Reviewerly/exHarmony huggingface dataset:
Optimized Path-Planning in Continuous Spaces for Unmanned Aerial Vehicles Using Meta-Heuristics

This work presents a novel path-planning approach for Unmanned Aerial Vehicles (UAVs) in continuous 3D environments. This proposal aims to minimize the path length while avoiding collisions through the suitable adjusting of control points (the points that take the UAV from a start position to a target location). The above is stated as a constrained global optimization problem. This problem considers the overall length of the path as the single objective function. Regarding the problem constraints, they are related to the collision of the obstacles with the 3D shape of a path. The assignment of the path shape is also proposed in this work to streamline the planning process. Due to the optimization problem features (high nonlinearity, multimodality, non-differentiability, and the lack of an initial guess solution), a constraints-handling mechanism is used in meta-heuristics to find suitable optimized paths. Also, an enhanced path-search mechanism is included in these algorithms to deal with complex planning scenarios. The enhanced mechanism incorporates a path computed by a variant of the A-Star method (the Pruned A-Star) in the first set of candidate solutions of the meta-heuristics. The proposed approach is tested through six complex scenarios. Moreover, the performance of three well-known meta-heuristics, Differential Evolution (DE), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA), is studied to find a potential candidate to solve the path-planning problem. In this way, the paths found by DE show outstanding performance. The paths obtained by the Pruned A-Star technique are adopted as a point of comparison to determine the advantages and drawbacks of the proposal.
An Ensemble Machine Learning Technique for Functional Requirement Classification

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.
Design of a privacy-preserving decentralized energy trading scheme in blockchain network environment

Small and medium-sized users with distributed generators or flexible demand response resources desire to participate in market transactions solely, rather than aggregated by aggregators. The commonly used decentralized bookkeeping mode, blockchain-based trading schemes can now realize uniform clearing of decentralized transactions with the help of smart contracts. However, there are still risks of excessive disclosure of information and privacy leakage caused by incomplete decentralization. To solve this problem, we propose a decentralized optimization-based market clearing model, which can be applied on blockchain. The model has three advantages: ① limited access to the consumers’ or generators’ private data, ② convenient calculation methods that can be deployed on the smart contract and ③ pure decentralization to realize transparent on-chain market clearing. Moreover, considering the insufficient throughput of the existing blockchain, a blockchain network, which can process the quotation calculation in parallel with various sub-chains, is also designed to support the proposed market clearing model. Finally, the effectiveness of proposed models is validated and tested on the Ethereum private blockchain.
A comparison of isometric and amalgamation logratio balances in compositional data analysis

The isometric logratio transformation, in the form of what has been called a "balance", has been promoted as a way to contrast two groups of parts in a compositional data set by forming ratios of their respective geometric means. This transformation has attractive theoretical properties and hence provides a useful reference, but geometric means are highly affected by parts with small relative values. When a comparison between two groups of parts is required in practical applications, such as the investigation and construction of models, while making use of substantive domain knowledge, it is demonstrated that the logratio of two amalgamations serves as an alternative, interpretable form of balance. A geochemical data set is considered, which has been analyzed previously by transforming to a set of isometric logratio balances. An alternative approach, using a reduced set of pairwise logratios of parts, optionally involving prescribed amalgamations, is very close to optimal in accounting for the variance in this compositional data set. These simpler transformations also have an exact back-transformation to the original parts. This approach highlights for this dataset which compositional parts are driving the data structure, using variables that are easy to interpret and that map well to research-driven objectives.
Lossless Compression of Deep Neural Networks

Deep neural networks have been successful in many predictive modeling tasks, such as image and language recognition, where large neural networks are often used to obtain good accuracy. Consequently, it is challenging to deploy these networks under limited computational resources, such as in mobile devices. In this work, we introduce an algorithm that removes units and layers of a neural network while not changing the output that is produced, which thus implies a lossless compression. This algorithm, which we denote as LEO (Lossless Expressiveness Optimization), relies on Mixed-Integer Linear Programming (MILP) to identify Rectified Linear Units (ReLUs) with linear behavior over the input domain. By using \(\ell _1\) regularization to induce such behavior, we can benefit from training over a larger architecture than we would later use in the environment where the trained neural network is deployed.
Distributed Resource Management for Blockchain in Fog-Enabled IoT Networks

Blockchain, an emerging decentralized but trusted system, has been applied in many applications, such as the Internet of Things (IoT), supply chains, and smart grid. However, due to the large amount of computing and storage resources blockchain typically demands, its wide deployment is faced with the sustainability issue. To resolve this issue, a viable solution is to empower the IoT system with fog computing that can offload the computation-demanding tasks. Due to varieties of mining tasks and heterogeneous resource capabilities at fog nodes (FNs), it is not an easy task to schedule mining tasks and manages resource allocation among FNs of conflicting interests and independent IoT devices in a distributed manner. In this article, under the framework of matching theory, we design a distributed matching mechanism to maximize the social welfare of resource-restricted FNs while guaranteeing various mining requirements of FNs. Besides, we also provide formal proof regarding the convergence and computational complexity of a distributed matching algorithm (DMA). Finally, we verify that DMA not only improves the social welfare of FNs but also reduces the mining latency compared with the existing algorithms through extensive simulations.
Map-Predictive Motion Planning in Unknown Environments

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient au-tonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot's observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot's position to the goal without requiring frontier selection. We applied this map-predictive motion planning strategy to randomly generated winding hallway environments, yielding substantial improvement in trajectory duration over a naïve frontier pursuit method. We also experimentally demonstrate similar performance to methods using more sophisticated fron-tier selection heuristics while significantly reducing computation time.
3D SIFT aided path independent digital volume correlation and its GPU acceleration

A novel path independent digital volume correlation (DVC) method is proposed, in which the internal deformation field is determined with the aid of the features extracted from the volume images. The deformation vector at each point of interest is first estimated by tracking the motion of the nearby keypoints obtained through 3D scale-invariant feature transform (SIFT), and then fed as the initial guess into the 3D inverse compositional Gauss-Newton algorithm to achieve high accuracy result. Benefiting from the robustness of SIFT to various deformation and distortion of image, the proposed DVC method demonstrates outstanding performance to automatically process the volume images containing large and complex deformation, which keep challenging to current DVC methods for decades. The proposed DVC method is further powered by the parallel computing on GPU. An ultrafast computation speed (about 10,000 POI/s) can be reached on a personal computer when dealing with the volume images of normal sizes. The 3D SIFT aided DVC method, which achieves unprecedented balance between accuracy, adaptability, and efficiency, shows great potential in the quantitative analysis of internal deformation.
Improving Deep Reinforcement Learning With Transitional Variational Autoencoders: A Healthcare Application

Reinforcement learning is a powerful tool for developing personalized treatment regimens from healthcare data. Yet training reinforcement learning agents through direct interactions with patients is often impractical for ethical reasons. One solution is to train reinforcement learning agents using an `environment model,' which is learned from retrospective patient data, and can simulate realistic patient trajectories. In this study, we propose transitional variational autoencoders (tVAE), a generative neural network architecture that learns a direct mapping between distributions over clinical measurements at adjacent time points. Unlike other models, the tVAE requires few distributional assumptions, and benefits from identical training, and testing architectures. This model produces more realistic patient trajectories than state-of-the-art sequential decision-making models, and generative neural networks, and can be used to learn effective treatment policies.
InHARD - Industrial Human Action Recognition Dataset in the Context of Industrial Collaborative Robotics

Nowadays, humans and robots are working more closely together. This increases business productivity and product quality, leading to efficiency and growth. However, human and robot collaboration is rather static; robots move to a specific position then humans perform their tasks while being assisted by the robots. In order to get a dynamic collaboration, robots need to understand the human's intention and learn to recognize the performed actions complementing therefore his capabilities and relieving him of arduous tasks. Consequently, there is a need for a human action recognition dataset for Machine Learning algorithms. Currently available depth-based and RGB+D+S based human action recognition datasets have a number of limitations, counting the lack of training samples along with distinct class labels, camera views, diversity of subjects and more importantly the absence of actual industrial human actions in an industrial environment. Actual action recognition datasets include simple daily, mutual, or health-related actions. Therefore, in this paper we introduce an RGB+ S dataset named “Industrial Human Action Recognition Dataset” (InHARD) from a real-world setting for industrial human action recognition with over 2 million frames, collected from 16 distinct subjects. This dataset contains 13 different industrial action classes and over 4800 action samples. The introduction of this dataset should allow us the study and development of various learning techniques for the task of human actions analysis inside industrial environments involving human robot collaborations.
Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle

Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.
Instance-Dependent ℓ<sub>∞</sub>-Bounds for Policy Evaluation in Tabular Reinforcement Learning

Markov reward processes (MRPs) are used to model stochastic phenomena arising in operations research, control engineering, robotics, and artificial intelligence, as well as communication and transportation networks. In many of these cases, such as in the policy evaluation problem encountered in reinforcement learning, the goal is to estimate the long-term value function of such a process without access to the underlying population transition and reward functions. Working with samples generated under the synchronous model, we study the problem of estimating the value function of an infinite-horizon discounted MRP with finite state space in the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm. We analyze both the standard plug-in approach to this problem and a more robust variant, and establish non-asymptotic bounds that depend on the (unknown) problem instance, as well as data-dependent bounds that can be evaluated based on the observations of state-transitions and rewards. We show that these approaches are minimax-optimal up to constant factors over natural sub-classes of MRPs. Our analysis makes use of a leave-one-out decoupling argument tailored to the policy evaluation problem, one which may be of independent interest.
Ebb-and-Flow Protocols: A Resolution of the Availability-Finality Dilemma

The CAP theorem says that no blockchain can be live under dynamic participation and safe under temporary network partitions. To resolve this availability-finality dilemma, we formulate a new class of flexible consensus protocols, ebb-and-flow protocols, which support a full dynamically available ledger in conjunction with a finalized prefix ledger. The finalized ledger falls behind the full ledger when the network partitions but catches up when the network heals. Gasper, the current candidate protocol for Ethereum 2.0’s beacon chain, combines the finality gadget Casper FFG with the LMD GHOST fork choice rule and aims to achieve this property. However, we discovered an attack in the standard synchronous network model, highlighting a general difficulty with existing finality-gadget-based designs. We present a construction of provably secure ebb-and-flow protocols with optimal resilience. Nodes run an off-the-shelf dynamically available protocol, take snapshots of the growing available ledger, and input them into a separate off-the-shelf BFT protocol to finalize a prefix. We explore connections with flexible BFT and improve upon the state-of-the-art for that problem.
SEDNN: Shared and enhanced deep neural network model for cross-prompt automated essay scoring

Most existing studies on Automated Essay Scoring (AES) focused on a single prompt, and only a few studies have addressed the problem of cross-prompt AES. This paper addresses the key question of how to extract more transferable rating knowledge from multiple source prompts. Different from the common approach which extracts an invariant part among all prompts, we propose to fuse all the data from the source prompts and to extract their shared knowledge with the target prompt, including prompt-independent features and some prompt-dependent features. We show that more rating knowledge can be extracted and transferred to the target prompt using this strategy. The transferred model is then used to generate a set of pseudo training data on the target prompt, with which another enhanced model incorporating more prompt-dependent features is built for the target prompt. Experiments show that this enhanced model can further improve the final performance and it outperforms the state-of-the-art methods on cross-prompt AES.
Automatic Speaker Recognition from Speech Signals Using Self Organizing Feature Map and Hybrid Neural Network

This paper explains a new hybrid method for Automatic Speaker Recognition using speech signals based on the Artificial Neural Network (ANN). ASR performance characteristics is regarded as the foremost challenge and necessitated to be improved. This research work mainly focusses on resolving the ASR problems as well as to improve the accuracy of the prediction of a speaker.. Mel Frequency Cepstral Coefficient (MFCC) is greatly exploited for signal feature extraction.The input samples are created using these extracted features and its dimensions have been reduced using Self Organizing Feature Map (SOFM). Finally, using the reduced input samples, recognition is performed using Multilayer Perceptron (MLP) with Bayesian Regularization.. The training of the network has been accomplished and verified by means of real speech datasets from the Multivariability speaker recognition database for 10 speakers. The proposed method is validated by performance estimation as well as classification accuracies in contradiction to other models.The proposed method gives better recognition rate and 93.33% accuracy is attained.
Selling LAND in Decentraland: The Regime of Non-fungible Tokens on the Ethereum Blockchain Under the Digital Content Directive

Rewind to the early 1990s: an infant World Wide Web recently created by Tim Berners-Lee was starting to redefine the way people were connected globally. First came communication services (e.g. e-mail) and a shift from physical to digital marketplaces (e.g. ecommerce). Then came the rise of Internet platforms, in what is now deemed to be Web 2.0. The critics of Web 2.0 claim it is a spoiled version of early Internet promises: freedom from surveillance, online safety (even through anonymity)—in a nutshell, more control and power for the user. The answer to the problems of Web 2.0 is thought to be the third era of the Internet, namely the Decentralized Internet, based on (among others) blockchain technology. While a lot of literature has focused on the legal implications of blockchain assets such as cryptocurrencies from a banking perspective, not the same can be said about the consumer protection angle necessary in tackling the hype that has affected users who spent valuable financial resources on investing, playing on or using blockchain-based platforms. This chapter aims to make a contribution to fill this research gap, and focus on Decentraland, a virtual world where LAND, a non-fungible token is traded in order to allow users to build their own spaces on these plots. In doing so, the chapter elaborates on the notion of Internet of Value, and looks at the inner workins of Decentraland from the perspective of European law, more specifically the Digital Content Directive.
VARID: Viewpoint-Aware Re-IDentification of Vehicle Based on Triplet Loss

With the increasing prevalence of intelligent traffic control and monitoring, research on vehicle re-identification (Re-ID) draws substantial attention in recent years. Different from other cross-view searching tasks such as person Re-ID, the vehicle Re-ID problem is more challenging and unpredictable as viewpoint variations can greatly affect the appearance of vehicles. Existing studies mainly focus on extracting global features based on visual appearance to represent the identity of the target vehicle, while the impact of viewpoint variation is rarely considered. In this paper, we take the view information into account to boost vehicle Re-ID, and introduce latent view labels by clustering and incorporates view information into deep metric learning to tackle the challenge. We also develop a stricter center constraint to further improve the intra-class compactness of feature space. Moreover, we adopt an orthogonal regularization to increase the separability between different vehicles. VARID achieves 79.3% mAP on VeRi-776 and 88.5% mAP on VehicleID which surpasses state-of-the-arts a lot. More comprehensive experimental analyses and evaluations on four benchmarks demonstrate that the proposed method outperforms significantly state-of-the-arts methods.
Accelerating Tiny YOLOv3 using FPGA-Based Hardware/Software Co-Design

Convolutional Neural Networks (CNNs) are influencing major breakthroughs in computer vision by achieving unprecedented accuracy on tasks such as image classification, object detection, landmark detection and semantic segmentation. Owing to high computational complexity of most modern CNN architectures, graphical processing units (GPUs) are being utilized to achieve real-time performance albeit at a high energy cost. Consequently, Field Programmable Gate Arrays (FPGAs) based hardware accelerators are also making their way as they demonstrate GPU-like performance with significantly lower energy consumption that is well-suited for embedded vision applications. In this paper, we employ Hardware/Software Co-Design approach to accelerate Tiny YOLOv3 - an efficient CNN architecture for object detection - by designing a hardware accelerator for convolution, the most complex operation involved in the CNNs. Experimental results show significant performance gains, in the range of 3.9× to 21.3×, over previous implementations of efficient object detection algorithms.