Reviewerly/exHarmony huggingface dataset:
Calibration-free Visual-Inertial Fusion with Deep Convolutional Recurrent Neural Networks

Visual-Inertial Odometry (VIO) has been one of the most popular yet affordable navigation systems for indoor and even outdoor applications. VIO can augment or replace Global Navigation Satellite Systems (GNSSs) under signal degradation or service interruptions. Conventionally, the fusion of visual and inertial modalities has been performed using optimization-based or filtering-based techniques such as nonlinear Least Squares (LS) or Extended Kalman Filter (EKF). These classic techniques, despite several simplifying approximations, involve sophisticated modelling and parameterization of the navigation problem, which necessitates expert fine-tuning of the navigation system. In this work, a calibration-free visual-inertial fusion technique using Deep Convolutional Recurrent Neural Networks (DCRNN) is proposed. The network employs a Convolutional Neural Network (CNN) to process the spatial information embedded in visual data and two Recurrent Neural Networks (RNNs) to process the inertial sensor measurements and the CNN output for final pose estimation. The network is trained with raw Inertial Measurement Unit (IMU) data and monocular camera frames as its inputs, and the relative pose as its output. Unlike the conventional VIO techniques, there is no need for IMU biases and scale factors, intrinsic and extrinsic parameters of the camera to be explicitly provided or modelled in the proposed navigation system, rather these parameters along with system dynamics are implicitly learned during the training phase. Moreover, since the inertial and visual data are fused at mid-layers in the network, deeper correlations of these two modalities are learned compared to a simple combination of the final pose estimates of both modalities at the output layers, hence, the fusion can be considered as a tightly-coupled fusion of visual and inertial modalities. The proposed VIO network is evaluated on real datasets and thorough discussion is provided on the capabilities of the deep learning approach toward VIO.
Removing the Traces of Median Filtering via Unsharp Masking as an Anti-forensic Approach in Medical Imaging

Development of post-processing algorithms which cannot be detected by forensic tools is an active area of research in image processing. Median Filter (MF) is one among the denoising schemes which is specifically targeted by the forensic toolsbecause of its wide application in commercial raster graphic editors, simplicity, fast computation and detail preserving characteristics. Methodsbased on Convolutional Neural Networks (CNN) and Variational Deconvolution (VD), meant for reducing the forensic detectability of MF by removing the traces of filtering from the output images are computationally intense. A simple and computationally feasible approach for removing the traces of median filtering from the output images, thereby to reduce the forensic detectability of MF is proposed in this paper. In the proposed approach, blurred edges in the output of MF are restored with the help of Unsharp Masking (UM). Optimum value of the amount which controls the degree of sharpening in the UM algorithm is determined via minimum error sense criterion by making use of Peak Signal to Noise Ratio (PSNR) between input and processed images as objective function. Values of PSNR and Structural Similarity Index Metric (SSIM) between input and output images exhibited by the proposed algorithm are found to be higher than those exhibited by methods based on CNN, VD and combined framework of VD and Total Variation (TV) minimisation.
Incremental scannerless generalized LR parsing

We present the Incremental Scannerless Generalized LR (ISGLR) parsing algorithm, which combines the benefits of Incremental Generalized LR (IGLR) parsing and Scannerless Generalized LR (SGLR) parsing. The parser preprocesses the input by modifying the previously saved parse forest. This allows the input to the parser to be a stream of parse nodes, instead of a stream of characters. Scannerless parsing relies heavily on non-determinism during parsing, negatively impacting the incrementality of ISGLR parsing. We evaluated the ISGLR parsing algorithm using file histories from Git, achieving a speedup of up to 25 times over non-incremental SGLR.
Virtual Machine Trading in Public Clouds

By obtaining virtual machines (VMs) from infrastructure providers (InPs) according to the demand in public cloud services, service providers (SPs) can elastically provide network services to users. As the charging methods of VMs, reserved instance (RI) and on-demand instance (ODI) are widely used. For InPs, RI is more desirable than ODI thanks to easiness of estimating long-term revenue, risk aversion of occurring idle VM resources, and reduction of charging cost. In this paper, to improve the ratio of RI in VMs prepared by an InP, we propose VM trading methods in which idle RI of SPs with VM demand falling below the number of contracted RI are transferred to SPs with VM demand exceeding the number of contracted RI. As the VM trading mechanisms, we investigate two approaches: RI with self-help effort (RISE) and RI with mutual aid (RIMA). Through numerical evaluation using the demand pattern of commercial VoD service, we show that the proposed VM trading methods decrease the number of VMs required for ODI by about 50% to 100% and increase the ratio of RI by about 15% to 85%.
Modified particle swarm optimization with novel population initialization

Particle swarm optimization (PSO) algorithm is such a simple, easily understood and realizable concept that it has been developing rapidly and has been applied widely since it was introduced. This paper proposes a modified particle swarm optimization(MPSO) algorithm with novel initializing particle swarms to improve the performance of the standard PSO. It is tested with a benchmark function compared with the standard PSO. Experimental results indicate that MPSO performs much better than the standard PSO both in terms of the speed of solutions and robustness.
Object Detection and Potential Field-based Trajectory Planning on LiDAR 3D-Point Clouds for UAV in Uncertain Environments

In this publication a new workflow of point cloud processing for LiDAR-based trajectory planning and collision avoidance is proposed, which enables autonomous vehicles such as UAV to navigate in semi-unknown environments. Semi-unknown means that no complete map of the area and of potential obstacles is available. To guarantee safe operation of the UAV, a low-level collision avoidance based on LiDAR-data is proposed. The approach consists of a voxel-grid-based downsampling, kd-tree-construction for efficient 3D range searching, a novel modification of the Euclidian clustering approach that is used for computationally inexpensive object detection, path planning based on artificial potential fields, and a final generation of a feasible trajectory by means of a model predictive controller (MPC).The proposed modification of the Euclidean clustering algorithm consists in choosing adaptive radii for the spherical search areas around the LiDAR points and yields superior clustering results for several test measurements in various environments than the existing algorithm that uses a fixed search radius while causing only negligible additional computational cost. Besides, the introduction of artificial potential fields to LiDAR measurements for trajectory planning and collision avoidance is proposed. By modelling point clouds as spatial distributions of electrical charges, an analytical expression for the potential field can be obtained. Subsequently, an arbitrarily smooth path to circumvent all obstacles is generated using gradient-descent based on which a feasible trajectory can be obtained by model predictive control. This novel approach for collision avoidance significantly reduces computational cost compared to graph-based state-of-the-art path planning solutions such as the A*-algorithm and its numerous derivatives, thus allowing near real-time solving of the 3D perception and cognition problem, i.e. obstacle detection and trajectory planning. Considering that the physical limits of payload capacity and power supply impose hard constraints for the available computing power on UAV, smaller computational cost offers a decisive advantage in order to enable low-latency on-boardprocessing of the sensor data. In order to evaluate the proposed approach, all algorithms were successfully applied to several static point clouds in a cluttered office environment. Additionally, near real-time capability was ensured by implementing the object detection and the trajectory planning on two asynchronous threads of an AscTec Mastermind board for UAV, equipped with an Intel Core i7-3517UE Dual-Core-CPU with 2 x 1.7 GHz. While the object detection thread was successfully running at variable refresh rates of approximately 1-10 Hz depending on the complexity of the scene, i.e. the size of the point cloud or the available free space, respectively, the trajectory planning including the MPC fulfilled the requirement of running independently on a separate thread at a fixed clock rate of 100 Hz.
The UDWT image denoising method based on the PDE model of a convexity-preserving diffusion function

Abstract It is a great challenge to maintain details while suppressing and eliminating noise of the image. Considering the nonconvexity property of the diffusion function and the hypersensitivity of the Laplace operator to noise in the Y-K model, a fourth-order PDE image denoising model (Con_G&amp;L model) is proposed in this paper. This model is constructed by a new convexity-preserving diffusion function which guarantees the corresponding energy functional has a globally unique minimum solution. At the same time, the Gaussian filter is combined with the Laplace operator in this model, and as a result, the noisy image is smoothed before the diffusion process, which improves the ability of capturing the details and edges of the noisy image greatly. Furthermore, by analyzing the statistical properties of the undecimated discrete wavelet transform (UDWT) coefficients of noisy image, we observe that the noise information is mainly distributed in the high-frequency sub-bands, and based on this, the proposed Con_G&amp;L model is applied in the high-frequency sub-bands of the UDWT to get the denoising method. The proposed method removes the image noise effectively with the image texture and other details of the image being maintained. Meanwhile, the generation of false edges and the staircase effect can be suppressed. A large number of simulation experiments verify the effectiveness of the proposed method.
Exploring Characteristic Features in Gait Patterns for Predicting Multiple Sclerosis

Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.
A multiscript gaze-based assistive virtual keyboard

The recent development of inexpensive and accurate eye-trackers allows the creation of gazed based virtual keyboards that can be used by a large population of disabled people in developing countries. Thanks to eye-tracking technology, gaze-based virtual keyboards can be designed in relation to constraints related to the gaze detection accuracy and the considered display device. In this paper, we propose a new multimodal multiscript gaze-based virtual keyboard where it is possible to change the layout of the graphical user interface in relation to the script. Traditionally, virtual keyboards are assessed for a single language (e.g. English). We propose a multiscript gaze based virtual keyboard that can be accessed for people who communicate with the Latin, Bangla, and/or Devanagari scripts. We evaluate the performance of the virtual keyboard with two main groups of participants: 28 people who can communicate with both Bangla and English, and 24 people who can communicate with both Devanagari and English. The performance is assessed in relation to the information transfer rate when participants had to spell a sentence using their gaze for pointing to the command, and a dedicated mouth switch for commands selection. The results support the conclusion that the system is efficient, with no difference in terms of information transfer rate between Bangla and Devanagari. However, the performance is higher with English, despite the fact it was the secondary language of the participants.
P4-391: CAREGIVER INPUT IN DESIGN AND USE OF NEW TECHNOLOGIES TO REDUCE AGITATION IN DEMENTIA IN THE HOME SETTING

Behavioral and Environmental Sensing and Intervention for Dementia Caregiver Empowerment (BESI) is a system of body-worn and in-home sensors developed to provide continuous, non-invasive agitation assessment and environmental context monitoring to detect early signals of agitation and environmental triggers. The goal to detect early stages of agitation in persons with dementia (PWD) opens up new and promising technological development of cyber-human systems to enable early caregiver intervention. Caregivers shoulder most of the burden of dementia caregiving, and the BESI project seeks to reduce burden and improve caregiver self-efficacy. This mixed methods, remote ethnographic study is explored in a 3-phase, multi-year plan. In Phase 1 we developed the BESI system, completed usability studies in Alzheimer's Disease support groups using the Systems Usability Scale (SUS), and refined the system. Dyads (caregivers + PWD) who live at home are studied for 30 days in Phase 2 with continuous data collection during the deployment period. A tablet application for caregivers is used to log PWD activities, agitation events, and input markers of caregiver self-efficacy. Using wearable wrist technology (e.g. Pebble®), agitation severity level, physical, behavioral, and social activities of the PWD are captured. Post-deployment surveys of all ten dyads provided data on the system usability from questions posed with Likert-type scaling response ratings between 1 and 6 plus qualitative feedback. Between phases 1 & 2, the tablet application was updated to enhance interface usability for caregivers. Scores for the ten questions (rated 1-6) on ease of use of the tablet were in the very easy range (5.11 - 5.90). Agreement on use of the table device yielded SUS scores (rated 1-5) with range of 2.67 - 4.56. Preliminary analysis of the subjective feedback indicates overall positive impressions in working with the technologies. Phase 2 results facilitated targeted changes in BESI, improving overall usability for the final phase of the study. Caregivers consistently demonstrated willingness to help – including working with technologies previously unfamiliar. These subject-oriented design decisions influenced the team in understanding caregiver and PWD dyad interactions with technologies. The full qualitative report will be available in June 2019.
Design and evaluation of mobile sensing technologies for identifying medicines by people with visual disabilities

Purpose People with visual impairments may have difficulty in identifying information, either on a bottle or package of medicine. As a result, many of them need the help of a third party to discern which medication they should take. In this perspective, this study aims to assist people with visual impairments to identify their medicine by using mobile sensing technologies. Design/methodology/approach The authors have developed three versions of a mobile application. They use three technologies, namely, near field communication, QR code and magnetic field, for drug identification. The entire development process was based on user-centered design. And, the authors also performed an initial usability assessment of these applications with ten users who were blind. Findings Preliminary results show user preference for the near field communication approach and reinforce some known drawbacks concerning the use of QR code by people with visual impairments. Research limitations/implications Although the authors have evaluated these technologies with few mobile devices, they perceived that the acquired values of the magnetic field suffered distortions culminating, in some cases, in a non-identification of the medicine, or even in incorrect identification. Practical implications Current mobile device magnet sensors are not yet robust to be used for medicine identification. Social implications Mobile apps combined with medicine identification can contribute to the autonomy of people who are blind. Originality/value The main contribution of this paper is to integrate and analyze distinct mobile-based sensing technologies for identifying medicines by people with visual impairments.
Cryptanalysis of NTRU where the private polynomial has one or more consecutive zero coefficients

Let h and (f, g) be the public and private keys of NTRU respectively. We present a new lattice to attack NTRU when the private polynomial g has one or more consecutive coefficients which are equal zeros, i.e., zero patterns. The target vector of the new lattice contains the private polynomial f, and the ratio between the size of the target vector and the expected size of a shortest non-zero vector in the new lattice is smaller than the ratio declared in the previous results. The zero patterns are not necessarily of the same or long lengths. Experimental results show that our attack succeeded to cryptanalyze NTRU faster than the previous attacks.
FITradeoff Decision Support System: An Exploratory Study with Neuroscience Tools

Multicriteria problems are present in several contexts and are often complex. To support the decision maker, decision support systems (DSS) have been developed. FITradeoff is a DSS focused on multicriteria additive problems and has a flexible and interactive approach. This study aimed to investigate how engagement and cognitive load is evoked during the use of DSS for different types of problems. It is a work in progress that seeks to raise hypotheses to be tested to promote changes in the DSS and generate recommendations for the decision analyst to make the experience with DSS and the whole decision-making process more efficient and effective.
Spherical Principal Component Analysis.

Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in some fields, angle distance is known to be more important and critical for analysis. In this paper, we propose a method by adding constraints on factors to unify the Euclidean distance and angle distance. However, due to the nonconvexity of the objective and constraints, the optimized solution is not easy to obtain. We propose an alternating linearized minimization method to solve it with provable convergence rate and guarantee. Experiments on synthetic data and real-world datasets have validated the effectiveness of our method and demonstrated its advantages over state-of-art clustering methods.
A New Chinese Financial Sentiment Dictionary for Textual Analysis in Accounting and Finance

Using a multi-stage filtering procedure based on both algorithms and human judgement, we develop a Chinese Financial Sentiment Dictionary (CFSD) with the hope to help advance textual analysis of Chinese documents in accounting and finance. There are 1,489 negative words and 1,108 positive words in the CFSD. We translate all 2,597 words into English. We also briefly discuss two unique settings in which researchers can investigate some important and interesting research questions that are rarely studied due to lack of data.
Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions

In this paper, we investigate the effect of enhancing lexical embeddings in LSTM language models (LM) with syntactic and semantic representations. We evaluate the language models using perplexity, and we evaluate the performance of the models on the task of predicting human sentence acceptability judgments. We train LSTM language models on sentences automatically annotated with universal syntactic dependency roles (Nivre, 2016), dependency depth and universal semantic tags (Abzianidze et al., 2017) to predict sentence acceptability judgments. Our experiments indicate that syntactic tags lower perplexity, while semantic tags increase it. Our experiments also show that neither syntactic nor semantic tags improve the performance of LSTM language models on the task of predicting sentence acceptability judgments.
On the Estimation of the Wasserstein Distance in Generative Models

Generative Adversarial Networks (GANs) have been used to model the underlying probability distribution of sample based datasets. GANs are notoriuos for training difficulties and their dependence on arbitrary hyperparameters. One recent improvement in GAN literature is to use the Wasserstein distance as loss function leading to Wasserstein Generative Adversarial Networks (WGANs). Using this as a basis, we show various ways in which the Wasserstein distance is estimated for the task of generative modelling. Additionally, the secrets in training such models are shown and summarized at the end of this work. Where applicable, we extend current works to different algorithms, different cost functions, and different regularization schemes to improve generative models.
Variable Record Table: A Run-time Solution for Mitigating Buffer Overflow Attack

We present a novel approach to mitigate buffer overflow attack using Variable Record Table (VRT). Dedicated memory space is used to automatically record base and bound information of variables extracted during runtime. We instrument frame pointer and function(s) related registers to decode variable memory space in stack and heap. We have modified Simplescalar/PISA simulator to extract variables space of six (6) benchmark suites from MiBench. We have tested 290 small C programs (MIT corpus suite) having 22 different buffer overflow vulnerabilities in stack and heap. Experimental results show that our approach can detect buffer overflow attack with zero instruction overhead with the memory space requirement up to 13Kb to maintain VRT for a program with 324 variables.
A Received Signal Strength Based Indoor Localization Algorithm Using ELM Technique and Ridge Regression

In this paper, a received signal strength (RSS) based localization algorithm by extreme learning machine (ELM) technique is proposed. In the offline phase, in order to cope with the environmental noise of the training data set, the improved ridge regression based ELM (IRR-ELM) algorithm is proposed to obtain more stable prediction which has better generalization ability, because the ridge parameter is obtained with the variance of the training error. In the online phase, the obtained prediction model is straightly used for position prediction. Since it can reduce the deviation in the off-line training phase, the proposed algorithm has more stable and accurate position estimation result in online phase. At last, the RSS measurements obtained from field test are used for performance evaluation. It shows that the localization performance of the proposed is better than that of the existing ELM based localization methods.