Reviewerly/exHarmony huggingface dataset:
High-Precision Sub-Pixel Displacement Algorithm Based on Temporal Sequence Digital Images

Digital image correlation algorithm (DIC) has the advantages of non-contact, full-field measurement, and easy automation. The previous sub-pixel positioning algorithms for temporal sequence digital images used the entire pixel displacement as the initial value of the fitting. When the displacement between consecutive multiple frames of images is small (less than 1 pixel), the fitting part of the sub-pixel will produce a larger To solve this problem, this article proposes a high-precision sub-pixel displacement algorithm based on time-series digital images. First, the entire pixel displacement is calculated by the particle swarm algorithm, and then the subpixel displacement is calculated using the bicubic interpolation algorithm, and finally the moving least square is used. The multiplication fits the sub-pixel displacement solved in the previous step on the time axis, and a high-precision sub-pixel displacement is obtained. At the end of the article, ANSYS was used to simulate the tensile experiment of duralumin, and 100 speckle images were simulated by Matlab to verify the effectiveness of the method. The experimental results show that the accuracy of the method can reach 0.01-0.03pixel, the mean error is 0.00263, and the standard deviation is 0.00222.
$P^{2}$ Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation

The target of human pose estimation is to determine the body parts and joint locations of persons in the image. Angular changes, motion blur and occlusion in the natural scenes make this task challenging, while some joints are more difficult to be detected than others. In this paper, we propose an augmented Parallel-Pyramid Net ( P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) with feature refinement by dilated bottleneck and attention module. During data preprocessing, we proposed a differentiable auto data augmentation ( DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) method. We formulate the problem of searching data augmentaion policy in a differentiable form, so that the optimal policy setting can be easily updated by back propagation during training. DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> improves the training efficiency. A parallel-pyramid structure is followed to compensate the information loss introduced by the network. We innovate two fusion structures, i.e. Parallel Fusion and Progressive Fusion, to process pyramid features from backbone network. Both fusion structures leverage the advantages of spatial information affluence at high resolution and semantic comprehension at low resolution effectively. We propose a refinement stage for the pyramid features to further boost the accuracy of our network. By introducing dilated bottleneck and attention module, we increase the receptive field for the features with limited complexity and tune the importance to different feature channels. To further refine the feature maps after completion of feature extraction stage, an Attention Module ( AM) is defined to extract weighted features from different scale feature maps generated by the parallel-pyramid structure. Compared with the traditional up-sampling refining, AM can better capture the relationship between channels. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the best performance on the challenging MSCOCO and MPII datasets.
An reinforcement learning approach for allocating software resources

Abstract Software resource allocation is an significant factor of system configuration which plays a critical role in guaranteeing the performance of multitier web service systems. Computing the optimal allocation of different software resources in order to meet performance requirements under dynamic workloads conditions is in highly challenging. Existing approaches mostly rely on translating domain knowledge from experts into computational solutions through heuristics‐based optimization techniques. While such techniques are useful, they cannot leverage actual usage data generated by system users which may contain allocation strategies that are not captured by domain experts' knowledge. In this paper, we propose an iterative feedback mechanism which solves the problem to some extent by optimizing software resource allocation of multitier web systems through imitating system users who have achieved excellent performance. Specifically, we propose a deep Q‐learning network‐based approach for performance prediction to deal with the dynamic changes of complex workloads. The performance prediction method involves the reinforcement learning method for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the multitier web benchmark system, and the experimental results demonstrated significant improvement compared to models built based on domain knowledge.
Analysis of the features of image processing using the Hamming network on the STM-32 microcontroller

Abstract The paper discusses the features of solving a class of problems for pattern recognition using the STM-32 microcontroller. The problem of pattern recognition can be solved on neural networks of different architectures, the main attention is paid to the Hamming neural network model. The features of the implementation of the Hamming network based on the STM-32 microcontroller for the recognition of images entered via the touch screen are analyzed. It is experimentally shown that the network cannot always correctly process the input value and compare it with the reference value of the class for digital test images. This is due to the high degree of similarity of some images and the presence of noise. In conclusion, recommendations on the implementation of neural network algorithms for image processing on microcontrollers are given.
Optimized MK-ELM by BAS and its Application in sEMG Gesture Recognition

In order to solve the problem of insufficient generalization performance and kernel parameter selection of the kernel extreme learning machine, a new model based on optimized multiple kernel extreme learning machine by beetle antennae search algorithm (BAS-MK-ELM) is proposed. In the model, the weighted combination of polynomial kernel function and Gaussian kernel function is selected as the kernel function of multiple kernel extreme learning machine (MK-ELM), and beetle antennae search algorithm is introduced to optimize the kernel function parameters to get the optimal kernel function parameters. This classification model is applied to sEMG gesture recognition, and four time-domain features of the pre-processed signalsl are extracted, and signal features are classified and recognized by BAS-MK-ELM. The experimental results show that the average recognition rate of the proposed classification model is higher than that of single-kernel extreme learning machine, multi-kernel support vector machine, MK-ELM and particle swarm optimization multi-kernel extreme learning machine, which verifies the effectiveness and superiority of the proposed classification model.
Elastic scheduler: Heterogeneous and dynamic deep Learning in the cloud

Abstract GPUs and CPUs have been widely used for model training of deep learning (DL) in the cloud, where both DL workloads and resource usage might heavily change over time. Traditional training methods require beforehand specification on the type (either GPUs or CPUs) and amount of computing devices, and thus cannot elastically schedule the dynamic DL workloads onto available GPUs/CPUs. In this paper, we propose Elastic Scheduler ( ES ), a novel approach that efficiently supports both heterogeneous training (with different device types) and dynamic training (with varying device numbers). ES (i) accumulates local gradients and simulates multiple virtual workers on one GPU to alleviate the performance gap between GPUs and CPUs for achieving similar accuracy in heterogeneous GPU‐CPU‐hybrid training as in homogeneous training and (ii) uses local gradients stabilizes batch sizes for high accuracy without long compensation. Experiments show that ES achieves significantly higher performance than existing methods for heterogeneous and dynamic training as well as inference.
Where are you? Influence of Redirected Walking on Audio-Visual Position Estimation of Co-Located Users

Two-user redirected walking (RDW) promises new possibilities for collaborative experiences in virtual reality (VR), but it also introduces challenges like the spatial de-synchronization emerging when one or more users are redirected in different ways. We analyzed the ability to estimate a co-located user's position, and assessed the feeling of representation consistency in an interactive game. Results show that a user's estimation of the other's position improves when their partner's virtual and physical position get closer together. Furthermore, larger redirection gains lead to a lower feeling of consistency, which also applies if only one person is redirected.
Analysis of pedagogical approaches to formation of technological competence

This article examines the problems of forming the technological competence of various scientists. In the process of training future specialists in various industries, the authors propose methods and means for the formation of technological competence: the introduction of a special course into the educational process, which is aimed at developing the professionally important qualities of a specialist; application of gaming technology and project method in the classroom in special disciplines; passing by students of industrial practice; creating a portfolio.
Corporate library’s staff. Functions and competences

The author clarifies the corporate library’s definition, specifies its particular qualities and distinctive features. She reviews the issues of education, competences, and advanced training of corporate library staff. The corporate libraries (during the Soviet era they were sci-tech units and trade-union libraries of organizations) are affiliated by both governmental and commercial organizations. As a result of cost optimization, the number of technical libraries decreased, while the trade union libraries completely ceased to exist. However, in every epoch, the mission of institutional or corporate library to satisfy the information needs and demands of workers and their on-the-job training. The collections of corporate libraries comprise digital resources, books, unpublished documents, periodicals and other information sources demanded by the corporate community. In fact, globally, the corporate libraries are the special libraries affiliated by companies and institutions, their units fulfilling information, cultural and educational functions and contributing to corporate efficiency improvement. In Russian library science, the corporate libraries have remained understudied; they are not represented in the Russian Library Association (RLA) or the National Library Bibliographic Classification (LBC). Meanwhile, in foreign countries, the corporate libraries have been well-established institutions with their specific features widely recognized. The author examines the problems the corporate library structure, efficiency and functionality, as well as the competences of staff, their education and experience. The competences and high qualification of corporate specialists enable them to meet information needs of corporate professionals immediately, completely and relevantly and to support strategic decisions. The author specifies the key competencies that contribute to the efficiency of corporate libraries.
Uzay Kafes Çatı Sistemlerinde PV Panel yerleşim optimizasyonu ile Maksimum Elektrik Enerjisi Elde Edilmesi

Solar panel is mostly applied in many roof types on buildings. That the angle of solar radiation is different in curved space truss system affects the amount of voltage and current of the solar panels and overall system efficiency. In this study, meteorological information of Isparta city, technical data of various solar panels, intra-year random shadow effect calculations are analyzed. The technic of genetic algorithm is applied, and the process of computerized simulation is performed in order to find the optimal solar panel combination. Based on the simulation results, the highest efficiency will be obtained from the PV panels if PV panels application is made on the curved space truss roof system.
Effective and Adaptive Refined Multi-metric Similarity Graph Fusion for Multi-view Clustering

Multi-view graph-based clustering aims to partition samples via fusing similarity graphs from different views into a unified graph. The clustering performance relies on the accuracy of similarity measurement. However, most existing methods utilize a single metric whose similarity measurement can be easily corrupted by noises thus lacking high accuracy and generalization capability. We propose an effective multi-metric similarity graph refinement and fusion method for multi-view clustering. We construct multiple similarity graphs for each view by different metric, exploit a novel refined similarity through symmetric conditional probability to preserve the important similarity information and finally adaptively fuse multiple refined similarity graphs to an informative unified one. Extensive experiments on eight benchmark datasets have validated the effectiveness and superiority of our proposed method comparing to thirteen state-of-the-art methods.
Digital Library SMK Negeri 3 Tanjungpinang Berbasis Web Menggunakan PHP dan JQuery Pada Framework Codeigniter

&#x0D; &#x0D; &#x0D; SMK Negeri 3 Tanjungpinang is a vocational high school located in Kampung Bulang, Tanjungpinang. This school is facing problems in managing the library that is owned so that it can serve library lending for users more quickly and accurately including in providing access to library materials directly to users. But, at the moment. The library still does not have a library circulation system that can provide library access. The web-based library system that is designed is expected to facilitate library members in SMK Negeri 3 Tanjungpinang in finding digital books about library materials owned by libraries without time and place limits. The web-based library sirkuasi system, which is designed according to the needs and conditions of the library is also expected to be able to facilitate and speed up officers in handling circulation services and making reports. Programming is done to make the system above is done using the PHP programming language with a MySQL database server. From the results of the implementation of the system and testing, it was concluded that the web-based library information system can answer the problems encountered so that it can help the library in serving users quickly and accurately.&#x0D; &#x0D; &#x0D;
Deep Active Learning for Autonomous Perception

Traditional supervised learning requires significant amounts of labeled training data to achieve satisfactory results. As autonomous perception systems collect continuous data, the labeling process becomes expensive and time-consuming. Active learning is a specialized semi-supervised learning strategy that allows a machine learning model to achieve high performance using less training data, thereby minimizing the cost of manual annotation. We explore active learning for autonomous vehicles, and propose a novel deep active learning framework for object detection and instance segmentation. We review prominent active learning approaches, study their performances in the aforementioned computer vision tasks, and perform several experiments using state-of-the-art R-CNN-based models for datasets in the self-driving domain. Our empirical experiments on a number of datasets reflect that active learning reduces the amount of training data required. We observe that early exploration with instance-rich training sets leads to good performance, and that false positives can have a negative impact if not dealt with appropriately. Furthermore, we perform a qualitative evaluation using autonomous driving data collected from Trondheim, illustrating that active learning can help in selecting more informative images to annotate.
Compressing Deep Networks Using Fisher Score of Feature Maps

In this paper, we propose a new structural technique for pruning deep neural networks with skip-connections. Our approach is based on measuring the importance of feature maps in predicting the output of the model using their Fisher scores. These scores subsequently used for removing the less informative layers from the graph of the network. Extensive experiments on the classification of CIFAR-10, CIFAR-100, and SVHN data sets demonstrate the efficacy of our compressing method both in the number of parameters and operations.
Anomaly Detection for Video Surveillance

Anomaly Detection is system which identifies inappropriate human behavior. One of the major problems in computer vision is identifying inappropriate human behavior. It is crucial as activity detection can help many numbers of applications. It can benefit applications like image monitoring, sign language recognization, object pursue and many more. Many alternatives are there such as low-cost depth sensors, but they do have some drawbacks such as limited indoor use also with lower resolution and clamorous depth information from deep images, it becomes nearly impossible to assess human poses. In order to resolve the above issues, the proposed system plans to utilize neural networks. One of the major research area is to recognize suspicious human behavior in video monitoring, in the field of computer vision. Several surveillance cameras are situated at places like airports, banks, bus station, malls, railway station, colleges, schools, etc to detect suspicious activities such as murder, heist, accidents, etc. It is a tedious job to detect and monitor these activities in crowded places, to trace real time human behavior and classify it into ordinary and unexpected scenarios the system needs to have a smart video surveillance. The experimental results show that the proposed methodology could assuredly detect the unexpected events in the video.
Mixture Distribution Graph Network for Few Shot Learning

Few-shot learning aims at heuristically resolving new tasks with limited labeled data; most of the existing approaches are affected by knowledge learned from similar experiences. However, interclass barriers and new samples insufficiency limit the transfer of knowledge. In this article, we propose a novel mixture distribution graph network, in which the interclass relation is explicitly modeled and propagated via graph generation. Owing to the weighted distribution features based on the Gaussian mixture model, we take class diversity into consideration, thereby utilizing information precisely and efficiently. Equipped with minimal gated units, the “memory” of similar tasks can be preserved and reused through episode training, which fills a gap in temporal characteristics and softens the impact of data insufficiency. Extensive trials are carried out based on the MiniImageNet and CIFAR-FS data sets. The results turn out that our method exceeds most state-of-the-art approaches, which shows the validity and universality of our method in few-shot learning.
Ontology-Driven and Integrated Automotive Systems Engineering

As cars become smart and connected, automotive development gets more complex. Traditional disciplines such as mechanical, electrical and software engineering can no longer remain separate, but are required to collaborate. Further, new regulations and standards require engineering activities across domains to be traceable and their outputs to be consistent. Current IT-architectures do not sufficiently support those new needs. Automotive engineering domains mainly rely on separate expert tools. Interfaces are, if at all existent, inefficient and come along with a loss of data and information. Sophisticated integration of IT-systems is the basis for solving the drawn problems, as it enables efficient and effective collaboration amongst domains. Hence, it also forms the basis for trending approaches in automotive engineering such as model-based systems engineering and function-orientation. Thus, a state-of-the-art integration architecture for the automotive engineering IT is presented in this paper. For instance, it suggests replacing former implicit relations between assets of separate systems by explicit ones through applying an ontology-driven Linked Data approach. This improves traceability and enables referencing, addressing and reusing IT-assets across expert tools. Therefore, it is further proposed to exchange data efficiently among systems in an innovative API-driven manner. The holistic integration architecture enables effective and consistent collaboration and ensures traceable work results across domains. The result is a future-proof architecture that serves as a blueprint for automotive companies to face the challenges of developing innovative cars as of today and tomorrow.
COMMUNICATIVE AND LINGUISTIC DEVICES USED IN ELON MUSK’S PERSONAL BRANDING (BASED ON TWITTER AND ONLINE PUBLICATIONS)

This research is dedicated to the study of Elon Musk’s personal brand. Communicative and linguistic devices used by Elon Musk to build, maintain and boost his personal brand, as well as to counter threats to its status, were analyzed on the basis of Twitter and online publications within the framework of the present research. The study traced the conflict between Elon Musk and short sellers with the participation of the US Securities and Exchange Commission and proved that regardless of the financial losses and legal challenges the businessman faced, the reaction of Elon Musk’s followers was mostly positive due to the effective brand-building techniques employed by Elon Musk. The conducted Twitter analysis dealt with both Elon Musk’s personal publications and the responses of his followers, and found that the main linguistic and communicative devices of Elon Musk’s personal branding were provocative tweets, popular culture references, connotatively charged notions, irony, allusions, acronyms and wordplay. As part of the present research, we studied how the linguistic phenomenon shaped reality, as Elon Musk took a literary technique and used it as a real-life enhancement of his personal brand. According to the results of the research, these devices were aimed at reinstating the credibility and authority of Elon Musk’s personal brand, attracting the attention of the followers, winning their support and counterattacking the opponents in order to re-establish a powerful personal brand. The research proves that these devices found positive response among Elon’s followers and had a positive effect on his brand attracting attention and boosting popularity and recognizability of Elon Musk’s personal brand.
Interactive Narrative Facial Expression Animation Generation by Intuitive Curve Drawing

This paper presents a type of interactive facial expressions animation generation system based on traditional montage techniques and generative narrative concepts. It could allow users to produce narrative facial expression animation by interactively and intuitively drawing a plot curve based on pre-recorded facial expression animation clips, thus provide high flexibility and creativity to users with interesting interaction.