Reviewerly/exHarmony huggingface dataset:
Moving Object Detection from Moving Camera Image Sequences Using an Inertial Measurement Unit Sensor

This paper describes a new method for the detection of moving objects from moving camera image sequences using an inertial measurement unit (IMU) sensor. Motion detection systems with vision sensors have become a global research subject recently. However, detecting moving objects from a moving camera is a difficult task because of egomotion. In the proposed method, the interesting points are extracted by a Harris detector, and the background and foreground are classified by epipolar geometry. In this procedure, an IMU sensor is used to calculate the initial fundamental matrix. After the feature point classification, a transformation matrix is obtained from matching background feature points. Image registration is then applied to the consecutive images, and a difference map is extracted to find the foreground region. Finally, a minimum bounding box is applied to mark the detected moving object. The proposed method is implemented and tested with numerous real-world driving videos, which show that it outperforms the previous work.
Learning Meshes for Dense Visual SLAM

Estimating motion and surrounding geometry of a moving camera remains a challenging inference problem. From an information theoretic point of view, estimates should get better as more information is included, such as is done in dense SLAM, but this is strongly dependent on the validity of the underlying models. In the present paper, we use triangular meshes as both compact and dense geometry representation. To allow for simple and fast usage, we propose a view-based formulation for which we predict the in-plane vertex coordinates directly from images and then employ the remaining vertex depth components as free variables. Flexible and continuous integration of information is achieved through the use of a residual based inference technique. This so-called factor graph encodes all information as mapping from free variables to residuals, the squared sum of which is minimised during inference. We propose the use of different types of learnable residuals, which are trained end-to-end to increase their suitability as information bearing models and to enable accurate and reliable estimation. Detailed evaluation of all components is provided on both synthetic and real data which confirms the practicability of the presented approach.
Sentiment Analysis in Turkish Text with Machine Learning Algorithms

With the developing technology, the number of comments made on the internet is increasing day by day. It has become almost impossible to make a manual sentiment analysis on these comments. Therefore, new algorithms should be developed to automatically perform sentiment analysis on these texts. In this study, a sentiment analysis model has been developed for Turkish texts. While developing this model, lexicon-based methods and machine learning algorithms were used together. As a naïve method of sentiment analysis, the root of each word in a sentence takes a score from a dictionary and the final polarity score of the relevant sentence is calculated by using additive score-based models. Machine learning models are trained to perform accurate sentiment annotations by using features based on polarity scores of texts. The final supervised machine learning model can achieve sentiment annotations of new Turkish texts within a 73% success rate without any human intervention.
How are you feeling?

The global population is ageing, leading to shifts in healthcare needs. Home healthcare monitoring systems currently focus on physical health, but there is an increasing recognition that psychological wellbeing also needs support. This raises the question of how to design devices that older adults can interact with to log their feelings. We designed three tangible prototypes, based on existing paper-based scales of affect. We report findings from a lab study in which participants used the prototypes to log the emotion from standardised emotional vignettes. We found that the prototypes allowed participants to accurately record identified emotions in a reasonable time. Our participants expressed a perceived need to record emotions, either to share with family/carers or for self-reflection. We conclude that our work demonstrates the potential for in-home tangible devices for recording the emotions of older adults to support wellbeing.
RGB-IR Cross Input and Sub-Pixel Upsampling Network for Infrared Image Super-Resolution

Deep learning-based image super-resolution has shown significantly good performance in improving image quality. In this paper, the RGB-IR cross input and sub-pixel upsampling network is proposed to increase the spatial resolution of an Infrared (IR) image by combining it with a color image of higher spatial resolution obtained with a different imaging modality. Specifically, this is accomplished by fusion of the features map of two RGB-IR inputs in the reconstruction of an infrared image. To improve the accuracy of feature extraction, deconvolution is replaced by sub-pixel convolution to upsample image in the network. Then, the guided filter layer is introduced for image denoising of IR images, and it can preserve the image detail. In addition, the experimental dataset, which is collected by us, contains large numbers of RGB images and corresponding IR images with the same scene. Experimental results on our dataset and other datasets demonstrate that the method is superior to existing methods in accuracy and visual improvement.
Adaptive Classifier Ensemble Method Based on Spatial Perception for High-Dimensional Data Classification

Classifying high-dimensional small-size data is challenging in the field of pattern recognition. Traditional ensemble learning methods have several limitations: 1) sample-space based methods are easily affected by noise and redundant features; 2) feature-space based methods cannot excavate the essential characteristics of features; 3) feature subspaces cause information loss, which leads to a decline in accuracy; 4) most selective ensemble methods only consider the diversity and performance of sub-classifiers and ignore the impact on integration systems. To address the above limitations, we propose an adaptive classifier ensemble learning method (AdaSPEL) based on spatial perception for high-dimensional data. First, we design a local-space perception method for feature transformation, which encourages both high performance and diversity of the ensemble members. Second, we design a cross-space perception method based on the distribution of samples to obtain the cross-space enhanced features to provide a macro analysis for the characteristics of data. Furthermore, an adaptive selective ensemble method based on local and global evaluation mechanisms is proposed, which considers the impact of sub-classifiers on integrated systems. Experimental results on 33 high-dimensional data sets verify that our method outperforms mainstream ensemble learning methods based on feature space and sample space, and neural network-based algorithms.
Evaluating DAG-Based Blockchains for IoT

Blockchain has been considered as the enabler to provide automated and trust-less Internet of things (IoT) services. However, legacy consensus mechanisms applied in most of the blockchain networks pose obstacles for deployment in IoT in respect of decentralization and scalability. The emerging Directed Acyclic Graph (DAG)-based models aim at delivering the benefits of blockchain without making compromise on performance. This paper gives a critical analysis on the two representative DAG-based models, IOTA and Hashgraph. In particular, we provide an insightful overview of the two blockchains, with their features and functionalities. We define desired criteria for evaluating these two blockchains, provide an in-depth analysis on the two systems, and identify open challenges IOTA and Hashgraph are facing for their application in IoT.
Locality-constrained affine subspace coding for image classification and retrieval

Feature coding is a key component of the bag of visual words (BoVW) model, which is designed to improve image classification and retrieval performance. In the feature coding process, each feature of an image is nonlinearly mapped via a dictionary of visual words to form a high-dimensional sparse vector. Inspired by the well-known locality-constrained linear coding (LLC), we present a locality-constrained affine subspace coding (LASC) method to address the limitation whereby LLC fails to consider the local geometric structure around visual words. LASC is distinguished from all the other coding methods since it constructs a dictionary consisting of an ensemble of affine subspaces. As such, the local geometric structure of a manifold is explicitly modeled by such a dictionary. In the process of coding, each feature is linearly decomposed and weighted to form the first-order LASC vector with respect to its top-k neighboring subspaces. To further boost performance, we propose the second-order LASC vector based on information geometry. We use the proposed coding method to perform both image classification and image retrieval tasks and the experimental results show that the method achieves superior or competitive performance in comparison to state-of-the-art methods.
Domain-Structured Chaos in a Hopfield Neural Network

In this paper, we provide a new method for constructing chaotic Hopfield neural networks. Our approach is based on structuring the domain to form a special set through the discrete evolution of the network state variables. In the chaotic regime, the formed set is invariant under the system governing the dynamics of the neural network. The approach can be viewed as an extension of the unimodality technique for one-dimensional map, thereby generating chaos from higher-dimensional systems. We show that the discrete Hopfield neural network considered is chaotic in the sense of Devaney, Li–Yorke, and Poincaré. Mathematical analysis and numerical simulation are provided to confirm the presence of chaos in the network.
Electro-Optical Neural Networks Based on Time-Stretch Method

In this paper, a novel architecture of electro-optical neural networks based on the time-stretch method is proposed and numerically simulated. By stretching time-domain ultrashort pulses, multiplications of large scale weight matrices and vectors can be implemented on light and multiple-layer of feedforward neural network operations can be easily implemented with fiber loops. Via simulation, the performance of a three-layer electro-optical neural network is tested by the handwriting digit recognition task and the accuracy reaches 88% under considerable noise.
Community-oriented attributed network embedding

Network embedding aims to map vertices in a complex network into a continuous low-dimensional vector space. Meanwhile, the original network structure and inherent properties must be preserved. Most of the existing methods merely focus on preserving local structural features of vertices, whereas they largely ignore the community patterns and rich attribute information. For example, the title of papers in an academic citation network could imply their research directions, which are potentially valuable in seeking more meaningful representations of these papers. In this paper, we propose a Community-oriented Attributed Network Embedding (COANE) framework, which can smoothly incorporate the community information and text contents of vertices into network embedding. We design a margin-based random walk procedure on the network coupled with flexible margins among communities, which limit the scope of random walks. Inspired by the analogy between vertex sequences and documents, the statistical topic model is adopted to extract community features in the network. Furthermore, COANE integrates textual semantics into representations through the topic model while preserving their structural correlations. Experiments on real-world networks indicate that our proposed method outperforms six state-of-the-art network embedding approaches on network visualization, vertex classification and link prediction.
Classification of 3D Terracotta Warrior Fragments Based on Deep Learning and Template Guidance

The Terracotta Warriors are terracotta sculptures created for China's first emperor more than 2,000 years ago.They are among the most precious unearthed cultural relics of China.However, these relics have been predominantly found in fragments.Fragment classification is currently performed manually on enormous quantities of fragments, which is a time-consuming, inaccurate, and subjective task for archaeologists and conservators.In this study, an automatic method based on a deep learning network combined with template guidance is proposed to classify 3D fragments of the Terracotta Warriors.The fragments are initially classified using PointNet.Then, misclassified fragments are secondly categorized based on their best match to a complete Terracotta Warrior model.Extensive experiments were performed to verify the effectiveness of the proposed method.The promising results demonstrate that the method is the most accurate technique for classifying 3D Terracotta Warrior fragments to date.Moreover, the proposed method can significantly increase the efficiency of future fragment reassembly for the Terracotta Warriors.
Modulation Pattern Recognition of Communication Signals Based on Fractional Low-Order Choi-Williams Distribution and Convolutional Neural Network in Impulsive Noise Environment

The majority of the existing automatic modulation recognition methods presume that the received wireless communication signal is corrupted by additive white Gaussian noise. The performance of the modulation recognition algorithms degrades severely under non-Gaussian impulsive noise. Hence, in this paper, we introduce a robust algorithm to identify the modulation type of radio signal contaminated by impulse noise by using convolutional neural network (CNN). The algorithm first employs fractional low-order Choi-Williams distribution (FLO-CWD) to get the time-frequency distribution of impulsive signal, then feature extraction and classification are performed with CNN, which has very high performance in image classification. Three types of digital modulation radio signals are used to validate the method. Simulation results demonstrate the superiority of the proposed method under impulse noise conditions.
Autonomous Car Racing in Simulation Environment Using Deep Reinforcement Learning

Self-Driving Cars are, currently a hot topic throughout the globe thanks to the advancements in Deep Learning techniques on computer vision problems. Since driving simulations are fairly important before real life autonomous implementations, there are multiple driving-racing simulations for testing purposes. The Open Racing Car Simulation (TORCS) is a highly portable open source car racing -self-driving- simulation. While it can be used as a game in which human players compete with scripted agents, TORCS provides observation and action API to develop an artificial intelligence agent. This study explores near-optimal Deep Reinforcement Learning agents for TORCS environment using Soft Actor-Critic and Rainbow DQN algorithms, exploration and generalization techniques.
3D digital breast cancer models with multimodal fusion algorithms

Breast cancer image fusion consists of registering and visualizing different sets of a patient synchronized torso and radiological images into a 3D model. Breast spatial interpretation and visualization by the treating physician can be augmented with a patient-specific digital breast model that integrates radiological images. But the absence of a ground truth for a good correlation between surface and radiological information has impaired the development of potential clinical applications. A new image acquisition protocol was designed to acquire breast Magnetic Resonance Imaging (MRI) and 3D surface scan data with surface markers on the patient's breasts and torso. A patient-specific digital breast model integrating the real breast torso and the tumor location was created and validated with a MRI/3D surface scan fusion algorithm in 16 breast cancer patients. This protocol was used to quantify breast shape differences between different modalities, and to measure the target registration error of several variants of the MRI/3D scan fusion algorithm. The fusion of single breasts without the biomechanical model of pose transformation had acceptable registration errors and accurate tumor locations. The performance of the fusion algorithm was not affected by breast volume. Further research and virtual clinical interfaces could lead to fast integration of this fusion technology into clinical practice.
Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period

To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the phase-plane was divided into statistical regions. Finally, anomaly detection and identification of the raw data were achieved by analyzing the statistical values of the phase-plane trajectory points in each partition region. This method was verified by a simulation test that used the measured data of the satellite momentum wheel rotation. The simulation results showed that the proposed method could achieve the pseudo-period extraction of the measured data and the detection and identification of the anomalous telemetry data.
A Multimodal Target-Source Classifier With Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects

In this study, we focus on multimodal language understanding for fetching instructions in the domestic service robots context. This task consists of predicting a target object, as instructed by the user, given an image and an unstructured sentence, such as “Bring me the yellow box (from the wooden cabinet).” This is challenging because of the ambiguity of natural language, i.e., the relevant information may be missing or there might be several candidates. To solve such a task, we propose the multimodal target-source classifier model with attention branches (MTCMAB), which is an extension of the MTCM. Our methodology uses the attention branch network (ABN) to develop a multimodal attention mechanism based on linguistic and visual inputs. Experimental validation using a standard dataset showed that the MTCM-AB outperformed both state-of-the-art methods and the MTCM. In particular, the MTCM-AB accuracy was 90.1% on average while human performance was 90.3% on the PFN-PIC dataset.
Rotational and Translational Velocity and Acceleration Thresholds for the Onset of Cybersickness in Virtual Reality

Cybersickness causes strong, discomfort and discourages the widespread use of VR system for both recreational and professional use. Visual motion or optic flow is known to be one of the main causes of cybersickness due to the sensory conflict it creates with the vestibular system. Participants were exposed, for few seconds per run, to to a particle field in VR. The field had different motion types, translation forward and lateral and rotation along the roll and yaw axis, different velocity profiles, steady and accelerating, and different densities. Using a staircase procedure, that controlled the speed or acceleration of the field, we detected the threshold at which participant started to feel temporary symptoms of cybersickness. The optic flow in each scene was modeled and by modifying the density of the fields it was ensured that all the scene had the same amount of optic flow to allow a direct comparison of the thresholds. The results showed that optic flow thresholds detected for rotational cases is significantly lower than the one for translation cases. Furthermore, accelerations significantly reduced the detected threshold but only for the translations cases. The thresholds suggestively decreased with the decreasing particle density of the scene. Finally, it was found that all the rotational and translational cases strongly correlate with each other. While the mean values of the thresholds could be used as guidelines to develop VR applications, the high individual variability implies that individual tuning of the motion controls would be more effective at reducing cybersickness while minimizing the impact on the experience of immersion.
Robust Neural Relation Extraction via Multi-Granularity Noises Reduction

Distant supervision is widely used to extract relational facts with automatically labeled datasets to reduce high cost of human annotation. However, current distantly supervised methods suffer from the common problems of word-level and sentence-level noises, which come from a large proportion of irrelevant words in a sentence and inaccurate relation labels for numerous sentences. The problems lead to unacceptable precision in relation extraction and are critical for the success of using distant supervision. In this paper, we propose a novel and robust neural approach to deal with both problems by reducing influences of the multi-granularity noises. Three levels of noises from word, sentence until knowledge type are carefully considered in this work. We first initiate a question-answering based relation extractor (QARE) to remove noisy words in a sentence. Then we use multi-focus multi-instance learning (MMIL) to alleviate the effects of sentence-level noise by utilizing wrongly labeled sentences properly. Finally, to enhance our method against all the noises, we initialize parameters in our method with a priori knowledge learned from the relevant task of entity type classification by transfer learning. Extensive experiments on both existing benchmark and an improved larger dataset demonstrate that our proposed approach remarkably achieves new state-of-the-art performance.
A Comparison of Desktop and Augmented Reality Scenario Based Training Authoring Tools

This work presents a study that explores the differences between authoring Scenario-Based Training (SBT) simulation content using Augmented Reality (AR) and a Desktop interface. Through an iterative design process two interface conditions were developed and then evaluated qualitatively and quantitatively. Our conceptual model is a graph based visualization that is presented to help designers understand the scenario learning artifacts and relationships. Our major contribution relies on the comparison made between the two authoring tools (AR, Desktop) with the same capabilities. Results show that no significant difference was found in time taken to complete tasks nor on the perceived usability of the systems. However, as expected the Desktop interface was perceived as more efficient. Based on these findings, insights on future directions for building AR immersive authoring tools are provided.