Reviewerly/exHarmony huggingface dataset:
Astigmatic Stokes lens revisited

Stokes lenses are variable power astigmatic lenses comprising of, in its standard version, two pure cylindrical lenses of equal but contrary power that rotate in opposite directions. Here, we present an optical device with variable and continuous astigmatic power which is based on a modified Stokes lens where two sphero-cylindrical lenses (in the form of pure astigmatic lenses) are combined in the classical way but merged with another fixed pure astigmatic lens for improving the capabilities of the resulting optical device concerning the expansion of the astigmatic range without worsening the dioptric power step resolution. The performance of this device is theoretically analyzed in virtue of the power vectors formalism including a three-dimensional (3-D) representation of the generated dioptric power as a function of both the meridian and the rotation angle between the cylinder's axes. In addition, we have assembled a lab-made prototype of the proposed modified Stokes lens and validated its theoretical behavior by dioptric power measurements with an automatic focimeter. As conventional Stokes lenses, the applications of this new optical device range from astigmatism compensation in optical instruments to measurement of refractive error in subjective routines with the previously commented improved capabilities.
Optimization of Linguistic Terms' Shapes and Parameters: Fuzzy Control System of a Quadrotor Drone

This study addresses the issues of structural-parametric optimization of the fuzzy control and decision-making systems for intelligent robotics. In particular, the comprehensive approach to structural-parametric optimization of linguistic terms membership functions (LTMF) for fuzzy systems (FS) is proposed, and the case of its application to design process of fuzzy control system for the quadrotor unmanned aerial vehicle (drone) is considered. The presented approach allows determining both the membership functions' optimal shapes and parameters, using the most effectively the advantages of local and global search techniques. The obtained research results confirm the high effectiveness of the proposed approach.
Research on Dynamic Assessment Method of Earthquake Scene Rescue Performance Based on AHP and Cloud Model

In view of the ambiguity and randomness of the assessment of earthquake scene rescue performance, this paper proposes an integrated assessment method considering quantitative and qualitative influencing factors from three levels: search, rescue and medical treatment. Firstly, this study constructs an assessment index system of rescue performance at the earthquake scene based on the International Search and Rescue Advisory Group (INSARAG) guidelines set by International Search and Response. Secondly, the subjective weights and objective weights of each index were calculated using the hierarchical analysis process (AHP) and cloud model, respectively. The comprehensive weights were calculated using weighted analysis, and the performance assessment results were visually expressed by cloud model images. Finally, the study was applied to an earthquake rescue case with an initial assessment of 4.0065, and its performance was improved by 4.36% when the rescue process was optimized. The assessment results show that earthquake rescue performance can be estimated, and the rationality and effectiveness of the method was validated.
Graph Neural Networks With Lifting-Based Adaptive Graph Wavelets

Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph filters with rigid transforms and cannot adapt to signals residing on graphs and tasks at hand. In this paper, we propose a novel class of graph neural networks that realizes graph filters with adaptive graph wavelets. Specifically, the adaptive graph wavelets are learned with neural network-parameterized lifting structures, where structure-aware attention-based lifting operations are developed to jointly consider graph structures and node features. We propose to lift based on diffusion wavelets to alleviate the structural information loss induced by partitioning non-bipartite graphs. By design, the locality and sparsity of the resulting wavelet transform as well as the scalability of the lifting structure are guaranteed. We further derive a soft-thresholding filtering operation by learning sparse graph representations in terms of the learned wavelets, yielding a localized, efficient, and scalable wavelet-based graph filters. To ensure that the learned graph representations are invariant to node permutations, a layer is employed at the input of the networks to reorder the nodes according to their local topology information. We evaluate the proposed networks in both node-level and graph-level representation learning tasks on benchmark citation and bioinformatics graph datasets. Extensive experiments demonstrate the superiority of the proposed networks over existing SGNNs in terms of accuracy, efficiency, and scalability.
Geometry-Based Person Re-Identification in Fisheye Stereo

Person re-identification using rectilinear cameras has been thoroughly researched to date. However, the topic has received little attention for fisheye cameras and the few developed methods are appearance-based. We propose a geometry-based approach to re-identification for overhead fisheye cameras with overlapping fields of view. The main idea is that a person visible in two camera views is uniquely located in the view of one camera given their height and location in the other camera’s view. We develop a height-dependent mathematical relationship between these locations using the unified spherical model for omnidirectional cameras. We also propose a new fisheye-camera calibration method and a novel automated approach to calibration-data collection. Finally, we propose four re-identification algorithms that leverage geometric constraints and demonstrate their excellent accuracy, which vastly exceeds that of a state-of-the-art appearance-based method, on a fisheye-camera dataset we collected.
Power Transmission Line Image Segmentation Method Based on Binocular Vision and Feature Pyramid Network

Power transmission line image segmentation is an important task, which is the preliminary task of power transmission line defect detection and intrusion detection of obstacles. It can also provide navigation and obstacle avoidance functions for the unmanned aerial vehicle. This paper uses the semantic segmentation method to detect power transmission lines, and an end-to-end semantic segmentation model of power transmission lines based on binocular vision and feature pyramid network is proposed, which is called binocular FPN. The model can detect the power transmission lines and their defects on binocular images. The experiment results show that the segmentation accuracy of binocular FPN is improved compared with the original monocular FPN without significantly increasing the number of parameters.
Toward Explainable AI—Genetic Fuzzy Systems—A Use Case

A fuzzy system trained by a genetic algorithm offers explainability and transparency in its decision making. Here, an aggregate fuzzy system works towards explainability while greatly reducing the number of rules needed to describe the system. The genetic algorithm, fuzzy logic and aggregate fuzzy tree are the separate parts that make up this system, and have been summarized. This system is trained on the Breast Cancer Wisconsin Data set. Two variations in the training of the system include the genetic algorithm mutation rate and the structure of the aggregate fuzzy tree. The method with the highest accuracy, where the tree structure is fixed and the mutation is varied, is examined closer to illustrate the level of explainability and transparency of such a system. An accuracy of 94.96% and a sensitivity of 98.08% is achieved on the test data, and while slightly lower than accuracy achieved in previous works on this data set, the model trained here works towards explainability, which is of high importance.
Block-Structured Deep Learning-Based OFDM Channel Equalization

This letter considers equalization of an optical signal under the Orthogonal Frequency Division Multiplexing modulation scheme. The equalizer mitigates nonlinear effects caused by a power amplifier (PA) in the transmitter and a transimpedance amplifier (TIA) in the receiver. We compare the Convolutional Neural Network (CNN) and the Wiener-Hammerstein (WH) linearization models using two PA and three TIA devices. We are first to demonstrate a great boost in CNN effectiveness if sequentially combined with a linear equalization. The optimal sequence of linear and nonlinear blocks depends on the device profiles, and is found to be the same between CNN and WH. We develop a new block-structured CNN-based solution that utilizes the optimal sequence and brings up to 2.88 dB Q-factor gain over the traditional WH approach.
Decreased Tactile Sensitivity Induced by Disownership: An Observational Study Utilizing the Rubber Hand Illusion

The sense of body ownership, the feeling that one's own body belongs to oneself, is generated from the integration of visual, tactile, and proprioceptive information. However, long-term non-use of parts of the body due to physical dysfunction caused by trauma or illness may disturb multisensory integration, resulting in a decreased sense of body ownership. The rubber hand illusion (RHI) is an experimental method of manipulating the sense of ownership (SoO). In this illusion, subjects feel as if the rubber hand in front of them were their own hand. The RHI elicits the disownership phenomenon; not only does the rubber hand feels like one's own hand, but one's own hand does not feel like one's own hand. The decrease of ownership of one's own body induced by the bodily illusion is accompanied by neurophysiological changes, such as attenuation of somatosensory evoked potential and decreases in skin temperature. If the loss of the SoO is associated with decreased neurophysiological function, the dysfunction of patients complaining of the loss of ownership can be exacerbated; appropriate rehabilitation prescriptions are urgently required. The present study attempted to induce a sense of disownership of subjects' own hands using the RHI and investigated whether the tactile sensitivity threshold was altered by disownership. Via questionnaire, subjects reported a decrease of ownership after the RHI manipulation; at the same time, tactile sensitivity thresholds were shown to increase in tactile evaluation using the Semmes-Weinstein monofilaments test. The tactile detection rate changes before and after the RHI were negatively correlated with the disownership-score changes. These results show that subjects' sense of disownership, that their own hands did not belong to them, led to decreases in tactile sensitivity. The study findings also suggest that manipulating of illusory ownership can be a tool for estimating the degree of exacerbation of sensory impairment in patients. Consideration of new interventions that optimize the sense of body ownership may contribute to new rehabilitation strategies for post-stroke sensory impairment.
A Machine Learning-Enabled Clustering Approach for Large-scale Classification of Solar Data

Solar power generation is one of the renewable energy sources that has gained prominence in recent times. Power generation companies and researchers are interested in the behavior of solar facilities with respect to daily weather fluctuations over time. Researchers have often tried to cluster solar spatio-temporal data. Some of the proposed methods contain unnecessary ambiguity with no clear justifications of the applicability of their models. This study aims to provide an accurate cluster-based representation of solar Photovoltaic (PV) production profiles, using the solar data from the University of Louisiana at Lafayette&#x0027;s 1.1 Megawatt(MW) Photovoltaic Applied Research and Testing Lab (PART lab) over a two-year interval. The availability of these clusters provides an easier basis for testing and modelling the behavior and response of an existing solar plant in the event of an upgrade, or incorporation into a power generating facility and solar forecasting. In this study,a machine learning approach is used to generate a clustering algorithm, employing three different existing accuracy metrics. A efficient Dynamic Time-Warping (DTW) k-Means model was developed to cluster the PV data with reasonable accuracy. The novelty of this study provides an efficient temporal-adaptive multi-model approach of clustering solar PV data. These clusters can reduce the computational expense of solar-connected microgrid modelling and simulation studies at the design and operational stages.
Unit-norm tight frame-based sparse representation with application to speech inpainting

This paper deals with the unit-norm tight frames (UNTFs) that better represent and solve sparse representation problems. Under this sparse representation framework, a novel model termed tight dictionary learning (TDL) is proposed. Unlike those dictionaries that only focus on the signals of interest, TDL intends to learn a more comprehensive dictionary that better represents the signals with the UNTF properties to facilitate sparse recovery. To better describe the physical meanings, the normalization constraints on the dictionary columns are imposed. A gradient-based approach is developed to obtain the tight dictionary where the normalization constraints are embedded into the cost function by a parametrization method. The learned tight dictionary is applied to the speech inpainting problem to restore speech signals corrupted by data missing. In order to compensate the degradation effect of the data missing, a UNTF-based preconditioning technique is developed to reform the frame properties of the degraded system. Parametrization and gradient-based approaches similar to those applied in the TDL are adopted to design a tight preconditioner. Extensive experiments on speech denoising and speech segment restoration are conducted to demonstrate the superiority of the proposed schemes.
A Simple Yet Robust Algorithm for Automatic Extraction of Parallel Sentences: A Case Study on Arabic-English Wikipedia Articles

Parallel corpora are vital components in several applications of Natural Language Processing (NLP), particularly in machine translation. In this paper, we present a novel method for automatically creating parallel sentences from comparable corpora. The method requires a bilingual dictionary as well as an adequate word-vectorisation method. We use Arabic and English Wikipedia as a comparable corpus to apply our proposed method and construct a parallel corpus between Arabic and English. The created Arabic-English corpus consists of 105,010 parallel sentences with a total number of 4.6M words. During our study, we compared two methods of word vectorisation, word embedding and term frequency-inverse document frequency, in terms of their usefulness in computing similarities between well-formed and syntactically ill-formed sentences. We also quantitatively and qualitatively examined the parallel corpus produced by our proposed method and compared it with other available Arabic-English parallel corpora counterparts: GlobalVoices, TED, and Wiki-OPUS. We explored the main advantages and shortcomings of these corpora when used for NLP applications, such as word semantic similarity identification and Neural Machine Translation (NMT). The word semantic similarity models trained on our parallel corpus outperformed models trained on other corpora in the task of English non-similar word identification. Our parallel corpus also proved competitive when building Arabic-English NMT systems, yielding results comparable to those of the automatically created Wiki-OPUS corpus and of the manually created TED corpus, while achieving results superior to the smaller GlobalVoices corpus.
Project Management Issues While Using Agile Methodology

Software engineering has many software development life cycle (SDLC) models to develop a software application and the latest SDLC models have been provided by agile methods. The agile methodology has been introduced due to some existing lacks in software development. Now agile methodology is used to overcome these deficiencies and improve software development. The use of the agile methodology is increased within software industries due to its distinctive features such as enabling change requests from the client at any stage of a project, client satisfaction, iterative development, and client-developer interaction. Another reason for agile adoption is the methods that are being used for agile software development. These methods include Scrum, Feature drive development, Extreme programming, and Dynamic system development methods. However, the agile methodology has some issues for project development and management. In this study, we discuss all these issues which are related to agile methods and individuals (i.e. team and developer). Further, we suggest the possible improvements that need to be introduced in the agile methodology. We believe such improvements is to make the agile methodology more productive for development environments.
Risk Conditioned Neural Motion Planning

Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. While existing mathematical programming methods offer theoretical guarantees in the context of constrained Markov decision processes, they either lack scalability in solving larger problems or produce conservative plans. Recent advances in deep reinforcement learning improve scalability by learning policy networks as function approximators. In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network. We define the execution risk in an accurate form, as opposed to approximating it through a summation of immediate risks at each time step that leads to conservative plans. Our proposed model is conditioned on a continuous spectrum of risk bounds, allowing the user to adjust the risk-averse level of the agent on the fly. Through a set of experiments, we show the advantage of our model in terms of both computational time and plan quality, compared to a state-of-the-art mathematical programming baseline, and validate its performance in more complicated scenarios, including nonlinear dynamics and larger state space.
Velocity Obstacle-based Trajectory Planner for Two-Link Planar Manipulators

Velocity Obstacle paradigm is one of the most popular and studied decentralized trajectory planning methods for multi-agent systems moving in dynamic environments. It has been successfully used in a multitude of real and simulated scenarios for the collision-free maneuver of ground or aerial mobile robots. In this paper we address the problem of adapting Velocity Obstacles to provide collision-free trajectories also for robotic manipulators with dynamic obstacles in their workspace. Simulation results show the effectiveness of the proposed approach.
”I Packed My Bag and in It I Put…”: A Taxonomy of Inventory Systems for Virtual Reality Games

On a journey, a backpack is a perfect place to store and organize the necessary provisions and tools. Similarly, carrying and managing items is a central part of most digital games, providing significant prospects for the player experience. Even though VR games are gradually becoming more mature, most of them still avoid this essential feature. Some of the reasons for this deficit are the additional requirements and challenges that VR imposes on developers to achieve a compelling user experience. We structure the ample design space of VR inventories by analyzing popular VR games and developing a structural taxonomy. We combine our insights with feedback from game developers to identify the essential building blocks and design choices. Finally, we propose meaningful design implications and demonstrate the practical use of our work in action.
Early Termination of Dyadic Region-Adaptive Hierarchical Transform for Efficient Attribute Compression of 3D Point Clouds

Thelow-complexity RAHT (Region-Adaptive Hierarchical Transform) and its corresponding Dyadic decomposition have been incorporated in the MPEG (Moving Picture Expert Group) standardization for encoding the attribute values in 3D point clouds. While significant gains were observed with the replacement of RAHT by Dyadic RAHT, the standard scheme uses only one type of decomposition for the entire point cloud. In this letter, we propose a novel adaptive scheme of switching between RAHT and Dyadic RAHT to achieve early termination of Dyadic RAHT by using 3D gradient filters. Improvements were made on the state-of-the-art scheme that uses only Dyadic RAHT transformation. The proposed method was tested on the publicly available 3D point cloud datasets. The results shows we can achieve cumulative compression gain of up to 11% over all-dyadic approach, by avoiding further decomposition in some cases.
Stochastic Ghost Batch for Self-distillation with Dynamic Soft Label

Deep neural networks excel at learning patterns from finite training data but often provide incorrect predictions with high confidence while faced out-of-distribution data. In this work, we propose a data-agnostic framework called Stochastic Ghost Batch Augmentation (SGBA) to address these issues. It stochastically augments activation units at training iterations to amendment the model’s irregular prediction behaviors by leveraging the partial generalization ability of intermediate model, in which a self-distilled dynamic soft label as regularization term is introduced to establish the aforementioned lost connection, that incorporates the similarity prior in the vicinity distribution respect to raw samples, rather than conform model to static hard label. Also, the induced stochasticity can reduce much unnecessary, redundant computational cost in conventional batch augmentation performed at each pass. The proposed regularization provides direct supervision by the KL-Divergence between the output soft-max distribution of original and virtual data, and enforces the distribution matching to fuse the complementary information in the model’s prediction, which are becoming gradually mature and stable with the training process. In essence, it is a dynamic check or test about the generalization of neural network during training. Extensive performance evaluations demonstrate the superiority of our proposed framework.
Differential characteristics of reflections of Kuznyechik encryption algorithm

The study of the differential characteristics of the Kuznyechik encryption algorithm is important for carrying out cryptanalysis methods relative to the algorithm. This article discusses a method for obtaining increments for bits that are more likely than a permutation table.
Storing and Securing the Digital Evidence in the Process of Digital Forensics through Blockchain Technology

Digital Forensics is a branch of forensic science which deals with the recovery and investigation of materials found in digital devices part of the crime scene, which often relates to computer crime. The evidence which is collected from the crime scene needs to be secured safely in such a way that the evidence isn't tampered by any means. Evidence in the case plays a vital role in the judgement, if such evidence gets tampered then the truth will be changed, and justice cannot prevail in such a situation. There are many methods and techniques for securing the digital evidence, one of such technology is blockchain technology, which has high-security properties in it. Blockchain can be defined as the chain of records which are cryptographically secured. In blockchain technology, one record is hashed with previous record, and this ensures the immutability in the ledger. In this way, blockchain technology can ensure the immutability in the digital evidence.