Reviewerly/exHarmony huggingface dataset:
An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.
Distributed Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Air Space

Small unmanned aerial vehicles (UAVs) have the potential to revolutionize various applications in civilian domain such as disaster management, search and rescue operations, law enforcement, precision agriculture, and package delivery. As the number of such UAVs rise, a robust and reliable traffic management is needed for their integration in national airspace system (NAS) to enable real-time, reliable, and safe operation. Management of UAVs traffic in NAS becomes quite challenging due to issues such as real-time path planning of large number of UAVs, communication delays, operational uncertainties, failures, and noncooperating agents. In this work, we present a novel UAV traffic management (UTM) architecture that enables the integration of such UAVs in NAS. A combined A*–mixed integer linear programming (MILP)-based solution is presented for initial path planning of multiple UAVs with individual mission requirements and dynamic constraints. We also present a distributed detect-and-avoid (DAA) algorithm based on the concept of resource allocation using a market-based approach. The results demonstrate the scalability, optimality, and ability of the proposed approach to provide feasible solutions that are versatile in dynamic environments.
A comparative analysis of adaptive consistency approaches in cloud storage

NoSQL storage systems are used extensively by web applications and provide an attractive alternative to conventional databases due to their high security and availability with a low cost. High data availability is achieved by replicating data in different servers in order to reduce access time lag, network bandwidth consumption and system unreliability. Hence, the data consistency is a major challenge in distributed systems. In this context, strong consistency guarantees data freshness but affects directly the performance and availability of the system. In contrast, weaker consistency enhances availability and performance but increases data staleness. Therefore, an adaptive consistency strategy is needed to tune, during runtime, the consistency level depending on the criticality of the requests or data items. Although there is a rich literature on adaptive consistency approaches in cloud storage, there is a need to classify as well as regroup the approaches based on their strategies. This paper will establish a set of comparative criteria and then make a comparative analysis of existing adaptive consistency approaches. A survey of this kind not only provides the user/researcher with a comparative performance analysis of the approaches but also clarifies the suitability of these for candidate cloud systems.
Robotic Services for New Paradigm Smart Cities Based on Decentralized Technologies

This article describes different methods of organizing robotic services for smart cities using secure encrypted decentralized technologies and market mechanisms—as opposed to models based on centralized solutions based (or not) on using cloud services and stripping citizens of the control of their own data. The basis of the proposed methods is the Ethereum decentralized computer with the mechanism of smart contracts. In this work, special attention is paid to the integration of technical and economic information into one network of transactions, which allows creating a unified way of interaction between robots—the robot economy. Three possible scenarios of robotic services for smart cities based on the economy of robots are presented: unmanned aerial vehicles (UAVs), environmental monitoring, and smart factories. In order to demonstrate the feasibility of the proposed scenarios, three experiments are presented and discussed. Our work shows that the Ethereum network can provide, through smart contracts and their ability to activate programs to interact with the physical world, an effective and practical way to manage robot services for smart cities.
Cyberthreat-Intelligence Information Sharing: Enhancing Collaborative Security

The term cyberthreat intelligence (CTI) is widespread and gaining much attention in the area of the Internet of Things (IoT), as CTI has shown capabilities regarding the defense of advanced persistent threats (APTs). The key component of CTI is the sharing of threat information, which conventionally was a time-consuming manual process.
Cascade Semantic Fusion for Image Captioning

Recent advances in deep visual attention methods accelerate greatly the research of image captioning. However, how to leverage hand-crafted features or deep features for the encoder of image captioning is not fully explored, due to the difficulty in finding a kind of all-purpose features to entail a set of visual semantics. In this paper, we introduce a cascade semantic fusion architecture (CSF) to mine the representative features to encode image content through attention mechanism without bells and whistles. Specifically, the CSF benefits from three types of visual attention semantics, including object-level, image-level, and spatial attention features, in a novel three-stage cascade manner. In the first stage, object-level attention features are extracted to capture the detailed contents of the objects based on the pretrained detector. Then, the middle stage devises a fusion module to merge object-level attention features with spatial features, thereby inducing image-level attention features to enrich the context information around the objects. In the last stage, spatial attention features are learned to unveil the salient region representation as a complement to two previously learned attention features. In a nutshell, we integrate attention mechanism with three types of features to organize context knowledge about images from different aspects. The empirical analysis shows that the CSF can assist image captioning model in selecting the object regions of interest. The experiments of image captioning on MSCOCO dataset show the efficacy of our semantic fusion architecture in depicting image content.
Efficient Data Privacy and Security in Autonomous Cars

As the advancement and testing of self-driving auto innovation has advanced, the possibility of exclusive self-ruling vehicles working on open streets is nearing. Industry specialists foresee that self-governing vehicles will be financially accessible inside the following five to ten years. As automation becomes more prevalent in the transportation industry, driverless vehicles are appearing more frequently in the news. Asymmetric algorithms have shown their impact on large amount of data that have been secured by generating a public key. Cyber security is the major concern for any autonomous vehicle. The main contribution of this research is to secure data using an Asymmetric algorithm technique which will be saved in cloud storage.
Towards Robust and Accurate Single-View Fast Human Motion Capture

This paper proposes a new method for fast human motion capture based on a single RGB-D sensor.By leveraging the human pose detection results for reinitializing the ICP-based sequential human motion tracking algorithm when tracking failure happens, our system achieves a highly robust and accurate human motion tracking performance even for fast motion.Moreover, the calculation and utilization of semantic tracking loss enable body-part-level motion tracking refinement, which is better than whole-body refinement.Finally, a simple yet effective post-processing method, semantic bidirectional motion blending, for single-view human motion capture is proposed to further improve the tracking accuracy, especially under severe occlusions and fast motion.The results and experiments demonstrate that the proposed method achieves highly accurate and robust human motion capture performance in a very efficient way.Applications include AR&VR, human motion analysis, movie, and gaming.
DeepMOT: A Differentiable Framework for Training Multiple Object Trackers

The recent trend in vision-based multi-object tracking (MOT) is heading towards leveraging the representational power of deep learning to jointly learn to detect and track objects. However, existing methods train only certain sub-modules using loss functions that often do not correlate with established tracking evaluation measures such as Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP). As these measures are not differentiable, the choice of appropriate loss functions for end-to-end training of multi-object tracking methods is still an open research problem. In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers. As a key ingredient, we propose a Deep Hungarian Net (DHN) module that approximates the Hungarian matching algorithm. DHN allows estimating the correspondence between object tracks and ground truth objects to compute differentiable proxies of MOTA and MOTP, which are in turn used to optimize deep trackers directly. We experimentally demonstrate that the proposed differentiable framework improves the performance of existing multi-object trackers, and we establish a new state of the art on the MOTChallenge benchmark. Our code is publicly available from this https URL.
PALOT: Profiling and Authenticating Users Leveraging Internet of Things

Continuous authentication was introduced to propose novel mechanisms to validate users’ identity and address the problems and limitations exposed by traditional techniques. However, this methodology poses several challenges that remain unsolved. In this paper, we present a novel framework, PALOT, that leverages IoT to provide context-aware, continuous and non-intrusive authentication and authorization services. To this end, we propose a formal information system model based on ontologies, representing the main source of knowledge of our framework. Furthermore, to recognize users’ behavioral patterns within the IoT ecosystem, we introduced a new module called “confidence manager”. The module is then integrated into an extended version of our early framework architecture, IoTCAF, which is consequently adapted to include the above-mentioned component. Exhaustive experiments demonstrated the efficacy, feasibility and scalability of the proposed solution.
Digital Forensics and Privacy-by-Design: Example in a Blockchain-Based Dynamic Navigation System

This research presents an experimental model and prototype to exploit digital evidence in Internet of Things (IoT). The novelty of this research is to consider new data privacy mechanisms that should be implemented in IoT, in compliance with the GDPR regulation, and their impact on digital forensic processes. The testbed is an innovative project for car navigation [1, 2], GDPR compatible, which offers users the possibility to submit their GPS position into a blockchain for obtaining road traffic information and alternative paths. The vehicles are communicating among themselves through IoTs and circumvent the use of third-party services. We propose a solution for forensic investigations of such a service by building a solid case thanks to the non-repudiable, immutable, identifiable as current and authentic properties of data logged into the blockchain. This solution applies to criminal and insurance cases, where law enforcement and individuals need to prove their claims.
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders

Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. However, recent studies demonstrate that, when equipped with expressive generative distributions (aka. decoders), VAE suffers from learning uninformative latent representations with the observation called KL Varnishing, in which case VAE collapses into an unconditional generative model. In this work, we introduce mutual posterior-divergence regularization, a novel regularization that is able to control the geometry of the latent space to accomplish meaningful representation learning, while achieving comparable or superior capability of density estimation. Experiments on three image benchmark datasets demonstrate that, when equipped with powerful decoders, our model performs well both on density estimation and representation learning.
An Efficient Stacking Model of Multi-Label Classification Based on Pareto Optimum

Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among those algorithms binary relevance (BR) is a widely used framework for multi-label classification. It constructs binary classifiers for each label by means of one-vs-rest style. BR approach is a simple and straight forward way of problem transformation for multi-label learning, but it ignores label correlations totally. Stacking based BR is a feasible way to tackle this problem. The key issue of stacking based BR is how to select label subset to extend the original features for each label. Existing methods of stacking based BR usually select identical label subset for all labels. It may be suboptimal as each label has its own most related label subset. In this paper, a novel stacking based method is introduced to utilize label correlations based on Pareto Optimum for improving the performance of BR. Our method builds a stack of two layers of BR classifiers. At the first layer, a group of binary classifiers are constructed, one for a label. At the second layer, for each label we employ Pareto Optimum to select most related label subset, then augment the original features by the selected label subset. The final binary classifiers for each label are constructed based on their corresponding reconstructed feature space. Comparing to other well-established stacking multi-label learning algorithms in terms of different multi-label classification criteria, experimental results on several multi-label benchmark datasets testify the superiority of the proposed methods.
Stablecoin as a New Financial Instrument

The author considers the legal and economic nature of stablecoin, the development stages of low-volatility assets, describes the existing models of their implementation in the public and private sectors. Particular attention is paid to the assessment of legal risks of mono-secured and multi-secured cryptocurrency on the example of projects in Venezuela and Russia, as well as the possibility of using stablecoin for the development of the financial system and evasion from economic sanctions.
Grey wolf optimization for global path planning of autonomous underwater vehicle

Path planning problem (PPP) deals with finding an optimized path between a source and a goal point. Global path planning (GPP) for Autonomous underwater vehicle (AUV), provides an optimized predefined path to reach the desired destination of the AUV. AUVs are largely useful in missions involving marine geoscience, scientific research, military warfare, along with commercial sectors of oil and gas industries. A time optimized path that can avoid collision helps in reducing time and energy expenses of such real time missions. Grey Wolf Optimization (GWO) is a nature inspired metaheuristic algorithm based on hunting behavior of the grey wolves. GWO provides better exploration of the solution space and good at avoiding local minima. This research presents an overview of GWO with its mathematical modelling. The research mainly contributes in applying GWO for path planning of an AUV to generate a global path in a two-dimensional underwater environment with static obstacles. Simulation results are obtained using MATLAB. The resultant path is optimized in time, distance travel and requires less processing time as compared to results obtained by applying Ant colony Optimization (ACO) for the same problem.
Quantum Coherence Witness with Untrusted Measurement Devices

Coherence is a fundamental resource in quantum information processing, which can be certified by a coherence witness. Due to the imperfection of measurement devices, a conventional coherence witness may lead to fallacious results. We show that the conventional witness could mistake an incoherent state as a state with coherence due to the inaccurate settings of measurement bases. In order to make the witness result reliable, we propose a measurement-device-independent coherence witness scheme without any assumptions on the measurement settings. We introduce the decoy-state method to significantly increase the capability of recognizing states with coherence. Furthermore, we experimentally demonstrate the scheme in a time-bin encoding optical system.
The effect of organizational culture on the organizational commitment: Evidence from hotel industry

This study seeks to investigate the association between organizational culture and employees' commitment in the Jordanian hotel sector.The data that were gathered from 248 hotel employees, were about the respondents' organizational culture and its impact on the employees' commitment.Data were collected using the Organizational Culture Index (Wallach, 1983) [Wallach, E. J. (1983).Individuals and organizations: The cultural match.
An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial communities, in order to support environmental monitoring efforts of contamination events. Models were trained on taxon count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the taxa primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species for glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep and branching architectures, demonstrating increasingly better object categorization performance, yet bringing into question how brain-like they still are. In particular, typical deep models from the machine learning community are often hard to map onto the brain's anatomy due to their vast number of layers and missing biologically-important connections, such as recurrence. Here we demonstrate that better anatomical alignment to the brain and high performance on machine learning as well as neuroscience measures do not have to be in contradiction. We developed CORnet-S, a shallow ANN with four anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a new large-scale composite of neural and behavioral benchmarks for quantifying the functional fidelity of models of the primate ventral visual stream. Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet. Moreover, our extensive analyses of CORnet-S circuitry variants reveal that recurrence is the main predictive factor of both Brain-Score and ImageNet top-1 performance. Finally, we report that the temporal evolution of the CORnet-S neural population resembles the actual monkey IT population dynamics. Taken together, these results establish CORnet-S, a compact, recurrent ANN, as the current best model of the primate ventral visual stream.
Explicit Disentanglement of Appearance and Perspective in Generative Models

Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transformations of the input data, and another that represents the transformed data. We find that the latter naturally captures the intrinsic appearance of the data. To realize the generative model, we propose a Variationally Inferred Transformational Autoencoder (VITAE) that incorporates a spatial ransformer into a variational autoencoder. We show how to perform inference in the model efficiently by carefully designing the encoders and restricting the transformation class to be diffeomorphic. Empirically, our model separates the visual style from digit type on MNIST, separates shape and pose in images of human bodies and facial features from facial shape on CelebA.