Reviewerly/exHarmony huggingface dataset:
A Model for Optimal Human Navigation with Stochastic Effects

We present a method for optimal path planning of human walking paths in mountainous terrain using a control theoretic formulation and a Hamilton--Jacobi--Bellman equation. Previous models for human navigation were entirely deterministic, assuming perfect knowledge of the ambient elevation data and human walking velocity as a function of the local slope of the terrain. Our model includes a stochastic component which can account for uncertainty in the problem and thus includes a Hamilton--Jacobi--Bellman equation with viscosity. We discuss the model in the presence and absence of stochastic effects and suggest numerical methods for simulating the model. We discuss two different notions of an optimal path when there is uncertainty in the problem. Finally, we compare the optimal paths suggested by the model at different levels of uncertainty and observe that as the size of the uncertainty tends to zero (and thus the viscosity in the equation tends to zero), the optimal path tends toward the deterministic optimal path.
CNN Based Adaptive Kalman Filter in High-Dynamic Condition for Low-Cost Navigation System on Highspeed UAV

Kalman Filter (KF) is widely used in navigation as a data-fusion algorithm. When KF is applied in high-speed Unmanned Aerial Vehicle (UAV) mounted with low-cost integrated navigation system, its performance always deteriorates in complicated and high-dynamic conditions. Facing such scenario, we proposed a new algorithm of adaptive Kalman Filter in this paper. The new method is based on 1-dimensional Convolutional Neural Network (CNN). The key component of the algorithm is a deep neural network estimator of system noise covariance. We modeled Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system and then trained the estimator using IMU and GNSS data which is sampled in real flight. Further, we tested the proposed algorithm on another real-sampled dataset of UAV and compared its performance with classical KF and Sage-Husa adaptive Filter (SHF). The results show a better adaptiveness of the proposed algorithm in high-dynamic condition and partly liberates researchers from parameter tuning.
Universal 2-Dimensional Terrain Marking for Autonomous Robot Swarms

This research introduces the design and implementation of a universal systematic 2-dimensional terrain marking and coverage solution. Real world applications such as lawn mowing, mine detection, chemical spill clean-up, and humanitarian search and rescue missions can be automated by employing swarms of autonomous mobile robots to complete the task. In most of these real world applications, efficiency is of utmost importance especially when human lives are involved. The solution proposed builds up on two graph traversal algorithms; Depth First Search (DFS) and Breadth First Search (BFS), where the algorithms are adapted and modified to be utilized for coverage of 2-dimensional isometric grid-like representations of terrains. The solution is developed so that each robot in the swarm would be fully capable of covering and marking any terrain by itself. The efficiency of the solution is optimized by increasing the swarm size, as robots benefit from data sharing in their path planning and self organization within the terrain. Communication between robots enable them to perform on a higher level by benefiting from the collaborative distributed behavior of the swarm as a whole. The communication between particles is decentralized and is carried out at a local level with no need for a central guidance mechanism. Robot abilities in this research are limited, where a robot can see and move only to locations that are adjacent to its current location. A simulation based evaluation is conducted in this research to assess the robots' area coverage and marking performance. The results show that the simulated robot swarm systems are suited for efficient flat area coverage, allowing for redundancy in data collection, and tolerating individual robot errors and shortcomings as the number of robots becomes more abundant.
Visception: An interactive visual framework for nested visualization design

Nesting is the embedding of charts into the marks of another chart. Related to principles such as Tufte’s rule of utilizing micro/macro readings, nested visualizations have been employed to increase information density, providing compact representations of multi-dimensional and multi-typed data entities. Visual authoring tools are becoming increasingly prevalent, as they make visualization technology accessible to non-expert users such as data journalists, but existing frameworks provide no or only very limited functionality related to the creation of nested visualizations. In this paper, we present an interactive visual approach for the flexible generation of nested multilayer visualizations. Based on a hierarchical representation of nesting relationships coupled with a highly customizable mechanism for specifying data mappings, we contribute a flexible framework that enables defining and editing data-driven multi-level visualizations. As a demonstration of the viability of our framework, we contribute a visual builder for exploring, customizing and switching between different designs, along with example visualizations to demonstrate the range of expression. The resulting system allows for the generation of complex nested charts with a high degree of flexibility and fluidity using a drag and drop interface.
Implementation of Teaching Quality Assessment System Using Android

Teaching Quality Assessment (TQA) is one of the measures to improve the planning and organization of the teaching process. However, very limited studies have been conducted to assess teaching quality within higher education context through the use of an online mobile system. This study, therefore, applied an android teaching quality assessment system (PKMD) to assess teaching quality within an Islamic Higher Education Institution. The system was developed using a prototype paradigm involving users from the Quality Assurance Center. The result shows that the system can increase students' involvement in each class in teaching quality assessment process. Most of the users perceived the PKMD system as high quality, information, relevancy, satisfaction, productivity, and high functionality. The limitation of this study is that the students may assess a lecturer who never teaches their class due to freedom to select on the database.
Self-dual codes using bisymmetric matrices and group rings

In this work, we describe a construction in which we combine together the idea of a bisymmetric matrix and group rings. Applying this construction over the ring F4+uF4 together with the well known extension and neighbour methods, we construct new self-dual codes of length 68. In particular, we find 41 new codes of length 68 that were not known in the literature before.
SLiKER: Sparse loss induced kernel ensemble regression

Kernel ridge regression (KRR) is an efficient method for regression task. However, KRR has a deficiency in finding appropriate type of kernel functions and their parameters. To overcome this shortcoming, a novel kernel ensemble framework is developed. In this ensemble framework, each kernel regressor is associated with a weight that can be adaptively determined according to its contribution to the regression result. By this way, more appropriate kernels and more accurate parameters can be learned directly from data without any manual intervention, which results in better performance in regression. In addition, to overcome the influence of existing outliers, the regressor loss is modeled as a sparse signal, thus a Sparse Loss induced Kernel Ensemble Regression (SLiKER) method is obtained. Experimental results on several UCI regression and computer vision datasets show that our proposed approach obtains best regression and classification performances among the state-of-art comparative methods.
Interactive visual exploration of metabolite ratios in MR spectroscopy studies

Magnetic resonance spectroscopy (MRS) is an advanced biochemical technique used to identify metabolic compounds in living tissue. While its sensitivity and specificity to chemical imbalances render it a valuable tool in clinical assessment, the results from this modality are abstract and difficult to interpret. With this design study we characterized and explored the tasks and requirements for evaluating these data from the perspective of a MRS research specialist. Our resulting tool, SpectraMosaic, links with upstream spectroscopy quantification software to provide a means for precise interactive visual analysis of metabolites with both single- and multi-peak spectral signatures. Using a layered visual approach, SpectraMosaic allows researchers to analyze any permutation of metabolites in ratio form for an entire cohort, or by sample region, individual, acquisition date, or brain activity status at the time of acquisition. A case study with three MRS researchers demonstrates the utility of our approach in rapid and iterative spectral data analysis.
DST-ML-EkNN: Data Space Transformation with Metric Learning and Elite k-Nearest Neighbor Cluster Formation for Classification of Imbalanced Datasets

Most of the real-world datasets suffer from the problem of imbalanced class representation, with some classes having more than sufficient samples, while some other classes are heavily underrepresented. The imbalance in the class distributions of the majority and minority samples renders conventional classifiers like the k-Nearest Neighbor (kNN) classifier ineffective due to the heavy bias toward the majority class. In this paper, we propose to counter the class-imbalance problem by data space transformation of the training set by distance metric learning prior to an enhanced classification phase. The classification phase comprises of partitioning the set of k-Nearest Neighbors of each transformed test sample into two clusters based on their distances from the two extreme members in the set. A majority voting of the training samples within the 'Elite' cluster that is closest to the transformed test sample indicates the label of the test sample. Our proposed method is called Data Space Transformation with Metric Learning and Elite k-Nearest Neighbor cluster formation (DST-ML-EkNN). Extensive experiments on benchmark datasets using a variety of metric learning methods, with comparisons to the state of the art, establish the supremacy of our learning approach for imbalanced datasets.
Single Image Super-resolution Reconstruction with Wavelet based Deep Residual Learning

We present a single-image super-resolution (SR) method for Remote Sensing Image based on deep learning within Discrete Wavelet Domain in this paper. Our method is inspired Residual Learning. Firstly, an input image is decomposed by single level 2D Discrete wavelet transform to get four sub-bands; The four sub-bands coefficients are feeding into the Deep Learning Residual Network to predict correspondingly residual images; Adding four sub-band images and residual images as the new sub-bands of 2D wavelet transform; Finally, uses the inverse 2D Discrete wavelet transform to get the final output Super Resolution HR image. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.
Adapting OCR with Limited Supervision

Text recognition systems of today (aka OCRs) are mostly based on supervised learning of deep neural networks. Performance of these is limited by the type of data that is used for training. In the presence of diverse style in the document images (eg. fonts, print, writer, imaging process), creating a large amount of training data is impossible. In this paper, we explore the problem of adapting an existing OCR, already trained for a specific collection to a new collection, with minimal supervision or human effort. We explore three popular strategies for this: (i) Fine Tuning (ii) Self Training (ii) Fine Tuning + Self Training. We discuss details on how these popular approaches in Machine Learning can be adapted to the text recognition problem of our interest. We hope, our empirical observations on two different languages will be of relevance to wider use cases in text recognition.
Saliency detection in human crowd images of different density levels using attention mechanism

The human visual system has the ability to rapidly identify and redirect attention to important visual information in high complexity scenes such as the human crowd. Saliency prediction in the human crowd scene is the process using computer vision techniques to imitate the human visual system, predicting which areas in a human crowd scene may attract human attention. However, it is a challenging task to identify which factors may attract human attention due to the high complexity of the human crowd scene. In this work, we propose Multiscale DenseNet — Dilated and Attention (MSDense-DAt), a convolutional neural network (CNN) using self-attention to integrate the result of knowledge-driven gaze in the human visual system to identify salient areas in the human crowd scene. Our method combines various state-of-the-art deep learning architectures to deal with the high complexity in human crowd image, such as multiscale DenseNet for multiscale deep features extraction, self-attention, and dilated convolution. Then the effectiveness of each component in our CNN architecture is evaluated by comparing different components combinations. Finally, the proposed method is further evaluated in different crowd density levels to appraise the effect of crowd density on model performance.
Aphasia-friendly medication instructions: effects on comprehension in persons with and without aphasia

Background: Written medication instructions provide critical information but are often presented at high reading levels resulting in poor comprehension. Comprehension of written health information can be particularly difficult for people with language impairments, such as aphasia.Methods & Procedures: Nine people with aphasia (PWA) and nine people without aphasia (PWoA) participated in this study. Each participant reviewed two unmodified medication instructions and two modified medication instructions using aphasia-friendly principles, then answered eight multiple-choice questions and provided their preferences.Outcomes & Results: Results showed that PWA demonstrated improved comprehension given modifications, but PWoA's comprehension did not significantly improve with modifications. Group comparison in the modified condition demonstrated that PWoA still demonstrated significantly higher comprehension compared to PWA. Most participants, in both groups, preferred aphasia-friendly instructions compared to the unmodified instructions.Conclusions: This study highlights the need for improvements in healthcare information to support comprehension and independence of all persons with regard to readability of complex health information.
Crowdsourcing as an approach to solving environmental problems by future construction engineers

Abstract The article studies the role of the University of Architecture and Engineering students in the formation of the ecological culture of the population. We consider crowdsourcing as an approach to solving problems and sharing the solutions through the implementation or offering the materials in the public domain. Crowdsourcing as an approach is different from a community-based research approach. Crowdsourcing is an innovative way of people’s self-realization. It is corporations’ and power structures’ attempt to open access to vigorous transformative activity for “smart crowd”. We also explore collaborative actions that are aimed at cooperating with communities on research participating and achieving environmental change. Within the crowdsourcing technology, the solution of the problem is transferred to a distributed and very large group of people, due to which the cost and time to achieve the result are significantly reduced. That must be used in the formation of people’s ecological culture, in particular students of the University of Architecture and Engineering.
Autonomous Industrial Management via Reinforcement Learning

Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε-greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents.
Human body gesture recognition method based on deep learning

Human pose recognition technology has always been an important research content in the field of computer vision and pattern recognition. So far, many pose recognition methods and theories have been proposed. However, in the process of human pose recognition, misjudgment still occurs due to the interference within the class, similar movements and other external factors. In this paper, a DBLSTM neural network model is constructed based on the methods of deep learning and data fusion for the similar motion confusion problem in the human pose recognition process. The wearable motion capture device is used to take the kinematics data of the key nodes of the human body and fuse the data with the human skeleton data extracted from the video image by Openpose. The experimental results show that the average recognition rate of similar actions of the human body is above 92%. The effectiveness of the proposed method is verified.
Uncertainty Quantification through Dropout in Time Series Prediction by Echo State Networks

The application of echo state networks to time series prediction has provided notable results, favored by their reduced computational cost, since the connection weights require no learning. However, there is a need for general methods that guide the choice of parameters (particularly the reservoir size and ridge regression coefficient), improve the prediction accuracy, and provide an assessment of the uncertainty of the estimates. In this paper we propose such a mechanism for uncertainty quantification based on Monte Carlo dropout, where the output of a subset of reservoir units is zeroed before the computation of the output. Dropout is only performed at the test stage, since the immediate goal is only the computation of a measure of the goodness of the prediction. Results show that the proposal is a promising method for uncertainty quantification, providing a value that is either strongly correlated with the prediction error or reflects the prediction of qualitative features of the time series. This mechanism could eventually be included into the learning algorithm in order to obtain performance enhancements and alleviate the burden of parameter choice.
Keystroke Dynamics for Biometric Recognition in Handheld Devices

With the recent widespread usage of mobile devices there has been an increasing research for automated recognition systems which could be implemented with the limited resources offered by such tools. Specifically, several biometric recognition approaches have been proposed in literature, most of them relying on the cameras, microphones, or gyroscopes installed from factories in smartphones to acquire biometric traits such as face, speech, and gait. Another interesting modality which could be exploited in handheld devices, without requiring any additional hardware upgrade, is keystroke dynamics, with this term referring to the typing behaviour characterizing each user. This trait is especially suited to perform recognition on mobile devices, leveraging on data recorded while typing a password or a personal identification number (PIN) to access any physical or logical facility. This paper evaluates the feasibility of performing keystroke-dynamics-based biometric verification by modeling the acquired sequences of typed characters with Gaussian mixture models. Three different public datasets, comprising recordings related to both passwords and PINs, are exploited to testify the effectiveness of the proposed approach.
Blockchain Enables Your Bill Safer

As one of the most frequently used Internet-of-Things (IoT) devices, energy smart meter has been widely adopted to facilitate the measures of residential energy use. Residents pay for the bills from energy suppliers according to their monthly/seasonal usage. Practically, there is a demand from residents/governments to check whether the bills are in line with their real consumptions. However, it is challenging to realize this demand due to two critical problems. The first problem refers to the nonrepudiated privacy issue caused by access to residents’ energy consumption history (e.g., data integrity may be questioned, and residents’ daily timetables may be exposed). The second problem comes from the efficiency requirement for bulk auditing requests on residents’ bills and consumptions, usually risen by governments. So far, we have not found any solutions that can be directly used in this case. In this article, we propose using homomorphic encryption cooperated with the blockchain technique to leverage the data auditing and privacy-preserving requirements. We also employe a certificateless signature to resolve the efficiency bottleneck in batch auditing. This framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pAuditChain</i> , not only accepts personal requests from residents for consumption checking but also handles bulk auditing requests issued by governments. To validate the correctness of the framework functions, we carried out a series of theoretical analysis, especially on the privacy preserving and auditing processes. To the best of our knowledge, the proposed framework is among the first solutions to improve the security and privacy of bills without losing the auditing function. Our approach concerns with IoT smart meters in energy supply industries and could be further extended to other forms of IoT devices with the bill demands.