Reviewerly/exHarmony huggingface dataset:
Procedures for the Integration of Drones into the Airspace Based on U-Space Services

A safe integration of drones into the airspace is fundamental to unblock the potential of drone applications. U-space is the drone traffic management solution for Europe, intended to handle a large number of drones in the airspace, especially at very low level (VLL). This paper presents the procedures we have designed and tested in real flights in the SAFEDRONE European project to pave the way for a safe integration of drones into the airspace using U-space services. We include three important aspects: Design of procedures related to no-fly zones, ensure separation with manned aircraft, and autonomous non-cooperative detect-and-avoid (DAA) technologies. A specific U-space architecture has been designed and implemented for flight campaigns with up to eight drones with different configurations and a manned aircraft. From this experience, specific recommendations about procedures to exit and avoiding no-fly zones are presented. Additionally, it has been concluded that the use of surveillance information of manned aircraft will allow a more efficient use of the airspace while maintaining a proper safety level, avoiding the creation of large geofence areas.
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization

Recently neural architecture search (NAS) has raised great interest in both academia and industry. However, it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a direct sparse optimization NAS (DSO-NAS) method. The motivation behind DSO-NAS is to address the task in the view of model pruning. To achieve this goal, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, sparse regularizations are imposed to prune useless connections in the architecture. Lastly, an efficient and theoretically sound optimization method is derived to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore it can be directly applied to large datasets like ImageNet and tasks beyond classification. Particularly, on the CIFAR-10 dataset, DSO-NAS achieves an average test error 2.74 percent, while on the ImageNet dataset DSO-NAS achieves 25.4 percent test error under 600M FLOPs with 8 GPUs in 18 hours. As for semantic segmentation task, DSO-NAS also achieve competitive result compared with manually designed architectures on the PASCAL VOC dataset. Code is available at https://github.com/XinbangZhang/DSO-NAS.
Constrained LSTM and Residual Attention for Image Captioning

Visual structure and syntactic structure are essential in images and texts, respectively. Visual structure depicts both entities in an image and their interactions, whereas syntactic structure in texts can reflect the part-of-speech constraints between adjacent words. Most existing methods either use visual global representation to guide the language model or generate captions without considering the relationships of different entities or adjacent words. Thus, their language models lack relevance in both visual and syntactic structure. To solve this problem, we propose a model that aligns the language model to certain visual structure and also constrains it with a specific part-of-speech template. In addition, most methods exploit the latent relationship between words in a sentence and pre-extracted visual regions in an image yet ignore the effects of unextracted regions on predicted words. We develop a residual attention mechanism to simultaneously focus on the pre-extracted visual objects and unextracted regions in an image. Residual attention is capable of capturing precise regions of an image corresponding to the predicted words considering both the effects of visual objects and unextracted regions. The effectiveness of our entire framework and each proposed module are verified on two classical datasets: MSCOCO and Flickr30k. Our framework is on par with or even better than the state-of-the-art methods and achieves superior performance on COCO captioning Leaderboard.
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation

In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on top of two-stream CNNs, GeoNet++ captures the geometric relationships between depth and surface normals with the proposed depth-to-normal and normal-to-depth modules. In particular, the "depth-to-normal" module exploits the least square solution of estimating surface normals from depth to improve their quality, while the "normal-to-depth" module refines the depth map based on the constraints on surface normals through kernel regression. Boundary information is exploited via an edge-aware refinement module. GeoNet++ effectively predicts depth and surface normals with high 3D consistency and sharp boundaries resulting in better reconstructed 3D scenes. Note that GeoNet++ is generic and can be used in other depth/normal prediction frameworks to improve 3D reconstruction quality and pixel-wise accuracy of depth and surface normals. Furthermore, we propose a new 3D geometric metric (3DGM) for evaluating depth prediction in 3D. In contrast to current metrics that focus on evaluating pixel-wise error/accuracy, 3DGM measures whether the predicted depth can reconstruct high quality 3D surface normals. This is a more natural metric for many 3D application domains. Our experiments on NYUD-V2 [1] and KITTI [2] datasets verify that GeoNet++ produces fine boundary details and the predicted depth can be used to reconstruct high quality 3D surfaces.
Transfer learning for nonlinear dynamics and its application to fluid turbulence

We introduce transfer learning for nonlinear dynamics, which enables efficient predictions of chaotic dynamics by utilizing a small amount of data. For the Lorenz chaos, by optimizing the transfer rate, we accomplish more accurate inference than the conventional method by an order of magnitude. Moreover, a surprisingly small amount of learning is enough to infer the energy dissipation rate of the Navier-Stokes turbulence because we can, thanks to the small-scale universality of turbulence, transfer a large amount of the knowledge learned from turbulence data at lower Reynolds number.
A COVID-19-Based Modified Epidemiological Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long-established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. The objective of this study is to investigate how a reduction in the exposure of vulnerable individuals to COVID-19 can minimise the number of deaths caused by the disease, using the UK as a case study. To overcome the limitation of long-established compartmental epidemiological models, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV-2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation (100,000 runs) along the proposed SEIR-v model is used to study the number of deaths which could be avoided as a function of the decrease in the exposure of vulnerable individuals to the disease. The results indicate a large number of deaths could be avoided by a slight realistic decrease in the exposure of vulnerable groups to the disease. The mean values across the simulations indicate 3681 and 7460 lives could be saved when such exposure is reduced by 10% and 20% respectively. From the encouraging results of the modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only. By combining very dense contact tracing data from smartphone apps and wristband signals with information about infection status and symptoms, vulnerable people can be protected and kept safer.
Financial sector development and Investment in selected countries of the Economic Community of West African States: empirical evidence using heterogeneous panel data method

Abstract This study investigated the impact of financial sector development on domestic investment in selected countries of the Economic Community of West African States (ECOWAS) for the years 1985–2017. The study employed the augmented mean group procedure, which accounts for country-specific heterogeneity and cross-sectional dependence, and the Granger non-causality test to test for causality in the presence of cross-sectional dependence. The results show that (1) The impact of financial sector development on domestic investment depends on the measure of financial sector development utilised; (2) Domestic credit to the private sector has a positive but insignificant impact on domestic investment in ECOWAS, whereas banking intermediation efficiency (i.e., ability of the banks to transform deposits into credit) and broad money supply negatively and significant influence domestic investment; (3) Cross-country differences exist in the impact of financial sector development on domestic investment in the selected ECOWAS countries; and (4) Domestic credit to the private sector Granger causes domestic investment in ECOWAS. The study recommends careful consideration in the measure of financial development that is utilised as a policy instrument to foster domestic investment. We also highlight the importance of employing country-specific domestic investment policies to avoid blanket policy measures. Domestic credit to the private sector should be given priority when forecasting domestic investment into the future.
Human activity recognition adapted to the type of movement

This paper analyzes the main motion characteristics of several types of movement using wearable sensor data. Based on this analysis, different deep learning-based strategies were evaluated to select the best alternative for separating movements within the same type. These strategies included Convolutional and Recurrent Neural Networks for feature learning and classification. Recordings from Pamap2 and Opportunity datasets were used to evaluate the alternatives considering a subject-wise cross-validation. Significant differences were obtained between the alternatives for the different types of movement, demonstrating the need of adapting feature extraction and classification modules to each type of movement. The best results on Pamap2 dataset showed accuracies of 96.7% and 88.7% for repetitive movements and postures, respectively. On Opportunity dataset, the best results reported accuracies of 66.9% and 97.1% for non-repetitive movements and postures, respectively.
E-Voting Systems using Blockchain: An Exploratory Literature Survey

E-Voting or electronic voting is a means for the election process to be conducted without the use of traditional paper ballots. The e-voting process, to be implemented in a large-scale scenario, requires the addressing of concerns concerning the security and reliability of such a system. The Blockchain technology, introduced by Satoshi Nakamoto using the cryptographic currency Bitcoin in 2008, opens up possibilities of designing and developing a secure, transparent and decentralized system with the absence of a third party for access and control, in the election procedure of casting and counting of votes.
Concept Representation by Learning Explicit and Implicit Concept Couplings

Generating the precise semantic representation of a word or concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually 1) model a word’s semantics from some explicit aspects while ignoring the intrinsic aspects of the word, 2) treat semantic knowledge as a supplement of word embeddings, and 3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between concept co-occurrences and hyperlinks. In human consciousness, a concept is always associated with various couplings that exist within/between descriptive texts and knowledge networks, which inspires us to capture as many concept couplings as possible for building a more informative concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings that are based on concept co-occurrences and hyperlink relations, respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings for better concept representation. Extensive experimental results on six real-world datasets show that CCE significantly outperforms eight state-of-the-art word embeddings and semantic representation methods.
A Learning Gaussian Process Approach for Maneuvering Target Tracking and Smoothing

Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This article proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process, and derivative-based Gaussian process approaches for target tracking, and smoothing are developed, with online training, and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80 and 62% performance improvement in the position, and 49 and 22% in the velocity estimation, respectively, as compared to the best model-based filter.
Model-Based Software Design and Testing in Blockchain Smart Contracts: A Systematic Literature Review

Blockchain technology promises to spark a real revolution.One of most important concepts associated with this technology is smart contracts, which enable the automatic execution of agreements and augur a world without intermediaries.The conditions and rules of ''contracts'' are established in a computer codes and trust is enforced by consensus among the participants.One relevant feature associated with smart contract is the immutability property, which establishes the non-alteration of blockchain network data after the clauses of the contract are been approved by all parties or entities involved.For this reason, smart contract development requires more effort and care than the development of other common programs.They require systematic mechanisms to collect requirements and functional specifications.In addition, it is necessary to verify and validate the agreed functionality and the implemented code before they are deployed in the blockchain platform.This article presents a systematic literature review of primary studies in the field of Software Development Life Cycle, focusing on model-based software design and testing in the blockchain domain of smart contracts.This research aims to identify gaps and/or opportunities for further research.After carried out this review, it was observed that no clear methodology exists for evaluating and validating the quality either of this software or the overall development process.This means that software developers may implement smart contract code in which bugs and serious security vulnerabilities appear when the software is delivered to their customers.
Survey on RNN and CRF models for de-identification of medical free text

Abstract The increasing reliance on electronic health record (EHR) in areas such as medical research should be addressed by using ample safeguards for patient privacy. These records often tend to be big data, and given that a significant portion is stored as free (unstructured) text, we decided to examine relevant work on automated free text de-identification with recurrent neural network (RNN) and conditional random field (CRF) approaches. Both methods involve machine learning and are widely used for the removal of protected health information (PHI) from free text. The outcome of our survey work produced several informative findings. Firstly, RNN models, particularly long short-term memory (LSTM) algorithms, generally outperformed CRF models and also other systems, namely rule-based algorithms. Secondly, hybrid or ensemble systems containing joint LSTM-CRF models showed no advantage over individual LSTM and CRF models. Thirdly, overfitting may be an issue when customized de-identification datasets are used during model training. Finally, statistical validation of performance scores and diversity during experimentation were largely ignored. In our comprehensive survey, we also identify major research gaps that should be considered for future work.
MAGNETO: Covert channel between air-gapped systems and nearby smartphones via CPU-generated magnetic fields

This papers shows that attackers can leak data from isolated, air-gapped computers to nearby smartphones via covert magnetic signals. The proposed covert channel works even if a smartphone is kept inside a Faraday shielding case, which aims to block any type of inbound and outbound wireless communication (Wi-Fi, cellular, Bluetooth, etc.). Malware is implemented to control the magnetic fields emanating from the computer by regulating workloads on the CPU cores. Sensitive data such as encryption keys, passwords, or keylogging data is encoded and transmitted over the magnetic signals. A smartphone located near the computer receives the covert signals with its magnetic sensor. I present technical background, discuss signal generation, data encoding, and signal reception. The proposed covert channel works from a user-level process, without requiring special privileges, and can successfully operate from within an isolated virtual machine (VM).
Efficient path planning of AUVs for container ship oil spill detection in coastal areas

Oil spills due to accidental leakage from Container Ships in a Marine environment poses a major challenge to aquatic living organisms and affects human health by increasing the environmental pollution. Autonomous Underwater Vehicles (AUV's) are deployed for detection and cleaning of oil spills. As the coastal areas are vast and lengthy, effective path planning of deployed AUVs is necessary. Many path planning algorithms for AUVs have been proposed in literature using approaches which necessitate search through large distance. The AUVs have to travel through a large distance to detect the spills in the search area resulting in more energy consumption. In this article we propose an efficient approach for determination of optimal AUV paths for detection of oil spills resulting in reduced search distance for a given area. The proposed approach implements a hybrid evolutionary optimization algorithm named Whale Cuckoo Search Optimization Algorithm (WCSOA) that results in increase in the search space, global search ability and the convergence speed. With the proposed approach the overall search delay and energy consumption of AUVs are reduced. It is observed from the simulation results obtained that the proposed approach provides reduction in AUV energy consumption by 57% compared to the existing Boustrophedon and WOA approach.
Time-aware sequence model for next-item recommendation

The sequences of users' behaviors generally indicate their preferences, and they can be used to improve next-item prediction in sequential recommendation. Unfortunately, users' behaviors may change over time, making it difficult to capture users' dynamic preferences directly from recent sequences of behaviors. Traditional methods such as Markov Chains (MC), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks only consider the relative order of items in a sequence and ignore important time information such as the time interval and duration in the sequence. In this paper, we propose a novel sequential recommendation model, named Interval- and Duration-aware LSTM with Embedding layer and Coupled input and forget gate (IDLSTM-EC), which leverages time interval and duration information to accurately capture users' long-term and short-term preferences. In particular, the model incorporates global context information about sequences in the input layer to make better use of long-term memory. Furthermore, the model introduces the coupled input and forget gate and embedding layer to further improve efficiency and effectiveness. Experiments on real-world datasets show that the proposed approaches outperform the state-of-the-art baselines and can handle the problem of data sparsity effectively.
Overcoming Data Sparsity in Group Recommendation

It has been an important task for recommender systems to suggest satisfying activities to a group of users in peoples daily social life. The major challenge in this task is how to aggregate personal preferences of group members to infer the decision of a group. In this paper, we propose a novel end-to-end group recommender system named CAGR (short for Centrality-Aware Group Recommender), which takes the Bipartite Graph Embedding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way. Specifically, we first extend BGEM to model group-item interactions, and then in order to overcome the sparsity of the interaction data generated by occasional groups, we propose a self-attentive mechanism to represent groups based on the group members. To further alleviate the group data sparsity problem, we propose two model optimization approaches to exploit an and integrate the user-item interaction data. To overcome the sparsity issue of user-item interaction data, we extend GCNs to leverage the social network to enhance user representation learning. We create two large-scale benchmark datasets and conduct extensive experiments on them. The experimental results show the superiority of our proposed CAGR.