Reviewerly/exHarmony huggingface dataset:
Nonlinear Dynamical Systems Modelling and Identification Using Type-2 Fuzzy Logic - Metaheuristic Algorithms Based Approach

This paper presents a novel type-2 fuzzy model for nonlinear dynamical systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. It is based on interval type-2 fuzzy systems and it is comprised of a parallel interconnection of two type-2 sub fuzzy models. The first sub fuzzy model is the primary model, which represents an ordinary model with low resolution for the nonlinear dynamical system under consideration. To overcome resolution quality problem, and obtain a model with higher resolution, we will introduce a second type-2 fuzzy sub model called error model which will represent a model for the error modelling between the primary model and the real nonlinear dynamical system. As the error model represents uncertainty in the primary model, it’s suitable to minimize this uncertainty by simple subtraction of the error model output from the primary model output, which will lead to a parallel interconnection between them, giving then a unique whole final model possessing higher resolution. To apply this approach successfully, the model’s representation and identification are considered in this investigation using type-2 fuzzy auto regressive (T2FAR) and type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithms, like as firefly and biogeography-based optimization algorithms. To evaluate the effectiveness of the proposed method, it will be tested on three types of nonlinear dynamical systems. Computer investigations indicate that the proposed model may significantly improves convergence and resolution.
ECC usage on X.509 digital certificates

This paper presents a review of the adoption of elliptic curve cryptography (ECC) algorithms in X.509 digital certificates. It was analyzed the cases of Estonia, United Arab Emirates (UAE), Australia, Brazil, and Egypt. Although all five countries have considered using ECC algorithms, only two (Estonia and UAE) use them throughout their entire PKI hierarchy (root, intermediates, and end-entity certificates). Our findings complement those of Bos et al. [1], which in 2014 identified only Austria as an adopter of ECC in end-entity X.509 digital certificates for electronic signatures.
Multitouch Interaction with Parallel Coordinates on Large Vertical Displays

This paper presents a multitouch vocabulary for interacting with parallel coordinates plots on wall-sized displays. The gesture set relies on principles such as two-finger range definition, a functional distinction of background and foreground for applying the Hold-and-Move concept to wall-sized displays as well as fling-based interaction for triggering and controlling long-range movements. Our implementation demonstrates that out-of-reach problems and limitations regarding multitouch technology and display size can be tackled by the coherent integration of our multitouch gestures. Expert reviews indicate that our gesture vocabulary helps to solve typical analysis tasks that require interaction beyond arms' reach, and it also shows how often certain gestures were used.
Sensitivity Analysis of Cascaded Quantum Feedback Amplifier

Quantum amplifier is an essential device in quantum information processing. As in the classical (non-quantum) case, its characteristic uncertainty needs to be suppressed by feedback, and in fact such a control theory for a single quantum amplifier has recently been developed. This letter extends this result to the case of cascaded quantum amplifier. In particular, we consider two types of structures: the case where controlled amplifiers are connected in series, and the case where a single feedback control is applied to the cascaded amplifier. Then, we prove that the latter is better in the sense of sensitivity to the uncertainty. A detailed numerical simulation is given to show actual performance of these two feedback schemes.
Neuromorphic Object Edge Detection with Artifical Photonic Spiking VCSEL-Neurons

Fast spiking Vertical Cavity Surface Emitting Laser neurons are used to reveal neuromorphic object edge information in digital images. Input integration and thresholding are used in tandem with convolution to create a photonic spiking node with high prospects for convolutional neural networks and computer vision.
Security and Privacy Solutions associated with NoSQL Data Stores

Technologies such as cloud computing and big data management, have lately made significant progress creating an urgent need for specific databases that can safely store extensive data along with high availability. Specifically, a growing number of companies have adopted various types of non-relational databases, commonly referred to as NoSQL databases. These databases provide a robust mechanism for the storage and retrieval of large amounts of data without using a predefined schema. NoSQL platforms are superior to RDBMS, especially in cases when we are dealing with big data and parallel processing, and in particular, when there is no need to use relational modeling. Sensitive data is stored daily in NoSQL Databases, making the privacy problem more serious while raising essential security issues. In our paper, security and privacy issues when dealing with NoSQL databases are introduced and in following, security mechanisms and privacy solutions are thoroughly examined.
Capsule Network Performance with Autonomous Navigation

Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs).This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios.In real world navigation, rewards external to agents may be rare.In turn, reinforcement learning algorithms can struggle to form meaningful policy functions.This paper's approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm.Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM).Caps-EM uses a more compact architecture without need for intrinsic rewards.Tested using ViZDoom, the Caps-EM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and D-ACM, respectively, for converging to a policy function across "My Way Home" scenarios.
Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data

Longitudinal observational and randomized controlled trials (RCT) are widely applied in biomedical behavioral studies and increasingly implemented in smart health systems. These trials frequently produce data that are high-dimensional, correlated, and contain missing values, posing significant analytic challenges. Notably, visual analytics are underdeveloped in this area. In this paper, we developed a longitudinal topic model to implement the systematic review of visual analytic methods presented at the IEEE VIS conference over its 28 year history, in comparison with MIFuzzy, an integrated and comprehensive soft computing tool for behavioral trajectory pattern recognition, validation, and visualization of incomplete longitudinal data. The findings of our longitudinal topic modeling highlight the trend patterns of visual analytics development in longitudinal behavioral trials and underscore the gigantic gap of existing robust visual analytic methods and actual working algorithms for longitudinal behavioral trial data. Future research areas for visual analytics in behavioral trial studies and smart health systems are discussed.
UIScreens: extracting user interface screens from mobile programming video tutorials

Mobile apps are one of the most widely used types of software systems in existence today and more programmers and students learn how to develop them everyday. One of the most popular resources for learning mobile programming are videos hosted on social platforms such as YouTube. While useful, this type of resource has also its limitations, especially when developers are looking for user interface (UI) designs for mobile applications, since these are hard to search for and locate in videos. We propose UIScreens, a web-based analysis and search engine that analyzes the visual contents of mobile programming video tutorials, then identifies and extracts the UI screens displayed in the videos. Our tool offers features such as searching for UI screens in videos, displaying an overview of the UI screens identified in a video under each search result, and navigating to the part of a video where a particular UI screen is being displayed and discussed. In a user study, participants agreed that UIScreens is usable and useful to quickly skim through videos, while the UI screens it extracts can help developers further determine the relevance of videos to a search topic.
Fine-grained outsourced data deletion in cloud storage

Abstract Due to the separation of outsourced data management and ownership, user loses the direct control over outsourced data. Therefore, the cloud executes all of the acts (e.g., data erasure) over outsourced data. However, the dishonest cloud may keep the data maliciously. To solve this problem, a new solution is proposed in this paper, which is able to reach fine-grained data erasure. In our protocol, if some outsourced data blocks are no longer needed, the user is able to remove them flexibly, while the useful data blocks remain in the cloud. After that, the user could check the erasure outcome efficiently. If the cloud remains the data blocks dishonestly, the user can fine the data reservation. Meanwhile, we provide the scheme analysis, which is able to show the security and practicality of our scheme.
Lifelong Learning Without a Task Oracle

Supervised deep neural networks are known to undergo a sharp decline in the accuracy of older tasks when new tasks are learned, termed “catastrophic forgetting”. Many state-of-the-art solutions to continual learning rely on biasing and/or partitioning a model to accommodate successive tasks incrementally. However, these methods largely depend on the availability of a task-oracle to confer task identities to each test sample, without which the models are entirely unable to perform. To address this shortcoming, we propose and compare several candidate task-assigning mappers which require very little memory overhead: (1) Incremental unsupervised prototype assignment using either nearest means, Gaussian Mixture Models or fuzzy ART backbones; (2) Supervised incremental prototype assignment with fast fuzzy ARTMAP; (3) Shallow perceptron trained via a dynamic coreset. Our proposed model variants are trained either from pre-trained feature extractors or task-dependent feature embeddings of the main classifier network. We apply these pipeline variants to continual learning benchmarks, comprised of either sequences of several datasets or within one single dataset. Overall, these methods, despite their simplicity and compactness, perform very close to a ground truth oracle, especially in experiments of inter-dataset task assignment. Moreover, best-performing variants only impose an average cost of 1.7% parameter memory increase.
Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Human-Robot Interfaces (HRIs) represent a crucial component in telerobotic systems. Body-Machine Interfaces (BoMIs) based on body motion can feel more intuitive than standard HRIs for naive users as they leverage humans’ natural control capability over their movements. Among the different methods used to map human gestures into robot commands, data-driven approaches select a set of body segments and transform their motion into commands for the robot based on the users’ spontaneous motion patterns. Despite being a versatile and generic method, there is no scientific evidence that implementing an interface based on spontaneous motion maximizes its effectiveness. In this study, we compare a set of BoMIs based on different body segments to investigate this aspect. We evaluate the interfaces in a teleoperation task of a fixed-wing drone and observe users’ performance and feedback. To this aim, we use a framework that allows a user to control the drone with a single Inertial Measurement Unit (IMU) and without prior instructions. All the interfaces are entirely data-driven and depend on the user’s spontaneous motion. We show through a user study that selecting the body segment for a BoMI based on spontaneous motion can lead to sub-optimal performance. Based on our findings, we suggest additional metrics based on biomechanical and behavioral factors that might improve data-driven methods for the design of HRIs.
Eyes Status Detector Based on Light-weight Convolutional Neural Networks supporting for Drowsiness Detection System

The drowsiness is the leading cause of many accidents on the road. These causes can be reduced by using the drowsiness alarm or drowsiness detection system. These systems monitor drivers while driving and alarm when they don't focus or have some abnormal signs in the driver's body. Currently, most methodologies use the analysis of human behaviors, vehicle behaviors, and human physiological conditions. This paper regards eyes status analysis based on deep learning method using proposed Convolutional Neural Networks (CNN) with two stages are face detection and eyes classification. The face detector employs a single detector module and shallow layer, then the eyes classifier using simple CNN without ignoring the accuracy. As a result, the average speed was tested in real-time by 50.03 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.
Target height measurement method based on stereo vision

The key to using binocular stereo vision technology to achieve target height measurement is to obtain accurate predicted box of target. We propose a disparity-method and an edge-method to correct the original predicted box of the target. The main idea of the disparity-method is to use the disparity discontinuity between the target area and the background to find the location of the highest point of the object, so as to correct the upper edge of the original predicted box. The main idea of the edge-method is to find the boundary between the target and the ground based on the edge map, so as to correct the lower side of the original predicted box. It can be seen from the experiment that the corrected predicted box has higher accuracy than the original predicted box obtained by Yolo. Finally, we give a specific method for measuring the height of the object based on binocular stereo vision.
Directional nonclassicality of resonance fluorescence from a three-body quantum antenna via geometry-dependent frequency engineering

By constructing a quantum filtering system to synthesize the spectral and spatial properties of stimulated radiation, the directionality of spectrally correlated nonclassical properties of resonance fluorescence from a three-body quantum antenna is investigated. The quantum antenna consists of three two-level quantum emitters, two of which are identical and strongly driven by an external laser field to radiate the collective resonance fluorescence with three distinct spectral components. The collective stimulated radiation is shaped by the remaining auxiliary emitter via its spontaneous emission, which is assumed to be resonant with the higher-frequency spectral component of the two identical emitters. Due to the presence of the auxiliary emitter, highly directional nonclassicality of the spectral correlations can be generated, which is the consequence of the internal interference of the source and the spatial interference of the radiation field. The electric dipole-dipole interaction between the undriven emitter and the dressed two-body radiating source plays a crucial role in breaking the rotational symmetry of nonclassical signals, which is embodied by various photon correlation functions with a frequency-resolved version for different spectral combinations. By correlating the two opposite sidebands, the frequency-resolved intensity-intensity correlation functions can signal highly directional nonclassicality, and the frequency-resolved two-mode entanglement along a specific emission direction can be tested by the frequency-resolved anomalous correlation function. While the directional nonclassical correlations between the central-frequency and the three-body collective higher-frequency components can be identified through the frequency-resolved intensity-field correlation function. These various frequency-resolved correlation functions characterize the spatial asymmetry of nonclassical properties of the collective stimulated radiation from different perspectives, including the particle properties and the quantum coherences of the filtered photons. When the three-body collective higher-frequency sidebands are correlated with the central band and the lower-frequency sideband, respectively, the schemes of using the auxiliary undriven emitter to generate optimal nonclassical correlations with well-defined directions are discussed analytically. In addition, the results obtained from the three-body quantum antenna and a dressed two-body quantum antenna are compared.
WorkerFirst: Worker-Centric Model Selection for Federated Learning in Mobile Edge Computing

Federated Learning (FL) is viewed as a promising manner of distributed machine learning, because it leverages the rich local datasets of various participants while preserving their privacy. Particularly under the fifth-generation communications (5G) networks, FL shows its overwhelming advantages in the context of mobile edge computing (MEC). However, from the participant's viewpoint, a puzzle is how to guarantee the tradeoff between the profit brought by participating in FL training and the restriction of its battery capacity. Because communicating with the FL server and training an FL model locally are energy-hungry. To address such a puzzle, different from existing studies, we particularly formulate the model-selection problem from the standpoint of mobile participants (i.e., workers). We then exploit the framework of deep reinforcement learning (DRL) to reformulate a joint optimization for all FL participants, by considering the energy consumption, training timespan, and communication overheads of workers, simultaneously. To address the proposed worker-centric selection problem, we devised a double deep Q-learning Network (DDQN) algorithm and a deep Q-Learning (DQL) algorithm to strive for the adaptive model-selection decisions of each energy-sensitive participant under a varying MEC environment. The simulation results show that the proposed DDQN and DQL algorithms can quickly learn a good policy without knowing any prior knowledge of network conditions, and outperform other baselines.
A Complete Anti-collusion Mechanism in Blockchain

Collusion attack is an issue existing in most blockchains, especially for token-based decentralized applications using voting as consensus mechanism and incentive method. Malicious users may collude others to get more votes, in order to get rewards. We present a complete anti-collusion mechanism (CACM), aiming to cheat the malicious users who want to collude others. Each vote will be committed on smart contract and consumes the commitment for last vote. Zero knowledge Succinct Non-interactive Argument of Knowledge (zkSNARKs) is used to ensure the correctness of the voting and tallying operations in CACM. We implement CACM on the Ethereum test network, and the CACM circuit on local machine. The experiments show the low cost of time and gas respectively in generating zkSNARKs proof and interactions with smart contract, which proves CACM is efficient.
A general multi-agent epistemic planner based on higher-order belief change

In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we consider centralized multi-agent epistemic planning from the viewpoint of a third person who coordinates all the agents to achieve the goal. We treat contingent planning, resulting in nonlinear plans. We model private actions and hence handle beliefs, formalized with the multi-agent KD45 logic. We handle static propositional common knowledge, which we call constraints. For such planning settings, we propose a general representation framework where the initial knowledge base (KB) and the goal, the preconditions and effects of actions can be arbitrary KD45n formulas, and the solution is an action tree branching on sensing results. In this framework, the progression of KBs w.r.t. actions is achieved through the operation of belief revision or update on KD45n formulas, that is, higher-order belief revision or update. To support efficient reasoning and progression, we make use of a normal form for KD45n called alternating cover disjunctive formulas (ACDFs). We propose reasoning, revision and update algorithms for ACDFs. Based on these algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.
Data Reduction Solution for Driving Simulator

This paper describes the methods that have been developed and implemented to process research participant data generated by a high fidelity driving simulator that has been integrated with eye tracking equipment. The driving simulator is used for experimental studies to understand driving behavior. Solutions are implemented to programmatically process the output of the simulator and transform the raw data from these research experiments to an analysis ready format. The algorithm is tested across the data for numerous participants with varying scenarios within the experiments and is further curated to meet the requirements and standards of the research studies that require the use of driving simulator to generate data.
Learning to extract transaction function from requirements: an industrial case on financial software

In practice, it is very important to determine the size of a proposed software system yet to be built based on its requirements, i.e., early in the development life cycle. The most widely used approach for size estimation is Function Point Analysis (FPA). However, since FPA involves human judgment, the estimation results are some degree of subjective, and the process is labor and cost intensive. In this paper, we propose a novel approach to identify transaction functions from textual requirements automatically by leveraging a set of natural language processing techniques and machine learning models. We evaluate our approach on 1,864 requirements and 104,691 transaction functions taken from 36 financial projects from one banking industry. The results show that the contents of the suggested transaction functions by our approach are high in quality, with low perplexity value of 8.5 and high BLEU score of 34 on average. The types of suggested transaction functions can also be accurately classified, with overall accuracy of 0.99 on average. Our approach can provide reasonable suggestions that assist industrial practitioners to identify transaction functions faster and easier.