Reviewerly/exHarmony huggingface dataset:
Various syncretic co‐attention network for multimodal sentiment analysis

Summary The multimedia contents shared on social network reveal public sentimental attitudes toward specific events. Therefore, it is necessary to conduct sentiment analysis automatically on abundant multimedia data posted by the public for real‐world applications. However, approaches to single‐modal sentiment analysis neglect the internal connections between textual and visual contents, and current multimodal methods fail to exploit the multilevel semantic relations of heterogeneous features. In this article, the various syncretic co‐attention network is proposed to excavate the intricate multilevel corresponding relations between multimodal data, and combine the unique information of each modality for integrated complementary sentiment classification. Specifically, a multilevel co‐attention module is constructed to explore localized correspondences between each image region and each text word, and holistic correspondences between global visual information and context‐based textual semantics. Then, all the single‐modal features can be fused from different levels, respectively. Except for fused multimodal features, our proposed VSCN also considers unique information of each modality simultaneously and integrates them into an end‐to‐end framework for sentiment analysis. The superior results of experiments on three constructed real‐world datasets and a benchmark dataset of Visual Sentiment Ontology (VSO) prove the effectiveness of our proposed VSCN. Especially qualitative analyses are given for deep explaining of our method.
BrokenStrokes

Wireless devices resorting to event-triggered communications have been proved to suffer critical privacy issues, due to the intrinsic leakage associated with radio-frequency (RF) emissions.
A non-parametric Bayesian change-point method for recurrent events

This paper proposes a non-parametric Bayesian approach to detect the change-points of intensity rates in the recurrent-event context and cluster subjects by the change-points. Recurrent events are commonly observed in medical and engineering research. The event counts are assumed to follow a non-homogeneous Poisson process with piecewise-constant intensity functions. We propose a Dirichlet process mixture model to accommodate heterogeneity in subject-specific change-points. The proposed approach provides an objective way of clustering subjects based on the change-points without the need of pre-specified number of latent clusters or model selection procedure. A simulation study shows that the proposed model outperforms the existing Bayesian finite mixture model in detecting the number of latent classes. The simulation study also suggests that the proposed method is robust to the violation of model assumptions. We apply the proposed methodology to the Naturalistic Teenage Driving Study data to assess the change in driving risk and detect subgroups of drivers.
Unsupervised recurrent deep learning scheme for process monitoring

Precisely detecting anomalies in process monitoring is beneficial to enhance the operation of the monitored process by avoiding catastrophic failures and reducing maintenance costs. Unsupervised deep learning techniques are increasingly popular because of their capacity to uncover relevant information from large and complex datasets without using labeled data. In this chapter, we review and evaluate the detection performance of recurrent neural networks (RNNs)-based approaches based on a multivariate time series. RNNs are a powerful tool to appropriately model temporal dependencies in multivariate time series data. We first offer a brief overview of RNNs, from the simplest RNNs with no memory states, to sophisticated architectures with several gates and memory components. Particularly, we focus on those that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a gated recurrent unit (GRU). We then present hybrid deep learning models that integrate the desirable features of RNNs and LSTM, which are capable of approximating complex distributions of deep belief networks and restricted Boltzmann machines. We then apply these models with numerous clustering algorithms for uncovering anomalies. We finally demonstrate these methods on real measurements of effluents from a coastal municipal wastewater treatment plant in Saudi Arabia.
A Proactive Trajectory Planning Algorithm for Autonomous Mobile Robots in Dynamic Social Environments

This paper proposes a proactive trajectory planning algorithm for autonomous mobile robots in dynamic social environments. The main idea of the proposed proactive timed elastic band (PTEB) system is to combine the advantages of the timed elastic band (TEB) technique and the hybrid reciprocal velocity obstacle (HRVO) model by incorporating the potential collision generated by the HRVO model into the objective function of the TEB technique. The output of the proposed PTEB system is the optimal trajectory, which enables the mobile robots to navigate safely in the dynamic social environments. We validate the effectiveness of the proposed model through a series of experiments in simulation environments. The simulation results show that, our proposed motion model is capable of driving the mobile robots to proactively avoid dynamic obstacles, providing the safe navigation for the robots.
CCTV Forensics in the Big Data Era: Challenges and Approaches

The video security market has seen rapid expansion over the last few years, expanding from the old analogue into new and emerging IP systems whilst running on multiple platform digital video recorders (DVR) of both Open and Proprietary format. Accordingly to the 2017 video surveillance report from IFSEC Global ( https://www.ifsecglobal.com/video-surveillance-report-2017 ), for example, proprietary formats accounted for 56% of their surveyed responses. It is primarily within these proprietary formats that numerous challenges arise with the forensic acquisition and analysis of the closed circuit television camera (CCTV) data. Such challenges are expanded when consideration is given to the volume of data and the complexity of data due to the different manufacturers, data formats, file systems, volume storage methods, etc. In this paper, we review current approach on CCTV forensics in literature, and identify challenges of CCTV forensics in the context of big data forensics. Then, we describe a new forensic process/workflow for acquiring and analysing artefacts from large amounts data from CCTV devices of different models/manufacturers, prior to presenting and analysing three real world case studies.
Adversarial Training-Based Hard Example Mining for Pedestrian Detection in Fish-Eye Images

Since fish-eye cameras are popular in the intelligent transportation systems, accurate pedestrian detection in fish-eye images becomes more and more critical in low-speed scenarios. Usually, big data based training is the key for detectors to handle the distortion problem in fish-eye images. Especially, hard examples are more important for detectors. They have more complex features and are hard to recognize. In conventional methods, fish-eye images are collected and labeled manually. These methods are expensive and labor-intense. More importantly, these methods are still hard to collect abundant hard examples since they are rare in reality. This work proposes the Distortion Generation Network, which generates generous fish-eye images automatically using only small samples. Moreover, the Adversarial Distortion Generation Network is proposed to mine hard examples via adversarial training. These hard examples benefit detectors to be more robust to seriously distorted objects in fish-eye images. Experiments with the ETH, the KITTI, the GM-ATCI and real fish-eye datasets demonstrate that the proposed methods achieve higher accuracy than conventional methods in pedestrian detection in fish-eye images.
Unfolding identity of financial institutions in bitcoin blockchain by weekly pattern of network flows

In this study, we analyzed bitcoin blockchain data for the period between 2013 and 2018. We constructed daily networks and analyzed the network properties of the bitcoin users and bitcoin flow attributed as edge flow circulated between users to focus on weekdays and weekends activities. In the real world, businesses take time off, particularly on weekends. This is no different in the crypto-asset world, which, theoretically, is operable 24/7. We also performed a threshold analysis of the flow of bitcoin to identify the big wallets and to compare it with the identity of real-world crypto-exchange companies, in particular, their weekly patterns. Finally, we propose a methodology to identify the financial institution in the bitcoin blockchain on the basis of fulfilling some key criteria. The criteria are having high frequency, appearing persistently on daily big trades and showing a distinct weekly pattern of total average network flow.
Design of Virtual Reality Application for Taharah Using 3D Blender

Abstract Taharah or purification is worship to God through cleaning some parts of the body with ablution and dry ablution or cleansing body from Najis or ritually unclean, such as small or big Hadas. Taharah is compulsory before pray and Tawaf. Moslem should understand the procedures of taharah. Now a days, taharah information is delivered through book, video compact disk (VCD) or websites. Taharah is not only knowledge but should be practice correctly. Therefore, this research aims to develop virtual reality application using 3D animation for learning Taharah, including ablution, dry ablution and cleaning ritually unclean. This application is using an Android operating system, 3D Blender modelling and unity for mobile device design. Furthermore, this application contains the Taharah theory and the rules. Moreover, this research makes a contribution by providing a virtual reality system of taharah based on 3D Blender. The testing result shows this application achieves 69.6% of effectiveness comparing to printing media for taharah learning media. The object testing is Moslem students with age of 11 years above.
Pitfalls of data-driven networking

This paper motivates the need to support counterfactual reasoning (i.e., answer "what-if " questions about events that did not occur) when collecting network data. We focus on video streaming - e.g., given logs of a video session, a video publisher may ask whether a user would continue to experience no rebuffering events if the lowest quality video choice were eliminated. We discuss potential pitfalls related to counterfactual reasoning, and argue that dynamic network state (e.g., bandwidth) serves as a confounding yet hidden (latent) feature that complicates such analyses. We illustrate the challenges, and present preliminary methods to address them using concrete examples. Our evaluations show that existing approaches, including randomized trials (collecting data from an algorithm that selects bitrates randomly), are by themselves inadequate for counterfactual reasoning related to video streaming, and must be supplemented by techniques that explicitly infer latent features.
Non-local attention association scheme for online multi-object tracking

Online multi-object tracking (MOT) is a fundamental problem in video analysis and multimedia applications. The major challenge in the popular tracking-by-detection framework is knowing how to associate candidate detections results with existing tracklets. In this, we propose a non-local attention association approach and apply it to a unified online MOT framework that integrates the merits of single object tracking and data association methods. Specifically, we use non-local attention association networks (NAAN) to incorporate both spatial and temporal characteristics to associate new detections. The non-local attention mechanism generates global attention maps across space and time, enabling the network to focus on the whole tracklet information, as opposed to the local attention mechanism to overcome the problems of noisy detections, occlusion, and frequent interactions between targets. Experimental results on MOT benchmark datasets show that the proposed algorithm performs favorably against various online trackers on the basis of identity-preserving metrics.
Security Implementations in IoT Using Digital Signature

In recent advancements, different types of embedded IoT devices are connected with the wired or wireless network and continuously access the internet for communication. Cybercriminals are finding vulnerabilities on IoT devices and compromise them to launch massive attacks (e.g. DDoS, Spamming, MITM, RFID Skimming) to destroy the network. IoT devices having default authentication credentials are an easy target. To avoid cybercriminals, we need a more sophisticated authentication mechanism embedded with existing security measures. Digital signature has become an integral part of IoT to restrict illegal users. The digital signature verification process is a time-consuming operation and not advisable in IoT however we can minimize the time of verification through some optimization schemes. This paper summarizes all the existing digital signature implementation aspects in IoT and states their technological properties as well as features and loopholes.
Cost-Based attraction recommendation for tour operators under stochastic demand

Attraction recommendation is a key functionality offered by tour operators. The main stakeholders of attraction recommendations include tourists and tour operators. The former use recommendations to make travel decisions, and the latter manage recommendations for their own benefits. Most existing attraction recommendation methods focus on providing recommendations that best match tourists’ preferences, yet overlook the benefits of tour operators. To address this gap, we conduct a two-phase study that focuses on cost-based attraction recommendations under stochastic tourist demand from the perspective of tour operators. In the first phase, we obtain preliminary recommendation solutions that best match tourists’ topic preferences. Then, with the consideration of cost factors, a stochastic programming model with a joint chance constraint is proposed to refine the preliminary recommendation solutions in the second phase, and a tractable model based upon Sample Average Approximation (SAA) method is further presented. To assess the performance of the proposed method, comprehensive experiments are conducted with both simulated instances and real-world data. The results indicate that the proposed optimization model can significantly reduce tour operators’ recommendation cost while maintaining a high service level and tourist satisfaction.
MFRep: Joint user and employer alignment across heterogeneous social networks

Nowadays people join multiple social networks simultaneously to enjoy a variety of social services. Apart from common users, social networks also share other information entities such as organizations/companies. Aligning common information entities across heterogeneous social networks is challenging, but will facilitate many applications. Existing approaches for network alignment do not fully leverage indirect relations among users (i.e., friends of friends). This leads to relatively poor performance, especially when there are little overlapping social relations between different networks. Meanwhile, the mutual promotion between two kinds of information entity alignment is often neglected. In this paper, we propose a novel Matrix Factorization based Representation learning (MFRep) framework. MFRep investigates the joint learning for user and employer alignment across different networks. (1). We first compute the cross-network similarities in user attributes to extract seed potential anchor users. Its efficiency can be boosted by considering the similarities in employer properties. (2). Considering social relation differences across networks, we construct user relational matrix to preserve multi-step relational information. This embodies the information propagation from known and seed potential anchor users to other potential anchor users. (3). We extend the user relational matrix to preserve consistent associations between users and employers across networks. (4). We perform semi-supervised user representation learning and unsupervised employer representation learning concurrently via efficient matrix decomposition. The correspondence between users/employers across networks can be inferred based on vector relevance. Extensive experiments on real-world social network datasets justify the utility of MFRep, even with dissimilar social structures across networks. MFRep illustrates the mutual promoted alignments of users and employers with a middle degree of scalability.
Multi-Objective Interdependent VM Placement Model based on Cloud Reliability Evaluation

Virtual Machine (VM) placement is considered as one of the crucial problems in Cloud Computing environments. From the perspective of Cloud Service Providers (CSPs), finding the optimal VM placement strategy is often related to optimal resource utilization, revenue maximization, and energy efficiency. However, to ensure the continuity of customer services, CSPs should also consider the reliability of deployed applications when placing VMs on their infrastructures. Existing research in this area either do not focus on the Cloud reliability evaluation aspect or do not account for the trade-off between reliability and performance in the VM placement process. In this paper, we propose a multi-objective placement model for interdependent VMs in the Cloud that considers both reliability and workload. Reliability in our model is quantitatively evaluated through a set of metrics that we propose. The model involves an Integer Linear Programming problem that aims at maximizing the reliability of the Cloud while minimizing network delay. A multi-objective genetic algorithm is then used to solve the problem heuristically. The proposed model introduces a level of flexibility and its parameters could be adjusted depending on the requirements of the infrastructure and services. The results show that our model achieves high Cloud reliability and allows to effectively control the trade-off between reliability and Quality of Service.
Analysing Log File Data from PIAAC

The OECD Programme for the International Assessment of Adult Competencies (PIAAC) was the first computer-based large-scale assessment to provide anonymised log file data from the cognitive assessment together with extensive online documentation and a data analysis support tool. The goal of the chapter is to familiarise researchers with how to access, understand, and analyse PIAAC log file data for their research purposes. After providing some conceptual background on the multiple uses of log file data and how to infer states of information processing from log file data, previous research using PIAAC log file data is reviewed. Then, the accessibility, structure, and documentation of the PIAAC log file data are described in detail, as well as how to use the PIAAC LogDataAnalyzer to extract predefined process indicators and how to create new process indicators based on the raw log data export.
Dynamic Selection of Mining Pool with Different Reward Sharing Strategy in Blockchain Networks

In a PoW-based blockchain network, miners participate in a block-discovery racing game for financial incentives. As the total computing power becomes overwhelming, miners join in the mining pool which combines the scattered computing power to win a stable profit. Miners in the same mining pool work as a team and once they successfully mine a valid block, mining pool plays a role in distributing the payoff to miners according to its reward sharing mechanism. Specifically, two main reward sharing strategies: Pay-Per-Share (PPS) and Pay-Per-Last-NShare (PPLNS) are considered. In the mining system model, a miner can choose to join a pool and adapt the selection for improving the expected reward. And we formulate the dynamic pool selection problem as an evolutionary game. We consider the required hash rate, network delay and reward sharing strategy as the main factors which affect the choice of miners. Evolutionary stable equilibrium (ESS) is considered to be the solution, and we conduct theoretical analysis on the existence and stability of the ESS for a case of two mining pools. A low complexity distributed algorithm is proposed for miners to reach the equilibrium. Numerical results show the evolution of miners and demonstrate the theoretical findings of our study.
A Las Vegas algorithm for computing the smith form of a nonsingular integer matrix

We present a Las Vegas randomized algorithm to compute the Smith normal form of a nonsingular integer matrix. For an A ∈ Zn×n, the algorithm requires O(n3(log n + log ||A||)2 (log n)2) bit operations using standard integer and matrix arithmetic, where ||A|| = maxij |Aij | denotes the largest entry in absolute value. Fast integer and matrix multiplication can also be used, establishing that the Smith form can be computed in about the same number of bit operations as required to multiply two matrices of the same dimension and size of entries as the input matrix.
Single image de-raining via clique recursive feedback mechanism

Deep learning methods have achieved significant progress in single image rain removal in recent years. However, the remaining rain streak phenomenon still exists in previous ways, provided that they did not adequately consider the interaction of features flowing among the network. To utilize the feature interaction of the network, we propose a new rain removal network based on the clique recursive feedback mechanism. Mainly, considering the interaction of the feature between different convolution layers, we construct a residual clique block (RCB) to infer the local information, allowing the network to rectify the model parameters in RCB alternately. Besides, a multi-path dilated convolutional unit is embedded into a scale clique block (SCB) to cover more scale components. In SCB, we consider the complementary correlation of different scales, and the multi-scale features are updated alternately, which is essential for excellent feature representation. Along with the clique recursive feedback mechanism, the information flowing among RCB and SCB is thus maximized during propagation. Experiments on synthetic and real-world images have shown the superiority of the proposed method over state-of-the-art methods.
Non-Bayesian Social Learning with Gaussian Uncertain Models

Non-Bayesian social learning theory provides a framework for distributed inference of a group of agents interacting over a social network by sequentially communicating and updating beliefs about the unknown state of the world through likelihood updates from their observations. Typically, likelihood models are assumed known precisely. However, in many situations the models are generated from sparse training data due to lack of data availability, high cost of collection/calibration, limits within the communications network, and/or the high dynamics of the operational environment. Recently, social learning theory was extended to handle those model uncertainties for categorical models. In this paper, we introduce the theory of Gaussian uncertain models and study the properties of the beliefs generated by the network of agents. We show that even with finite amounts of training data, non-Bayesian social learning can be achieved and all agents in the network will converge to a consensus belief that provably identifies the best estimate for the state of the world given the set of prior information.