Reviewerly/exHarmony huggingface dataset:
Integration of Fuzzy and Deep Learning in Three-Way Decisions

The problem of uncertainty is a challenging issue to solve in opinion mining models. Existing models that use machine learning algorithms are unable to identify uncertainty within online customer reviews because of broad uncertain boundaries. Many researchers have developed fuzzy models to solve this problem. However, the problem of large uncertain boundaries remains with fuzzy models. The common challenging issue is that there is a big uncertain boundary between positive and negative classes as user reviews (or opinions) include many uncertainties. Dealing with these uncertainties is problematic due in many frequently used words may be non-relevant. This paper proposes a three-way based framework which integrates fuzzy concepts and deep learning together to solve the problem of uncertainty. Many experiments were conducted using movie review and ebook review datasets. The experimental results show that the proposed three-way framework is useful for dealing with uncertainties in opinions and we were able to show that significant F-measure for two benchmark dataset.
Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings

With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. Deep learning models perform well when trained on standardized datasets from artificial settings. However, generalization and translation to real-world clinical settings are challenging. The complexity of real-world applications in healthcare emanates from different data distributions across multiple device domains, variations in image resolution, human errors, and the lack of manual grading. Moreover, healthcare applications not only suffer from scarcity in labeled data, but also face limited access to un-labeled data. These limitations pose additional challenges to developing translatable applications for clinical care. In this paper, we utilize self-supervised representation learning methods, formulated effectively in transfer learning settings, to address limited data availability and assess the importance of real-world data for generalizations to clinical settings. We show that by employing a self-supervised approach with transfer learning on a multi-domain real-world dataset, we can achieve 16% relative improvement on a standardized dataset over supervised baselines.
D<sup>2</sup>VO: Monocular Deep Direct Visual Odometry

In this paper, we present a novel deep learning and direct method based monocular visual odometry system named D2VO. Our system reconstructs the dense depth map of each keyframe and tracks camera poses based on these keyframes. Combining direct method and deep learning, both tracking and mapping of the system could benefit from the geometric measurement and semantic information. For each input frame, a feature pyramid is built and shared by both tracking and mapping process. The depth map of keyframe is efficiently estimated from coarse to fine with the followed multi-view hierarchical depth estimation network. We optimize the camera pose by minimizing photometric error between re-projected features of each frame and its reference keyframe with bundle adjustment. Experimental results on TUM dataset demonstrate that our approach outperforms the state-of-the-art methods on both tracking and mapping.
Neuroevolution of augmented topologies with difference-based mutation

Abstract This study proposes the modification of the neuroevolution of augmented topologies, namely the difference-based mutation operator. The difference-based mutation changes the weights of the neural network by combining the weights of several other networks at the position of the connections having same innovation numbers. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses several mutation operators, including connections deletion. The algorithm is tested on a set of classification problems and a rotary inverted pendulum problem and compared to the same approach without difference-based mutation. The experimental results show that the proposed weight tuning scheme allows significant improvements of classification quality in several cases and finding better control algorithms.
Compliance Control of a Cable-Suspended Aerial Manipulator using Hierarchical Control Framework

Aerial robotic manipulation is an emergent trend that poses several challenges. To overcome some of these, the DLR cable-Suspended Aerial Manipulator (SAM) has been envisioned. SAM is composed of a fully actuated multi-rotor anchored to a main carrier through a cable and a KUKA LWR attached below the multi-rotor. This work presents a control method to allow SAM, which is a holonomically constrained system, to perform such interaction tasks using a hierarchical control framework. Within this framework, compliance control of the manipulator end-effector is considered to have the highest priority. The second priority is the control of the oscillations induced by, for example, the motion of the arm or physical contact with the environment. A third priority task is related to the internal motion of the manipulator. The proposed approach is validated through simulations and experiments.
CLAHE implementation on a low-end FPGA board by high-level synthesis

Automobile companies have been trying to replace side mirrors of cars with small cameras for reducing air resistance. It enables us to apply some image processing for improving quality. This paper describes a design of Contrast Limited Adaptive Histogram Equalization (CLAHE), which improves the quality of the dark image for the side mirror camera, on a low-end FPGA board by high-level synthesis. By constituting CLAHE as a data flow design flow, we could achieve performance comparable to the implementation with HDL. The code is parameterized so that the number of tiles and the size of the image can be easily changed. The source code for this research can be downloaded from https://github.com/kokihonda/fpga_clahe.
UAV Image Mosaicing Based Multi-Region Local Projection Deformation

The goal of unmanned aerial vehicle (UAV) image mosaicing is to create natural-looking mosaics free of artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on images. In this paper, we apply local homography to match UAV images, which can reduce misalignment artifacts or “ghosting” in the results compared with 2D projective transforms or global homography. In addition, when an object in three dimensions is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which still causes artifacts near some tall buildings if we only use local homography. We propose a novel stitching method based multi-region local projection deformation, that divides the overlapping regions of input images into several regions, then meshes image to calculate local projections by partitioned regions. Specifically, we use a strategy where multiple regions have different weights for calculating local projections, which can significantly reduce ghosting due to these projections vary with the viewpoint and parallax. The benefits of the proposed approach are demonstrated using a variety of challenging cases.
Blockchain-Based Electronic Medical Records Management of Hospital Emergency Ward

The handling of electronic medical records in the emergency ward is very crucial because it is the basis of patients' medical treatment information before transferring to other ward in hospitals. Although, the current Electronic Medical Records (EMR) system has provided several advantages in handling patients' relevant information such as quick searching and eliminating duplicated records. However, the system is still facing the challenges of security concern particularly data manipulation issue that is considered a serious threat to data integrity. In fact, the application of blockchain technology overcomes data security problem by validating this issue based on smart contract supported with hash method of peer to peer network. This method has allowed the safe sharing of information across nodes and secures the transaction over networks through the accuracy of data transmission. Therefore, this paper aims to perform data transmission analysis of a smart contract-based hash validation method through peer to peer network (P2P). The analysis result shows that the data transmission is accurate and non-error in every node on P2P. When the number of peers and data size is increased, it has a greater effect on the processing latency. Each peer can act as both a server and a client. With 8 peers, maximum data size 2048 bytes, latency was 3,249 milliseconds which shows the latency does not increase more than the smaller data size. After that, we brought the questionnaire relating to our system to ask 34 people, the results show that 50% of them aware of the importance of medical data and 100% of data integrity.
Wigner function, Wigner-Yanase skew information, and parity asymmetry

The Wigner function, a position-momentum joint distribution introduced by Wigner in 1932 as a quantum correction for thermodynamics, plays a fundamental role in the phase-space formulation of quantum mechanics. The Wigner-Yanase skew information, an information-theoretic concept introduced by Wigner and Yanase in 1963 for quantifying information contents of quantum states, remains almost unnoticed until 2000s and is now playing an increasingly important role in quantum information theory. Apart from the appearance of the name Wigner, the relations between these two important concepts seem to have attracted little attention. In this work, by exploiting symmetry and asymmetry of bosonic field states with respect to the parity, we quantify parity asymmetry in term of the Wigner-Yanase skew information, and reveal some intrinsic complementary relations between the Wigner function and the Wigner-Yanase skew information. The results can be extended to a rather general setup.
Socially Assistive Robots at Work: Making Break-Taking Interventions More Pleasant, Enjoyable, and Engaging

More than ever, people spend the workday seated in front of a computer, which contributes to health issues caused by excess sedentary behavior. While breaking up long periods of sitting can alleviate these issues, no scalable interventions have had long-term success in motivating activity breaks at work. We believe that socially assistive robotics (SAR), which combines the scalability of e-health interventions with the motivational social ability of a companion or coach, may offer a solution for changing sedentary habits. To begin this work, we designed a SAR system and conducted a within-subjects study with N = 19 participants to compare their experiences taking breaks using the SAR system versus an alarm-like device for one day each in participants' normal workplaces. Results indicate that both systems had similar effects on sedentary behavior, but the SAR system led to greater feelings of pleasure, enjoyment, and engagement. Interviews yielded design recommendations for future systems. We find that SAR systems hold promise for further investigations of aiding healthy habit formation in work settings.
An Empirical Study of Task-Specific Limitations of the Overview+Detail Technique for Interactive Time Series Analysis

Interactive visualisation and analysis of time series data is a critical part of many data-driven optimisation processes, particularly in Industry 4.0 and Smart Manufacturing. Time series visualisation enables data analysts or domain experts to visually identify problems such as missing values, sensor drift, precision degradation, or faulty data, before or after algorithmic analysis. A common technique to support the visual exploration of large time series is the overview+detail (O+D) technique. O+D provides both detail and context information by displaying a detailed view showing the actual data and a thumbnail overview for showing its context. User studies have shown that users analyse and navigate data sets more efficently and effectively with than without O+D, but that this strongly depends on the task and the nature and amount of the data to be displayed. We present results of a quantitative user study that was performed on Amazon Mechanical Turk with 95 participants to identify scenarios in which O+D could not effectively solve the challenge of visualising large time series. By this, we identify potential usability issues of O+D for typical time series analysis tasks and discuss their origins. For each of these usability issues, we also propose alternative interaction and visualisation designs or other strategies to maintain good usability, even for challenging task types and data densities.
Consistent Covariance Pre-Integration for Invariant Filters with Delayed Measurements

Sensor fusion systems merging (multiple) delayed sensor signals through a statistical approach are challenging setups, particularly for resource constrained platforms. For statistical consistency, one would be required to keep an appropriate history, apply the correcting signal at the given time stamp in the past, and re-apply all information received until the present time. This re-calculation becomes impractical (the bottleneck being the re-propagation of the covariance matrices for estimator consistency) for platforms with multiple sensors/states and low compute power.This work presents a novel approach for consistent covariance pre-integration allowing delayed sensor signals to be incorporated in a statistically consistent fashion with very low complexity. We leverage recent insights in Invariant Extended Kalman Filters (IEKF) and their log-linear, state independent error propagation together with insights from the scattering theory to mimic the re-calculation process as a medium through which we can propagate waves (covariance information in this case) in single operation steps. We support our findings in simulation and with real data.
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view Data

Since many real-world data can be described from multiple views, multi-view learning has attracted considerable attention. Various methods have been proposed and successfully applied to multi-view learning, typically based on matrix factorization models. Recently, it is extended to the deep structure to exploit the hierarchical information of multi-view data, but the view-specific features and the label information are seldom considered. To address these concerns, we present a partially shared semi-supervised deep matrix factorization model (PSDMF). By integrating the partially shared deep decomposition structure, graph regularization and the semi-supervised regression model, PSDMF can learn a compact and discriminative representation through eliminating the effects of uncorrelated information. In addition, we develop an efficient iterative updating algorithm for PSDMF. Extensive experiments on five benchmark datasets demonstrate that PSDMF can achieve better performance than the state-of-the-art multi-view learning approaches. The MATLAB source code is available at https://github.com/libertyhhn/PartiallySharedDMF.
CU-MSDSp: A flexible parallelized Reversible jump Markov chain Monte Carlo method

Reversible jump Markov chain Monte Carlo (RJMCMC) is a powerful Bayesian trans-dimensional algorithm for performing model selection while inferring the distribution of model parameters. The present work introduces CU-MSDSp as an open source and fully automated parallel RJMCMC implementation that aims to increase the accessibility of RJMCMC to practitioners. CU-MSDSp begins by independently forming Markov Chains to approximate the posterior distribution of each model’s parameters. These approximations are then used to estimate the posterior distribution of the model space. This embarrassingly parallelizable software eliminates the need of designing a trans-dimensional proposal distribution and Jacobian all while ensuring the same theoretical guarantees as the non-parallel RJMCMC algorithm. Finally, CU-MSDSp enables practitioners to rely on their previous knowledge of fixed dimension MCMC convergence assessment and simulation design.
Perturbed Chaotic Map with Varying Number of Iterations and Application in Image Encryption

In this paper, the model of perturbed chaotic map with varying number of iterations (PCM-VNI) is proposed, in which values of chaotic states and control parameters are represented in the format of fixed-point number. The perturbation is at bit level applied to both state variables and control parameters. The proposed PCM-VNI is employed in the image diffusion, where the chaotic map can be iterated with a number of iterations differently for different pixels. Number of iterations can be seen to be perturbed for varying to get the advantage for the chaos-based image encryption. The exemplar simulation and statistical analyses will demonstrate the effectiveness of the PCM-VNI in the application.
Improved Human-Object Interaction Detection Through On-the-Fly Stacked Generalization

Human-object interaction (HOI) detection, which finds the relationships between humans and objects, is an important research area, but current HOI detection performance is unsatisfactory. One of the main problems is that CNN-based HOI detection algorithms fail to predict correct outputs for unseen test data based on a limited number of available training examples. Herein, we propose a novel framework for HOI detection called the on-the-fly stacked generalization deep neural network (OSGNet). OSGNet consists of three main components: (1) feature extraction modules, (2) HOI relationship detection networks, and (3) a meta-learner for combining the outputs of sub-models. Here, components (1) and (2) are considered to be sub-models. Any task-based feature extraction modules, such as classification or human pose estimation modules, can be used as sub-models. To achieve on-the-fly stacked generalization, the sub-models and meta-learner are trained simultaneously. The sub-models are trained to provide complementary information, and the meta-learner improves the generalization performance for unseen test data. Extensive experiments demonstrate that the proposed method achieves state-of-the-art accuracy, particularly in cases involving rare classes.
Avoiding data loss and corruption for file transfers with Fast Integrity Verification

End-to-end integrity verification is used to avoid silent data corruption in file transfers by comparing the checksum of files at source and destination end points. However, it increases transfer times significantly as checksum computation requires reading files back from the storage and running compute-intensive hash computation. In this paper, we propose Fast Integrity VERification (FIVER) algorithm which alleviates the overhead of end-to-end integrity verification by overlapping checksum computation with transfer operation and enabling I/O sharing between the two. The results obtained from various network and dataset settings show that FIVER is able to bring down the cost of end-to-end integrity verification from up to 120% by the state-of-the-art solutions to below 15%. Moreover, existing implementations of end-to-end integrity verification are vulnerable to permanent data loss in the case of an unexpected power outage due to completing the integrity verification process while data is still on memory. FIVER addresses this issue by enforcing dirty data on memory to be flushed to disk before finishing integrity verification such that power loss during any phase of a transfer would cause integrity verification to fail and transfer application to retransfer lost data.
Textual Lyrics Based Emotion Analysis of Bengali Songs

The type of song or music a person is listening to, usually represents his state of mind. We find many online music libraries/ repositories and music streaming and media services providers play or store songs or music based on the human emotions. This makes the emotion or mood detection of songs a very interesting study which has got the above mentioned areas of application along with many others. In this paper, we have proposed methods to identify the connotation of any song based on the textual lyrics only. The methods used are the very basic language independent features which show competent performance with F1-score of more than 81%.
Functional Resignification and Technological Innovation as a Digital Teaching Competence

The documentary screening carried out in this study supports arguments about the relationship between institutional conditioning in the implementation of digital media in educational practice and the digital competence required by teachers to undertake it. Also, this review justifies the innovative double response of teachers aimed at avoiding determinism in two educational settings differentiated by the presence or lack of digital hybrids. One of them is based on reassigning the secondary functions, and incorporating them into the primary, or essential, functions that have used the tools of a technological family since their origin. The second one entails the resignificance, or change of primary function between the two that support digital hybrids, as applied to production or reproduction of discourses.
Implementation of Sobel filter using CUDA

Abstract The Sobel filter is an edge detection filter that results in highlighting and focusing the edges. It is used for extracting the image gradient and contours in image processing applications. The data sets we are using in the paper are PGM and TIFF images with different rows and column pixel dimensions. The sobel filter operations used are implemented in C and CUDA programming language and the time taken for the operations in the serial and parallel platforms respectively are analyzed.
Poster: Scaling Up Deep Neural Network optimization for Edge Inference

Deep neural networks (DNNs) have been increasingly deployed on and integrated with edge devices, such as mobile phones, drones, robots and wearables. Compared to cloud-based inference, running DNN inference directly on edge devices (a.k. a. edge inference) has major advantages, including being free from the network connection requirement, saving bandwidths, and better protecting user privacy [1].
Kennard-Stone Balance Algorithm for Time-series Big Data Stream Mining

Nowadays time series are generated relatively more easily and in larger quantity than ever, by the advances of IoT and sensor applications. Training a prediction model effectively using such big data streams poses certain challenges in machine learning. Data sampling has been an important technique in handling over-sized data in pre-processing which converts the huge data streams into a manageable and representative subset before loading them into a model induction process. In this paper a novel data conversion method, namely Kennard-Stone Balance (KSB) Algorithm is proposed. In the past decades, KS has been used by researchers for partitioning a bounded dataset into appropriate portions of training and testing data in cross-validation. In this new proposal, we extend KS into balancing the sub-sampled data in consideration of the class distribution by round-robin. It is also the first time KS is applied on time-series for the purpose of extracting a meaningful representation of big data streams, for improving the performance of a machine learning model. Preliminary simulation results show the advantages of KBS. Analysis, discussion and future works are reported in this short paper. It is anticipated that KBS brings a new alternative of data sampling to data stream mining with lots of potentials.
Monolithic Integration of a Phase Noise Based Quantum Random Number Generator on InP platform

We present the experimental characterization of a fully integrated InP-based quantum random number generator chip composed from a single gain switched DBR laser and two Mach Zehnder interferometers. We demonstrate high degree of randomness by testing the QRNG output in the NIST Statistical Test Suite.