Reviewerly/exHarmony huggingface dataset:
Generic Adaptive Monitoring Based on Executed Architecture Runtime Model Queries and Events

Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant.
Characterization method of mechanical properties of rubber materials based on in-situ stereo finite-element-model updating

The mechanical properties of rubber materials changes with the service time and the material parameters have a corresponding relationship with the aging time. In this paper, an in-situ non-destructive characterization technique of structural material parameters evaluation was developed, and the mechanical parameters for the silicone rubber during accelerated aging were measured by the developed method. This work combined the local indentation method to develop an in-situ stereo Finite element model updating method based on optical full-field deformation measurement, which realized constitutive parameters inversion of rubber. By comparing the inversion results with the test results, the validity and accuracy of the developed method were proved. Then, this method was applied to determine changes in the mechanical parameters during rubber aging. The proposed method is potential to predict the remaining lifetime of rubber material by using the parameter-aging time curve.
GANmera: Reproducing Aesthetically Pleasing Photographs Using Deep Adversarial Networks

Generative adversarial networks (GANs) have become increasingly popular in recent years owing to its ability to synthesize and transfer. The image enhancement task can also be modeled as an image-to-image translation problem. In this paper, we propose GANmera, a deep adversarial network which is capable of performing aesthetically-driven enhancement of photographs. The network adopts a 2-way GAN architecture and is semi-supervised with aesthetic-based binary labels (good and bad). The network is trained with unpaired image sets, hence eliminating the need for strongly supervised before-after pairs. Using CycleGAN as the base architecture, several fine-grained modifications are made to the loss functions, activation functions and resizing schemes, to achieve improved stability in the generator. Two training strategies are devised to produce results with varying aesthetic output. Quantitative evaluation on the recent benchmark MIT-Adobe-5K dataset demonstrate the capability of our method in achieving state-of-the-art PSNR results. We also show qualitatively that the proposed approach produces aesthetically-pleasing images. This work is a shortlisted submission to the CVPR 2019 NTIRE Image Enhancement Challenge.
Dependencies of elements recognized as critical infrastructure of the state

Critical infrastructure and the network of its interconnections have a complicated and not fully recognized character. The analysis of the literature on the subject allows to register the fact that individual authors perceive the multidimensionality of the dependence, which means the relationship between elements defined as critical infrastructure. The dependencies cause that elements of critical infrastructure have the so-called "double dimension". On the one hand, they determine the efficiency of the functioning of critical infrastructures, on the other hand they increase their susceptibility to all kinds of disruptions. In practice, this means that any disturbances in the functioning of one of the critical infrastructures negatively affect other dependents.
Towards Scalable Spectral Sparsification of Directed Graphs

Recent spectral graph sparsification research allows constructing nearly-linear-sized subgraphs that can well preserve the spectral (structural) properties of the original graph, such as the the first few eigenvalues and eigenvectors of the graph Laplacian, leading to the development of a variety of nearly-linear time numerical and graph algorithms. However, there is not a unified approach that allows for truly-scalable spectral sparsification of both directed and undirected graphs. For the first time, we prove the existence of linear-sized spectral sparsifiers for general directed graphs, and introduce a practically-efficient yet unified spectral graph sparsification approach that allows sparsifying real-world, large-scale directed and undirected graphs with guaranteed preservation of the original graph spectra. By exploiting a highly-scalable (nearly-linear complexity) spectral matrix perturbation analysis framework for constructing nearly-linear sized (directed) subgraphs, it enables to well preserve the key eigenvalues and eigenvectors of the original (directed) graph Laplacians.
BFA fuzzy logic based control allocation for fault-tolerant control of multirotor UAVs

ABSTRACT This paper deals with the problem of fault-tolerant control (FTC) for redundant multirotor unmanned aerial vehicles (UAVs) subject to actuators failures. A fuzzy logic approach is used to solve the constrained control allocation problem by adjusting the components of the multiplexing vector once a motor failure is detected. This fuzzy logic allocation problem is tuned using the Bacterial Foraging Algorithm (BFA), a powerful bio-inspired optimisation technique. The effectiveness of this approach is illustrated through real experimental application to a hexarotor UAV, where up to two motors failures are considered.
Research on Data Mining Technology based on Association Rules Algorithm

The current data mining technology has the mismatch problem of mining keywords and used words, which leads to low mining precision and low efficiency. In this paper, a new data mining algorithm based on association rule algorithm is proposed. the clustering analysis of new mining results are carried out by K-means clustering algorithm, and according to the descending order, the relevance is ordered, to output mining results. The experimental results show that the algorithm is accurate and efficient.
Classifying Pedagogical Material to Improve Adoption of Parallel and Distributed Computing Topics

The NSF/IEEE-TCPP Parallel and Distributed Computing curriculum guidelines released in 2012 (PDC12) is an effort to bring more parallel computing education to early computer science courses. It has been moderately successful, with the inclusion of some PDC topics in the ACM/IEEE Computer Science curriculum guidelines in 2013 (CS13) and some coverage of topics in early CS courses in some universities in the U.S. and around the world. A reason often cited for the lack of a broader adoption is the difficulty for instructors who are not already knowledgable in PDC topics to learn how to teach those topics and align their learning objectives with early CS courses. There have been attempts at bringing textbook chapters, lecture slides, assignments, and demos to the hands of the instructors of early CS classes. However, the effort required to plow through all the available materials and figure out what is relevant to a particular class is daunting. This paper argues that classifying pedagogical materials against the CS13 guidelines and the PDC12 guidelines can provide the means necessary to reduce the burden of adoption for instructors. In this paper, we present CAR-CS, a system that can be used to categorize pedagogical materials according to well-known and established curricular guidelines and show that CAR-CS can be leveraged 1) by PDC experts to identify topics for which pedagogical material does not exist and that should be developed, 2) by instructors of early CS courses to find materials that are similar to the one that they use but that also cover PDC topics, 3) by instructors to check the topics that a course currently covers and those it does not cover.
An Algorithm of Bidirectional RNN for Offline Handwritten Chinese Text Recognition

Handwritten Chinese text recognition characters is a challenging problem as it involves a imbalanced training data, and the samples are very different even in same character. In this paper, we propose a novel algorithm based on the bidirectional Recurrent Neural Network (BiRNN) to recognize the characters in the text regions. We solve the problems with pre-processing and improved CNN network. In addition, we utilize RNN to analyze the correlation between characters. Compared with previous works, the algorithm has three distinctive properties: (1) It can predict characters by context analyzing from forward and backward. (2) It solve the problem of sample imbalance effectively. (3) The convergence rate of training has increased. Moreover, the proposed algorithm has achieved good results in recognition.
Detecting Pedestrians with YOLOv3 and Semantic Segmentation Infusion

Pedestrian detection has been considered essential for many real-world applications. Continuous progress in the detection accuracy tends to be supported by solutions of increasing complexity, which inflicts scaling processing costs. In this paper, we counter that tendency by exploring the fastest generic object detector, YOLOv3, by applying in the learning phase the weak semantic segmentation infusion technique - inspired in the SD-SRCNN - increasing its accuracy without affecting the inference speed. Experiments are run on the Caltech Pedestrian Detection Benchmark, as well as in a real-world video surveillance scenario with the PTI01 Pedestrian Detection Dataset. Our proposed method demonstrates infusion benefits in some scenarios achieving competitive accuracy on Caltech, staying less than 3% behind the best method on the primary metric, while being 5 faster. We verify that, among the compared methods, YOLOv3 seems to be the most feasible one for practical applications. Lastly, we also contribute by providing improved annotations for the PTI01 dataset.
Remote Diagnosis of Architectural Heritage Based on 5W1H Model-Based Metadata in Virtual Reality

We propose a framework based on the 5W1H model-based metadata for remote diagnosis in virtual reality (VR). For this purpose, we suggest unique metadata composed of Point of Interest (POI)-extended anchor (xAnchor)-content for a context-aware service in virtual and augmented reality. We define the attributes of the metadata based on the 5W1H context for information retrieval according to the context in a remote diagnosis. Second, we propose the ontology-based linker metadata that express the relations between AR scenes and that retrieve external information. Moreover, we suggest heritage building information metadata for information retrieval according to context. For evaluation, we created a geo-tagged content tool and a remote diagnosis VR application. We conducted focus-group interviews and heuristic evaluations for remote diagnosis in VR to verify the methodology of this study. As a result, we found that experts were most satisfied with the functions that provide the contextualized information. This study contributes to the geospatial metadata for a context-aware service in VR/AR as well as the remote diagnosis framework to overcome the time-consuming problem of the existing remote diagnosis.
Informatization Teaching of Optional Courses of Physical Education in Colleges and Universities

Currently, physical education in colleges and universities mostly adopts optional courses, including theoretical and practical courses. Optional courses in colleges and universities are one of the great achievements of physical education reform in colleges and universities, but in the course of implementation, the disadvantages have been exposed day by day. As a first-line physical education teacher, how to apply modern information technology to the theoretical and practical courses of physical options, and which links can be applied, is obviously a problem to be solved. Based on the author’s learning and practical experience, this paper first interpreted the meaning of education informatization and physical informatization in ordinary colleges and universities, and then put forward the application of informatization in the theory teaching and practice teaching of optional courses of physical education in colleges and universities respectively.
A Visual Approach for the Comparative Analysis of Character Networks in Narrative Texts

The analysis of a novel's plot and characters are challenging and time-consuming tasks in literary criticism. Typically, humanities scholars want to describe and compare characters' personality traits, their roles, their relationships, and the evolution of these aspects over the course of a novel. Nowadays, due to the digitization of literature, humanities scholars can be supported in these endeavors with computational methods. In this paper, we present an approach that offers several means to analyze the plot and characters of a novel visually. Analysts can easily switch between an adjacency matrix and a node-link representation, which provide an overview of the characters and the relationships between them. Both views enable analysts to select different text ranges of the novel for studying the commonalities and differences of the character constellations within these ranges. We offer interactive visual representations to help investigate the relationships between the characters in more detail. Additionally, we link the visual representations with the novels' texts to support the inspection and verification of previously generated ideas and hypotheses. To demonstrate the benefits and limitations of our approach, we present two usage scenarios. The first one is based on a fictitious analysis and the second one discusses applications that were carried out during joint workshops with humanities scholars. Finally, we present and discuss the insights gained by an expert study and the design decisions of our approach.
Bundled Round Bars Counting Based on Iteratively Trained SVM

Bundled bars counting is difficult in the cases of overlap or varied illumination. An iteratively trained SVM method is proposed to count bundled round bars from a bottom side image. Using Hough transformation, the sizes of bars are extracted and normalized. A SVM classifier using HOG features of the image are applied to determine the center points of bars. These center points generate central regions corresponding to bars. By counting the number of connected regions with great area in the image, the number of bars is obtained. In SVM training process, sample selection affects the classifier significantly. From an iteratively selection process, typical samples are selected and used for training the SVM classifier. The experimental results showed this strategy improved the performance of SVM classifier effectively, and the method works well in overlapped or varied illumination situation.
Robust Face Recognition with Assistance of Pose and Expression Normalized Albedo Images

Facial albedo images are believed to be invariant to external factors of pose, illumination and expression that can greatly affect the appearance of face images and thus face recognition accuracy as well. Unlike most existing face recognition methods that address the impact of one or two of these external factors, we propose an end-to-end network, which consists of De-Light Network (DL-Net) and Normalization Network (N-Net), to generate normalized albedo images with neutral expression and frontal pose for input face images. DL-Net aims to eliminate the effects of illumination and reconstruct a posed albedo image that has the same pose and expression as the input image. N-Net attempts to generate a pose and expression normalized albedo image and extract identity features under the supervision of the normalized albedo images. Our experiments on the Multi-PIE database show that the extracted identity features can effectively assist conventional face recognition methods to improve face recognition accuracy under varying poses, illuminations and expressions.
A Controlled Generative Model for Segmentation of Liver Tumors

In this paper, we introduce a generative model representing the formation of liver CT images and employ it for segmentation of hepatic tumors. We extend the graphical model used in label fusion techniques for the segmentation of multi-modality Magnetic Resonance brain images. To resemble atlas data, we segment the first slice by a physician and propagate it for processing of subsequent slices. We include a knowledge-based constraint in the model that is considered as a key difference with classical generative models. We compared our method with conventional Bayesian techniques and state-of-the-art hepatic tumor segmentation algorithms. We achieved a measure of 0.86 ± 0.06 and 0.76 ± 0.09 regarding Dice and Jaccard metrics respectively that is better than recent researches.
Partial person re-identification with two-stream network and reconstruction

Partial person re-identification is a challenging issue at present. However, affected by occlusions, features in person re-identification cannot be detected and the traditional person re-identification methods can not accurately deal with it. In order to solve this problem, we propose to match query and gallery by combining different modes from two-stream network with sparse reconstruction to realize partial person re-identification. For acquiring features, bilinear pooling is applied to fuse the two different modes from the appearance network and pose network aiming at better performance. For matching query and galley, the robust sparse representation reconstructs the features extracted by the network for flexible solution, using the parameters learned from galley. The reconstruction process achieves arbitrary size images in partial person re-identification. In addition, we extract mid-level feature and fuse it with the high-level feature for more accuracy. Experiments demonstrate the performance of the proposed method better compared with the methods of state-of-the-art person re-identification methods on dataset Market1501, CUHK03, DukeMTMC-reID and partial person dataset Partial-REID, Partial-iLIDS.
Interactive Machine Learning for More Expressive Game Interactions

Videogame systems incorporate varied sensors to increase the range of player interactions and improve player experience. However, implementing robust recognisers for player actions with sensors presents significant challenges to developers. Further, sensor-based controls offer little player customisation compared to traditional input interfaces (gamepads, keyboards and joysticks). Past research on motion-driven music systems has successfully used interactive machine learning (IML) techniques to facilitate the development and customisation of sensor-based interfaces, both by developers and end users. However, existing standalone software tools for IML are not ideal for use in game development and distribution. In order to support more effective and flexible use of sensors by game developers and players, we developed an integrated IML solution for Unity3D in the form of a visual node system supporting classification, regression and time series analysis of sensor data.
Edge-Preserving Camera Trajectories for Improved Optical Character Recognition on Static Scenes With Text

Camera systems in fast motion suffer from the effects of motion blur, which degrades image quality and can have a large impact on the performance of visual tasks. The degradation in image quality can be mitigated through the use of image reconstruction. Blur effects and the resulting reconstruction performance are highly dependent on the point-spread function resulting from camera motion. This letter focuses on the motion planning problem for a camera system with boundary conditions on time and position, with the objective of improving the performance of optical character recognition. Tuned edge-preserving trajectories are shown to result in higher recognition accuracy when compared to inverse error and linear trajectories. Simulation and experimental results provide quantitative measures to verify edge-preservation and greater recognition performance.
Speckle Noise Removal Technique in SAR Images using SRAD and Weighted Least Squares Filter

In the process of obtaining synthetic aperture radar (SAR) images, speckle noise exhibited in the SAR images. Speckle noise in the SAR images degrades an image quality and become difficult to interpret the image. Speckle noise reduction technique is a crucial step for using various application of SAR images. In this paper, we proposed a novel algorithm based on speckle reducing anisotropic diffusion (SRAD) and weighted least squares (WLS) filter for removing speckle noise and preserving edge information. The SRAD filter is applied as a preprocessing filter. A logarithmic transformation employed to convert a multiplicative noise remaining in the SRAD filter result image. The multiplicative noise changed as an additive noise. The additive noise in the SRAD filtering result image removed by the WLS filter. We can obtain a despeckled image employing an exponential transform. Experimental results demonstrate that the proposed method exhibits the best speckle noise reduction and edge preservation performance than the conventional filtering techniques.
A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder

For a long time, image enhancement techniques have been widely used to improve the image quality in many image processing applications. Recently, deep learning models have been applied to image enhancement problems with great success. In the domain of biometric, fingerprint and face play a vital role to authenticate a person in the right way. Hence, the enhancement of these images significantly improves the recognition rate. In this chapter, undecimated wavelet transform (UDWT) and deep autoencoder are hydridized to enhance the quality of images. Initially, the images are decomposed with Daubechies wavelet filter. Then, deep autoencoder is trained to minimize the error between reconstructed and actual input. The experiments have been conducted on real-time fingerprint and face images collected from 150 subjects, each with 10 orientations. The signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and root mean square error (RMSE) have been computed and compared. It was observed that the proposed model produced a biometric image with high quality.