Reviewerly/exHarmony huggingface dataset:
Collaborative Path Planning based on MAXQ Hierarchical Reinforcement Learning for Manned/Unmanned Aerial Vehicles

Under the complex situation of Manned/Unmanned Aerial Vehicles(MAV/UAV) mixed operation, it is difficult to design proper control strategy and parameter for UAV collaborative actions. To solve this problem, a UAV reinforcement learning algorithm based on game theory is proposed. Considering the characteristics of multi-Vehicles collaboration, the merits of MAXQ hierarchical reinforcement learning and multi-agent are combined, and propose a UAV coordination method based on MAXQ multi-agent hierarchical reinforcement learning, in order to improve the UAV adaptation and self-coordination ability under complex situation of mixed operation. It is demonstrated by the simulation results that the proposed hierarchical reinforcement learning algorithm performs better and its learning rate and efficiency are higher than the traditional hierarchical reinforcement learning.
Designable quantum beamsplitter using the two-level InGaAs quantum dot interacting with a Y-Type plasmonic waveguide

We have theoretically studied the designable quantum beamsplitter of single photons interacting with the two-level quantum dot placed in the node of a Y-type waveguide on the basis of the real-space approach. We showed that splitting of incoming single photons from one quantum channel to the others could be achieved by controlling the coupling strength of two-level quantum dot with waveguide, detuning between the quantum dot and the incoming single photon, and so on. Such quantum devices based on the controllable single photon transport behave as quantum beamsplitter at the single photon level, which provides us rich ways of configuration of important elements for the practical realization of the quantum information network.
Ensuring Inclusion and Diversity in Research and Research Output: A Case for a Language-Sensitive NLP Crowdsourcing Platform

In the context of the debate on the need to place citizens at the center of the technological revolution, this paper makes a case for a natural language processing (NLP) crowdsourcing platform that ensures inclusion and diversity, thus making the research outcome relevant and applicable across issues and domains. This paper also makes the case that by enabling participation for a wide variety of stakeholders, this NLP crowdsourcing platform might ultimately prove useful in the decision- and policy-making processes at city, community, and country levels. Against the backdrop of the debates on artificial intelligence (AI) and NLP research, and considering substantial differentiation specific to the Arab language, this paper introduces and evaluates an Arab language-sensitive NLP crowdsourcing platform. The value of the platform and its accuracy are measured via the System Usability Scale (SUS), where it scores 72.5, i.e., above the accepted usability average. These findings are crucial for NLP research and the research community in general. They are equally promising in view of the practical application of the research findings.
Multi-robot Task Allocation and Path Planning System Design

This paper studies the task allocation and path planning of multi-robot system in a two-dimensional warehouse logistics environment. A task allocation algorithm based on market auctions is used, and an improved Astar algorithm is proposed to implement multi-robot path planning. During the task assignment, robots make bids in conjunction with the cost of the task itself and the associated cost between tasks, in order to optimize the two performance indicators of the total distance of all robots and the total running time of the robots. Compared with the traditional Astar algorithm, the improved Astar algorithm is combined with traffic rules and reservation tables to obtain the shortest path while avoiding problems such as collisions. The simulation results show that the system can effectively implement the tasks of multi-robot task allocation and path planning in a two-dimensional warehouse logistics environment.
Probabilistic Inference and Trustworthiness Evaluation of Associative Links toward Malicious Attack Detection for Online Recommendations

The increasing use of recommender systems as personalization recommendation services such as Amazon, TripAdvisor, and Yelp, has stressed the demand for secure and usable abnormality detection techniques, due to fundamental vulnerabilities of recommender systems and their openness. With the emergence of new attacks, how to defend diverse malicious attacks for online recommendations is a challenging issue. Moreover, characterizing and evaluating sparse rating behaviors are a long-standing problem that still remains open, leading to an upsurge of research, as well as real application. This article investigates probabilistic inference and trustworthiness evaluation of behavioral links according to coupled association networks converted from rating behaviors, and presents a unified detection framework from a novel perspective to spot diverse malicious threats. First, an association graph is constructed from the original rating matrix based on both the inherent rating motivation of users and atomic propagation rules of coupled networks. Then, we evaluate the trustworthiness of link behaviors in the targeted network of coupled association network by exploiting a factor graph model of coupled network, and redetermine concerned links in the targeted network. Finally, suspicious users and items can be empirically inferred by comprehensively evaluating the trustworthiness of both links and nodes in the targeted network. Extensive experiments on synthetic data for profile injection attacks and co-visitation injection attacks, as well as real-world data including Amazon and TripAdvisor, demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks.
hPRESS: A Hardware-Enhanced Proxy Re-Encryption Scheme Using Secure Enclave

Proxy re-encryption (PRE) allows a proxy to transform one ciphertext to another under different encryption keys while keeping the underlying plaintext secret. Because of the ciphertext transformability of PRE, there are many potential private communicating applications of this feature. However, existing PRE schemes are not as full-fledged as expected. The lack of necessary features makes them hard to apply in real-world scenarios. So far, there does not exist a unidirectional multihop PRE scheme with constant decryption efficiency and constant ciphertext size without extensions. Impractical performance and weak scalability also hinder PRE from most real-world applications. In this work, we present a new PRE scheme with secure hardware enclave named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hPRESS</monospace> (hardware-enhanced PRE scheme using secure enclave). To the best of our knowledge, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hPRESS</monospace> is the first unidirectional multihop PRE scheme which achieves both constant decryption efficiency and constant ciphertext size without extensions. A detailed security analysis demonstrates that our proposal is CCA secure based on the security of the underlying encryption schemes and the secure enclave. We also implement a prototype based on Intel SGX, one of the most popular secure enclave techniques in recent years, and evaluate its performance. The experimental results show that, compared with previous PRE schemes, our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hPRESS</monospace> is almost one order of magnitude faster in terms of the decryption and transformation.
REMOVED: Detecting anomalies within Unmanned Aerial Vehicle (UAV) video based on contextual saliency

This article has been removed: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been removed at the request of the Editor-in-Chief of Applied Soft Computing. The author has plagiarized the thesis work “Contextual Saliency for Detecting Anomalies within Unmanned Aerial Vehicle (UAV) Video,” (Simon Gokstorp), Master’s Thesis for the degree of Master of Engineering, Durham University, (Supervisor: T.P. Breckon), 2019” by duplicating material from the online repository of the Master Thesis project (https://github.com/Hoclor/CoSADUV-Contextual-Saliency-for-Detecting-Anomalies-in-UAV-Video). One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
Reliable Convolution in Point-Mass Filter for a Class of Nonlinear Models

This paper is devoted to the Bayesian state estimation of the nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Bayesian recursive relations by the point-mass filter for a class of state-space models with linear dynamics and nonlinear measurement. In particular, a novel reliable technique for convolution computation is proposed. The technique combines the standard point-mass-based convolution with a density-weighted integration to provide accurate results even for systems with small state noise. Several implementations of the technique are developed, theoretically analysed, and evaluated in a numerical study.
Dynamic Relation Extraction with A Learnable Temporal Encoding Method

The need to judge the relations between two entities at a specific time arises in many natural language understanding and knowledge graph related tasks, where the traditional relation extraction (RE) task without considering specific time is not feasible. Therefore, it is an important task to extract the dynamic relation from sentences containing the two entities. However, existing studies focus on extracting the static relation while ignoring temporal information in sentences or encode temporal information as a sequence to infer the relation. Considering these limitations of existing studies, we propose a Learnable Temporal Encoding (LTE) model, which encodes explicit temporal information in sentences. Specifically, we introduce a key-value memory network in LTE to identify the relation between an entity pair at a specific time. Through experiments on a general temporal relation extraction dataset, we show that the proposed model outperforms other state-of-the-art baselines, which demonstrate the effectiveness of LTE for dynamic relation extraction. We also conduct visual analysis to demonstrate that our model can fully represent the temporal information in the embedding space for any time spots.
Decentralized Mining Pool Games in Blockchain

We use cooperative game theory to model mining pools and design reward allocation schemes in this paper. Specifically, we propose a cooperative game model named as “Decentralized Mining Pool Game (DMPG)” The player set of DMPG is the set of all pool managers and the utility function is defined as the sum of block rewards and transaction fees. In our model, we take miners in pools as normal nodes rather than only as computational powers, that is, all miners joining mining pools also participate in the propagation and validation of transactions in the network, this setting can effectively avoid the formation of centralized mining pools. We design two kinds of reward allocation schemes for DMPG and present efficient methods to compute them. One scheme is the stable allocation scheme which focuses on maintaining the rationality of miners (i.e. the core of DMPG) and the security of mining pools (i.e. resistance to pool block withholding attack). The other kind of scheme is the fair allocation scheme which focuses on the fairness of miners (i.e. the Shapley value of DMPG).
BBM: A Blockchain-Based Model for Open Banking via Self-sovereign Identity

Open banking technology is an emerging data-sharing paradigm that can facilitate and inspire new businesses vie efficient data-sharing with banks, such as quick approval of loan applications and finding a better investment return. However, the majority of customers are reluctant to use the open banking service, because they feel unsafe to share data with third-party service providers. In this paper, we proposed (BBM) a blockchain-based self-sovereign identity system model for open banking. The model provides a reliable secured communication network between users and third-party service providers and enables users to control their own identities and data. Through a comparison with the existing related work, the superiority of the BBM model has been demonstrated and analysed.
Predicting Perceptual Quality in Internet Television Based on Unsupervised Learning

Quality of service (QoS) and quality of experience (QoE) are two major concepts for the quality evaluation of video services. QoS analyzes the technical performance of a network transmission chain (e.g., utilization or packet loss rate). On the other hand, subjective evaluation (QoE) relies on the observer’s opinion, so it cannot provide output in a form of score immediately (extensive time requirements). Although several well-known methods for objective evaluation exist (trying to adopt psychological principles of the human visual system via mathematical models), each of them has its own rating scale without an existing symmetric conversion to a standardized subjective output like MOS (mean opinion score), typically represented by a five-point rating scale. This makes it difficult for network operators to recognize when they have to apply resource reservation control mechanisms. For this reason, we propose an application (classifier) that derivates the subjective end-user quality perception based on a score of objective assessment and selected parameters of each video sequence. Our model integrates the unique benefits of unsupervised learning and clustering techniques such as overfitting avoidance or small dataset requirements. In fact, most of the published papers are based on regression models or supervised clustering. In this article, we also investigate the possibility of a graphical SOM (self-organizing map) representation called a U-matrix as a feature selection method.
LUNAR: Cellular automata for drifting data streams

With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.
Bernoulli merging for the Poisson multi-Bernoulli mixture filter

Under the standard multiple target tracking models and a Poisson point process birth model, the Poisson multi-Bernoulli mixture (PMBM) filter provides the closed-form recursion to computing the posterior density over the set of targets. Without approximations, the PMBM computational complexity rapidly rises in time due to the increasing number of data association hypotheses. This paper presents innovative strategies for merging Bernoulli components for the same potential target reducing the number of single-target hypotheses in the PMBM filter, aiming to lower its computational complexity while keeping its performance high. We use several measures to compute the similarity between different Bernoulli components. Simulation results show that the proposed algorithms show performance close to the PMBM filter without Bernoulli merging, as measured by the generalized optimal sub-pattern assignment (GOSPA) metric, with a significantly reduced execution time.
Recursive Algorithms for Computing Sliding DCT With Arbitrary Step

Discrete cosine transform (DCT) is one of the most popular transforms in digital signal processing. Its close approximation to the Karhunen-Loeve transform implies a high degree of energy compaction. Therefore, it can be used in a wide range of applications such as sensor noise removal, spectral analysis, linear filtering, feature extraction and pattern recognition. When it is required to estimate the spectrum of a nonstationary process such as radar, speech, biomedical and communication signals, a short-time (sliding) transform can be used. Basically, the sliding transform means that the transform is calculated on a fixed-length window of the signal, which is constantly updated with new samples while the oldest ones are discarded. In some engineering applications, when the spectral content changes slowly, the window can slide more than one sample at a time to speed up the spectral analysis. In this paper, second-order recursive algorithms are proposed for fast computing the DCT in windows that are located at a given equal distance from each other. The computational complexity of the algorithms is compared with that of common fast and sliding DCT algorithms.
Anomaly Detection using Clustered Deep One-Class Classification

Anomalies on Cyber-Physical System (CPS) can have a devastating effect on the entire system of complex CPS. Thus, it is important to detect anomalies quickly. Since CPS can collect sensor data in near real-time throughout the process, many attempts have been made to solve this problem from the perspective of data-driven security based on the collected data. However, since the CPS datasets are big data and most of the data are normal data, it has always been a great challenge to analyze the data and implement the anomaly detection model. In this paper, we propose and evaluate the Clustered Deep One-Class Classification (CD-OCC) model that combines the clustering algorithm and deep learning (DL) models using only a normal dataset for anomaly detection. We classify normal data into optimal cluster size using the K-means clustering algorithm. DL models train to classify each cluster based on clustered normal data, and we can obtain the softmax values in the process of predicting the cluster. We use the softmax values as a dataset with distilled knowledge of the DL model for anomaly detection. We transfer the softmax values to one-class classification (OCC) models to detect anomalies. As a result of the experiment, the F1-score of the proposed model shows performance close to 0.8 and performance improvement of about 0.5 compared to the encoded OCC model, which has reduced-dimensionality through auto-encoder as well as the basic OCC model.
State Estimation from Range-Only Measurements

In this paper, a method of state estimation from range-only measurements is presented. This makes it possible to use low-cost sensor networks which cannot provide accurate azimuth information to replace large antennas for target tracking and other applications. Firstly, the time evolving equation of target range is formulated. The sub-state vector is constructed by range, Doppler and the derivative of the product of range and Doppler with respect to time. The sub-state equation corresponding to the nearly constant velocity (NCV) motion is derived by explicit substitutions according to the relationship between sub-state and Cartesian states. Then the unscented Kalman filter (UKF) is employed to extract the sub-state from range-only measurements. The filter initialization is derived by two-point differencing method. At last, the performance of the proposed method is compared against the posterior Cramer-Rao Lower Bound (PCRLB) for filtering in sub-state space and the existing method with approximate model in the numerical simulations. The results demonstrate the effectiveness of the proposed method.
Big Data, Anonymisation and Governance to Personal Data Protection

In a massive processing data era, an emerging impasse has taking scenario: privacy. In this context, personal data receive particular attention, witch its laws and guidelines that ensure better and legal use of data. The General Data Protection Regulation (GDPR) - in the European Union - and the Brazilian General Data Protection Law (LGPD) - in Brazil - lead to anonymisation (and its processes and techniques) as a way to reach secure use of personal data. However, expectations placed on this tool must be reconsidered according to risks and limits of its use, mainly when this technique is applied to Big Data. We discussed whether anonymisation used in conjunction with good data governance practices could provide greater protection for privacy. We conclude that good governance practices can strengthen privacy in anonymous data belonging to a Big Data, and we present a suggestive governance framework aimed at privacy.
Image manipulation with natural language using Two-sided Attentive Conditional Generative Adversarial Network

Altering the content of an image with photo editing tools is a tedious task for an inexperienced user, especially, when modifying the visual attributes of a specific object in an image without affecting other constituents such as background etc. To simplify the process of image manipulation and to provide more control to users, it is better to utilize a simpler interface like natural language. It also enables to semantically modify parts of an image according to the given text. Therefore, in this paper, we address the challenge of manipulating images using natural language descriptions. We propose the Two-sidEd Attentive conditional Generative Adversarial Network (TEA-cGAN) to generate semantically manipulated images. TEA-cGAN’s contribution is seen as two-fold. The first contribution aims to attend locations that need to be modified during generation. It introduces two types of architectures that provide fine-grained attention both in the generator and discriminator of Generative Adversarial Network (GAN). To be specific, the first one i.e., the Single-scale architecture used in the generator focuses to modify only the text-relevant regions in an image and leaves other regions untouched. While the second one i.e., Multi-scale architecture further extended this idea by taking the different scales of image features into account. The second contribution purpose is to generate higher resolution images (e.g., 256 × 256) as they provide better quality and stability. Quantitative and qualitative experiments conducted on CUB and Oxford-102 datasets confirm that TEA-cGAN different scale architectures outperform existing methods while generating 128 × 128 resolution images including generating higher resolution image i.e., 256 × 256.