Reviewerly/exHarmony huggingface dataset:
The Pixogram: Addressing High Payload Demands for Video Steganography

This paper introduces the concept of a pixogram which makes possible a fresh approach to high payload video steganography. The pixogram allows for a new perspective by investigating the temporal changes that take place at the individual pixel level across frames of a video segment. Simply put, a pixogram has the property of converting highly uncorrelated spatial areas of individual frames of a video scene into highly correlated temporal segments by making use of the temporal correlation between the frames of the same scene in a given video segment, thus maximizing the redundant area suitable for hiding in the transform domain. Experimental results demonstrate the effectiveness of this new approach for increased payload capacity while maintaining visual fidelity of the stego-video as compared to competing video steganography schemes.
Design and Social Innovation at the Margins: Finding and Making Cultures of Plurality

Design and social innovation at the margins: finding and making
Choosing the Appropriate System for Cloud Computing Implementation by Using the Interval-Valued Intuitionistic Fuzzy CODAS Multiattribute Decision-Making Method (Case Study: Faculty of New Sciences and Technologies of Tehran University)

The development of information and communications technology has led organizations to use various solutions to meet their communication needs. One of the best practices for this purpose is the use of cloud computing. Cloud computing allows users to access IT-based services without having to specialize in or control technology infrastructure. At the moment, there are several software applications for implementation of cloud computing in organizations, which make it difficult to select the appropriate tool for the organization. This paper aims to help select the appropriate software for cloud computing system implementation at the Faculty of New Sciences and Technologies, Tehran University. For this purpose, at first, the appropriate criteria are extracted from the related literatures and then finalized using expert opinions. Then, by using an interval-valued intuitionistic fuzzy CODAS multiattribute decision-making method, the criteria are weighted and the alternatives are prioritized. The results indicate that the open stack cloud computing system can be selected as the best alternative. Choosing this alternative can be due to its remarkable distinction in criteria such as performance, system reliability and security, system stability, usability, serviceability, and being open source. According to the experts of this project, the most important criteria for decision making are software availability and system reliability and security.
A novel extended phase correlation algorithm based on Log-Gabor filtering for multimodal remote sensing image registration

Automatic registration of multimodal remote sensing images, which is a critical prerequisite in a range of applications (e.g. image fusion, image mosaic, and image analysis), continues to be a fundamental and challenging problem. In this paper, we propose a novel extended phase correlation algorithm based on Log-Gabor filtering (LGEPC) for the registration of images with nonlinear radiometric differences and geometric differences (e.g. rotation, scale, and translation). Our algorithm focuses on two problems that the traditional extended phase correlation algorithms cannot well handle: 1) significant nonlinear radiometric differences and 2) large-scale differences between image pairs. After an over-complete multi-scale atlas space of the original image is built based on the filtered magnitudes obtained by using Log-Gabor filters with different central frequencies, the phase correlation of the single scale images is extended by LGEPC to atlases phase correlation, which is conducive to solving the problem of large scale and rotation differences between the image pairs. Subsequently, LGEPC eliminates the interface of the significant nonlinear radiometric differences by superimposing multi-scale geometric structural spectra and carrying out the phase correlation module, so that the translation can be well determined. Our experiments on synthetic images demonstrated the rationality and effectiveness of LGEPC, and the experiments on a variety of multimodal images confirmed that LGEPC can ideally achieve pixel-wise registration accuracy for multimodal image pairs that conform to the similarity transformation model.
BotCamp: Bot-driven Interactions in Social Campaigns

Bots (i.e. automated accounts) involve in social campaigns typically for two obvious reasons: to inorganically sway public opinion and to build social capital exploiting the organic popularity of social campaigns. In the process, bots interact with each other and engage in human activities (e.g. likes, retweets, and following).
A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment

Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run.
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning

A large amount of research effort has been dedicated to adapting boosting for imbalanced classification. However, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems. This is because most of the existing solutions for handling class imbalance rely on expensive cost set tuning for determining the proper level of compensation. We show that the assignment of weights to the component classifiers of a boosted ensemble can be thought of as a game of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tug of War</i> between the classes in the margin space. We then demonstrate how this insight can be used to attain a good compromise between the rare and abundant classes without having to resort to cost set tuning, which has long been the norm for imbalanced classification. The solution is based on a lexicographic linear programming framework which requires two stages. Initially, class-specific component weight combinations are found so as to minimize a hinge loss individually for each of the classes. Subsequently, the final component weights are assigned so that the maximum deviation from the class-specific minimum loss values (obtained in the previous stage) is minimized. Hence, the proposal is not only restricted to two-class situations, but is also readily applicable to multi-class problems. Additionally, we also derive the dual formulation corresponding to the proposed framework. Experiments conducted on artificial and real-world imbalanced datasets as well as on challenging applications such as hyperspectral image classification and ImageNet classification establish the efficacy of the proposal.
Stacked U-shape networks with channel-wise attention for image super-resolution

Recent years have witnessed great success of Single Image Super-Resolution (SISR) with convolutional neural network (CNN) based models. Most existing Super-Resolution (SR) networks use bicubic upscaled images as input or directly use low-resolution images as input and do transposed convolution or sub-pixel convolution only in the reconstruction stage which do not use the hierarchical features across the network for final reconstruction. In this paper, we propose a novel stacked U-shape networks with channel-wise attention (SUSR) for SISR. In general, the proposed network consists of four parts, which are shallow feature extraction block, stacked U-shape blocks which produce high-resolution features, residual channel-wise attention blocks and reconstruction block respectively. The hierarchical high-resolution features produced by U-shape blocks have the same size with the final super-resolved image, thus different to existing methods we do upsampling operator in U-shape blocks. In order to fully exploit the different hierarchical features, we propose residual attention block (RAB) to perform feature refinement which explicitly model relationships between channels. Experiments on five public datasets show that our method can achieve much higher Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) scores than the state-of-the-art methods.
Automatic Boolean Query Refinement for Systematic Review Literature Search

In the medical domain, systematic reviews are a highly trustworthy evidence source used to inform clinical diagnosis and treatment, and governmental policy making. Systematic reviews must be complete in that all relevant literature for the research question of the review must be synthesised in order to produce a recommendation. To identify the literature to screen for inclusion in systematic reviews, information specialists construct complex Boolean queries that capture the information needs defined by the research questions of the systemic review. However, in the quest for total recall, these Boolean queries return many non relevant results.
Feature Engineering and Ensemble-Based Approach for Improving Automatic Short-Answer Grading Performance

In this paper, we studied different automatic short answer grading (ASAG) systems to provide a comprehensive view of the feature spaces explored by previous works. While the performance reported in previous works have been encouraging, systematic study of the features is lacking. Apart from providing systematic feature space exploration, we also presented ensemble methods that have been experimentally validated to exhibit significantly higher grading performance over the existing papers in almost all the datasets in ASAG domain. A comparative study over different features and regression models toward short-answer grading has been performed with respect to evaluation metrics used in evaluating ASAG. Apart from traditional text similarity based features like WordNet similarity, latent semantic analysis, and others, we have introduced novel features like topic models suited for short text, relevance feedback based features. An ensemble-based model has been built using a combination of different regression models with an approach based on stacked regression. The proposed ASAG has been tested on the University of North Texas dataset for the regression task, whereas in case of classification task, the student response analysis (SRA) based ScientsBank and Beetle corpus have been used for evaluation. The grading performance in case of ensemble-based ASAG is highly boosted from that exhibited by an individual regression model. Extensive experimentation has revealed that feature selection, introduction of novel features, and regressor stacking have been instrumental in achieving considerable improvement in performance over the existing methods in ASAG domain.
Improving Top-K Recommendation via JointCollaborative Autoencoders

In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives.
Heterogeneous Track-to-Track Fusion in 3-D Using IRST Sensor and Air MTI Radar

Only a few publications exist at present on heterogeneous track-to-track fusion (T2TF). A common limitation of the current work on heterogeneous T2TF is that the cross covariance due to common process noise cannot be computed. This is due to the fact that two local trackers use different dynamic models, and hence, it is difficult to account for the common process noise. We consider a heterogeneous T2TF problem in three dimension (3-D) using a passive infrared search and track (IRST) sensor and an active air moving target indicator (AMTI) radar with the nearly constant velocity motion of the target. The active AMTI tracker uses the Cartesian state vector with 3-D position and velocity, and the dynamic model is linear. A passive IRST tracker commonly uses modified spherical coordinates (MSCs) for the state vector, where the dynamic model is nonlinear. In this formulation, the common process noise is explicitly modeled in both dynamic models. Therefore, it is possible to take into account the common process noise. We use the cubature Kalman filter (CKF) in both trackers due to its numerical stability and improved state estimation accuracy over existing nonlinear filters. The passive tracker uses a range-parameterized MSC-based CKF, and the active tracker uses a Cartesian CKF. We perform T2TF using the information filter (IF), where each local tracker sends its information matrix and the corresponding information state estimate to the fusion center. The IF handles the common process noise in an approximate way. Results from Monte Carlo simulations show that the accuracy of the proposed IF-based T2TF is close to that of the centralized fusion with varying levels of process noise and communication data rate.
Automatic modulation classification based on joint feature map and convolutional neural network

The authors propose an automatic modulation classification algorithm to discriminate the radar emitter signals exploiting deep learning of convolutional neural network (CNN) based on a joint feature map. The joint feature map including time-frequency map and instantaneous autocorrelation (IA) map is generated to achieve superior classification performance on the identification of both phase modulated signals and frequency modulated signals. Afterwards, a CNN is designed to extract the features of the joint feature map. Training and test samples are designed to evaluate the recognition rate of the CNN. Simulation results show that the joint feature map makes up for the weakness of a single feature map for the classification of frequency modulated signals and phase modulated signals simultaneously, and superior classification accuracy is obtained.
Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods

Lithological mapping using satellite images, particularly the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data help in effectively defining the best initial targets for regional exploration. ASTER data allow for the discrimination of rock units in the broader region. This research work is focused on the use of remote sensing techniques for geological mapping using ASTER satellite image and generating a geological map of Sangan region. The study area is located in southeast of Khorasan-e-Razavi province and at the eastern edge of the Khaf-Kashmar-Bardskan Volcano-Plutonic Metallogenic Belt in northeast Iran. Band ratio (BR), spectral angle mapper (SAM) and support vector machines (SVMs) methods were used for classifying the main lithologic units in Sangan region. The results of BR, SAM and SVM techniques were quantitatively compared with geological boundaries mapped in the field showing an accuracy of nearly 79 %. SVMs method, in comparison to conventional methods of classification, was known to provide superior results. As the final result of this research, integration of remote sensing and field investigations led to generating high accuracy geological map in the Sangan region. Application of the methods has invaluable implications for geological mapping and mineral exploration in inaccessible regions.
Improving Performance of Graph Processing on FPGA-DRAM Platform by Two-level Vertex Caching

In recent years, graph processing attracts lots of attention due to its broad applicability in solving real-world problems. With the flexibility and programmability, FPGA platforms provide the opportunity of processing the graph data with high efficiency. On FPGA-DRAM platforms, the state-of-art solution of graph processing (i.e., ForeGraph) attaches each pipeline with local vertex buffers to cache the source and destination vertices during processing. Such one-level vertex caching mechanism, however, results in excessive amounts of vertex data transmissions that consume the precious DRAM bandwidth, and frequent pipeline stalls that waste the processing power of the FPGA. In this paper, we propose a two-level vertex caching mechanism to improve the performance of graph processing on FPGA-DRAM platforms by reducing the amounts of vertex data transmissions and pipeline stalls during the execution of graph algorithms. We build a system, named as FabGraph, to implement such two-level vertex caching mechanism by using available on-chip storage resources, including BRAM and UltraRAM. Experimental results show that: FabGraph achieves up to 3.1x and 2.5x speedups over ForeGraph for BFS and PageRank respectively, on the FPGA board with relatively large BRAM; and up to 3.1x and 3.0x speedups over ForeGraph for BFS and PageRank respectively, on the FPGA board with small BRAM but large UltraRAM. Our experience in this paper suggests that the two-level vertex caching design is effective in improving the performance of graph processing on FPGA-DRAM platforms.
Finding a High-Quality Initial Solution for the RRTs Algorithms in 2D Environments

Summary In this paper, we propose a bioinspired path planning algorithm for finding a high-quality initial solution based on the pipeline of the Rapidly exploring Random Tree (RRT) method by modifying the sampling process. The modification mainly includes controlling the sampling space and using the probabilistic sampling with the two-dimensional Gaussian mixture model. Inspired by the tropism of plants, we use a Gaussian mixture model to imitate the tree’s growth in nature. In a 2D environment, we can get an approximate moving point’s probabilistic distribution, and the initial path can be found much quickly guided by the probabilistic heuristic. At the same time, only a small number of nodes are generated, which can reduce the memory usage. As a meta-algorithm, it can be applicable to other RRT methods and the performance of underlying algorithm is improved dramatically. We also prove that the probabilistic completeness and the asymptotic optimality depend on the original algorithm (other RRTs). We demonstrate the application of our algorithm in different simulated 2D environments. On these scenarios, our algorithm outperforms the RRT and the RRT* methods on finding the initial solution. When embedded into post-processing algorithms like the Informed RRT*, it also promotes the convergence speed and saves the memory usage.
Mobile Apps for Green Food Practices and the Role for Consumers: A Case Study on Dining Out Practices with Chinese and Dutch Young Consumers

Mobile applications (apps) have become popular among consumers to facilitate their existing food practices like cooking, shopping, and dining out. However, the feasibility of using mobile apps to facilitate sustainability transitions in food consumption is not well researched. In this study, we, therefore, propose a conceptual framework to illustrate how mobile apps can be developed in linking everyday food practices with sustainability transitions. Through the case study of dining out and with the help of focus group discussions, we seek to illustrate that practice theory might serve as a useful starting point for understanding the dynamics of food practices, their relevant sustainability dimensions, and the ways in which mobile apps can be used for changing current food practices into more sustainable ones. Among our main results are the findings that consumers prefer the sustainability food app to be integrated with dominant or mainstream apps, which are already used by consumers in the context of dining out. Besides being simple, functional, flexible, and rewarding, the information provided by the app should be reliable and trustworthy. Moreover, both science-based and practice-based information is necessary to provide sufficient guidance to consumers on how changes in food practice can be operationalized and implemented.
Asynchronous multi-rate multi-sensor fusion based on random finite set

This paper considers the asynchronous sensor fusion problem for an arbitrary number of sensors with different sampling rates in the framework of the random finite set (RFS) theory. By sequentially predicting and updating the posteriors with measurements according to the arrival time sequence of measurement sets, a centralized asynchronous fusion algorithm, centralized Sequential Processing (SP), is proposed first. It is optimal due to the usage of original measurement information. Considering the reliability, survivability and communication bandwidth as well as flexibility of output time, two distributed asynchronous fusion algorithms are also proposed by assuming that the process of tracking among sensors is independent. The first distributed asynchronous fusion algorithm, namely Batch Generalization Covariance Intersection (B-GCI), utilizes all the predicted local posteriors (LPs) from different sensors and fuses them simultaneously at the fusion center (FC) based on GCI rule, which avoids the complicated calculations of cross-covariance among the LPs of sensors. Considering the large computational burden of the B-GCI fusion algorithm, another one method, Sequential GCI (S-GCI) fusion algorithm, is proposed. The method sequentially fuses the predicted LPs in pairs based on GCI rule at the FC. Performance of proposed three algorithms, including a centralized fusion algorithm and two distributed fusion algorithms, is realized by using Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter and some numerical simulations are given.