Reviewerly/exHarmony huggingface dataset:
Multi-poses Face Frontalization based on Pose Weighted GAN

In many scenes, the frontal face image is the only criterion for judging the identity of a person. However, it is difficult to collect a standard frontal image in an uncontrolled environment. To get a clear frontal image from a large variety of profile images, there are many studies on face frontalization. Some researches need three-dimension face data or prior pose information while others do not take into account the effect of pose information. And there are restrictions on the number of poses of input face images. Because of the ill-consideration of pose information, the authenticity of generated frontal face images is not high when we input multi-poses profile images. To resolve this problem, this paper proposes a Pose-weighted Generative Adversarial Network (PWGAN), which adds a pre-trained pose certification module to learn face pose information. For the single input image, PWGAN combines fusion features with pose features. And for multiple input images, PWGAN uses pose information to dynamic distribute weights when fusing feature maps. PWGAN makes full use of pose information to make the generation network learn more about facial features and get better-generating effect. Through contrastive experiments, this paper proves that PWGAN has a better effect on multi-poses face frontalization than the above methods.
Healthcare Augmentation

Healthcare sector is recognized as one the most important sectors for the well-being of humanity. The recent technological advancements has brought about great enhancements in healthcare delivery. Augmented Reality (AR) is a phenomenon whereby real life objects in a recognized environments are amplified by additional visual information in order to support the process of augmentation. The benefits afford the opportunity to diagnose patient disease conditions during surgical procedure with high accuracy and precisions thereby reducing the incidence of medical errors. In spite of the numerous benefits AR technology has offered, its widespread adoption in clinical perspectives most especially towards enhancing medical practice and education in developing countries and Sub-Saharan African countries in particular have received little academic attention. The objective of this paper therefore is to examine some of these factors and propose a theoretical model of adoption for AR technology which can be validated in future studies.
Steering evolution of two-mode Gaussian states in noisy environments

Steering, a quantum property existing in some bipartite entangled systems, causes the local measurements on one part to change the state of the other part at any distance. In this paper, the dynamics of steering between two uncoupled oscillators in squeezed thermal, thermal and vacuum environments are compared. Assuming that the two oscillators are coupled to a common environment or two similar environments, the evolution of steering is obtained using Linblad equation in Born–Markov approximation. It is observed that when the two oscillators are initially in uni-modal squeezed states, the interaction with a common squeezed thermal environment induces steering between them after a short time. However, in other common and the two similar environments, steering does not appear. In addition, when two oscillators are initially in a squeezed thermal state, it is observed that steering decreases monotonically and disappears in a short time in common thermal, common vacuum and the two similar environments. Moreover, steering survives the most in a common squeezed thermal environment.
PRODUCTIVE LEARNING TECHNOLOGY AS MEANS OF EDUCATION QUALITY PROVISION

This article presents a review that provides insight in the concept, characteristics of productive learning in its correlation with education quality as well as the purposes of a productive learning environment. The problem of education inner and outer quality in higher educational establishments has been highlighted. The most important inner and outer components of productive learning as well as main turning points of productive learning history and basic definitions have been succinctly studied. This article defines three the most important components of a Productive Learning both inner and outer: 1) learning opportunity that helps to a select number of relevant learning choices, not a high volume and variety; 2) learning capability that helps to gain competences not just new business skills and knowledge; 3) learning environment that is necessary to share ownership of the learning environment, not just their individual learning. Thus, technology can play a key role in education quality provision.
Simulation analysis of athletes’ motion recognition based on deep learning method and convolution algorithm

Athletes have a large amount of video information, so how to capture effective information is the key to improving athletes’ training efficiency and improving the quality of the game. From the perspective of deep learning, this study analyzes and improves traditional algorithm models based actual n eeds, and jointly learns multi-scale features. At the same time, in view of the problem of over-fitting in the model training process, this study uses the sparse pyramid pool strategy to adjust the pool parameterization process and reduce the complexity of feature description. In addition, the research designs experiment to analyze the performance of the improved algorithm model and select the appropriate database to analyze the recognition effect of the algorithm model. The research shows that the algorithm of this research has a certain improvement in the recognition effect of athletes, and the recognition effect matching the artificial design features can be obtained, and it can provide theoretical reference for subsequent related research.
Integration Distance Similarity with Keyword Algorithm for Improving Cohesion between Sentences in Text Summarization

Abstract In recent time the exponential growth of textual information available on the Web, end user need to be able to access information in summary form. Commonly the method to get the summary is extraction method. One of extraction method that easier and commonly used is Keyword Algorithm, but this algorithm has a weakness in the cohesion between the sentences. Distance similarity method is one method used for solving the cohesion problem. The idea of this paper is to improve cohesion between sentences based on extraction of keyword algorithm. The hybrid keyword algorithm and the distance similarity method is proposed. The proposed method was compared three distance similarity such as Cosine, Dice and Jaccard that looking for the cohesiveness between sentences according to keyword algorithm extraction and performance as standard of evaluation. The result showed that Dice has the highest cohesion degree is 45.87 %. Although the best performance is Cosine that performance is influenced with gold standard of abstractive human summary.
Sparsification Scale-Spaces

We introduce a novel scale-space concept that is inspired by inpainting-based lossy image compression and the recent denoising by inpainting method of Adam et al. (2017). In the discrete setting, the main idea behind these so-called sparsification scale-spaces is as follows: Starting with the original image, one subsequently removes a pixel until a single pixel is left. In each removal step the missing data are interpolated with an inpainting method based on a partial differential equation. We demonstrate that under fairly mild assumptions on the inpainting operator this general concept indeed satisfies crucial scale-space properties such as gradual image simplification, a discrete semigroup property or invariances. Moreover, our experiments show that it can be tailored towards specific needs by selecting the inpainting operator and the pixel sparsification strategy in an appropriate way. This may lead either to uncommitted scale-spaces or to highly committed, image-adapted ones.
A Relevance Inference Model of Electronic Evidence Based on Case Characteristics

To combat cyber crimes, electronic evidence have played an increasing role, but currently electronic evidence usually have been questioned in the courts because of their relevancy judgement in judicial practice. In this paper, we put forward a relevance inference model of electronic evidence and discuss their analytical process combined with the case study. The initial practice shows the model is feasible and has a consulting value in analyzing the relevancy of electronic evidence.
Scene-based Imperceptible-visible Watermarking for HDR Video Content

This paper presents the High Dynamic Range - Imperceptible Visible Watermarking for HDR video content (HDR-IVW-V) based on scene detection for robust copyright protection of HDR videos using a visually imperceptible watermarking methodology. HDR-IVW-V employs scene detection to reduce both computational complexity and undesired visual attention to watermarked regions. Visual imperceptibility is achieved by finding the region of a frame with the highest hiding capacities on which the Human Visual System (HVS) cannot recognize the embedded watermark. The embedded watermark remains visually imperceptible as long as the normal color calibration parameters are held. HDR-IVW-V is evaluated on PQ-encoded HDR video content successfully attaining visual imperceptibility, robustness to tone mapping operations and image quality preservation.
A Method for Analyzing the Quality-in-Use in Collaborative Contexts

Quality-in-use is a concept that establishes a direct relationship between the effects of the user interaction and the context in which the system is used. Groupware systems enable collaborative contexts in which users interact between them to achieve a common goal. Therefore, the evaluation of the quality-in-use of groupware systems can involve an analysis of the social dimension of the context and an assessment of the collaborative interactions between the users. This article proposes a method to analyze the interaction and collaboration supported by groupware with the goal of inferring models of the user's behaviors and to calculate a set of metrics that assesses the collaborative dimension of the quality-in-use evaluation. We describe a case study that puts the method in action to analyze the quality-in-use of a groupware application for smartphones that supports collaborative betting.
Bilevel Optimization for Planning Through Contact: A Semidirect Method

Many robotics applications, from object manipulation to locomotion, require planning methods that are capable of handling the dynamics of contact. Trajectory optimization has been shown to be a viable approach that can be made to support contact dynamics. However, the current state-of-the art methods remain slow and are often difficult to get to converge. In this work, we leverage recent advances in bilevel optimization to design an algorithm capable of efficiently generating trajectories that involve making and breaking contact. We demonstrate our method’s efficiency by outperforming an alternative state-of-the-art method on two benchmark problems. We moreover demonstrate the method’s ability to design a simple periodic gait for a quadruped with 15 degrees of freedom and four contact points.
Joint-Sparse Decentralized Heterogeneous Data Fusion for Target Estimation

In our recent work, we developed a new joint-sparse data-level fusion (JSDLF) approach to integrate heterogeneous sensor data for target discovery. In the approach, the target state space is discretized and data fusion is formulated as a joint sparse signal reconstruction problem, which is solved by using simultaneous orthogonal matching pursuit (SOMP). In our previous work, the joint sparse signal recovery approach has been implemented in a centralized manner. Namely, all the raw sensor data are transmitted to a fusion center, where they are fused to detect and estimate the targets. The drawback of the centralized network is its high communication cost and its lack of robustness, since the global information is stored and processed at a single point, the fusion center. In this paper, several decentralized JSDLF approaches have been developed, that provide exactly the same estimation result at each sensor node as the centralized algorithm does. Further, two distributed database query algorithms, Threshold Algorithm (TA) and Three-Phase Uniform Threshold (TPUT) have been combined with the SOMP algorithm to reduce communication costs. Numerical examples are provided to demonstrate that the proposed decentralized JSDLF approaches obtain excellent performance with accurate target position and velocity estimates to support situational awareness, while at the same time achieving dramatic communication savings.
Two-shot fringe pattern phase demodulation using the extreme value of interference with Hilbert-Huang per-filtering

The extreme value of interference (EVI) algorithm is a very fast and efficient method for the fringe pattern phase demodulation. It requires only two arbitrarily phase-shifted frames in which the phase shift between interferograms can be determined by searching the maximum and the minimum of the normalized interference patterns, then the measured phase is obtained by an arctangent function. Compared with other two-frame demodulation algorithms, the EVI algorithm has great advantages. Firstly, the EVI algorithm is simple and the calculation speed is fast. Secondly and more importantly, it works very well even if the number of fringes of the interferogram is less than one. However, to make this method work, the fringe should be normalized in advance, which is sometimes not a satisfactory requirement. The effects of uneven background terms, modulation amplitude variations, and random noise in the fringe pattern will make the normalization of the fringes extremely complex. Therefore, by employing the HilbertHuang transform (HHT) based prefiltering in this paper, the background intensities and modulation amplitudes of the two interferograms are suppressed and normalized respectively. Then, phase demodulation is implemented using the EVI method. Because of the HHT process, the demodulation result is greatly improved in plenty of situations. Both simulation and experimental studies have shown that the proposed improved method makes it easier to determine the phase distribution with high precision even under complex conditions.
Distributed cat modeling based agile framework for software development

Software development is a challenging process that requires in-depth understanding and an effective model such that the developed software inherits good quality and reliability, and attains customer satisfaction towards achieving the goals successfully. The effectiveness of the software is enabled by modifying the operating modules of the software through a model, like agility. In this paper, the catastrophic and distributed computing models are integrated into the software development process. The proposed model is termed as a distributed cat model that is developed with the aim to handle the risk factors engaged in various developing stages of the agile model. The risk factors that affect the communication, planning, release, design, coding and testing modules of the agile modules are deeply learned and executed such that the risk factors are tackled by various modules present in the proposed distributed cat model. The effectiveness of the proposed model is analysed based on the performance metrics such as Index of Integration (IoI) and Usability Goals Achievement Metric (UGAM), for which five products, including the hotel management system, Customer Relationship Management system (CRM), rainfall prediction system, temperature monitoring system and meta-search system, are employed. The analysis is performed using the parameters like mean difference, variance, standard deviation and correlation coefficient. The result proves that the proposed model offers a great positive deviation contributing to high degree of performance in software development.
Do Low Cost Virtual Reality Devices Support Learning Acquisition?

Virtual Reality (VR) can support engaging and effective learning and current VR devices commercially available are opening up opportunities and expectations in higher education. An open research question is determining whether the fidelity of the VR device (e.g. resolution, framerate, etc.) impacts learning. This work aims to address this question by conducting a study wherein the quality of the experience and the knowledge acquired using a high fidelity (and high priced) VR headset is compared against a lower fidelity (and lower priced) VR headset. The results of the study do not reveal significant difference in spatial and experiential learning. The user satisfaction and experience are also similar regardless of the headset used.
Leveraging Online Audio Commons Content for Media Production

With the advent of online audio resources and web technologies, digital tools for sound designers and music producers are changing. The Internet provides access to hundreds of thousands of digital audio files, from human- and nature-related environmental sounds, instrument samples and sound effects, to produced songs ready to use in media production. In relation to the vast amount of creative content available online, an emerging community has forged a culture of sharing. Creative Commons (CC) appears as a legal framework to support such initiative enabling the reuse and remix of creative artifacts. Through exploring five use cases, we discuss key concepts and challenges related to the use of Audio Commons content for linear media production.
Uncertainty evaluation for absolute flatness measurement on horizontally aligned fizeau interferometer

This paper describes and reports uncertainties associated with the absolute flatness measurement for an Ø 300 mm optical flat. Absolute flatness of 3 flats denoted as optical flat A, B and C was determined by conducting a three-flat test method. The measurements were carried out using a Fizeau interferometer. Sources of uncertainty that contributed to the measurement uncertainty were examined. The dominant contributions include off-center alignment during three-flat test measurement, temperature instability and deformation due to gravity. In our experiment, although the alignment was performed crucially, deviation up to 4 nm was observed from 6 sets of three-flat test. Since the optical flat was mounted using a sling type module, deformation due to clamping force can be neglected. Deformation due to gravitational force was investigated by comparing the contour map of the optical flat oriented at 0° and 180°. The maximum difference up to 99 nm was observed which is higher than accuracy of the reference flat (λ/20) itself.
Success-history based biology-inspired algorithms for global trajectory optimization

Abstract Biology-inspired algorithms are computationally efficient for real-parameter optimization. However, the search efficiency of such algorithms depends significantly on their ability in keeping the balance between exploration and exploitation when solving complex multimodal problems. A new technique for generating potential solutions in biology-inspired algorithms is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions, thereby improving their exploration and exploitation abilities. Thus, this paper describes the application of modified biology-inspired algorithms, namely the Firefly Algorithm, the Cuckoo Search Algorithm and the Bat Algorithm to global trajectory optimization problems. The problems are provided by the European Space Agency and represent trajectories of several well-known spacecraft, such as Cassini and Messenger. Firstly, modified versions of the listed heuristics as well as their original variants were evaluated on a set of various test functions. Then their performance was evaluated on two global trajectory optimization problems: Cassini-1 and Messenger. The experimental results obtained by them are presented and compared. It was established that success-history based position adaptation allows better solutions to be found with the same computational effort while solving complex real-world problems. Thus, the usefulness of the proposed position adaptation technique was demonstrated.
Optical form measurement system using a line-scan interferometer and distance measuring interferometers for run-out compensation of the rotational object stage

Positioning an interferometric sensor with respect to the specimen requires a precise and expensive motion system to retain the sensors accuracy. A cost-efficient and precise setup requires a compensation of any run-out that the motion system induces. This contribution demonstrates that run-outs of a rotational axis can be compensated by implementing a low-cost interferometric point sensor to a previously presented interferometric line-scan system. Furthermore, the setup is extended by an evaluating algorithm that is capable of evaluating the line-sensor’s measurement data in real-time, using either a CPU or a GPU. An improved mechanical design of the interferometric sensor is introduced. It consists of fewer mechanical parts compared to previous versions, thus making the sensor more efficient in production and more robust against vibrations. An up-to-date high-speed line camera with a length of 4,000 pixels and line rate of up to 200 kHz increases the measurement rate of the sensor to up to 2,000 height values per second per camera pixel, enabling the sensor to evaluate 8 million height values per second.
Pushing Visualization Effects into Pushed Schema Enumerated Tree-Based Support Constraints

Based on the datasets from UCI and Obstructive Sleep Apnea, a disparate methodology of uncovering the visualization effects into the pushed support constraints of schema enumerated tree-based classification techniques is proposed and presented in this paper. This is to actively ‘wipe out’ the redundant growing effects of decision trees through itemset generation when visualization techniques are applied using Principal Component Analysis (PCA) and/or Principal Component Variable Grouping (PCVG) algorithms. Enumeration specification is based on the schema enumerated tree (SET) drawn after sorting out the features and characteristics on each dataset applied. The linchpin is to streamline the pre-tree classification effects for post-tree classification by using visualization techniques, i.e. PCA and/or PCVG, which are applied during the SET development. The over-fitting effects done during the SET development by the pushed support constraints can be counter-corrected by fewer PCA and/or PCVG imposed during visualization processes. The under-fitting effects done by the imprecise ‘early stopping’ of the SET development can be counter-corrected by greater PCA and/or PCVG imposed during the post-tree classification techniques through pushed SET support constraint learning. Research outcome on all the investigated datasets showed that the prediction accuracies have been profoundly improved after applying visualization of PCA and/or PCVG algorithms into the pushed SET-based or SET-based support constraints.