Reviewerly/exHarmony huggingface dataset:
Deep Single Image Enhancer

Surveillance cameras can be deployed in various environments where lighting conditions are constantly changing. However, due to the limited dynamic range of current image sensors, the captured images are only low dynamic range images that usually suffer from over-exposure and under-exposure situations where important details are lost. Therefore, it is critical to recover the lost details of such images in order to improve visual experience for observers and performance for possible computer vision processing. In this paper, we propose a reformulated Laplacian pyramid and a convolutional neural network (CNN) model to enhance and recover the lost detail of a degraded image. The reformulated Laplacian first decomposes the image into two sub-images that contain global and local image features, respectively. The global features and local features are processed by the proposed CNN model to manipulate the global luminance terrain and enhance local details. The final image is obtained by reconstructing the CNN generated local and global features. Various experiments have been conducted. The results demonstrate that the proposed model outperforms the state-of-the-art methods.
Adversarial Variational Optimization of Non-Differentiable Simulators.

Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes. We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator. We solve the resulting non-differentiable minimax problem by minimizing variational upper bounds of the two adversarial objectives. Effectively, the procedure results in learning a proposal distribution over simulator parameters, such that the JS divergence between the marginal distribution of the synthetic data and the empirical distribution of observed data is minimized. We evaluate and compare the method with simulators producing both discrete and continuous data.
Fast Trajectory Optimization using Sequential Convex Programming with No-Fly Zone Constraints

This paper proposes a new trajectory optimization method for Unmanned Aerial Vehicle (UAV). The proposed L1-Penalized Sequential Convex Programming (LPSCP) method improves the initial infeasibility of the standard sequential convex programming. LPSCP method converges within 0.2 seconds which is more than one-tenth of the pseudospectral (PS) method. Therefore, LPSCP has the potential to enable UAV’s real-time autonomous air mission if implemented on-board. The UAV trajectory optimization problem is defined at the beginning of the paper and a convexification process is performed when there are several no-fly zones along the trajectory. Then the LPSCP method iteratively solves locally approximated subproblems in conic forms. Simulation results illustrate the proposed method satisfies the required constraints and has a computation time advantage over the conventional PS method.
Blind Wavelet-Based Image Watermarking

In this chapter, the watermarking technique is blind; blind watermarking does not need any of the original images or any information about it to recover watermark.In this technique the watermark is inserted into the high frequencies.Threelevel wavelet transform is applied to the image, and the size of the watermark is equal to the size of the detailed sub-band.Significant coefficients are used to embed the watermark.The proposed technique depends on quantization.The proposed watermarking technique generates images with less degradation.
Evaluation of the OIRE Method in a Case Study

To (semi-)automatically classify user needs collected from online open sources, we propose the Open Innovation in Requirements Engineering (OIRE) method. OIRE is mimicking the well-known Kano-model exclusively using data from online reviews instead of conducting interviews with select focus groups. In our previous research, we introduced the design, implementation and preliminary validation of the OIRE method. In this paper, we introduce the tool support for the OIRE method, OIRE-System (OIRE-S), and evaluate the OIRE method using OIRE-S. For that purpose, we conducted a case study with two Chinese companies using OIRE-S as tool support when applying the OIRE method. Based on the results of the case study, we conclude that the analysis results of the OIRE method provide valuable information for stakeholders and, thus, is useful to decision-makers in the industry, in particular as a complement to traditional requirement elicitation and analysis activities.
HYBRID MOBILE AUGMENTED REALITY: WEB-LIKE CONCEPTS APPLIED TO HIGH RESOLUTION 3D OVERLAYS

Abstract. Mobile Augmented Reality (MAR) aligns toward current technological advances with more intuitive interfaces, realistic graphic content and flexible development processes. The case of overlaying precise 3D representations exploits their high penetration to induct users to a world where data are perceived as real counterparts. The work presented in this paper integrates web-like concepts with hybrid mobile tools to visualize high-quality and complex 3D geometry on the real environment. The implementation involves two different operational mechanisms: anchors and location-sensitive tracking. Three scenarios, for indoors and outdoors are developed using open-source and with no limit on distribution SDKs, APIs and rendering engines. The JavaScript-driven prototype consolidates some of the overarching principles of AR, such as pose estimation, registration and 3D tracking to an interactive User Interface under the scene graph concept. The 3D overlays are shown to the end user i) on top of an image target ii) on real-world planar surfaces and iii) at predefined points of interest (POI). The evaluation in terms of performance, rendering efficacy and responsiveness is made through various testing strategies: system and trace logs, profiling and ‗end-to-end‖ tests. The final benchmarking elucidates the slow and computationally intensive procedures induced by the big data rendering and optimization patterns are proposed to mitigate the performance impact to the non-native technologies.
SALT-Viz: Real-Time Visualization for Large-Scale Traffic Simulation

The visualization of large-scale traffic simulation is a key problem for most big cities. Moreover, it is very important to visualize results of traffic simulations for each timestep in real-time, in order to analysis urban traffic congestion status accurately. However, large-scale traffic simulation and visualization has always been considered a challenging research topic, due to the preparation difficulty of massive road networks to simulate. In this paper, we present the SALT1, a novel traffic simulator and visualizer, that can handle large-scale urban traffic simulation. It introduces the concept of dynamic detailed geometry generation using base geometric information with attributes (e.g., the number of lanes) of links in road networks. Our system can provide efficient geometry processing and real-time level-of-detail rendering of traffic simulation results. The experimental results demonstrate that our system can be successfully applied to large- scale real-world urban traffic simulation.1 SALT is the abbreviation for “Simulation for AnaLyzing urban Traffic”, which is our project code name.
Indoor path planning and obstacle avoidance simulation

Abstract The paper is describing the work performed for choosing and designing a path planning and obstacle avoidance solution for a car-like vehicle moving in an indoor environment. The proposed algorithm first consists in providing the vehicle with a set of passing points generated by a Dijkstra path planning algorithm. The vehicle is using the points for establishing its path and is avoiding the obstacles using scanned data from a distance sensor. In a simulation environment, initial tests were performed for estimating correct values of the vehicle’s forward and angular velocities and for estimating the effect of the scanning speed on the vehicle’s behaviour. Obstacle avoidance tests were performed for identifying specific situations possible to appear due to high velocities or to low scanning speed. Even not always choosing a smooth avoidance path, the algorithm proved to find a way for avoiding the obstacle in a clear and fast enough manner.
Classifying Imbalanced Road Accident Data Using Recurring Concept Drift

In New Zealand, road accident casualties have been increasing. Factor analyses and time series analyses show what types of accidents result in casualties, but the results from the analysis can become outdated. We propose a stream classification framework with drift detection to signal and adapt when the factors associated with crash casualties change over time. We propose a drift detection framework, G-mean Adaptive drift Detection (GAD), which adapts a classifier threshold to maximise G-mean. This metric rewards maximising accuracy on each class while keeping these accuracies balanced. As a result, GAD can make concept drift in the minority class easier to detect. We also propose a recurring concept classification framework, G-mean Concept Profiling Framework (GCPF), which reuses previously trained classifiers and uses GAD’s approach to drift detection. Through experimentation, we show GAD improves G-mean without increasing false positive drift detection on imbalanced synthetic and real world datasets. We also show GCPF achieves better G-mean than other state-of-the-art stream classification approaches on the NZ crash data set.
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series.

Recurrent neural networks (RNNs) are commonly applied to clinical time-series data with the goal of learning patient risk stratification models. Their effectiveness is due, in part, to their use of parameter sharing over time (i.e., cells are repeated hence the name recurrent). We hypothesize, however, that this trait also contributes to the increased difficulty such models have with learning relationships that change over time. Conditional shift, i.e., changes in the relationship between the input X and the output y, arises when risk factors associated with the event of interest change over the course of a patient admission. While in theory, RNNs and gated RNNs (e.g., LSTMs) in particular should be capable of learning time-varying relationships, when training data are limited, such models often fail to accurately capture these dynamics. We illustrate the advantages and disadvantages of complete parameter sharing (RNNs) by comparing an LSTM with shared parameters to a sequential architecture with time-varying parameters on prediction tasks involving three clinically-relevant outcomes: acute respiratory failure (ARF), shock, and in-hospital mortality. In experiments using synthetic data, we demonstrate how parameter sharing in LSTMs leads to worse performance in the presence of conditional shift. To improve upon the dichotomy between complete parameter sharing and no parameter sharing, we propose a novel RNN formulation based on a mixture model in which we relax parameter sharing over time. The proposed method outperforms standard LSTMs and other state-of-the-art baselines across all tasks. In settings with limited data, relaxed parameter sharing can lead to improved patient risk stratification performance.
Infrastructural sovereignty over agreement and transaction data ('metadata') in an open network-model for multilateral sharing of sensitive data

Organizations are becoming ever more aware that their data is a valuable asset requiring protection against mis-use. Therefore, being in control over the usage conditions (i.e. data sovereignty) is a prerequisite for sharing sensitive data in (increasingly complex) supply chains. Maintaining sovereignty applies to both the primary shared data and to the 'metadata' stemming from the data sharing support processes. However, maintaining sovereignty over this metadata creates an area of tension. Data providers must balance operational efficiency through outsourcing the data sharing support processes and the associated metadata to external, trusted, organizations against the added risk of transferring control over the metadata. At the same time, lock-in by community providers and major integration efforts due to multiple data sharing relationships need to be avoided. To address these issues, this paper elaborates an open network-model approach for maintaining sovereignty over metadata. © 40th International Conference on Information Systems, ICIS 2019. All rights reserved.
Application Design of Electronic ID Card

The development and advancement of information technology has a lot of benefits for the institutions that implement it included in the field of government bureaucracy. The application of e-government to the government bureaucracy certainly can make the performance of government better. The application of information technology in the field of government such as in matters of making Identity Card, Bahasa: Kartu Tanda Penduduk (KTP), is certainly able to provide a change in the bureaucratic system of making Identity Card. As in the Pringsewu District that is originally used a manual system in making Identity Card that is certainly very inefficient and requires a long time so that many residents are reluctant to take care of making Identity Card. Surely this is very unfortunate remembering the importance of Identity Card. Because of this reason, the author tries to examine the design of an application for making an Electronic Identity Card with the aim to ease the public or to take care of making Citizenship Cards online, in addition to that, this application will also help the performance of the Population and Civil Registry Office so that the service provided to the public is more optimal. It is expected that people will no longer to be reluctant to take care of making a Electronic Identity Card with the application.
What Is Bitcoin?

There are four major factors that make up Bitcoin—money, decentralized network, Proof-of-Work, and time. We’ll explore each in this chapter to understand the basics of what we’re going to be working with.
Camera System Performance Derived from Natural Scenes

The Modulation Transfer Function (MTF) is a wellestablished measure of camera system performance, commonly employed to characterize optical and image capture systems. It is a measure based on Linear System Theory; thus, its use relies on the assumption that the system is linear and stationary. This is not the case with modern-day camera systems that incorporate non-linear image signal processes (ISP) to improve the output image. Nonlinearities result in variations in camera system performance, which are dependent upon the specific input signals. This paper discusses the development of a novel framework, designed to acquire MTFs directly from images of natural complex scenes, thus making the use of traditional test charts with set patterns redundant. The framework is based on extraction, characterization and classification of edges found within images of natural scenes. Scene derived performance measures aim to characterize non-linear image processes incorporated in modern cameras more faithfully. Further, they can produce &#x2018;live&#x2019; performance measures, acquired directly from camera feeds.
Improving Intent Extraction Using Ensemble Neural Network

User intent extraction from social media texts is aimed at identifying user intent keyword and its related information. This topic has attracted a lot of researches since its various applications in online marketing, e-commerce and business services. One of such studies is to model this problem as a sequence labeling task and apply state-of-the-art sequential tagging models such as BiLSTM [12] and BiLSTM-CRFs [12]. In this paper, we take a further step to enhance intent extraction results based on tri-training [23] and ensemble learning [2]. Specifically, we simultaneously use three BiLSTM-CRFs models, each of them is different from others by the type of word embeddings, and apply majority voting scheme over their predicted labels when decoding final labels. Extensive experiments on data from three domains Real Estate, Tourism and Transportation show that our proposed methods enjoy a better performance compared to single model based approach.
Exploring players' user experience in a high-embodied virtual reality game

In the virtual reality (VR) field, embodiment is a concept related to the degree that a player can naturally control an avatar in a virtual world. Prior studies demonstrated that when the effects of VR on players' cognition are evaluated, both the level of immersion and embodiment should be considered. However, few studies have explored the value of the embodiment dimension in assessing the effect of VR on players' user experience. This study aims to fill the gap and explore the characteristics of players' user experience in a high-embodied VR game. Sixty-seven gamers were quasirandomly assigned to either desktop or VR groups and played the same first-person shooter (FPS) game for 10 minute per person after a short training. The VR-version FPS game afforded participants the capability of gestures and physical movement like being in a real battlefield. Participants' in-game biometric data and post-game questionnaire data were collected and then analyzed. This study's results showed that the characteristics of players' user experience in a highembodied VR game condition can be summarized as achieving a significantly higher level of emotional arousal, more positive experience and attitude towards the video game than a traditional desktop condition. All the evidence demonstrated that the embodiment dimension is a critical factor that designers should consider when designing for effective games.
Sampling-based Trajectory Planning and Control for a Collision Avoidance System

The contribution at hand combines a sampling-based trajectory planning approach and a model predictive trajectory tracking controller to a collision avoidance system. The planner generates candidate trajectories by the suitable selection of breakpoints which are connected by a spline interpolation. A procedure is presented to systematically select sample states to perform a collision avoidance maneuver in case of an emergency situation. The vehicle is controlled to the optimal trajectory of the planner by comparison with a model predictive trajectory set. This is determined by the prediction of a detailed nonlinear vehicle model for constant input variables. A suitable objective function with a time weighting is utilized to evaluate the individual trajectories and to select the optimal input variables.
A Hybrid Multi-Filter Wrapper Feature Selection Method for Software Defect Predictors

Software Defect Prediction (SDP) is an approach used for identifying defect-prone software modules or components. It helps software engineer to optimally, allocate limited resources to defective software modules or components in the testing or maintenance phases of software development life cycle (SDLC). Nonetheless, the predictive performance of SDP models reckons largely on the quality of dataset utilized for training the predictive models. The high dimensionality of software metric features has been noted as a data quality problem which negatively affects the predictive performance of SDP models. Feature Selection (FS) is a well-known method for solving high dimensionality problem and can be divided into filter-based and wrapper-based methods. Filter-based FS has low computational cost, but the predictive performance of its classification algorithm on the filtered data cannot be guaranteed. On the contrary, wrapper-based FS have good predictive performance but with high computational cost and lack of generalizability. Therefore, this study proposes a hybrid multi-filter wrapper method for feature selection of relevant and irredundant features in software defect prediction. The proposed hybrid feature selection will be developed to take advantage of filter-filter and filter-wrapper relationships to give optimal feature subsets, reduce its evaluation cycle and subsequently improve SDP models overall predictive performance in terms of Accuracy, Precision and Recall values.
Designing Design for Safety: How emergent methods indicate new safer future design practices

A series of research projects by the authors has supported an understanding of a new type of design practice geared towards ensuring a safer relationship between people and technologies in a number of contexts including rapidly changing dynamic environments. Here we report on the research methods, tools and approaches that were created and adapted for investigating what emerged as a new approach for design in dealing with human behaviours and complex relations between physical and digital technologies. In particular these methods were used to research across both applied and strategic focus areas ranging from saving lives at sea to a future safety foresight review. A particular consideration was to ask whether current design principles and practices were adequate in order to tackle mature and emerging design for safety risks from local to global levels. Our methods emerged across a series of projects in a grand challenge and a foresight review. The first aimed to tackle loss of life at sea while the foresight review investigated the more specific strategic interdisciplinary role of design in tackling major future global risks. We report here on the thinking involved in developing the methods, how these were adapted and changed throughout the process and report on findings at key stages alongside insights from the data captured. The results from this research have been significant in helping to frame what may become a new approach to design practice, one that is capable of enabling change for increasing safety through new methods, practices, ethics and cultures.
Development of An POF-based Navigation System for People with Mild Cognitive Impairment Based on Wandering Detection

This study develops a point of familiarity (POF)-based navigation system for people with mild cognitive impairment (PMCI) based on wandering detection. The proposed system has four main functionalities: optimal path planning, POF-based navigation, wandering detection, and remote route tracking. An improved A* algorithm which introduced the θ_WD approach is employed to plan an optimal path for the PMCI. As any wandering behavior is detected, the PMCI who holds a mobile phone will receive the pof data sent by the remote monitoring system for guiding to the point of familiarity (POF) to wait for rescue. The travelling status of the PMCI will be recorded by the remote route tracking unit. Experiment results show that the proposed system achieves the accuracy of 91.7% for wandering detection in the normal GPS environments. Furthermore, the POF-based navigation saves about 19% of the traveling time as compared to the MAP-based navigation approach. Therefore, the system has great potential in helping the PMCI to go home safely.