Reviewerly/exHarmony huggingface dataset:
Implementation of hand gesture recognition based on flexible sensor array for mobile devices

As the digital devices have been developed, hand gesture recognition plays an important role in Human-Computer interface (HCI) for direct interaction without contact. Among them, the non-contact methods do not require attaching sensors to human's body and mostly use visual technologies such as Kinect or camera sensors. Various wearable devices have been developed using contact sensors such as inertial sensor, magnetic sensor, gyro sensor, electromyography (EMG), FSRs, and others to identify gestures. However, various drawbacks remain for these sensor in detecting gestures. In this paper, we propose a new method using strain gauges-based Flexible Epidermal Tactile Sensor Array to overcome the drawbacks of the existing sensors. It acquires electric signals generated according to the movements of the wrist based on strain gauge estimating the degree of deformation such as tension, compression, and twist. The proposed sensor array shows robustness, stability, and repeatability through various experiments for testing the sensor array. Furthermore, the proposed method is verified by confirming gesture recognition accuracy with multiple subjects and has a greater performance than the existing gesture recognition devices in accuracy tests.
An Aitken-extrapolated Gauss-Seidel method for computing PageRank

Power method is one of the basic iterative method used by Brin and Page to compute the PageRank vector. However, this method may not be efficient when the hyperlink matrix becomes large or damping factor value closes to 1. In last few years, Researchers have considered several numerical methods like Gauss-Seidel, Monte-Carlo, and Arnoldi extrapolation to accelerate the convergence of PageRank computation. In this paper, we have proposed a novel iterative method to compute the PageRank vector of hyperlink matrix by using Aitken extrapolation algorithm. Also, we have analyzed convergence behavior of proposed algorithm i.e. Aitken-extrapolation Gauss Seidel. Further, we have compared Aitken-extrapolation Gauss Seidel algorithm to existing methods such as Power method, Aitken-Power, and Gauss-Seidel method experimentally and found that our method is more feasible and efficient as compared to these existing approaches.
Research on Docker Role Access Control Mechanism Based on DRBAC

With the rapid development of virtualization technology, security issues have also become increasingly prominent. All kinds of virtualization platforms use access control mechanisms to prevent illegal or legal entities from illegally accessing unauthorized resources.This paper discusses the access control mechanism adopted in Docker container, analyzes its shortcomings, and proposes a DRBAC based Docker role access control mechanism. This method is based on the role allocation method, dynamically expands the existing model, and adds a role access control mechanism to the Docker container. Through the distributed trust management and access control mechanism of DRBAC, we can solve the hidden danger brought by the main body because of delegated authority to other entities autonomously. When the system accesses Docker resources, this method can effectively control the access management and delegate problem of teh main body. In this way, we improve the access security of Docker container to host resources.
Convolutional Neural Network based Inverse Tone Mapping for High Dynamic Range Display using LUCORE

The popularity of high dynamic range (HDR) makes the inverse tone mapping become an important technique for HDR display. In this paper, we propose a convolutional neural network (CNN)-based inverse tone mapping method to generate a high-quality HDR image from one single standard dynamic range (SDR) image. First, we present a CNN design with a three- channel input, which considers both luminance and chrominance. Second, we propose to use overlapped inputs to remove the boundary artifacts, caused by zero paddings in CNN. Experimental results demonstrate the high quality of our generated HDR images compared to the ground truth.
SAR image edge detection via directional Bhattacharyya coefficient with its application on image segmentation

In this paper, a novel edge detector for synthetic aperture radar (SAR) images is proposed by introducing the Bhattacharyya coefficient (BC) combining with the rotated biwindow configuration. Based on the quantified input image, the BC is computed from two sample distribution histograms of local regions supported by the subwindows on the opposite sides of the pixel to be detected. With biwindows of different directions sliding through the image, multiple directional Bhattacharyya coefficient matrices are obtained, which are utilized to extract the edge strength map (ESM), characterizing the intensity variation in SAR images. Sequent nonmaximum suppression and hysteresis thresholding refine the extracted ESM into thin edges. Experiment results show that the proposed edge detector can accurately extract edges. Moreover, the BC-based ESM can act as a good precursor to guide SAR image segmentation based on region merging.
A new watermarking method for vector map data

In this paper, we propose a new digital watermarking method for protection of vector maps in geographic information systems. The method is based on a combination of two novel approaches. Firstly, the watermark is embedded into polygon objects of the map by cyclically shifting the vertex list of each polygon. Secondly, a raster image, superimposed on the map, is considered as a watermark. The major advantage of this method is that, unlike most existing watermarking techniques, it does not distort the map by altering the coordinate values. Experimental results demonstrate the efficiency of the proposed method, as well as the robustness of the embedded watermark against common geometric transformations: translation, scaling, rotation and cropping.
Multi-agent System Using Scrum Methodology for Software Process Management

Multi-agent modeling has evolved in multiple disciplines of research and development. While choosing between traditional and agile based software development process, we have proposed scrum methodology using multi-agent system to meet the requirements of stakeholders. Scrum process as well as facilitates key stakeholders of the project to save their lessons learnt during the development of their products. We have implemented multi-agent modelling to extract meta-data; Nouns and Verbs to structure a team for task distribution. With the help of task distribution, we are motivated to construct a system that estimates the cost of development for project using scrum methodology. We have devised a framework that shows partial implementation that takes an input of single SRS document and produces the expected results.
Diagnosis with Linked Open Data for Question Decomposition in Web-based Investigative Learning

In Web-based investigative learning, learners are expected to construct wider and deeper knowledge by navigating a great number and variety of Web resources/pages. On the other hand, they tend to search a limited number of them, which often results in limited knowledge construction. In order to make the investigation with an initial question elaborate, learners need to decompose the question into related ones. They also need to create a scenario like a table of contents implying the questions to be investigated and their sequence. We have built a model of Web-based investigative learning, and developed the system so far. However, it remains an open problem to diagnose learner-created scenario without preventing self-directed investigation. Toward this problem, this paper proposes a diagnosis method with Linked Open Data (LOD), and reports a case study whose purpose was to evaluate the diagnosis method.
Design of the Software Architecture for Starcraft Video Game on the Basis of Finite State Machines

Video game StarCraft is a tool for development of control algorithms. Authors describe different approaches to win the game: coordination of independent rational agents and central control of squad of agents as a whole. Authors select classical approach of finite state machines. The article shows specific in software design for both approaches. Authors describe differences in software architecture and in finite state machines in these approaches and format of storing the information in separate files. Authors propose mathematical model of the solution, realization of generic algorithms for game control, discuss differences in variations of finite state machines. Authors discuss possibility of using state machines in combination with decision trees. Authors conducted the experiment; the results showed advantages and limitations of the discussed architecture.
Rule-based hidden relation recognition for large scale knowledge graphs

Knowledge graphs usually contain much implicit semantic information, which need to be further recognized through semantic inference. However, existing approaches are either not good at processing large scale data or not powerful enough for digging hidden relations thoroughly. This paper proposes a distributed OWL2 RL/RDF rule-based theory closure reasoning algorithm, named KGRL, for recognizing hidden relations in knowledge graphs. Since hidden relations derived from knowledge graph usually contain a lot of redundancies, a redundancy reduction strategy is proposed for eliminating redundant data without effect further queries on the knowledge graph. Extensive experiments and comprehensive evaluations are conducted. The experimental result shows that KGRL recognizes more hidden relations efficiently than Cichlid at different scales of the LUBM benchmark, and it only has a constant increase of runtime. Further more, the redundancy reduction strategy effectively reduces the size of the resulting knowledge graphs of hidden relation recognition on both synthetic and real-world knowledge graphs.
Data Center Simulations Deployed in MATLAB and Simulink Using a Cyber-Physical Systems Lens

This paper describes data center simulations deployed in MATLAB and Simulink using a cyber-physical systems lens. The simulation models, developed in discrete and continuous time, contribute to the assessment of data centers under certain a-priori known conditions. The results of the simulations, with different workloads and IT resources, show quality of service as well as power, airflow and energy parameters, which help better understand and effectively communicate data center behavior. Detailed explanation provided on how to create simulations can be useful for learning purposes. Simulation results enable informed or evidence-based decision-making in the case of uncertainty and risk and help to evaluate areas of improvement.
Pattern augmentation for classifying handwritten japanese hiragana characters of 46 classes by using CNN pre-trained with object images

Neural networks are powerful technology for classifying character patterns and object images. A huge number of training samples is very important for classification accuracy. A novel method for recognizing handwritten hiragana characters is proposed that combines pre-trained convolutional neural networks (CNN) and support vector machines (SVM). The training samples are augmented by pattern distortion such as by cosine translation and elastic distortion. A pre-trained CNN, Alex-Net, can be used as the pattern feature extractor. Alex-Net is pre-trained for large-scale object image datasets. An SVM is used as a trainable classifier. Original hiragana samples of 46 classes on the ETL9B are divided in two-fold by odd and even dataset numbers. Samples with the odd dataset number and augmented patterns on the ETL9B database are trained by the SVM. The feature vectors of character patterns are passed to the SVM from AlexNet. The average error rate was 1.130% for 100 test patterns of each of the 46 classes for a 5-times test, and the lowest error rate was 0.978% with 506138 training patterns of distorted hiragana characters. Experimental results showed that the proposed method is effective in recognizing handwritten hiragana characters.
End to end multi-scale convolutional neural network for crowd counting

Crowd counting is a challenging task in computer vison field and haven’t been well addressed until now. In this paper, we intend to develop an end to end multi-scale deep convolutional neural network(CNN) model that can accurately estimate the crowd count from an individual image with arbitrary crowd density and perspective. The proposed model extract multi-scale deep CNN features from the input image and regress the crwod count directly, without any post-processing . Hence our model could handle muti-scale targets well in various crowd scene. We evaluate our model on several benchmark datasets and the performance outperforms some state-of-the-art methods. What’s more, due to the end-to-end characteristics, our model demonstrates good practical application performance.
Deep convolutional neural networks compression method based on linear representation of kernels

Convolutional Neural Networks (CNNs) are getting larger and deeper, and thus becoming harder to be deployed on systems with limited resources. Though convolutional filters benefit from the concept of receptive field, they still take up lots of resources to store these parameters in the large amounts of filters. Therefore, a compression method of pre-trained CNN models using "Linear Representation" of convolutional kernels is introduced in this paper. First, a codebook of template kernels "K<sub>t</sub>". are generated by conducting unsupervised clustering on all convolutional kernels, with Pearson Correlation Coefficient set as distance. Then all the convolutional kernels are represented by the closest templates using linear fitting function a &bull; K<sub>t</sub> + b , which means that only two parameters and a codebook index are enough to represent a kernel. After that, the model is retrained with fixed template kernels and only two related parameters need to be finetuned for each kernel. Experiments show that convolutional kernels of a large CNN model can be represented using only a small amount of templates. Thus, this method can reach a compression rate of convolutional layers near 4&times;, with tiny impact on precision after retraining. Nevertheless, the proposed method can be performed with other compression approaches to get higher compression rate.
Expressive Tactile Controls

Push buttons, sliders, switches, and dials-we use such controls everyday and everywhere, but we barely notice them. Expressive Tactile Controls is a research experiment with a series of controls that are augmented by giving human personalities. What if each control had a unique personality and they could express their emotion only through haptic feedback? How could our interaction with controls be improved? The research approached the question by constructing a series of button prototypes able to express themselves with varying tactile and kinesthetic feedback according to the interaction between the user and controls.
Direct Spherical Calibration of Omnidirectional Far Infrared Camera System

We investigate the calibration of omnidirectional (O-D) infrared (IR) camera for intelligent perception applications. Current O-D camera approaches are primarily focused on O-D color vision applications. The low-resolution O-D IR image edge boundaries are not as sharp as with color vision cameras, and as a result, the standard calibration methods were harder to use and less accurate with the low definition of the O-D IR camera. In order to more fully address O-D IR camera calibration, we propose a new calibration grid center which coordinates control point discovery methodology and a direct spherical calibration (DSC) approach for a more robust and accurate method of calibration. DSC will address the limitations of the existing methods by using the spherical coordinates of the centroid of the calibration board to directly triangulate the location of the camera center and iteratively solve the camera parameters. We compare DSC to three baseline visual calibration methodologies and augment them with additional output of the spherical results for comparison. We also look at the optimum number of calibration boards using an evolutionary algorithm and Pareto optimization to find the best method and combination of accuracy, methodology, and number of calibration boards. The benefits of DSC are both a more efficient calibration board geometry selection approach, and better accuracy than the three baseline visual calibration methodologies.
Examining Sentiment Analysis When Evaluating Survey Responses

Surveys have become a ubiquitous tool used by decision makers to determine the opinion of a population. Recent research suggests that executing surveys soliciting written text responses provides the most statistically informative data to decision makers, allowing for more accurate inference concerning the opinion of a large population. Current surveys quantify population sentiment with questions requiring Likert scaled responses, forcing respondents to map their opinion to a discreet and constructed quantitative scale. This mapping from qualitative opinion to quantitative sentiment measure often results in inaccurate data with high variance. We examine the accuracy of leveraging sentiment analysis to infer the opinion of a population concerning topical questions on a digital survey. We design an experiment to conduct this investigation, administering a survey to a population of over 300 individuals, gathering thousands of responses to three questions - each requiring Likert scaled and written text responses. We employ a baseline sentiment analysis algorithm with a domain specific lexicon to quantify written text responses, and subsequently execute a experimental hypothesis test to evaluate the statistical difference between written sentiment and Likert scaled distributions and means. Statistical tests used include the Randomization Test and Two Sample t Test. Our results indicate that surveyors may substitute questions requiring written text for Likert scaled responses, while maintaining similar distributions of results.
2D art recognition in uncontrolled conditions using one-shot learning

The paper considers the problem of 2D art identification in photos acquired with mobile devices under the conditions of museum exhibition. The proposed approach is based on a compact description of an image with a constellation of keypoints and corresponding local descriptors. A two-step comparison scheme is described for finding the best reference image matching the query. Bag-of-features approach is used as a first step, then mutual disposition of points is analyzed. Rejection of the query is performed if no suitable matches are found. Geometrical normalization of the query image is proposed to achieve higher robustness against scale and viewpoint variations. After the normalization, mutual disposition of points is estimated using a simplified geometric model. Advantages of the described approach over state-of-the-art solutions are considered. The results of the experiments conducted on the open WikiArt dataset are presented along with processing times for different hardware platforms.
Amazigh POS Tagging Using TreeTagger: A Language Independant Model

Part of Speech (POS) tagging has high importance in the domain of Natural Language Processing (NLP). POS tagging determines grammatical category to any token, such as noun, verb, adjective, person, gender, etc. Some of the words are ambiguous in their categories and what tagging does is to clear of ambiguous word according to their context. Many taggers are designed with different approaches to reach high accuracy. In this paper we present a Machine Learning algorithm, which combines decision trees model and HMM model to tag Amazigh unknown words. In case of statistical methods such as TreeTagger, this will have added practical advantages also. This paper presents creation of a POS tagged corpus and evaluation of TreeTagger on Amazigh text. The results of experiments on Amazigh text show that TreeTagger provides overall tagging accuracy of 93.19%, specifically, 94.10% on known words and 70.29% on unknown words.
Arabic Poem Generation with Hierarchical Recurrent Attentional Network

Automatic Poem generation is an interesting research topic which has attracted the Natural Language Processing (NLP) deep learning community's attention. However, the majority of poetry-generation-related research is performed in English and Chinese, ignoring other languages. In this paper, we take a first step in training a deep learning model of Arabic poetry generation. We proposed two approaches to generate Arabic poetry: (1) A Bi-directional Gated Recurrent Unit (Bi-GRU) model to compose the first line; (2) A modified Bi-GRU encoder-decoder with a hierarchical neural attention framework to generate other verses sequentially, which can adequately capture word, phrase, and verse information between contexts. A comprehensive evaluation with human judgments confirms that the generated poems by our model outperforms the base models in criteria such as Meaning, Coherence, and Poticness. Extensive quantitative experiments using BLEU scores also demonstrate significant improvements over strong baselines. To the best of the authors' knowledge, this work in generating Arabic poetry is considered an essential development in this line of the field.
On permutation symmetry of subradiant states and its application

Group theory is an extremely powerful mathematical tool for many physical problems, in particular for classification of quantum states by exploiting their symmetry. In this paper, we address symmetry properties of single- and double-excited subradiant states and show how they can be useful for experimental design of sidewise excitation method for generation of subradiant states. Experimental realization of the considered method can be achieved using superconducting qubits in circuit quantum electrodynamics.
Multispectral Direct-Global Separation of Dynamic Scenes

In this paper, we propose a method for separating direct and global components of a dynamic scene per illumination color by using a projector-camera system; it exploits both the color switch and the temporal dithering of a DLP projector. Our proposed method is easy-to-implement because it does not require any self-built equipment and temporal synchronization between a projector and a camera. In addition, our method automatically calibrates the projector-camera correspondence in a dynamic scene on the basis of the consistency in pixel intensities, and optimizes the projection pattern on the basis of noise propagation analysis. We implemented the prototype setup and achieved multispectral direct-global separation of dynamic scenes in 60 Hz. Furthermore, we demonstrated that our method is effective for applications such as image-based material editing and multispectral relighting of dynamic scenes where wavelength-dependent phenomena such as fluorescence are observed.
Single Image Dehazing Based on Weighted Dark Channel

Most single image dehazing methods are based on the dark channel prior. However, since the use of the dark channel prior results in halo artifacts, these conventional dehazing methods employ computationally complex post-refinement processes to eliminate the halo artifacts. In this paper, we propose a single image dehazing method using a weighted dark channel that does not require any post-refinement process. Unlike the conventional dark channel obtained by selecting the minimum intensity value in the local patch, the weighted dark channel is generated by considering the color similarity and spatial distance in the local patch. Experimental results show that the proposed method does not only remove the haze effectively, but also prevent the halo artifacts.