Only Look Once (YOLO) v3, is applied to detect vehicles globally.Then an image registration method based on Shi-Tomasi corner detection is applied for camera motion compensation.Trajectory construction methods are proposed to obtain accurate vehicle trajectories based on data correlation and trajectory compensation.At last, the ensemble empirical mode decomposition (EEMD) is applied for trajectory data denoising.Our framework is tested on three aerial videos taken by an UAV on urban roads with one including intersection.The extracted vehicle trajectories are compared with manual counts.The results show that the proposed framework achieves an average Recall of 91.91% for motor vehicles, 81.98% for non-motorized vehicles and 78.13% for pedestrians in three videos.
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions

Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the increasing model size has raised challenges in employing them in resource-limited applications. In this work, we propose to compress deep models by using channel-wise convolutions, which replace dense connections among feature maps with sparse ones in CNNs. Based on this novel operation, we build light-weight CNNs known as ChannelNets. ChannelNets use three instances of channel-wise convolutions; namely group channel-wise convolutions, depth-wise separable channel-wise convolutions, and the convolutional classification layer. Compared to prior CNNs designed for mobile devices, ChannelNets achieve a significant reduction in terms of the number of parameters and computational cost without loss in accuracy. Notably, our work represents an attempt to compress the fully-connected classification layer, which usually accounts for about 25 percent of total parameters in compact CNNs. Along this new direction, we investigate the behavior of our proposed convolutional classification layer and conduct detailed analysis. Based on our in-depth analysis, we further propose convolutional classification layers without weight-sharing. This new classification layer achieves a good trade-off between fully-connected classification layers and the convolutional classification layer. Experimental results on the ImageNet dataset demonstrate that ChannelNets achieve consistently better performance compared to prior methods.
Reliable Task Management Based on a Smart Contract for Runtime Verification of Sensing and Actuating Tasks in IoT Environments

With the gradual popularization of Internet-of-Things (IoT) applications and the development of wireless networking technologies, the use of heterogeneous devices and runtime verification of task fulfillment with different constraints are required in real-world IoT scenarios. As far as IoT systems are concerned, most of them are built on centralized architectures, which reveal various assailable points in data security and privacy threats. Hence, this paper aims to investigate these issues by delegating the responsibility of a verification monitor from a centralized architecture to a decentralized manner using blockchain technology. We present a smart contract-based task management scheme to provide runtime verification of device behaviors and allows trustworthy access control to these devices. The business logic of the proposed system is specified by the smart contract, which automates all time-consuming processes cryptographically and correctly. The usability of the proposed solution is further demonstrated by implementing a prototype application in which the Hyperledger Fabric is utilized to implement the business logic for runtime verification and access control with one desktop and one Raspberry Pi. A comprehensive evaluation experiment is conducted, and the results indicate the effectiveness and efficiency of the proposed system.
Rutile as a pathfinder for metals exploration

The use of detrital rutile as an indicator of mineralisation is contingent on the identification of trace element signatures that uniquely distinguish rutile grains derived from mineralisation from those sourced from barren rocks. In order to discriminate rutile sources, principal component analysis was applied to a total of 3149 LA-ICP-MS analyses of rutile from ore environments (Au, Au-Cu, pegmatites, carbonatites, lamprophyres, VHMS) and barren igneous and metamorphic rocks. Hierarchical clustering of the results of principal component analysis (PCA) determined Sb and Cr exhibit the most chemical variation in ore-derived rutile. Rutile sourced from Au deposits and enriched in Sb, can be distinguished from rutile from pegmatites, which are enriched in Nb, Ta and Sn. Discriminating rutile from barren rocks can be done primarily using W and Fe contents, which can also provide insight on source rocks based on metamorphic grade and degree of crustal input. Clustering all rutile chemistries based on PCA of 12 elements, identified nine chemically distinct groups from ore mineralisation, and barren igneous and metamorphic rocks, primarily using variation in W and Cr contents. Two groups are solely derived of rutile from Au ores (high versus low Sb rutile). Of the remaining seven groups, one group is comprised mainly of rutile from pegmatites (high Nb, Ta, Sn), four groups are comprised solely of rutile from barren rocks and two groups are a mix of ore and non-ore rutile. Rutile from Au ore, similar to the giant Kalgoorlie and Big Bell Au deposits, is distinctive compared to rutile from all other rocks and can be distinguished from other ores and barren environments using multi-element clustering of PCA analysis. Thus rutile chemistry of detrital rutile grains is a valuable tool in Au exploration.
Deep Representation Learning for Location-Based Recommendation

Location-based recommendation has recently received a lot of attention in the communities of information service and mobile application. Its task is to provide personalized recommendations of points of interest (POIs) to users at a certain time and location. However, existing location-based recommendation models have at least two main drawbacks: first they cannot adequately capture semantic features of POIs and users, which may lead to unsatisfactory recommendations and second they cannot effectively address the cold-start problem. To address the above drawbacks, in this article, we first propose a novel deep representation learning-based model (DRLM) for improving the recommendation accuracy. In DRLM, we mainly focus on learning to accurately represent semantic features of POIs and users. Specifically, four co-occurrence matrices are constructed to produce four different original features for each POI, and a principal component analysis (PCA) algorithm is utilized to generate a semantic feature of each POI from its four original features. On the other hand, a three-modal simple recurrent unit (TMSRU) network is given to constructed semantic features of users using semantic features of POIs, times, and locations. We further propose minimum description length (MDL)-based and skyline-based strategies to address the cold-start issues for new users and new POIs, respectively. Through experiments on two real-world data sets, we show that compared with the state-of-the-art approaches, the proposed model DRLM can achieve the superior performance in terms of high recommendation accuracy and effectiveness in handling the cold-start problem.
An efficient truthfulness privacy-preserving tendering framework for vehicular fog computing

This paper presents a proposal for a tendering-based incentive framework in order to encourage vehicle owners to join in announced tasks in the vehicular fog computing. The truthfulness of users is ensured by using the incentive mechanism that also assists a fog node server to choose suitable resources for the task. An illustrative language, which is a novel approach to guaranteeing fairness amongst vehicles, is designed based on heterogeneous vehicular resource types. The signcryption technique and a homomorphic concept are integrated in the proposed framework in order to preserve vehicles privacy. Moreover, a detailed performance analysis demonstrates that the communication and computational overheads of this privacy-preserving scheme are significantly more efficient than the available alternatives.