Reviewerly/exHarmony huggingface dataset:
Till We Meet Again: A Cinévoqué Experience

Virtual Reality movies, with its possibility of being immersed in 360° spaces, have both inherent challenges and advantages associated with creating and experiencing them. While the grammar of storytelling in traditional media is well established, filmmakers cannot utilize them effectively in the context of VR due to its immersive nature, as the viewers could end up looking elsewhere and miss important parts of the story. Taking this into account, our framework Cinévoqué leverages the unique features of this immersive medium for creating seamless movie experiences, where the narrative alters itself with respect to the viewer’s passive interactions, without making them aware of the changes. In our demo, we present Till We Meet Again, a VR film that utilizes our framework to provide different storylines that evolve seamlessly for each user.
A Data Security Scheme for the Secure Transmission of Images

The transfer of image is happening over the unsecured network needs good security mechanisms to keep the image away from the unauthorized access. This paper is suggesting a new approach for providing security of images using Elliptic Curve Cryptography (ECC) and DNA encoding. The RGB image is encoded using DNA encoding followed by DNA addition to increase the randomness. After that a hybrid encryption is performed based on Elliptic Curve Cryptography and Hill Cipher. This will increase the security of the image and thus prevent attacks from hackers.
Automatic processing of Z-transform artificial neural networks using parallel programming

The article describes the process of computing the Z-transform neural network on the basis of input and output signals of analyzed object. Parallel algorithms for performing these calculations are presented and different parallel architectures with different number of processors showing their advantages and limitations are analyzed.
Ensemble of Neural Networks

The most frequent approach to data-driven modeling consists to estimate only a single strong predictive model. A different strategy is to build a bucket, or an ensemble of models for some particular learning task. One can consider building a set of weak or relatively weak models like small neural networks, which can be further combined altogether to produce a reliable prediction. The most prominent examples of such machine-learning ensemble techniques are random forests Breiman (Mach Learn 45:5–32, 2001) and neural network ensembles Hansen and Salamon (IEEE Trans Pattern Anal Mach Intell 12:993–1001, 1990), which have found many successful applications in different domains. Liu et al. (Earthquake prediction by RBF neural network ensemble. In: Yin F-L, Wang J, Guo C (eds) Advances in neural networks ISNN2004. Springer, Berlin, p 962–969, 2004) use this approach for predicting earthquakes. Shu and Burn (Water Resour Res 40:1–10, 2004) forecast flood frequencies with an ensemble of networks. We start this chapter by describing the bias-variance decomposition of the prediction error. Next, we discuss how aggregated models and randomized models reduce the prediction error by decreasing the variance term in the bias-variance decomposition. Theoretical developments are inspired from the PhD thesis of Louppe (Understanding random forests: from theory to practice. PhD Dissertation, Faculty of Applied Sciences, Liége University) on random forests, 2014.
Issues in creating digital archives. (Experience of building VAK documentary e-archive)

The author reveals and analyses the problems of digital archive building. She summarizes the experience of VAK (Higher Attestation Commission) documentary e-archive. Building and development of VAK archival information system is discussed; the problems of each stage are specified. The author discusses the processes of archival materials analysis and systematization. The processes of archival documents verification, digitization of archival materials, information system development, storage of digitized materials are examined. The analysis goal is to summarize complicated problems of the project implementation and to design viable approaches to be used in similar projects. The ways to populate digital archives are identified; efficient procedure for building digital archives is proposed.
An efficient edge-assisted mobile system for video photorealistic style transfer

In the past decade, convolutional neural networks (CNNs) have achieved great practical success in image transformation tasks, including style transfer, semantic segmentation, etc. CNN-based style transfer, which denotes transforming an image into a desired output image according to a user-specified style image, is one of the most popular techniques in image transformation. It has led to to many successful industrial applications with significant commercial impacts, such as Prisma and DeepArt. Figure 1 shows the general workflow of the CNN-based style transfer. Given a content image and a user-specified style image, the content features and style features can be extracted using a pre-trained CNN, and then be merged to generate the stylized image. The CNN model is trained for generating a stylized image that has similar content features as the content image's and similar style features as the style image's. In this example, we can see the content image is captured at a lake in the daytime, while the style image is another similar scene captured at dusk. After performing style transfer, the content image is successfully transformed to the dusky scene while keeping the content unchanged as the content image.
Secured Hybrid Prediction Based Reversible Watermarking For Medical Images

Advancement in storage of patient record is changing in today's world and the art of secretly hiding the patient's medical information in the medical image is called as medical image watermarking. In case of medical images embedding of data in it can cause distortions and it may lead to misdiagnosis. Distortions in medical images are not acceptable and hence a secured hybrid prediction based reversible watermarking technique is used for complete restoration of the original image and the also the patient record embedded in it without any distortion. In this paper a secured hybrid prediction based technique along with hashing algorithm is used to enlarge the payload capacity of the medical images and also data authentication has been used for protecting the integrity of the message and validating the identity of the originator. Message authentication is provided by using MD5 hashing algorithm. The simulation results shows that this technique is robust and in the presence of noise, upto 99.76% of data can be recovered.
Hierarchical Correlation Stereo Matching Network

Recently, stereo matching from a pair of rectified images has been cast as a supervised learning task using the powerful representation of convolutional neural networks. However, existing methods only utilize last feature maps output from Siamese Networks to compute similarity measurement, which are lack of multi-levels similarity information to construct an informative cost volume. To solve this problem, we propose a hierarchical correlation operation to compute similarity of stereo pairs at multiple levels. In addition, to yield accurate disparity in ill-posed region, we propose a stacked hourglass feature network with dense connections to effectively incorporate context information. Then, hybrid matching cost volume is built leveraging hierarchical correlation features and concatenation features of left and right. 3D CNN encoder-decoder architecture is utilized to regularize the cost volume and regress disparity. Experiments demonstrate that our network achieves competitive performance with state-of-the-art methods on Scene Flow, KITTI 2012, and KITTI 2015 datasets.
Chinese Reviews Generation Based on HM-BiLSTM Model

Abstract HM-BiLSTM model is proposed to solve the problem of inaccurate expression of Chinese comment with specific topic, which is generated by existing deep learning models. Firstly, HM-BiLSTM model was created to generate comments with subject attention mechanism algorithm whose purpose was to assist to constrain the theme. Then, imitation writing sentence was retrieved to extract syntactic structure information from Chinese reviews corpus. The subject as well as structure information was both encoded. Lastly, Chinese comments were generated recurrently by HM-BiLSTM model under the constraints of the subject attention mechanism algorithm and syntactic structure information. The experiment results showed that the model generated comments with better quality and accurate theme.
Research on the Control Principle of “Four Wheels and Hexapod” Robot System

Abstract A set of “four-wheeled and hexapod” deformable robotic system, which can be remotely controlled and highly integrated, has been designed and implemented. It consists of a four-wheeled robot body, a hexapod robot body, a control system and a control terminal of computer. The sensor module and camera mounted on the robot can collect environmental information and transmit it to the control terminal by wireless communication. The “four-wheeled hexapod” robot is controlled remotely to complete the deformation, search and rescue and other related operations by the control terminal. With the great characteristics of low power consumption, low cost, high scalability and high portability, the implementation can also have a significant effort on tasks such as detection, search and rescue, and transportation of materials in a complex rescue environment.
Towards a Logic of Epistemic Theory of Measurement

We propose a logic to reason about data collected by a number of measurement systems. The semantic of this logic is grounded on the epistemic theory of measurement that gives a central role to measurement devices and calibration. In this perspective, the lack of evidences (in the available data) for the truth or falsehood of a proposition requires the introduction of a third truth-value (the undetermined). Moreover, the data collected by a given source are here represented by means of a possible world, which provide a contextual view on the objects in the domain. We approach (possibly) conflicting data coming from different sources in a social choice theoretic fashion: we investigate viable operators to aggregate data and we represent them in our logic by means of suitable (minimal) modal operators.
Apply Deep Learning Techniques on Classification of Single-Band SAR Satellite Images

Synthetic Aperture Radar (SAR) images have been widely applied to compensate the optical satellite images. SAR can be performed night and day, as well as be able to penetrate the cloud is one of advantages. It is famously used in disaster applications for gathering the damaged area information. Apart from that, SAR images are also contained with speckle noises, which cause the difficulty of the images for human visualization as well as the one band of SAR image is insufficient, they are additionally required skillful specialists. Therefore, to work on manual classification purpose would face the questionable objects and easy to be misclassified. Nowadays, the deep learning techniques could be capable of solving classification problems due to outperformed accuracy. In this study, we proposed the end-to-end classification framework using single band SAR image, which applied the deep learning to solve the problems. Our framework is divided into three main parts consisting of preparing and the dataset, developing the classification model, and classifying our testing images. We experimented on four classes using ALOS 2 satellite images. Finally, the prediction model reached to 91% accuracy on our test dataset with adding dropout techniques. Expectedly, our framework could be practically adopted in different applications in advance.
Abnormal events detection method for surveillance video using an improved autoencoder with multi-modal input

This paper introduces an algorithm to solve the the anomaly behavior detection problem of surveillance video through an improved autoencoder with multimodal inputs. Using 3D convolution and 3D deconvolution, and the decoder adds a feature map corresponding to the encoder on a specific layer to enhance the image detail information. Taking the RGB frame and the optical flow as inputs, abnormality scores are calculated according to the reconstruction error for locating the abnormal segment. Experiments conducted in the CUHK Avenue dataset, the UCSD Pedestrian dataset and the Behave dataset, our approach works best compare to the original approach. While improving the AUC, due to the use of unsupervised learning, a lot of labeling time is saved, which is more in line with the diversity and contingency of abnormal behavior in real life.
Active Child Program by Electronic Craft Work

Recently, children no longer play outside. Therefore, the athletic ability of children has to declined. We have been doing a workshop to let children have interest in manufacturing and traditional culture continuously. In this study, we devised the active child program that increased the interest in play in addition to the workshop to achieve these aims. We tried two programs. The first one is the theme of Hachiman of the Iwami-kagura. Because of the ogre extermination story with a bow and arrows, it is an electronic craft work lesson including Sports Onigokko and archery game. The second one is four-frame manga. It is the creation of a short story using a matrix LED including a gesture game. In these workshops, we gave a stage of performance to express children's ideas. This is considered to be a logical communication training method including the element of directing.
pFilter

Enterprise needs to process customer data for providing tailored services to them, however, the data often includes sensitive and personally identifiable information. This leads to a difficult situation wherein the enterprise has to balance the necessity to process the sensitive data with the requirement to safeguard its privacy. The problem is more prominent in legacy applications with almost no privacy controls in place. A well-studied technique to retrofit legacy application is to mask sensitive content before it is rendered on the screen using path based methods. In this work we show the gap in the existing state of art and describe a dynamic system which utilizes a context to perform locality based searching and masking of sensitive content.
The 5R adaptation framework for cultural heritage management information system of the Dayak tribe Borneo

Abstract The Dayak tribe Borneo is divided into 405 sub-tribes which are formed based on clumps, habitats and their origins, despite being Dayak ethnic groups, but each has a wealth of cultural heritage and local wisdom that is diverse and different according to the nature they live in. This paper proposes a framework for cultural heritage management information system of The Dayak Tribe Borneo. The development method approach refers to the 5R Adaptation Framework concept; Right Time (timing), Right Location (location), Right Device (device), Right Learner (user), and The Right Contents (cultural information). The development results have provided a framework called “e-Dayaknese”, a framework that integrates information content: location, time, devices, users, and content (cultural heritage information of the Borneo Dayak tribe referring to the UNESCO cultural heritage domain).
Photon-pair characterization using one single-photon detector by time multiplexing

We experimentally demonstrate coincidence measurements as well as <italic>g</italic><sup>(2)</sup> ( nτ ) correlation function measurements with only one single-photon detector (SPD) with the aid of field-programmable gate array and 2 × 2 fast optical switches. Both experiments produced similar results compared to conventional experimental methods, requiring more than one SPD. When detecting correlations between multiple single photons, this technique can be employed to reduce demands for the number of SPDs and potentially would be able to offer new options to researchers and institutions requiring coincidence measurement systems with lower complexity as well as smaller form factors.
Effects of Transparency and Controller Type on Performance in A Multi-Layer Display

Augmented reality application development faces the challenge of balancing key information visualization with user’s cognitive constraints. Virtual Reality Rehab, Inc. is developing an application blending augmented reality (AR) with synthetic vision for use in complex, time-sensitive military operations. This study investigated the effects of controller use and transparency on marker placement speed and accuracy. Method: Participants were required to mark targets by placing icons on the synthetic environment using either a mouse, speech recognition, or Xbox One controller. They were also asked to recall AR building labels. For each scenario, the display was set to 100%, 25%, 50%, or 75% transparency. Results: The 50% transparency condition resulted in the lowest speed and accuracy, while 25% and 75% were not significantly different. Participants performed the best while using the Xbox One controller. Discussion: Findings suggest that transparency should be investigated within the context of task, controller type, and interface design.
Archetypes and design

Identification of basic archetypes and their remote structure in the context of civilization design, industrial and communicative design makes it possible to specify age-long stability of certain patterns and instruments. This contributes to design of effective models and necessary things. Archetypes as constantly recurring patterns of actions reflected in the mind in the form of unconscious schemes, patterns of thinking, behaviour and perception. Archetypes are a level of connection between being and thinking in the process of work, an area of contact of human corporeality and material side of the world. In the context of technical progress, miniaturization of gadgets and integration of functions, archetypal items remain highly-demanded. The clock with a round dial symbolizes the archetype of “eternal return”. Instruments and lever control systems, a lamp with the mechanism of “lever balance”, which simulates levels of human arm freedom, express the archetype of action. A jar made by a master on a potter’s wheel symbolizes a “vessel of life”, embodies the archetype of rotation, “wheel of life”. Symbolic figures of visual communication – circle, spiral, swastika and concentric circles – also present the archetype of rotation. Mandala, a special symbol that can be noticed in the design of visual communications, as well as in city plans in ancient civilizations, expresses the archetype of selfness. The design of civilizations has constants established by the contact of human corporeality and material side of the world. However, most of contemporary things that have a symbolic meaning lack being universal, since they are made within the structure of civilization, beyond incrustation of human corporeality in the world. Acting in the world, a person constantly repeats life patterns; this archetype of “eternal return” remains a basic attitude of the design.
Discovering Cyclic and Partial Cyclic Patterns Using the FP Growth Method Incorporated with Special Constraints

Analyzing the temporal behavior of frequent patterns and decreasing the size of discovered patterns are two major challenges in the area of temporal data mining. Several methods are available in this context, among them, constraint based pattern mining approach contributed a lot in this field. There are several methods have been proposed in this direction. However, while exploring the patterns based on time granularities: cyclic and partial cyclic patterns, the existing methods use the traditional Apriori algorithm or Interleaved algorithm, that takes lots of time while generating candidates. In this paper, a new strategy – Frequent Pattern Growth technique Incorporated with Special Constraints (FPGSC) is proposed. Here, complete cyclic and partial cyclic constraints are imposed on the framework consists of a Frequent pattern growth method for generating frequent patterns. This algorithm is able to discover complete cyclic and partial cyclic patterns in an efficient way. We also analyze the experimental results that show that it is as efficient as other algorithms in this field and it is better to generate more appropriate temporal patterns.
The problem of neural networks communication

Abstract In spite of the successful application of artificial neural networks (ANNs) for the solution of multiple problems (forecasting, language translation, image classification, voice recognition etc.), ANNs are still autonomous entities incapable of communication or exchange of their knowledge. Meanwhile, the ability to communicate is critical for further development of methods of artificial intelligence. We propose and test several methods of communication and knowledge fusion of ANNs. These methods do not require the presence of the initial training data and use only the internal parameters of ANNs. We propose generative iterative and non-iterative methods of ANNs communication. Noniterative methods show the classification accuracy similar to that provided by an ensemble of ANNs. The accuracy of generative methods is similar to a network trained on the joint dataset.
Information Technologies in Education

The article deals with the issue of using the latest information technologies in education and provides examples of using them in this field. The significance and effectiveness of implementing and using the information technology in Russian education are also analyzed in this article. The study suggests that information technologies in education allow using a greater number of teaching methods than traditional techniques, and that they are an innovative way to improve and organize the educational process.
Measuring the Capability of a Speech Translation System

This chapter introduces an evaluation method for speech translation systems. Since the method estimates the system’s capability in a commonly known English proficiency measurement score such as TOEIC, the evaluation results are very comprehensible not only for researchers but also for system users. This chapter also formulates the evaluation errors and discusses the costs necessary for evaluation. Lastly, we provide a solution to the evaluation cost issue by applying an automatic evaluation method.
Accounting Science with Technological Development

Abstract The purpose of this study is to identify the development of application technology in the field of accounting, and the benefits of using accurate applications for users of accounting services. The method used in this research is descriptive method, which is to describe the events that are happening. The results of this study are to identify the extent to which the development of accounting-based application technology in the times, as well as the benefits provided by accurate applications in the field of accounting. This research was conducted to discuss how easy it is for accurate application users to produce complicated accountancy activities, which range from the process of entering a purchase transaction and a sales transaction, recording the inventory, the journal process, the classification of records, until the process of presenting the financial statements. The complexity of accounting activities carried out by an accountant and the length of time presenting financial statements can be done by anyone quickly without having to understand deep accounting knowledge.