Reviewerly/exHarmony huggingface dataset:
An Alternative Deep Feature Approach to Line Level Keyword Spotting

Keyword spotting (KWS) is defined as the problem of detecting all instances of a given word, provided by the user either as a query word image (Query-by-Example, QbE) or a query word string (Query-by-String, QbS) in a body of digitized documents. Keyword detection is typically preceded by a preprocessing step where the text is segmented into text lines (line-level KWS). Methods following this paradigm are monopolized by test-time computationally expensive handwritten text recognition (HTR)-based approaches; furthermore, they typically cannot handle image queries (QbE). In this work, we propose a time and storage-efficient, deep feature-based approach that enables both the image and textual search options. Three distinct components, all modeled as neural networks, are combined: normalization, feature extraction and representation of image and textual input into a common space. These components, even if designed on word level image representations, collaborate in order to achieve an efficient line level keyword spotting system. The experimental results indicate that the proposed system is on par with state-of-the-art KWS methods.
Mathematical Variable Detection based on Convolutional Neural Network and Support Vector Machine

Mathematical expression detection in scientific documents is a prerequisite step for developing a mathematical retrieval system that has attracted many researches recently. In the detecting process, a challenging issue is the detection of variable. The similar properties of variable and narrative text cause many errors in the detection in existing approaches. In the paper, pre-trained deep Convolutional Neural Networks (CNN) are employed and optimized to automatically extract visual features of images and Support Vector Machine (SVM) is used to improve the accuracy of the detection. The accuracy of 99.5% is achieved for the detection of variable in inline expressions in document images in public benchmark datasets. The performance comparison with traditional methods demonstrates the effectiveness of the proposed method.
Learning the Orientation of a Loosely-Fixed Wearable IMU Relative to the Body Improves the Recognition Rate of Human Postures and Activities

Features were developed which accounted for the changing orientation of the inertial measurement unit (IMU) relative to the body, and demonstrably improved the performance of models for human activity recognition (HAR). The method is proficient at separating periods of standing and sedentary activity (i.e., sitting and/or lying) using only one IMU, even if it is arbitrarily oriented or subsequently re-oriented relative to the body; since the body is upright during walking, learning the IMU orientation during walking provides a reference orientation against which sitting and/or lying can be inferred. Thus, the two activities can be identified (irrespective of the cohort) by analyzing the magnitude of the angle of shortest rotation which would be required to bring the upright direction into coincidence with the average orientation from the most recent 2.5 s of IMU data. Models for HAR were trained using data obtained from a cohort of 37 older adults (83.9 ± 3.4 years) or 20 younger adults (21.9 ± 1.7 years). Test data were generated from the training data by virtually re-orienting the IMU so that it is representative of carrying the phone in five different orientations (relative to the thigh). The overall performance of the model for HAR was consistent whether the model was trained with the data from the younger cohort, and tested with the data from the older cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.782, 0.793]; total class sensitivity 95% confidence interval [84.9%, 85.6%]), or the reciprocal scenario in which the model was trained with the data from the older cohort, and tested with the data from the younger cohort after it had been virtually re-oriented (Cohen's Kappa 95% confidence interval [0.765, 0.784]; total class sensitivity 95% confidence interval [82.3%, 83.7%]).
Fast Training of Deep LSTM Networks

Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks. However, the LSTM training method, such as backward propagation through time (BPTT), is really slow. In this paper, by separating the LSTM cell into forward and recurrent substructures, we propose a much simpler and faster training method than the BPTT. The deep LSTM is modified by combining the deep RNN with the multilayer perceptron (MLP). The simulation results show that our fast training method for LSTM is better than BPTT for LSTM.
Dynamic social privacy protection based on graph mode partition in complex social network

Differential privacy protection model provides strict and quantitative risk representation for privacy disclosure, which greatly ensures the availability of data. However, most existing methods do not consider the semantic context, so they are vulnerable to attacks based on semantic information. Therefore, dynamic social privacy protection based on graph pattern partitioning is designed to satisfy differential privacy protection. Firstly, the structure of social network is represented as a graph model, and the original graph is classified into several sub-graphs according to the characteristics of nodes. Then, the dense area of each sub-graph is divided by quad-tree method, and the noise of differential privacy protection is added to the leaf nodes of the tree, and the graph publishing is generated by sub-graph reconstruction. Finally, the feasibility and practicability of the model are verified by statistical analysis, such as degree distribution, shortest path, and clustering coefficient. The simulation results show the validity and applicability of the privacy protection method proposed in this paper.
An Efficient Storage of Infrared Video of Drone Inspections via Iterative Aerial Map Construction

In this letter, we present a novel compression algorithm of infrared video sequences captured during drone inspections based on iterative aerial map construction. In our approach, we first apply a stitching algorithm to construct a map of an inspected area assuming that a drone is flying at the same altitude by trajectory close to meander, so that each frame can have a partial overlap with other frame captured much earlier or later. Then, we extract position and rotation angle within the map for each frame and use them as a side information for the video coding. In order to compress an input video sequence, we utilize a multi-view H.265/HEVC with two views. First view is a virtual view generated utilizing the decoded frames of the second view and the side information, whereas the input video is considered as the second view, which is encoded utilizing the virtual view as a reference for the inter-view prediction. The proposed approach has two main benefits. First, the aerial map is generated during decoding utilizing the side information, i.e., the map is not embedded into a bit stream. Second, the inter-view prediction allows to exploit an additional redundancy, which is typical for a drone video. Experimental results show that the proposed algorithm provides 1.4%-2.4% bit rate savings comparing to H.265/HEVC. The maximum possible bit rate savings are estimated from 15.5% to 18.9% assuming that the drone is repeatedly flying many times at exactly the same trajectory.
Parallel conjugate gradient-particle swarm optimization and the parameters design based on the polygonal fuzzy neural network

Simple binary coded genetic algorithm (GA) and particle swarm optimization (PSO) fall easily into local minimums and fail to find the global optimal solution to the algorithm. Thus, the development of a hybrid algorithm between GA and PSO is urgently demanded. In this paper, a three-layer polygonal fuzzy neural network (PFNN) model and its error function are first given by the arithmetic operations of the polygonal fuzzy numbers. Second, the random sequences are constructed by a chaos random generator, these random sequences are used as the initial population of chaos GA and the optimal individuals for sub-populations gained by chaos search are used as the initial population of PSO, and then an new parallel conjugate gradient-particle swarm optimization (PCG-PSO) is designed. Finally, a case study shows the proposed parallel CG-PS algorithm not only avoids dependence of traditional GA on initial values and overcomes the poor global optimization capability of traditional PSO, but also possesses advantages of rapid convergence and high stability.
Parallel Shortest Path Big Data Graph Computations of US Road Network Using Apache Spark: Survey, Architecture, and Evaluation

This chapter reports our continuing work on single source shortest path computations of big data road network graphs using Apache Spark. Smart applications and infrastructures are increasingly relying on graph computations to model real-life problems. Big data is being generated from various sources such as Internet of Things (IoT) and social media. Big data cannot be processed by traditional tools and technologies due to their properties, volume, velocity, veracity, and variety. The problems and relevant data are typically large and, hence, give rise to large graphs, which could be analyzed and solved using big data technologies. We use the US road network data, modelled as graphs, and calculate shortest paths between a set of large numbers of vertices in parallel. The experiments are performed on the Aziz supercomputer. We analyze Spark's parallelization behavior by solving problems of varying graph sizes, i.e., various states of the USA (with over 58 million edges), and varying number of shortest path queries up to one million. We achieve good performance, and as expected, the speedup is dependent on both the size of the data and the number of parallel nodes. The system architecture for graph computing in Spark is explained. A detailed review of the relevant work is provided. We call our system, the Big Data Shortest Path Graph Computing (BDSPG) system.
Are you from North or South India? A hard face-classification task reveals systematic representational differences between humans and machines

We make a rich variety of judgments on faces, but the underlying features are poorly understood. Here we describe a challenging geographical-origin classification problem that elucidates feature representations in both humans and machine algorithms. In Experiment 1, we collected a diverse set of 1,647 faces from India labeled with their fine-grained geographical origin (North vs. South India), characterized the categorization performance of 129 human subjects on these faces, and compared this with the performance of machine vision algorithms. Our main finding is that while many machine algorithms achieved an overall performance comparable to that of humans (64%), their error patterns across faces were qualitatively different despite training. To elucidate the face parts used by humans for classification, we trained linear classifiers on overcomplete sets of features derived from each face part. This revealed mouth shape to be the most discriminative part compared to eyes, nose, or external contour. In Experiment 2, we confirmed that humans relied the most on mouth shape for classification using an additional experiment in which subjects classified faces with occluded parts. In Experiment 3, we compared human performance for briefly viewed faces and for inverted faces. Interestingly, human performance on inverted faces was predicted better by computational models compared to upright faces, suggesting that humans use relatively more generic features on inverted faces. Taken together, our results show that studying hard classification tasks can lead to useful insights into both machine and human vision.
Microscopic Image Analysis and Counting of Red Blood Cells and White Blood Cells in a Urine Sample

Traditional counting of red blood cells (RBC) and white blood cells (WBC) in a urine sample is done manually by a medical technologist. However, this makes the blood cell count subjective to the skill of the laboratory technician and will take much longer time in doing the task. This paper proposes the use of image processing in counting the WBCs and RBCs in a urine sample through use of Canny Edge Detection and Circular Hough Transform algorithm. The process consists of two (2) main parts. First is the Canny Edge Detection and the final part is Circular Hough Transform algorithms. It shows that the proposed system has a percentage accuracy of at least 93.229% in reference to the actual RBC and WBC count result.
Optimal b-symbol constacyclic codes with respect to the Singleton bound

Let [Formula: see text] be the finite field of order [Formula: see text], where [Formula: see text] is a power of odd prime [Formula: see text]. Assume that [Formula: see text], [Formula: see text] are nonzero elements of the finite field [Formula: see text] such that [Formula: see text]. In this paper, we determine the [Formula: see text]-distance of [Formula: see text]-constacyclic codes with generator polynomials [Formula: see text] of length [Formula: see text], where [Formula: see text] and [Formula: see text]. As an application, all maximum distance separable (MDS) [Formula: see text]-symbol constacyclic codes of length [Formula: see text] over [Formula: see text] are established. Among other results, we construct several classes of new MDS symbol-pair codes with minimum symbol-pair distance six or seven by using repeated-root cyclic codes of length [Formula: see text] and [Formula: see text], respectively, where [Formula: see text] is an odd prime.
Compressed range minimum queries

Given a string S of n integers in [0,σ), a range minimum query RMQ(i,j) asks for the index of the smallest integer in S[i…j]. It is well known that the problem can be solved with a succinct data structure of size 2n+o(n) and constant query-time. In this paper we show how to preprocess S into a compressed representation that allows fast range minimum queries. This allows for sublinear size data structures with logarithmic query time. The most natural approach is to use string compression and construct a data structure for answering range minimum queries directly on the compressed string. We investigate this approach in the context of grammar compression. We then consider an alternative approach. Instead of compressing S using string compression, we compress the Cartesian tree of S using tree compression. We show that this approach can be exponentially better than the former, is never worse by more than an O(σ) factor (i.e. for constant alphabets it is never asymptotically worse), and can in fact be worse by an Ω(σ) factor.
Data fusion of Lidar and Thermal Camera for Autonomous driving

The adverse environmental conditions build a bottleneck for the autonomous driving. This challenge is resolved by data fusion of sensor modalities. Here, thermal and Lidar data are fused together for the precise perception of environment.
Using Design Fiction to Explore the Ethics of VR ‘In the Wild’

In this half-day workshop, we will explore the ethics of Virtual Reality (VR) through conversations framed around design fictions. Affordable head-mounted displays (HMDs) and accessible VR content are now within reach of large audiences, yet many of VR's most urgent challenges remain under-explored. In addition to the many known unknowns (e.g. how do we manage sensory conflicts and spatial limitations in VR?), there are many more unknown unknowns (e.g. what kinds of psychological, social and cultural impact will VR provoke?). By bringing together diverse scenarios from workshop participants, and bespoke design fictions created specifically to explore the ethics of VR, we will facilitate a rich discussion that will inform the development of three high-fidelity design fictions that will be used to explore the ethics of VR in future workshops, including one in Bristol, UK in November 2019, part of the Virtual Realities Immersive Documentary Encounters project.
Kinematics analysis and self - collision detection of Truss type multi-robot cooperative welding platform

A collaborative welding platform consists of a XYZ-mobile truss, a 3R (revolute joint) clamping robot and a dual-arm welding robot. Based on Denavit-Hartenberg (D-H) method, the analytical inverse kinematics solution of the platform is obtained. Dual-arm coordination is a technical difficulty in robot control field. Focusing on self-collision problem during dual-arm robot motion planning, this paper proposes a new obstacle avoidance algorithm based on Rapidly Exploring Random Tree (RRT) algorithm. Setting searching area founded on artificial potential field (APF) algorithm and adopting note-pruning strategy achieves high searching efficiency. Simulation results verify the correctness of kinematics analysis and self-collision detection algorithm.
A Secure Image Steganography Using Advanced Encryption Standard and Discrete Cosine Transform

Cryptography and Steganography are two prominent techniques to obtain secure communication over the shared media like the Internet.Steganography is slightly ahead of cryptography because of its stealthy characteristics.In this paper, a new method has been proposed which combines cryptography and steganography to ensure even more secure communication.The Advanced Encryption Standard (AES) in spatial domain of the carrier/cover image and Least Significant Bit (LSB) replacement in the transformed domain of the same image has been used after performing a Discrete Cosine Transform (DCT) on the pixels.An additional layer of security has been introduced by applying XOR operation on the AES encrypted message with the pixel values of the carrier image.The Peak Signal to Noise Ratio (PSNR) of the proposed algorithm is better than most of the similar algorithms.With better PSNR, the proposed method depicts a three layer of security of the information and error free decryption.
A novel task scheduling algorithm integrated with priority and greedy strategy in cloud computing

To deal with the problem of unbalanced load and ignoring task priority in previous task scheduling algorithms, this paper proposes a task scheduling algorithm named P-Min-Min-Max, which combines with priority and greedy strategy. Under the condition of considering the task priority, the algorithm l everages the execution time of tasks for greedy strategy, and binding large task and small task in the task list to form “task pair” to perform scheduling, so as to effectively solve the problem of unbalanced load. To reduce the average response time of tasks, the algorithm priority schedules the small task from the “task pair”. The experimental results show that, compared with the Min-Min and P-Min-Min algorithm, the proposed algorithm improves the system resource utilization and service quality of user, and saves the total execution time for tasks. Compared with Max-Min and P-Max-Min algorithm, the proposed algorithm improves the system resource utilization and service quality of user, and reduces the total completion time and average response time.
An Automatic Detection Scheme of Acute Stanford Type A Aortic Dissection Based on DCNNs in CTA Images

As a severely life-threatening disease, acute Stanford type A aortic dissection brings patients with high potential of death. Computed Tomography Angiography (CTA) images become the effective diagnostic basis of aortic dissection. In this paper, we propose a scheme for automatic patient-level and site-level detection of aortic dissection based on Deep Convolutional Neural Networks (DCNNs) in order to relieve the burden on doctors and diagnose accurately and timely. Firstly, Mask R-CNN is adopted to precisely detect and segment the aorta from the original CTA images. Taking advantage of spatial continuity among CTA images, we put forward an area-wise screening method to remove a handful of unsatisfactory segmentation results. Then we process the segmented aorta images to extract the edges by Canny edge detector. Finally, two ResNets are employed in processed aorta images for aortic dissection detection. Specially, we can detect not only the ascending and descending aorta, but also the aortic arch, which may be the site of onset. We tested on over 3500 images, achieving an accuracy of 98.66% in slice-level detection, 96.51% in patient-level detection and 91.47% in site-level detection.
Manipulating Nonclassicality via Quantum State Engineering Processes: Vacuum Filtration and Single Photon Addition

The effect of two quantum state engineering processes that can be used to burn hole at vacuum in the photon number distribution of quantum states of radiation field are compared using various witnesses of lower- and higher-order nonclassicality as well as a measure of nonclassicality. Specifically, the witnesses of nonclassical properties due to the effect of vacuum state filtration and a single photon addition on an even coherent state, binomial state and Kerr state are investigated using the criteria of lower- and higher-order antibunching, squeezing and sub-Poissonian photon statistics. Further, the amount of nonclassicality present in these engineered quantum states is quantified and analyzed by using an entanglement potential based on linear entropy. It is observed that all the quantum states studied here are highly nonclassical, and on many occasions the hole burning processes are found to introduce/enhance nonclassical features. However, it is not true in general. The investigation has further revealed that despite the fact that a hole at vacuum implies a maximally nonclassical state (as far as Lee's nonclassical depth is used as the quantitative measure of nonclassicality). However, any particular process of hole burning at vacuum does not ensure the existence of a particular nonclassical feature. Specifically,lower- and higher-order squeezing are not observed for photon added even coherent state and vacuum filtered even coherent state.
Estimating Predictive Rate–Distortion Curves via Neural Variational Inference

The Predictive Rate–Distortion curve quantifies the trade-off between compressing information about the past of a stochastic process and predicting its future accurately. Existing estimation methods for this curve work by clustering finite sequences of observations or by utilizing analytically known causal states. Neither type of approach scales to processes such as natural languages, which have large alphabets and long dependencies, and where the causal states are not known analytically. We describe Neural Predictive Rate–Distortion (NPRD), an estimation method that scales to such processes, leveraging the universal approximation capabilities of neural networks. Taking only time series data as input, the method computes a variational bound on the Predictive Rate–Distortion curve. We validate the method on processes where Predictive Rate–Distortion is analytically known. As an application, we provide bounds on the Predictive Rate–Distortion of natural language, improving on bounds provided by clustering sequences. Based on the results, we argue that the Predictive Rate–Distortion curve is more useful than the usual notion of statistical complexity for characterizing highly complex processes such as natural language.