Reviewerly/exHarmony huggingface dataset:
Reactive Navigation in Partially Known Non-convex Environments

This paper presents a provably correct method for robot navigation in 2D environments cluttered with familiar but unexpected non-convex, star-shaped obstacles as well as completely unknown, convex obstacles. We presuppose a limited range onboard sensor, capable of recognizing, localizing and (leveraging ideas from constructive solid geometry) generating online from its catalogue of the familiar, non-convex shapes an implicit representation of each one. These representations underlie an online change of coordinates to a completely convex model planning space wherein a previously developed online construction yields a provably correct reactive controller that is pulled back to the physically sensed representation to generate the actual robot commands. We extend the construction to differential drive robots, and suggest the empirical utility of the proposed control architecture using both formal proofs and numerical simulations.
FANCFIS: Fast adaptive neuro-complex fuzzy inference system

Large-scale multivariate time series forecasting is an increasingly important problem, with sensor networks pouring out sampled data at unprecedented rates, trillions of dollar's worth of stock trades made every data, and zettabytes of traffic transmitted over the Internet every year. While they are only examples of the much larger domain of general data streams, the uniformly-sampled time series still remains a very large and important subdomain. Extensive research has shown that machine-learning algorithms can often be very effective forecasting models, but many of these algorithms do not scale well. The Adaptive Neuro-Complex-Fuzzy Inferential System is one such approach; built to leverage complex fuzzy sets, it is both an accurate and parsimonious forecasting algorithm. However, its scaling is poor due to a relatively slow training algorithm (gradient descent hybridized with chaotic simulated annealing). Before the algorithm can be used for large-scale forecasting, a fast training algorithm that preserves the system's accuracy and compactness must be developed. We propose the Fast Adaptive Neuro-Complex Fuzzy Inference System, which is designed for fast training of a compact, accurate forecasting model. We use the Fast Fourier Transform algorithm to identify the dominant frequencies in a time series, and then create complex fuzzy sets to match them as the antecedents of complex fuzzy rules. Consequent linear functions are then learned via recursive least-squares. We evaluate this algorithm on both univariate and multivariate time series, finding that this incremental-learning algorithm is as accurate and compact as its slower predecessor, and can be trained much more quickly.
Optimization for Deep Convolutional Neural Networks: How Slim Can It Go?

The deep convolutional neural networks (CNN) have a vast amount of parameters, especially in the fully connected (FC) layers, which have become a bottleneck for real-time applications where processing latency is high due to computational cost. In this paper, we propose to optimize the FC layers in CNN via making it much slimmer. We make analysis of the statistical distribution of the weights in FC layer, and observe each column follows Gaussian distribution. Regression model analysis of the weights of FC layer based on Akaike information criteria and Bayesian information criterion demonstrates that they have Granger causality, which means the columns are correlated and they follow colored Gaussian distribution. Based on this distribution, we derive a CNN design and optimization theorem for FC layers from information theory point of view. The theorem provides two design criteria, rank and singular values. Further, we show that FC layer with weights of colored Gaussian is more efficient than that of white Gaussian. The optimization criteria is singular-values-based, so we apply singular value decomposition to find the maximal singular values and QR to identify the corresponding columns in FC layer. We evaluate our optimization approach to AlexNet and apply the slimmer CNN to ImageNet classification. Simulation results show our approach performs much better than random dropout. Specifically, with only around 28% of weights, the AlexNet could perform as well as the original AlexNet in terms of top one error and top five error.
Fast Laplacian twin support vector machine with active learning for pattern classification

In this paper, we propose a semi-supervised classifier termed as Fast Laplacian Twin Support Vector Machine (FLap−TWSVM) with an objective to reduce the requirement of labeled data and simultaneously lessen the training time complexity of a traditional Laplacian Twin Support Vector Machine semi-supervised classifier. FLap−TWSVM is faster than existing Laplacian twin support vector machine as it solves a smaller size Quadratic Programming Problem (QPP) along with an Unconstrained Minimization Problem (UMP) to obtain decision hyperplanes which can also handle heteroscedastic noise present in the training data. Traditional semi-supervised classifiers generally have no explicit control over the choice of labeled data available for training, hence to overcome this limitation, we propose a pool-based active learning framework which identifies most informative examples to train the learning model. Moreover, the aforementioned framework has been extended to deal with multi-category classification scenarios. Several experiments have been performed on machine learning benchmark datasets which proves the utility of the proposed classifier over traditional Laplacian Twin Support Vector Machine (Lap−TWSVM) and active learning based Support Vector Machine (SVMAL). The efficacy of the proposed framework has also been tested on human activity recognition problem and content based image retrieval system.
Reduced algorithm of laser measurement data for geometric parameters of ring forgings based on Artificial Immune Algorithm

Measuring the geometric parameters of ring forgings by laser scanner is an important way to ensure the quality of processing. However, the amount of data obtained by scanner is huge and the noise is complex. The process of data processing is slow. It is impossible to ensure the real-time quality monitoring. Therefore, an improved Quadratic Error Metric (QEM) point cloud data reduction algorithm based on Artificial Immune Algorithm (AIA) is proposed in this study. As for AIA, the choice of antigen and antibody is the critical step. The candidate point cloud is used as the antibody. The antigen is divided into two parts. The first part is the cost function. The cost function is made up of the squares sum of the volumes for two tetrahedra. This is an improved algorithm of QEM. The second part is the optimal solution of the cost function. The antibodies are screened by using the specific reactions between antigens and antibodies, antibodies and antibodies. The point cloud data can be reduced by these specific reactions. Finally, the algorithm is applied to the experiment of measuring the geometric parameters of typical ring forging. The experimental results show that the reduced data can quickly reconstruct the model. The precision of the size parameters for the model can be ensured through the algorithm proposed in the study. Through the comparison experiments, the advantages of the reduced method of the algorithm are further proved. Meanwhile, the real-time performance of the on-line monitoring of the quality for the ring forging can be improved. The measuring method proposed is feasible according to the experimental results.
Autonomous robot photographer with KL divergence optimization of image composition and human facial direction

In this paper, we propose a robot photography system that can autonomously search for the optimal target viewpoint. Two technical issues of scene composition evaluation and viewpoint selection are solved by this system. A composition evaluation method for photos is developed using well-known composition rules based on Kullback–Leibler (KL) divergence, considering the directional information of each target. To reduce the calculation cost of the composition evaluation in the case where the number of targets is large, automatic target grouping is conducted via variational Bayes. The optimal viewpoint with respect to the composition is selected from a number of candidate viewpoints around the targets based on KL divergence. Finally, the fact that better composed photos can be autonomously photographed by the proposed system is validated via experiments and human evaluations.
Self Recurrent Neural Network Based Target Tracking in Wireless Sensor Network using State Observer

Background &amp; Objective: Mobile target tracking based on data from wireless sensor networks (WSN) is a hot topic that has been investigated both from a theoretical and practical point of view in the literature. Tracking the position and velocity of a target moving in WSN (especially in the context of uncertain noisy measurement channel) is a very challenging task. To deal with the uncertainty in system dynamics as well as uncertainty in target states, an Observer Based Self Recurrent Neural Network (OBSRNN) is proposed in this paper. Methods: The proposed algorithm employs a state observer based tracking control strategy and thereby allows for accurate estimation of mobile target moving along a predefined route in WSN. The Self Recurrent Neural Network (SRNN) framework is used to approximate the uncertainty in the system dynamics, while a full-order state observer is used to estimate the unknown state vector. Conclusion: The simulation analysis is performed to evaluate the efficacy of the proposed work.
Neuroevolution of a Modular Memory-Augmented Neural Network for Deep Memory Problems

We present Modular Memory Units (MMUs), a new class of memory-augmented neural network. MMU builds on the gated neural architecture of Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTMs), to incorporate an external memory block, similar to a Neural Turing Machine (NTM). MMU interacts with the memory block using independent read and write gates that serve to decouple the memory from the central feedforward operation. This allows for regimented memory access and update, giving our network the ability to choose when to read from memory, update it, or simply ignore it. This capacity to act in detachment allows the network to shield the memory from noise and other distractions, while simultaneously using it to effectively retain and propagate information over an extended period of time. We train MMU using both neuroevolution and gradient descent, and perform experiments on two deep memory benchmarks. Results demonstrate that MMU performs significantly faster and more accurately than traditional LSTM-based methods, and is robust to dramatic increases in the sequence depth of these memory benchmarks.
Group selfies and Snapchat: From sociality to synthetic collectivisation

This paper sets out a multimodal framework that can be used to explore the ways in which people are positioned as individuals and groups within the selfies and other kinds of representations found in video-sharing practices. Unlike earlier, monomodal accounts of selfies, the framework accounts for the visual, aural and verbal resources that are used in video-sharing. The analysis focuses on the videos and photos that are produced and consumed in the Featured Stories of Snapchat as collective accounts of public events of different kinds. The results show that, in Snapchat, constructing group identities is prominent, both in selfies and quasi-selfies. This reflects a discourse of 'us-ness' current in many forms of social media, and which prizes particular forms of sociality. The uses of this discourse are ideologically charged, and include the strategic use of synthetic collectivisation as an emerging form of audio-visual communication which contrives to position the individual member of the audience as if they were part of a larger group, sharing the same experience and perspective as the person creating the video. The framework is used in this paper to examine the ways in which the collective identities of fans, mourners and protestors are constructed in Featured Stories from Snapchat, but is of relevance to many other forms of multimodal communication that are shared through social media sites and services.
A Filtered Sun Sensor for Solar Tracking in HCPV and CSP Systems

Tracking systems with stringent performance are required to take advantage of solar energy produced through the use of high concentration photovolatics and concentrating solar power technologies. Closed-loop tracking systems use measurements provided by a sun sensor, which picks up direct and diffuse sunlight. High levels of diffuse sunlight produce increased levels of measurement noise at the output of the sensor, which in turn limits the maximal achievable gains in the closed-loop controller and consequently the tracking performance deteriorates. The goal of this paper is to present the advantages of equipping a four-quadrant photodiode sun sensor with infrared and linear polarizing optical filters. Experimental results show that these filters reduce the effect of diffuse radiation and allow increasing the performance of sun tracking systems regardless of the type of controller used in the solar tracker. Tracking experiments are presented under indoor and outdoor conditions employing algorithms currently used in solar trackers, including proportional-integral (PI), and proportional-integral-derivative (PID) controllers, and a recently proposed cascade control law. In all the cases, the best performance is obtained with the sun sensor equipped with an infrared filter. Experiments show that an infrared optical filter reduces the filtered mean squared tracking error up to 75% under artificial light conditions and 90% under direct sunlight.
Absolute flat test using rotated and multi-shifted maps with relative tilt measurement

In classical three-flat test, error in reference surface can not be identified fully. What we obtain is only the absolute radial profile instead. In addition, at least three flats with similarly high accuracy are required. The test procedures are very complex and it takes a long time and requires high precision adjustment. In this paper, we proposed a new method for absolute test of flatness. There are only two flats used in our method and it does not need precise alignment of the specimens. The method requires multiple translations of the test surface and one 90° rotation step. The relative tilt during translation is measured by a compact wavefront interferometer. We analyze the effect of noise, tilt estimation error and rotation error on reconstruction result. The method is experimentally verified and compared with the direct interferometric result using high precision reference surface.
Rapid Integration of CPS Security and Safety

The security and safety of cyber-physical systems (CPSs) often influence each other. Ensuring that this does not have negative implications might require a large and rigorous effort during the development of CPS. However, early in the lifecycle, quick feedback can be valuable helping security and safety engineers to understand how seemingly trivial design choices in their domain may have unacceptable implications in the other. We propose the cyber risk assessment framework (CRAF) for this purpose. The CRAF is based on openly available and widely used taxonomies from the safety and security domains, and a unique mapping of where loss of data security may impact aspects of data with safety implications. This letter represents the first time these different elements have been brought together into a single framework with an associated process. Through examples from within our organizations we show how this framework can be put to good use.
Precision Position Measurement of Linear Motors Mover Based on Temporal Image Correlation

High-precision position measurement is very important for precision motion control of a linear motor. This paper presents an accurate mover position detection method with high precision stability based on temporal image correlation and fence image for the linear motors. First, sequence fence images with the information of mover position are recorded by a position measurement system for linear motor, and the integer pixel displacement of each image is obtained by image correlation method. Then, according to the continuity of the motion, the subpixel displacement can be extracted by fitting the integer pixel displacement. Finally, the displacement and velocity curves can be calculated according to system calibration. Compared to digital image measurement used in position detection of linear motors, the proposed method can detect mover position accurately with longer measurement range and smaller uncertainty. The moving least square (MLS) algorithm is used to ensure the stability of measurement uncertainty under different motion modes. Simulations and mover position detection experiments with different motion conditions were performed to demonstrate the effectiveness of the proposed method in linear motors. In this paper, the average measurement error of displacement is 0.006 mm with the maximum displacement of 120 mm. The maximum and average relative errors are less than 0.06% and 0.017%, respectively.
StreamSW: A density-based approach for clustering data streams over sliding windows

Density-based clustering is a prominent and an essential technique in mining data streams. It can discover clusters of arbitrary or irregular shape and handle noisy data. This paper presents StreamSW, a new density-based approach for clustering streaming data over a sliding window (SW). A Sliding window is an extensively adopted window model for capturing and mining streaming data. The StreamSW approach adopts a two-phase framework for performing clustering on streaming data. In the online phase, the p-micro-clusters and grid structure are adopted to hold a synopsis of the streaming data. In the offline phase, final macro-clusters are created from the p-micro-clusters of online phase using the conventional DBSCAN algorithm. The StreamSW approach discovers the clusters with an irregular shape in limited memory and time. Experimental results exhibit that the speed and quality of StreamSW are better than the current approaches.
Image restoration via DOST and total variation regularisation

In this study, the author proposes a total variational (TV) driven image restoration usingdiscrete orthogonal Stockwell transform (DOST) when the noise (in the image) isan outcome of a Poisson process. Stockwell transform or S-transform (ST) is wellknown for its efficiency in resolving spatio-frequency components with highaccuracy compared with many other transforms such as short-term Fouriertransform, wavelet transform etc. This property of ST makes it more suitable formany image processing applications such as image restoration and imageinpainting. By deriving the objective function and constraints of theoptimisation problem (image restoration problem) based on the ST coefficients,the model becomes more robust in terms of preserving high resolution in thespatio-frequency domain. Images are modelled as an outcome of a Poisson processin many medical and telescopic imaging applications. The Poisson noisecorruption is mainly due to the lack of a sufficient number of photons toreconstruct the data. In this study, corrupted images are restored due to thePoisson process (by which the data is formed) using the DOST under a non-localTV framework. The model is analysed and compared with the state-of-the-artPoisson noise removal methods using visual and statistical measures.
Detection and tracking based tubelet generation for video object detection

Video object detection (VID) is a more challenging task compared with still-image object detection, which not only needs to detect objects accurately per frame but also needs to track objects for a long period of time. In order to detect objects from videos, we propose a Detection And Tracking (DAT) based tubelet generation framework. Under this framework, we first propose a detection-based tubelet generation method which can generate tubelets with more accurate bounding boxes compared with traditional tracking-based methods. On the other hand, the latter can produce a higher recall of bounding boxes than the former in general. To take advantage of their complementary attributes, we further propose a novel tubelet fusion method to combine these multi-modal information (appearance information in independent images and contextual information in videos). Our extensive experiments on the well-known ILSVRC 2016 VID dataset show that our proposed method can achieve state-of-the-art performances.
Generative RNNs for OOV Keyword Search

The modeling of text queries as sequences of embeddings for conducting similarity matching based search within speech features has been recently shown to improve keyword search (KWS) performance, especially for the out-of-vocabulary (OOV) terms. This technique uses a dynamic time warping based search methodology, converting the KWS problem into a pattern search problem by artificially modeling the text queries as pronunciation-based embedding sequences. This query modeling is done by concatenating and repeating frame representations for each phoneme in the keyword's pronunciation. In this letter, we propose a query model that incorporates temporal context information using recurrent neural networks (RNN) trained to generate realistic query representations. With experiments conducted on the IARPA Babel Program's Turkish and Zulu datasets, we show that the proposed RNN-based query generation yields significant improvements over the statistical query models of earlier work, and yields a comparable performance to the state-of-the-art techniques for OOV KWS.
An integrated algorithm for multi-agent fault-tolerant scheduling based on MOEA

With the increasing functionality of multi-agent systems, fault tolerance has become an essential requirement, especially for workflow with interdependencies among tasks. In this paper, we propose a scheme that combines a novel intrinsic-plasticity-inspired rescheduling execution model (IPIREM) with an improved NSGA-III with knee points. The IPIREM provides a relatively stable evaluation without any estimation of the failure distribution when transient failures happen. Based on the evaluation result of IPIREM, the multi-objective optimization problem is solved by use of an improved NSGA-III, which accelerates convergence and promotes diversity. Finally, experimental results show that the proposed algorithm is effective in enhancing efficiency of the workflow.