Reviewerly/exHarmony huggingface dataset:
OctreeNet: A Novel Sparse 3-D Convolutional Neural Network for Real-Time 3-D Outdoor Scene Analysis

Convolutional neural networks (CNNs) for 3-D data analyses require a large size of memory and fast computation power, making real-time applications difficult.This article proposes a novel OctreeNet (a sparse 3-D CNN) to analyze the sparse 3-D laser scanning data gathered from outdoor environments.It uses a collection of shallow octrees for 3-D scene representation to reduce the memory footprint of 3-D-CNNs and performs point cloud classification on every single octree.Furthermore, the smallest non-trivial and non-overlapped kernel (SNNK) implements convolution directly on the octree structure to reduce dense 3-D convolutions to matrix operations at sparse locations.The proposed neural network implements a depthfirst search algorithm for real-time predictions.A conditional random field model is utilized for learning global semantic relationships and refining point cloud classification results.Two public data sets (Semantic3D.netand Oakland) are selected to test the classification performance in outdoor scenes with different spatial sparsity.The experiments and benchmark test results show that the proposed approach can be effectively used in realtime 3-D laser data analyses.Note to Practitioners-This article was motivated by the limitations of existing deep learning technologies for analyzing 3-D laser scanning data.This technology enables robots to infer what the surroundings are, which is closely linked to semantic mapping and navigation tasks.Previous deep neural networks have seldom been used in robotic systems since they require a large amount of memory and fast computation power to apply dense 3-D operations.This article presents a sparse 3-D-Convolutional neural network (CNN) for real-time point cloud classification by exploiting the sparsity of 3-D data.This framework requires no GPUs.The practicality of the proposed method is verified on data sets gathered from different platforms and sensors.The proposed network can be adopted for other classification tasks with laser sensors.
Matwo-CapsNet: A Multi-label Semantic Segmentation Capsules Network

Despite some design limitations, CNNs have been largely adopted by the computer vision community due to their efficacy and versatility. Introduced by Sabour et al. to circumvent some limitations of CNNs, capsules replace scalars with vectors to encode appearance feature representation, allowing better preservation of spatial relationships between whole objects and its parts. They also introduced the dynamic routing mechanism, which allows to weight the contributions of parts to a whole object differently at each inference step. Recently, Hinton et al. have proposed to solely encode pose information to model such part-whole relationships. Additionally, they used a matrix instead of a vector encoding in the capsules framework. In this work, we introduce several improvements to the capsules framework, allowing it to be applied for multi-label semantic segmentation. More specifically, we combine pose and appearance information encoded as matrices into a new type of capsule, i.e. Matwo-Caps. Additionally, we propose a novel routing mechanism, i.e. Dual Routing, which effectively combines these two kinds of information. We evaluate our resulting Matwo-CapsNet on the JSRT chest X-ray dataset by comparing it to SegCaps, a capsule based network for binary segmentation, as well as to other CNN based state-of-the-art segmentation methods, where we show that our Matwo-CapsNet achieves competitive results, while requiring only a fraction of the parameters of other previously proposed methods.
A Feature‐Based Procedure for Detecting Technical Outliers in Water‐Quality Data From In Situ Sensors

Abstract Outliers due to technical errors in water‐quality data from in situ sensors can reduce data quality and have a direct impact on inference drawn from subsequent data analysis. However, outlier detection through manual monitoring is infeasible given the volume and velocity of data the sensors produce. Here we introduce an automated procedure, named oddwater, that provides early detection of outliers in water‐quality data from in situ sensors caused by technical issues. Our oddwater procedure is used to first identify the data features that differentiate outlying instances from typical behaviors. Then, statistical transformations are applied to make the outlying instances stand out in a transformed data space. Unsupervised outlier scoring techniques are applied to the transformed data space, and an approach based on extreme value theory is used to calculate a threshold for each potential outlier. Using two data sets obtained from in situ sensors in rivers flowing into the Great Barrier Reef lagoon, Australia, we show that oddwater successfully identifies outliers involving abrupt changes in turbidity, conductivity, and river level, including sudden spikes, sudden isolated drops, and level shifts, while maintaining very low false detection rates. We have implemented this oddwater procedure in the open source R package oddwater .
A DNA Computation-Based Image Encryption Scheme for Cloud CCTV Systems

With the rapid development of information technology, huge amounts of images stored in cloud-based CCTV systems.How to protect the transmitted image efficiently and reducing the storage cost for the system become to critical research issues.The concept of DNA computing has been intrigued researchers in recent years.It has many potentials, for example, the high density and low cost storage provided by a double stranded DNA; or a realization of low cost parallel computation to solve non-deterministic polynomial time problem using replication of DNA strands.As a result, a vast number of theoretical studies of the application of DNA computing on information security, which require extensive computation power, has been published.In this paper we study DNA computation based image encryption scheme which applies to cloud CCTV system.In many different image encryption research the artifact hyperchaotic map was used.We proposed an image encryption algorithm based on DNA coding and hyperchaotic map which utilizes the chaotic property of the hyperchaotic map on top of DNA computing.Experiment showed that our algorithm is more secure and more computation efficient than some of the recent similar works.
Measuring User Experience With 3, 5, 7, or 11 Points

To assess versions of the shorter form variant of Usability Metric for User Experience (UMUX-LITE) questionnaire differing in the number of response options for the items (3, 5, 7, and 11).The UMUX-LITE is an efficient (two-item) standardized questionnaire that measures perceived usability. A growing body of evidence shows it closely corresponds to one of the most widely used standardized usability questionnaires, the System Usability Scale (SUS), with regard to both correlation and magnitude of concurrently collected means. Although the "standard" version of the UMUX-LITE uses items with seven response options, there is some variance in practice.Members of a corporate user experience panel (n = 242) completed surveys rating a recent Web site experience with the SUS and UMUX-LITE, also providing ratings of overall experience and likelihood-to-recommend.Scale reliabilities were acceptable (coefficient α >.70) with the exception of UMUX-LITE with three response options. All UMUX-LITE correlations with SUS, overall experience, and likelihood-to-recommend were highly significant. For likelihood-to-recommend, there was a significant difference in the magnitude of correlations, with 11 response options higher than three. Although some statistically significant differences were observed in correspondence between SUS and UMUX-LITE scores, these did not seem to translate to practically significant differences.The number of UMUX-LITE response options does not matter much, especially in practice. Because the version with three response options showed some weakness with regard to reliability and correlation with likelihood-to-recommend, practitioners should avoid it.Unless there is a strong reason to do otherwise, use the "standard" version with seven response options.
Multitemporal Hyperspectral Image Super-Resolution through 3D Generative Adversarial Network

The super-resolution of multitemporal hyperspectral imagery is considered, wherein a 3D generative adversarial network (GAN) is promoted and employed. Firstly, we put the SR process in a generative adversarial network (GAN) framework, so that the resulted high resolution HSI can keep more texture details. Secondly, the input of our method is of full bands due to 3D kernel exploited. Furthermore, a series of spatial-spectral constraints or loss functions are imposed to guide the training of our generative network so as to further alleviate spectral distortion and texture blur. The experiments on the houston datasets demonstrate that the proposed GAN-based SR method with the best generalization ability can yield very high quality results.
High Performance Accelerator for CNN Applications

The continuing advancement of the neural networks based techniques led to their exploitation in many applications, such as computer vision and the natural language processing systems where they provide high accuracy results at the cost of their high computational complexity. Hardware implemented AI accelerators provide the needed performance improvement for applications in specific areas, including robotics, autonomous systems and internet of things. The current study presents an FPGA based accelerator for Convolutional Neural Networks (CNN). The CNN model is trained for the MNIST dataset and the VHDL design targets high throughput, low power while using only on chip memory. The architecture uses parallel computations at the convolutional and fully connected layers and it has a highly pipelined output layer. The architecture implementation on a Xilinx Virtex VC707 validates the results.
Liver Segmentation in Magnetic Resonance Imaging via Mean Shape Fitting with Fully Convolutional Neural Networks

In this work, we propose a novel learning-based segmentation technique for delineating liver volumes in magnetic resonance images. The method utilizes the shape prior of the liver for improved accuracy. Instead of labeling the tissue via binary classification, our method completes the segmentation by deforming a label template of the liver average shape based on the learned image features. The average shape of the liver we used is estimated from a large set of expert-labeled computed tomography images. A fully convolutional neural network (FCN) is trained to maximize the overlap between the deformed liver label template and the ground truth segmentation. The proposed method is validated with 51 T2-weighted liver image volumes and achieves an average Dice coefficient of 95.2% with a mean Hausdorff distance of 20.0 mm. Compared to the results obtained with a standard FCN-based method, a three-fold improvement of the Hausdorff distance is observed, indicating the substantial gains achieved by incorporating the shape prior.
Strengthening Password Authentication using Keystroke Dynamics and Smartphone Sensors

Presently, a password authentication is a weak point for security in the authentication scheme because a password is easy to be stolen and a user may ignore the security by using a simple password, which is easy to remember or using the same password for all accounts. From the related works, basic keystroke dynamics features, i.e. key hold time, latency time, and interkey time, were studied on a smartphone. The results showed the weak aspect to use only basic keystroke dynamics for authentication on the phone.
Detecting usability problems in mobile applications on the basis of dissimilarity in user behavior

Usability is one of the critical success factors for mobile applications. However, usability is not easy to improve. Many usability testing studies have been conducted to detect usability problems easily, however they usually carry a substantial cost and require specific expertise. In this paper, we present methods and tools to detect usability problems effectively via usability testing in mobile application. We expect users to interact with mobile applications in dissimilar ways when encountering usability problems. Based on this approach, we propose tracing, modeling and comparing methods for measuring the similarity of user behaviors. We evaluate the similarity of user behavior and use it in detecting problems. In addition, we developed tools for our methods to run in automatic way to save usability testing costs. An experiment was conducted using two mobile applications to confirm that our proposed method is useful. We tested eight tasks and found lower similarity when users encountered a usability problem. The experimental results show that our methods and tools can help find usability problems in mobile applications.
PSO-based Dynamic Distributed Algorithm for Automatic Task Clustering in a Robotic Swarm

The Multi-Robot Task Allocation (MRTA) problem has recently become a key research topic. Task allocation is the problem of mapping tasks to robots, such that the most appropriate robot is selected to perform the most fitting task, leading to all tasks being optimally accomplished. Expanding the number of tasks and robots may cause the collaboration among the robots to become tougher. Since this process requires high computational time, this paper describes a technique that reduces the size of the explored state space, by partitioning the tasks into clusters. In real-world problems, the absence of information regarding the number of clusters is ordinarily occurring. Hence, a dynamic clustering is auspicious for partitioning the tasks to an appropriate number of clusters. In this paper, we address the problem of MRTA by putting forward a new simple, automatic and efficient clustering algorithm of the robots’ tasks based on a dynamic distributed particle swarm optimization, namely, ACD2PSO. Our approach is made out of two stages: stage I groups the tasks into clusters using the dynamic distributed particle swarm optimization (D2PSO) algorithm and stage II allocates the robots to the clusters. The assignment of robots to the clusters is represented as multiple traveling salesman problems (MTSP). Computational experiments were carried out to prove the effectiveness of our approach in term of clustering time, cost, and the MRTA time, compared to the distributed particle swarm optimization (dPSO) and genetic algorithm (GA). Thanks to the D2PSO algorithm, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing the fast convergence of PSO.
Solving LPN Using Covering Codes

Abstract We present a new algorithm for solving the LPN problem. The algorithm has a similar form as some previous methods, but includes a new key step that makes use of approximations of random words to a nearest codeword in a linear code. It outperforms previous methods for many parameter choices. In particular, we can now solve the $$(512,\frac{1}{8})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mn>512</mml:mn><mml:mo>,</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>8</mml:mn></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math> LPN instance with complexity less than $$2^{80}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mn>2</mml:mn><mml:mn>80</mml:mn></mml:msup></mml:math> operations in expectation, indicating that cryptographic schemes like HB variants and LPN-C should increase their parameter size for 80-bit security.
Design a Data Warehouse Schema from Document-Oriented database

Abstract Traditional data warehouses are unable to meet the growing needs of the modern enterprise to integrate and analyze a wide variety of data generated by social, mobile and sensor sources. What is remarkable is that many companies have changed their data storage in NoSQL databases. Given the important role played by this type of databases, it is necessary to study NoSQL databases as a data source for the modeling and the implementation of data warehouses. This paper proposes a method to design the data warehouse schema from schema free databases known as NoSQL databases. Our proposal starts with the extraction of schemes from document-oriented databases as an example of NoSQL database. This extraction is performed based on the MapReduce paradigm. Then, it defines the structure identification graph from the extracted schemes. This graph will help the designer to identify the multidimensional concepts for each schema extracted in order to design the global data warehouse schema.
Fault and Noise Tolerance in the Incremental Extreme Learning Machine

The extreme learning machine (ELM) is an efficient way to build single-hidden-layer feedforward networks (SLFNs).However, its fault tolerant ability is very weak.When node noise or node failure exist in a network trained by the ELM concept, the performance of the network is greatly degraded if a countermeasure is not taken.However, this kind of countermeasure for the ELM or incremental learning is seldom reported.This paper considers the situation that a trained SLFN suffers from the coexistence of node fault and node noise.We develop two fault tolerant incremental ELM algorithms for the regression problem, namely node fault tolerant incremental ELM (NFTI-ELM) and node fault tolerant convex incremental ELM (NFTCI-ELM).The NFTI-ELM determines the output weight of the newly inserted node only.We prove that in terms of the training set mean squared error (MSE) of faulty SLFNs, the NFTI-ELM converges.Our numerical results show that the NFTI-ELM is superior to the conventional ELM and incremental ELM algorithms under faulty situations.To further improve the performance, we propose the NFTCI-ELM algorithm.It not only determines the output weight of the newly inserted node, but also updates all previously trained output weights.In terms of training set MSE of faulty SLFNs, the NFTCI-ELM converges, and it is superior to the NFTI-ELM.
Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors

We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset. A comparative study between the proposed method and four state-of-the art preprocessing algorithm attests that the proposed approach could yield a competitive performance for magnetic resonance brain glioblastomas tumor preprocessing. In fact, the result of this step of image preprocessing is very crucial for the efficiency of the remaining brain image processing steps: i.e., segmentation, classification, and reconstruction.
An Enhanced Feature Pyramid Object Detection Network for Autonomous Driving

Feature Pyramid Network (FPN) builds a high-level semantic feature pyramid and detects objects of different scales in corresponding pyramid levels. Usually, features within the same pyramid levels have the same weight for subsequent object detection, which ignores the feature requirements of different scale objects. As we know, for most detection networks, it is hard to detect small objects and occluded objects because there is little information to exploit. To solve the above problems, we propose an Enhanced Feature Pyramid Object Detection Network (EFPN), which innovatively constructs an enhanced feature extraction subnet and adaptive parallel detection subnet. Enhanced feature extraction subnet introduces Feature Weight Module (FWM) to enhance pyramid features by weighting the fusion feature map. Adaptive parallel detection subnet introduces Adaptive Context Expansion (ACE) and Parallel Detection Branch (PDB). ACE aims to generate the features of adaptively enlarged object context region and original region. PDB predicts classification and regression results separately with the two features. Experiments showed that EFPN outperforms FPN in detection accuracy on Pascal VOC and KITTI datasets. Furthermore, the performance of EFPN meets the real-time requirements of autonomous driving systems.
Type, Then Correct: Intelligent Text Correction Techniques for Mobile Text Entry Using Neural Networks

Current text correction processes on mobile touch devices are laborious: users either extensively use backspace, or navigate the cursor to the error position, make a correction, and navigate back, usually by employing multiple taps or drags over small targets. In this paper, we present three novel text correction techniques to improve the correction process: Drag-n-Drop, Drag-n-Throw, and Magic Key. All of the techniques skip error-deletion and cursor-positioning procedures, and instead allow the user to type the correction first, and then apply that correction to a previously committed error. Specifically, Drag-n-Drop allows a user to drag a correction and drop it on the error position. Drag-n-Throw lets a user drag a correction from the keyboard suggestion list and "throw" it to the approximate area of the error text, with a neural network determining the most likely error in that area. Magic Key allows a user to type a correction and tap a designated key to highlight possible error candidates, which are also determined by a neural network. The user can navigate among these candidates by directionally dragging from atop the key, and can apply the correction by simply tapping the key. We evaluated these techniques in both text correction and text composition tasks. Our results show that correction with the new techniques was faster than de facto cursor and backspace-based correction. Our techniques apply to any touch-based text entry method.
Granular structure-based incremental updating for multi-label classification

Incremental learning is an efficient computational paradigm of acquiring approximate knowledge of data in dynamic environment. Most of the research focuses on knowledge updating for single-label classification, whereas incremental mechanism for multi-label classification is of preliminary nature. This leads to considerable computation complexity to maintain desired performance. To address this challenge, we formulate a granular structure system (GSS). The proposed granular structure system in bottom-up way provides a systematic view on label-specific based classification. We demonstrate that the three-way selective ensemble (TSEN) model, a state-of-the-art solution for multi-label classification, is compatible with GSS in granulation. An incremental mechanism of GSS is introduced for both label-specific feature generation and optimization, and an incremental three-way selective ensemble algorithm for multiple instances immigration (IMOTSEN) is presented. Experiments completed on six datasets show that the proposed algorithm can maintain considerable classification performance while significantly accelerating the knowledge (GSS) updating.