Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain

Safe unmanned ground vehicle navigation in unknown rough terrain is crucial for various tasks such as exploration, search and rescue and agriculture. Offline global planning is often not possible when operating in harsh, unknown environments, and therefore, online local planning must be used. Most online rough terrain local planners require heavy computational resources, used for optimal trajectory searching and estimating vehicle orientation in positions within the range of the sensors. In this work, we present a deep reinforcement learning approach for local planning in unknown rough terrain with zero-range to local-range sensing, achieving superior results compared to potential fields or local motion planning search spaces methods. Our approach includes reward shaping which provides a dense reward signal. We incorporate self-attention modules into our deep reinforcement learning architecture in order to increase the explainability of the learnt policy. The attention modules provide insight regarding the relative importance of sensed inputs during training and planning. We extend and validate our approach in a dynamic simulation, demonstrating successful safe local planning in environments with a continuous terrain and a variety of discrete obstacles. By adding the geometric transformation between two successive timesteps and the corresponding action as inputs, our architecture is able to navigate on surfaces with different levels of friction. Reinforcement learning, autonomous vehicle navigation, motion and path planning.
Students Perception of Mobile Learning during COVID-19 in Bangladesh: University Student Perspective

The aim of this study is to measures the student's perception of using mobile for learning during COVID-19 in Bangladesh especially at the university student's perspective. During the COVID-19 pandemic period, mobile learning may help the students to fulfill the study gap. Due to COVID-19 pandemic 213 countries, higher education has affected all over the world of June 2020. Although all of the developed countries considered mobile learning as an effective tool for education, it is not used properly in Bangladesh. This survey method conducted on 416 students from different university students in Bangladesh to understand the student's perception of using mobile phones as a learning system. The findings of this study show that most of the students at the university level have a positive perception of m-learning. This study revealed that m-learning is very helpful to recover the study gap during this COVID-19 pandemic time and the findings of this study will help the education policymaker as well as the educational institutions to incorporate mobile learning technology for the whole system where social media may enhance the process of teaching and learning.
A trustworthiness-based vehicular recruitment scheme for information collections in Distributed Networked Systems

Because of high mobility, large number of vehicles are utilized to achieve timely and quality-based information in the smart Internet of Things, which has formulated into a dynamic Distributed Networked Systems (DNS). However, designing a vehicular recruitment scheme to enhance a security-based DNS is challenging since it is hard to judge trustworthiness values of vehicular sensors. Therefore, in this paper, a novel vehicular trust evaluation scheme is proposed to analyze and supervise the data collected by vehicular sensors with a trust and low-cost style. To obtain vehicular trusts, the proposed scheme that considers time factor and gap between sensed data and real data is designed to calculate trustworthiness of vehicles. Moreover, sensing data in the vehicle sparse regions has more contributions because of its rareness. Thus, to inspire vehicles to sense data in the vehicle sparse regions, a trustworthiness-based gradient pricing method is designed to pay rewards for the vehicular sensors. Finally, with real vehicular GPS datasets, simulation results demonstrate that the proposed scheme can improve accuracy rate of data sensing by 37.72% and can improve data quality by 76.95%. By incentive pricing method, coverage ratio of data sensing is improved by 13.1%. In general, performances of the proposed scheme can be improved by 19. 39% to 22.32% approximately. Future works focus on improving information security by advanced machine learning methods in the dynamic DNS.
Self-adaptive salp swarm algorithm for engineering optimization problems

Salp swarm algorithm is a recent introduction in the field of swarm intelligent algorithms and has proved its worth over various research domains. Though it is a competitive algorithm but it has been found that salp swarm algorithm suffers from various problems including poor exploitation, slow convergence and unbalanced exploration and exploitation operation. In present work, four major modifications have been added to salp swarm algorithm in order to make it self-adaptive and the proposed algorithm has been named as adaptive salp swarm algorithm. The modifications include division of generations and logarithmic adaptive parameters to control the extent of exploration and exploitation, enhanced exploitation phase to improve the local search and linearly decreasing population adaptation to reduce the total number of function evaluations. The performance of the proposed algorithm is tested on benchmark problems and further applied for optimization of transmission parameters in cognitive radio system. From the experimental results, it has been found that the proposed adaptive salp swarm algorithm is highly competitive and provides better results when compared with bat algorithm, grey wolf optimization, teacher learning based algorithm, dragonfly algorithm and others. Convergence profiles and statistical tests further validate the results.
Entangled Polynomial Codes for Secure, Private, and Batch Distributed Matrix Multiplication: Breaking the "Cubic" Barrier

In distributed matrix multiplication, a common scenario is to assign each worker a fraction of the multiplication task, by partitioning the input matrices into smaller submatrices. In particular, by dividing two input matrices into m-by-p and p-by-n subblocks, a single multiplication task can be viewed as computing linear combinations of pmn submatrix products, which can be assigned to pmn workers. Such block-partitioning based designs have been widely studied under the topics of secure, private, and batch computation, where the state of the arts all require computing at least "cubic" (pmn) number of submatrix multiplications. Entangled polynomial codes, first presented for straggler mitigation, provides a powerful method for breaking the cubic barrier. It achieves a subcubic recovery threshold, meaning that the final product can be recovered from any subset of multiplication results with a size order-wise smaller than pmn. In this work, we show that entangled polynomial codes can be further extended to also include these three important settings, and provide a unified framework that order-wise reduces the total computational costs upon the state of the arts by achieving subcubic recovery thresholds.
NodeAug: Semi-Supervised Node Classification with Data Augmentation

By using Data Augmentation (DA), we present a new method to enhance Graph Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised node classification. DA for graph data remains under-explored. Due to the connections built by edges, DA for different nodes influence each other and lead to undesired results, such as uncontrollable DA magnitudes and changes of ground-truth labels. To address this issue, we present the NodeAug (Node-Parallel Augmentation) scheme, that creates a 'parallel universe' for each node to conduct DA, to block the undesired effects from other nodes. NodeAug regularizes the model prediction of every node (including unlabeled) to be invariant with respect to changes induced by Data Augmentation (DA), so as to improve the effectiveness. To augment the input features from different aspects, we propose three DA strategies by modifying both node attributes and the graph structure. In addition, we introduce the subgraph mini-batch training for the efficient implementation of NodeAug. The approach takes the subgraph corresponding to the receptive fields of a batch of nodes as the input per iteration, rather than the whole graph that the prior full-batch training takes. Empirically, NodeAug yields significant gains for strong GCN models on the Cora, Citeseer, Pubmed, and two co-authorship networks, with a more efficient training process thanks to the proposed subgraph mini-batch training approach.
Keeping customers' data secure: A cross-cultural study of cybersecurity compliance among the Gen-Mobile workforce

Employees are increasingly relying on mobile devices. In international organizations, more employees are using their personal smartphones for work purposes. Meanwhile, the number of data breaches is rising and affecting the security of customers' data. However, employees' cybersecurity compliance with cybersecurity policies is poorly understood. Researchers have called for a more holistic approach to information security. We propose an employee smartphone-security compliance (ESSC) model, which deepens understanding of employees' information-security behavior by considering influences on the national, organizational, technological (smartphone-specific), and personal levels. The research focuses on secure smartphone use in the workplace among Gen-Mobile (aged 18–35) employees in a cross-cultural context: the United Kingdom (UK), United States (US) and United Arab Emirates (UAE) where 1735 questionnaires were collected. Our findings suggest that those who wish to understand employees' smartphone-security behavior should consider national cybersecurity policies, cultural differences in different countries, and threats specific to smartphone use. In addition, our findings help companies to increase customers' trust and maintain a positive reputation.
A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.
Privacy enhancing technologies for solving the privacy-personalization paradox: Taxonomy and survey

Personal data are often collected and processed in a decentralized fashion, within different contexts. For instance, with the emergence of distributed applications, several providers are usually correlating their records, and providing personalized services to their clients. Collected data include geographical and indoor positions of users, their movement patterns as well as sensor-acquired data that may reveal users' physical conditions, habits and interests. Consequently, this may lead to undesired consequences such as unsolicited advertisement and even to discrimination and stalking. To mitigate privacy threats, several techniques emerged, referred to as Privacy Enhancing Technologies, PETs for short. On one hand, the increasing pressure on service providers to protect users' privacy resulted in PETs being adopted. One the other hand, service providers have built their business model on personalized services, e.g. targeted ads and news. The objective of the paper is then to identify which of the PETs have the potential to satisfy both usually divergent - economical and ethical - purposes. This paper identifies a taxonomy classifying eight categories of PETs into three groups, and for better clarity, it considers three categories of personalized services. After defining and presenting the main features of PETs with illustrative examples, the paper points out which PETs best fit each personalized service category. Then, it discusses some of the inter-disciplinary privacy challenges that may slow down the adoption of these techniques, namely: technical, social, legal and economic concerns. Finally, it provides recommendations and highlights several research directions.
Efficient Data Access Control With Fine-Grained Data Protection in Cloud-Assisted IIoT

The Industrial Internet of Things (IIoT) has provided a promising opportunity to build digitalized industrial systems. A fundamental technology of IIoT is the radio-frequency identification (RFID) technique, which allows industrial participants to identify items and anchor time-series IoT data for them. They can further share the IoT data through the cloud service to enable information exchange and support critical decisions in production operations. Storing IoT data in the cloud, however, requires a data access control mechanism to protect sensitive business issues. Unfortunately, using traditional cryptographic access control schemes for time-series IoT data face severe efficiency and key leakage problems. In this article, we design a secure industrial data access control scheme for cloud-assisted IIoT. Our scheme enables participants to enforce fine-grained access control policies for their IoT data via ciphertext policy-attribute-based encryption (CP-ABE) scheme. Our scheme adopts a hybrid cloud infrastructure for participants to outsource expensive CP-ABE tasks to the cloud service with strong privacy guarantees. Importantly, our scheme guarantees a new privacy notion named item-level data protection for IoT data to prevent key leakage problem. We achieve these goals via several encryption and optimization techniques. Our performance assessments combine system implementation with large-scale emulations and confirm the security and efficiency of our design.
Forecasting the price of Bitcoin using deep learning

After constructing a feature system with 40 determinants that affect the price of Bitcoin considering aspects of the cryptocurrency market, public attention, and the macroeconomic environment, a deep learning method named stacked denoising autoencoders (SDAE) is utilized to predict the price of Bitcoin. The results show that compared with the most popular machine learning methods, such as back propagation neural network (BPNN) and support vector regression (SVR) methods, the SDAE model performs better in both directional and level prediction, measured using commonly used indicators, i.e., mean absolute percentage error (MAPE), root mean squared error (RMSE), and directional accuracy (DA).
Cascaded hierarchical atrous spatial pyramid pooling module for semantic segmentation

Atrous Spatial Pyramid Pooling (ASPP) is a module that can collect semantic information distributed in different scopes. However, because of the limited number of sampling ranges of ASPP, much valuable global features and contextual information cannot be sufficiently sampled, which degrades the representation ability of the segmentation network. Besides, due to the sparse distribution of the effective sampling points in the atrous convolution kernels of ASPP, large amount of local detail characteristics are easily discarded. To overcome the above two problems, a new Cascaded Hierarchical Atrous Pyramid Pooling (CHASPP) module, consisting of two cascaded components, is proposed. Each component is a hierarchical pyramid pooling structure containing two layers of atrous convolutions with the aim to densify the sampling distribution. On the foundation of such a hierarchical structure, another same structure is appended to form a cascaded module which can further enlarge the diversity of sampling ranges. Based on this cascaded module, not only rich local detail characteristics can be comprehensively presented, but also important global contextual information can be effectively exploited to improve the prediction accuracy. To demonstrate the performance of our CHASPP module, experiments on the benchmarks PASCAL VOC 2012 and Cityscape are conducted.
Gesture recognition based on multi‐modal feature weight

Summary With the continuous development of sensor technology, the acquisition cost of RGB‐D images is getting lower and lower, and gesture recognition based on depth images and Red‐Green‐Blue (RGB) images has gradually become a research direction in the field of pattern recognition. However, most of the current processing methods for RGB‐D gesture images are relatively simple, ignoring the relationship and influence between its two modes, and unable to make full use of the correlation factors between different modes. In view of the above problems, this paper optimizes the effect of RGB‐D information processing by considering the independent features and related features of multi‐modal data to construct a weight adaptive algorithm to fuse different features. Simulation experiments show that the method proposed in this paper is better than the traditional RGB‐D gesture image processing method and the gesture recognition rate is higher. Comparing the current more advanced gesture recognition methods, the method proposed in this paper also achieves higher recognition accuracy, which verifies the feasibility and robustness of this method.
An overview of decentralized autonomous organizations on the blockchain

Blockchain technology has emerged as a new paradigm to build decentralized systems which do not require a central authority. It is most popular for enabling Bitcoin and other crypto-currencies. However, blockchain applications span beyond Finance, and recently it has been applied to decentralized governance. Blockchain-enabled "Decentralized Autonomous Organizations" (DAOs) have emerged as a new form of collective governance, in which communities may organize themselves relying on decentralized infrastructure. In this article, we introduce the concept of DAO and review the main software platforms that offer DAO creation as a service, which simplifies the use of DAOs to non-blockchain experts; namely: Aragon, DAOstack, DAOhaus and Colony. These platforms will be compared by showing their key features. Finally, we will review the available visualisation tools for DAOs, and we will introduce our open-source tool to plot DAOs activity, DAO-Analyzer. We will illustrate its potential with the case of the DAO Genesis Alpha, which is the main DAO of the DAOstack project.
A Serial Image Copy-Move Forgery Localization Scheme With Source/Target Distinguishment

In this paper, we improve the parallel deep neural network (DNN) scheme BusterNet for image copy-move forgery localization with source/target region distinguishment. BusterNet is based on two branches, i.e., Simi-Det and Mani-Det, and suffers from two main drawbacks: (a) it should ensure that both branches correctly locate regions; (b) the Simi-Det branch only extracts single-level and low-resolution features using VGG16 with four pooling layers. To ensure the identification of the source and target regions, we introduce two subnetworks that are constructed serially: the copy-move similarity detection network (CMSDNet) and the source/target region distinguishment network (STRDNet). Regarding the second drawback, the CMSDNet subnetwork improves Simi-Det by removing the last pooling layer in VGG16 and by introducing atrous convolution into VGG16 to preserve field-of-views of filters after the removal of the fourth pooling layer; double-level self-correlation is also considered for matching hierarchical features. Moreover, atrous spatial pyramid pooling and attention mechanism allow the capture of multiscale features and provide evidence for important information. Finally, STRDNet is designed to determine the similar regions obtained from CMSDNet directly as tampered regions and untampered regions. It determines regions at the image-level rather than at the pixel-level as made by Mani-Det of BusterNet. Experimental results on four publicly available datasets (new synthetic dataset, CASIA, CoMoFoD, and COVERAGE) demonstrate that the proposed algorithm is superior to the state-of-the-art algorithms in terms of similarity detection ability and source/target distinguishment ability.
Improved salient object detection using hybrid Convolution Recurrent Neural Network

Salient object detection is a critical and active field that aims at the detection of objects in a video, however, it draws increased attention among researchers. With increasing dynamic video data, the performance of saliency object detection method has been degrading with conventional object detection methods. The challenges lie with blurry moving targets, rapid movement of objects and background occlusion or dynamic background change on foreground regions in video frames. Such challenges result in poor saliency detection. In this paper, we design a deep learning model to address the issues, which uses a novel framework by combining the idea of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) for video saliency detection. The proposed method aims at developing a spatiotemporal model that exploits temporal, spatial and local constraint cues to achieve global optimization. The task of finding the salient objects in benchmark dynamic video datasets is then carried out by capturing the temporal, spatial and local constraint features with the Convolution Recurrent Neural Network (CRNN). The CRNN is evaluated on benchmark datasets against conventional video salient object detection methods in terms of precision, F-measure, mean absolute error (MAE) and computational load. The experiments reveal that the CRNN model achieves improved performance than other state-of-the-art saliency models in terms of increased speed and reduced computational load.
Stateful Detection of Black-Box Adversarial Attacks

The problem of adversarial examples, evasion attacks on machine learning classifiers, has proven extremely difficult to solve. This is true even in the black-box threat model, as is the case in many practical settings. Here, the classifier is hosted as a remote service and the adversary does not have direct access to the model parameters.