Reviewerly/exHarmony huggingface dataset:
Analysis of the Efficiency of Speculative Parallel Computation

Parallel computing is a method of increasing the efficiency of computation in order to solve combinatorial optimization problems, which involve enormous computational complexity. However, conventional parallelization cannot fully utilize computational resources. Speculative computation is another method of parallel computation. We have analyzed theoretically that speculative computation is more effective than conventional parallel processing in some cases. However, analysis of the condition is insufficient. In this paper, we confirm one of the conditions experimentally.
Observability Enhancement of the Target Tracking Model Based on Infrared Semi-strapdown Seeker

As an important device for target interception, The seeker is used to search, recognize and track the target autonomously. Infrared semi-strapdown seekers are widely used in applications due to their low cost and small size. However, one non-negligible disadvantage of semi-strapdown infrared seekers is that the observability of target tracking model is poor, especially for the interception of high-speed maneuvering weapons. In this paper, the target tracking model ( i.e., the filtering estimation model) based on the infrared simi-strapdown seeker is established. The observability of the target tracking model is analyzed and the evaluation method of the observability is studied. Simulation results show that the observability of the filtering estimation model can be improved by increasing the relative motion between missile and target which results in faster changes of the line-of-sight (LOS) angle. According to the results on the observability analysis, the desired trajectory is obtained through the design of proper guidance law. Nonlinear filtering algorithm is also introduced for the trajectory design. Simulation experiments are conducted for the integrated filtering and guidance, and simulation results illustrate the effectiveness of the proposed observability enhancement method.
OGCTL: Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition

Concatenation of the deep network representations extracted from different facial patches helps to improve face recognition performance. However, the concatenated facial template increases in size and contains redundant information. Previous solutions aim to reduce the dimensionality of the facial template without considering the occlusion pattern of the facial patches. In this paper, we propose an occlusion-guided compact template learning (OGCTL) approach that only uses the information from visible patches to construct the compact template. The compact face representation is not sensitive to the number of patches that are used to construct the facial template, and is more suitable for incorporating the information from different view angles for image-set based face recognition. Instead of using occlusion masks in face matching (e.g., DPRFS [38]), the proposed method uses occlusion masks in template construction and achieves significantly better image-set based face verification performance on a challenging database with a template size that is an order-of-magnitude smaller than DPRFS.
Understanding the Role of Context on Memory for Maximally Counterintuitive Concepts

The current study examined how contextual information, defined as information represented at the level of the situation model, influenced memory for minimally and maximally counterintuitive stories. The first two experiments investigated whether the presence of explicit instructions to make sense of “strange information” in the stories influenced memory for maximally counterintuitive stories. Although no such effect was observed, post hoc analyses indicated that maximally counterintuitive stories that support a global inference that integrates the counterintuitive ideas are better recalled compared to stories that do not support such an inference. A third experiment was conducted to directly test the “global-inference” hypothesis. The results from the three experiments highlight the significant role of inferential processing in the integration of counterintuitive ideas, especially for maximally counterintuitive studies.
An Adaptive Cross-Layer Sampling-Based Node Embedding for Multiplex Networks

Network embedding aims to learn a latent representation of each node which preserves the structure information. Many real-world networks have multiple dimensions of nodes and multiple types of relations. Therefore, it is more appropriate to represent such kind of networks as multiplex networks. A multiplex network is formed by a set of nodes connected in different layers by links indicating interactions of different types. However, existing random walk based multiplex networks embedding algorithms have problems with sampling bias and imbalanced relation types, thus leading the poor performance in the downstream tasks. In this paper, we propose a node embedding method based on adaptive cross-layer forest fire sampling (FFS) for multiplex networks (FFME). We first focus on the sampling strategies of FFS to address the bias issue of random walk. We utilize a fixed-length queue to record previously visited layers, which can balance the edge distribution over different layers in sampled node sequences. In addition, to adaptively sample node's context, we also propose a metric for node called Neighbors Partition Coefficient (N P C ). The generation process of node sequence is supervised by NPC for adaptive cross-layer sampling. Experiments on real-world networks in diverse fields show that our method outperforms the state-of-the-art methods in application tasks such as cross-domain link prediction and shared community structure detection.
Image Enhancement using Dark Channel and Adaptive Fuzzy Filter

Image enhancement is a technique used to improve the quality of the image which will be carried out further. The improvement of image quality consists of returning blur image, noise removal and others as. In the research carried out a combination of two algorithms, namely dark channel and adaptive fuzzy filter. The results obtained in the form of a combination can work well on blurred images with an average success rate of 28.00 dB (decibels) and not very good if the image is noisy.
Restoration of Blur video using Blind Deconvolution Method

Instability in the atmosphere and incompatibility camera settings leads to blurring of video. Blur of a video is due to the motion of subjects during capturing, quality of lens and improper holding of the camera. The IR cameras usually have a low resolution mostly 160x120 and 320x240 for technical reasons. In this case Image Processing is one of the boons for business, engineering, forensics and medical field to extract the required values from the image data. To resolve this, we introduce an effective method to deblur low resolution videos. First, Blind Deconvolution method is applied to low resolution frames. Then, restored gray frames are converted into RGB images and write each RGB frame into the video object to make a video. The experimental results depict a high resolution video which is the sharpen form of low resolution video.
Generalized Differential Gray-level Histogram Equalization

Histogram equalization (HE) is a simple and effective method for contrast enhancement as it can automatically define the gray-level transformation function based on the distribution of gray-level included in the image. HE fails to produce satisfactory results for broad range of low-contrast images because the HE does not use a spatial feature included in the input image. The differential gray-level histogram which is contained edge information of the input image, were defined. Furthermore, the differential gray-level histogram equalization (DHE) has been proposed. The DHE shows better enhancement results compared to the HE results for many kinds of images. In this paper, we propose a generalized DHE (GDHE) method. In GDHE, histograms are created using powers of gradients. If the power is set as 0, GHE is equivalent to HE, and if the power is set as 1, GHE is equivalent to DHE. That is, GDHE includes HE and DHE. GHE can preserve the mean brightness of the input image perfectly by setting the power appropriately and shows good contrast enhancement results at the same time.
A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

Texture classification is a classic problem in pattern recognition. It is an effective strategy for improving texture classification to find the texture features with both powerful discrimination and various invariant properties. In this paper, we provide a new insight into texture images, that is, texture images can be treated as quasi-periodic signals. Some new concepts such as Dominant Period Component (DPC), periodic degree (PD), and Main Frequency (MF) are proposed to characterize the properties of quasi-periodic signals. DPC controls the oscillation rate of a quasi-periodic signal and plays a key role in controlling the behavior of the whole signal. So it can serve as a key feature for texture classification. Based on this idea, we propose a new method to extract texture features. The proposed features have both powerful classification ability and rotation-illumination- invariance as well as robustness to noise. Experimental results on three texture data sets demonstrate the validity of this method.
Multi-supervised CML for Small Sample Low-resolution Image Matching

The coupled metric learning(CML) is an effective method for low-resolution image matching. However, the existing coupled metrics generally use class label as the supervision, which easily leads to changes in the samples distribution after coupling transformation. These changes affect image matching accuracy seriously. To address this problem, we propose a multi-supervised coupled metric learning method fusing class label and distribution information. In this work, a novel multi-supervised objective function is constructed, which consists of the main and auxiliary objective functions. The class label in the main objective function plays key supervisory role, and the distribution information in the auxiliary objective function plays auxiliary supervisory role. Then, by solving the objective function, the samples with different resolution are transformed into a common space for distance measurement. Experimental results on two image datasets validate the efficacy of the proposed method.
A High Detection Rate Pupil Detection Algorithm Based on Contour Circularity Evaluation

A pupil detection algorithm that exhibits a high detection rate is presented. This work builds on two previously proposed algorithms and includes a procedure that provides an initial global adaptive threshold that is further adjusted by a feedback loop. The processing steps involve a blurring phase, two morphological operations, contour determination using Laplacian, a convex-hull enclosure as well as a novel contour processing solution that ensures that the contour of maximum circularity is always chosen as the pupil. Finally, the Least Square Fit to Ellipse (LSFE) procedure is used to accurately estimate the position of the pupil center. The proposed algorithm achieves a 95% average detection rate at five pixels for two representative databases.
A Dimension Reduction Method Using LSDA For Human Action Recognition

Since dimensionality disaster easily occurs in the processing of dealing with video data, a dimensionality reduction method, linear sequence discriminate analysis (LSDA), is proposed for human action recognition. The experimental results conducted on KTH datasets and UCF101 datasets show that the proposed method has improved the recognition accuracy.
Efficient Bayesian Sequential Classification Under the Markov Assumption for Various Loss Functions

Optimal sequential classification theory generalizes naturally from that of single-hypothesis classification. However, longer sequences require characterization with higher-order probability distributions; consequently, the design of optimal classifiers can become computationally prohibitive for real-time applications. A Markov assumption for the class random process provides structure to the distribution, enabling efficient evaluation with a limited number of arithmetic operations; the Viterbi algorithm exploits this property to provide a computationally inexpensive maximum a posteriori classifier when the Markov process is first-order. This letter extends this classical result for general order Markov processes and assesses the additional computational cost in terms of multiplication operations. Additionally, a second efficient algorithm is provided which determines the optimal hypothesis if a sample-level loss function is used. In contrast to the Viterbi classifier which implicitly penalizes all erroneous sequences equally, this classifier judges misclassifications more gradually, providing higher flexibility for challenging classification tasks.
THE HIERARCHY OF PROFESSIONAL WELL-BEING FACTORS IN DIFFERENT PROFESSIONAL GROUPS

The article studies peculiarities of the hierarchy and the structure of professional well-being factors characteristic for the representatives of different professional groups. Based on the empirical data obtained for the representatives of different professions, and based on the performed correlation analysis between the factors of the 6-factor model of professional well-being (the mixed professional sample) and indicators of professional well-being in different professional groups, it was found that the hierarchy (strength of correlations) of the professional well-being factors is significantly different for different professions, in particular, for architects, police inspectors, managers, sales managers, sale representatives (medical salespersons), police investigators, economists and programmers. Significant differences in the hierarchy of professional well-being factors between some professional groups were determined, first and foremost, for the following factors: “professional motivation”, “flow at work” and “evaluation of professionalism and professional identity”. There were no significant differences for the factors such as “job satisfaction”, “satisfaction with the made professional choice” and “satisfaction with work processes and content” for different professional groups. The performed analysis of the research results suggests that some statistically significant differences in the hierarchy of professional well-being factors can be found out between “self-sufficient” (architects, police investigators, managers) and “dependent” professions, such as police inspectors or sale representatives (medical salespersons). The invariance of the factor structure for professional well-being and the proven significant differences in the factor hierarchy for different professional groups testify to the possibility to develop the author’s model of professional well-being for representatives of different professions.
A Modified Structural Similarity Index with Low Computational Complexity

Structural Similarity Index (SSIM) has been a benchmark method for image quality assessment (IQA). This is due to its simplicity and good performance. In this paper, we propose a modified SSIM method that reduces the computational complexity with comparable performance. Instead of computing similarities on local windows, the proposed method computes global information similarities. The proposed method omits the luminance part similarities in SSIM due to its less crucial role in assessing image quality. From the presented results, the proposed method has a much lower computational time and comparable performance compared to SSIM.
Virtual Reality Applications in Tourism

Through modern ages, tourism becomes a vital part of human life. With the advance of technology, tourism movements gain new tools. Those tools gave tourism an edge on management, marketing, education, and economic areas. Also, on demand side, new types of tourists with their brand-new demands and motivations became an important factor for those responsible for tourism supply. One of the most noticeable tools that new technology era has brought is virtual reality applications. Despite dating back to the 1950s, VR (virtual reality) has gained an interesting reputation in the last few years with the advances of related technologies. VR applications are used in many sectors like construction, military, communication, marketing, education, etc. Tourism industry is one of them of course. Especially, VR is used as both a tool and product in hotel &amp; accommodation sector, recreation &amp; entertainment, transportation, destination marketing, and a touristic product itself.
Framework Index security information to support evaluation, adoption and improvement of methods in developing variables

Abstract The governance of information security in the Indonesian Government has not been completely improved. Many related variables components are not measured. This study aims to observe an evaluation for application of information security index framework in Indonesian government institutions as the implementation of electronic government. The analysis is carried out by adopting an information security index framework produced in the rank of e-government. The study uses a triangulation approach within a mixed method. This study results in a novel adoption model of information security index framework equipped by two new variables. Therefore, a proper policy of the Indonesian government is required for its implementation.
Convex Decomposition Model for Salient Object Detection

An efficient background-salient object separation using background priors is proposed in this paper. When the image is cluttered, it is difficult to decompose the image into foreground and background regions. In order to overcome this limitation, feature vectors are computed from the image using Simple Linear Iterative Clustering algorithm (SLIC). In the next step feature matrix is decomposed into low-rank and sparse component using a convex model. The separation between background and salient object is increased by incorporating background priors into the model. The background priors chosen are location, color and boundary connectivity. In contrast with the bench mark methods, the proposed method delivers accurate result at a faster rate.
Clustering and Classification to Evaluate Data Reduction via Johnson-Lindenstrauss Transform

A dataset is a matrix X with $$n\times d$$ entries, where n is the number of observations and d is the number of variables (dimensions). Johnson and Lindenstrauss assert that a transformation exists to achieve a matrix with $$n\times k$$ entries, $$k<< d$$, such that certain geometric properties of the original matrix are preserved. The property that we seek is that if we look at all pairs of points in matrix X, the distance between any two points should be the same within a given small acceptable level of distortion as the corresponding distance between the same two points in the reduced dataset. Does it follow that semantic content of the data is preserved in the transformation? We can answer in the affirmative that meaning in the original dataset was preserved in the reduced dataset. This was confirmed by comparison of clustering and classification results on the original and reduced datasets.
Line-point feature based structure-preserving image stitching

The naturalness of the panoramic image is an important criterion for evaluating the result of image stitching. When the overlapping region of the image has a low-texture area, the alignment is hard to accomplish due to incorrect and inadequate feature point matching pairs, and the salient structures are easily destroyed due to the unreasonable position of the optimal seam. To solve the problem, we use line segment matching information to assist the alignment of low-texture regions and use superpixel segmentation regions to filter the unreliable point correspondences. An improved seam-finding algorithm is finally used to blend images together. The experimental results demonstrate that the proposed method can get more natural stitching results.
GLSE: Global-Local Selective Encoding for Response Generation in Neural Conversation Model

How to generate relevant and informative response is one of the core topics in response generation area. Following the task formulation of neural machine translation, previous works mainly consider response generation task as a mapping from a source sentence to a target sentence. However, the dialogue model tends to generate safe, commonplace responses (e.g., I don't know) regardless of the input, when learning to maximize the likelihood of response for the given message in an almost loss-less manner just like MT. Different from existing works, we propose a Global-Local Selective Encoding model (GLSE) to extend the seq2seq framework to generate more relevant and informative responses. Specifically, two types of selective gate network are introduced in this work: (i) A local selective word-sentence gate is added after encoding phase of Seq2Seq learning framework, which learns to tailor the original message information and generates a selected input representation. (ii) A global selective bidirectional-context gate is set to control the bidirectional information flow from a BiGRU based encoder to decoder. Empirical studies indicate the advantage of our model over several classical and strong baselines.
Maneuvering Target Tracking based on Adaptive Cooperative Cubature Kalman Filter

For tracking a target with a sudden maneuver change by using multiple sensors, a distributed estimation approach based on adaptive cooperative cubature Kalman filter (ACCKF) is proposed. For alleviating tracking performance degradation caused by target abrupt maneuver, an adaptive cubature Kalman filter is designed by adding a time-varying fading factor, which can adjust filtering gain matrix in real-time. In ACCKF, a consensus-based distributed cubature Kalman filtering algorithm is developed, where a consensus weight matrix is present in the sensor network with communication topology. The ACCKF guarantees that all the local sensors can achieve a consensus in state estimation. Compared to centralized estimation approach, ACCKF can reduce the communication and computation burden. Simulation results show that ACCKF has good performance in tracking the target with a sudden maneuver change. Compared with the existing estimation approaches including the adaptive cubature Kalman filter, ACCKF has the best overall filtering performance.
Knowledgeware Design Towards O-DA

Although, the dependability of architecture is widely expected in the digital age, it is not clear what content shall the knowledgeware to learn the dependability has. In this paper, we propose the knowledgeware design on the open dependability to certify enterprise architects who have the capability to develop dependable architectures. The knowledgeware is based on the open standard of The Open Group named O-DA that means Open Dependability through Assuredness. The knowledge configuration and development process of the knowledgeware have also described.