Reviewerly/exHarmony huggingface dataset:
Block Chain and Its Application

In this research article, the investigation has been made possible to gather some ideas about Block Chain technology and its recent applications. It has also been tried to explore some other implementations of the related protocols. At the initial stage, discussions on the Bitcoin (the first-ever application of the technology) are being provided. Block Chain technology is known as the technological basis on which Bitcoin is built. This technology has made it possible that every kind of transaction is executed in a proper manner of decentralized way. It does not require any trusted kind of the third party in between the whole transaction. By offering a distributed, undisputable, public verified records of the transactions, the Block Chain technology potentials to renovate many industries. In this chapter, a critical and detailed survey has been performed aiming toward the area of application of the Block Chain technology, its impact on the society, and its possible use cases. The idea purpose of this chapter is to provide that much ability to the readers to familiarize and to understand the state of the art of the Block Chain world, in both the society and technology also.
Adaptivity: A Continual Adaptive Online Knowledge Assessment System

The main goal of this paper is to provide an insight into implementation of a model for continual adaptive online knowledge assessment throughout Adaptivity, a web-based application. Adaptivity enables continual and cumulative knowledge assessment process, which comprises of a sequence of at least two (but preferably more) interconnected tests, carried-out throughout a reasonably long period of time (i.e. one semester). It also provides personalized post-assessment feedback, which is based on each student’s current results, to guide each student in preparations for the upcoming tests. In this paper, we provide description of adaptation model, reveal the design of Adaptivity and results of testing of the proposed model.
Application and research of GIS intelligent environmental protection Internet of things system construction technology

the wisdom of this project is based on GIS environmental iot system building technical application and research, is the use of geographic space information technology, satellite remote sensing technology, global satellite positioning technology, radio frequency identification technology, based on the electronic map, image data, which is based on Internet, metropolitan area network, or LAN, in different spatial location of environmentally friendly iot devices intuitive visual control, intelligent identification, location and tracking, monitoring, alarm, inspection and maintenance of various environmental iot information flow, gathering and processing and statistical analysis, system management and comprehensive application of socialized service system. This project studies the application of the GIS based intelligent environmental protection Internet of things system platform with universal applicability, realizing the unified access, storage, exchange, analysis and application framework of environmental perception information, and establishing the data center of the platform. The system of "digital environmental protection" has been constructed, and the Internet of things and sensor network in the field of environmental protection have been gradually constructed to promote the transformation from "digital environmental protection" to "smart environmental protection" and "digital city" to "smart city", thus revolutionizing the improvement of urban management level.
A Game Logic Specification Proposal for 2D Video Games

The game engines are one of the essential and daily used applications on game development. These applications are designed to assist in the creation of games' contents. However, the games definition and specification is still complex. The mechanisms involved in the behaviour definition are not easy to standardise, given their dependency with the subjective game logic design and the selected programming language features. In this sense, this work presents the design and development of a game logic system for 2D video games. It draws from the study and analysis of the behaviour and game logic definition literature. From this, a game logic system has been developed from a first-order logic semantics and a reduced set of actions and conditions. The model has been tested with several games to determine its potential. Finally, one of these games is described with the proposed semantics, and further on it is also used as a reference for a user test against other game logic systems.
Removal of the Infrared Light Reflection of Eyeglass Using Multi-Channel CycleGAN Applied for the Gaze Estimation Images

In virtual reality (VR) environments, the importance of the eye gaze estimation is rapidly increasing. The geometrical model base method is commonly used by equipping infrared (IR) light sources and cameras inside of the head mount displays (HMDs). Some HMDs enable users to wear with spectacles on, and, in this case, IR light reflections of the eyeglass often causes serious obstruction to detect those of the corneal. In this study, we propose the multi-channel CycleGAN to generate the eye images with no eyeglass from those with eyeglass. Proposed method has 4 channels input, which consists of three normal eye images at different time points and an image in blinking, in order to distinguish stationary and moving reflections in images. Proposal achieved to selectively remove the eyeglass reflections and keep the corneal reflections alive in sequential gaze estimation images.
Proposing Ontology-Driven Content Modularization in Documents Based on the Normalized Systems Theory

A problem of evolvability is widely discussed in the current world, and still, it has not been fully addressed yet. Our approach tries to improve evolvability in a domain of documents. Our approach is based on principles and recommendations from the Normalized Systems Theory. We try to redefine the process of how the document is created and maintained by involving ontologies. We offer a solution which should increase evolvability for a sort of documents which is created by a template and which is often updated. We demonstrate our solution to an example of a Data Management Plan document.
Set Constraints, Pattern Match Analysis, and SMT

Set constraints provide a highly general way to formulate program analyses. However, solving arbitrary boolean combinations of set constraints is \(\mathsf {NEXPTIME}\)-hard. Moreover, while theoretical algorithms to solve arbitrary set constraints exist, they are either too complex to realistically implement or too slow to ever run.We present a translation that converts a set constraint formula into an SMT problem. Our technique allows for arbitrary boolean combinations of set constraints, and leverages the performance of modern SMT solvers. To show the usefulness of unrestricted set constraints, we use them to devise a pattern match analysis for functional languages, which ensures that missing cases of pattern matches are always unreachable. We implement our analysis in the Elm compiler and show that our translation is fast enough to be used in practical verification.
On Decoding Algebraic Codes Using Radical Locators

It is well-known that the decoding of algebraic codes with error locators have been extensively conceived in the literature over a half century. The radical locator, which is an ω th root of error locator, has recently been discovered. This paper focuses on the two classes of radical locators. The first is complete radical locators, where all the locators are assigned to radical locators. The second is partial radical locators in that there are only a few locators being radical locators. In the former case based on complete radical locators, a new square matrix whose determinant is a univariate radical-locator polynomial is proposed. In particular, this matrix is modified to allow a large square matrix. It can be transformed by Gaussian elimination to the matrix in a row-echelon form in which zeros appearing in the diagonal entries of the current matrix are able to determine errors implicitly. Furthermore, this work further extends the previous results from the univariate case to multivariate cases. The matrix methods found herein enable one to decode a large class of cyclic codes efficiently. In the latter case, the cyclotomic cosets are a practical approach to select a small subset of positive integers. They are employed to develop partial radical locators. Finally, in contrast with the complete radical locators, the algebraic decoding methods make a natural use of partial radical locators more widely and flexibly to arbitrary cyclic codes.
Trial and Error Experience Replay Based Deep Reinforcement Learning

The environment with sparse rewards in reinforcement learning is a common problem and the agent learns inefficiently using general methods. A new solution called trialand-error experience replay is proposed. In this method, the general hindsight experience replay is combined with a curiositydriven model, by which the sample-efficiency will be improved although extrinsic rewards are sparse. It is demonstrated as an algorithm to control a virtual robotic arm to reach a mobile goal. Through analysis the robotic arm can explore and learn based on failure trajectories which shows that the agent mimics a human who failed repeatedly but still tries to learn something from the unexpected outcomes.
Audible Code, a Voice-Enabled Programming Extension of Visual Studio Code

Though there are improved programming languages and modern compilers, writing code is not an easy job as it requires patience and consistency. Having a piece of software specially designed and developed for developers that automatically inserts code needed for defining various classes will make it much more convenient and productive for programmers and will increase their productivity by decreasing programming time. Hence, in this paper we present an implementation of a Visual Studio Code extension that uses Artificial Intelligence and Machine Learning to make writing code easier. It is also an attempt to design a product following fundamental product development guidelines.
«Digital Twin» technology in medical information systems

Abstract Given the high mortality rate from cardiovascular diseases and the need to transfer medicine to a more high-tech level of development, the authors propose the use of digital twin technology for the diagnosis and treatment of heart diseases. As a digital twin of the human heart, it is proposed to use the combination of an equivalent electric heart generator and the of D. Noble computer heart model within the subsystem for supporting medical decision-making. The use of such digital twin will make it possible to reliably conduct non-invasive cardiodiagnosis to select drugs within the treatment regimen.
Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams

Mining data streams has become an important topic due to the increased availability of vast amounts of online data. In such incremental learning scenarios, observations arrive in a sequence over time and are subject to changes in data distributions, also known as concept drifts. Interleaved test-then-train evaluations are often used during supervised learning from streaming data. The idea is intuitive: we first use each instance to test a model, then it is used for training. However, true class labels may be missing or arrive well after the prediction, which implies that they cannot be used for training and/or drift detection. Based on these considerations, we introduce our LESS-TWE ensemble-based method for online learning in domains where full reliance on labels would be unfeasible. Our approach combines weighted soft voting and unsupervised drift detection to reduce the dependency on labels during model construction. In cases where the label is unavailable, the most confident label, as predicted through weighted soft voting, is selected. Similarly, our unlabelled drift detector flags for drifts based on the voting confidence, rather than relying on the true label. Our experimental evaluation indicates that our algorithm is very fast, achieves comparable predictive accuracy when compared to the state-of-the-art and outperforms baseline methods.
Classification of Tissue Regions in Histopathological Images: Comparison Between Pre-trained Convolutional Neural Networks and Local Binary Patterns Variants

The identification of tissue regions within histopathological images represents a fundamental step for diagnosis, patient stratification and follow-up. However, the huge amount of image data made available by the ever improving whole-slide imaging devices gives rise to a bottleneck in manual, microscopy-based evaluation. Furthermore, manual procedures generally show a significant intra- and/or inter-observer variability. In this scenario the objective of this chapter is to investigate the effectiveness of image features from last-generation, pre-trained convolutional networks against variants of Local Binary Patterns for classifying tissue sub-regions into meaningful classes such as epithelium, stroma, lymphocytes and necrosis. Experimenting with seven datasets of histopathological images we show that both classes of methods can be quite effective for the task, but with a noticeable superiority of descriptors based on convolutional neural networks. In particular, we show that these can be seamlessly integrated with standard classifiers (e.g. Support Vector Machines) to attain overall discrimination accuracy between 95 and 99%.
Research on Personalized Recommendation Technology Based on Collaborative Filtering

Personalized recommendation system is hot territory in the research and application of Internet enterprises in recent years. In this paper, based on the main idea of collaborative filtering, the traditional collaborative filtering algorithm is improved, including data model and algorithm process, considering the actual data, calculation consumption and effect of hotel personalized recommendation. Based on the improved data model, when calculating the recommendation results, the similarity between users and items is calculated directly, which greatly reduces the computational pressure. Finally, considering the different effects of different data, two sub-processes of candidate set fusion and reordering are added to further improve the final recommendation effect. The computations are divided into offline and online computations to maximize performance and fast response algorithms.
Moving objects multi-classification based on information fusion

This paper aims to present a new model for multi-classification in video surveillance, based on data fusion. First, features are extracted. Then, a pre-classification is conducted using each feature separately. Second, the obtained posterior probabilities, are combined using the T-conorm operator. At last, the maximum is applied to specify the label of each detected object. The performance of our model was evaluated using two public datasets. In addition, the used number of classes and features were varied, in order to, validate the efficiency of our model. The obtained results showed that our model improved the classification accuracy up to an average of 99% using SVM, also, it outperformed the other methods.
Visualizing the Newtons Fractal from the Recurring Linear Sequence with Google Colab: An Example of Brazil X Portugal Research

In this work, recurrent and linear sequences are studied, exploring the teaching of these numbers with the aid of a computational resource, known as Google Colab.Initially, a brief historical exploration inherent to these sequences is carried out, as well as the construction of the characteristic equation of each one.Thus, their respective roots will be investigated and analyzed, through fractal theory based on Newton's method.For that, Google Colab is used as a technological tool, collaborating to teach Fibonacci, Lucas, Mersenne, Oresme, Jacobsthal, Pell, Leonardo, Padovan, Perrin and Narayana sequences in Brazil and Portugal.It is also possible to notice the similarity of some of these sequences, in addition to relating them with some figures present and their corresponding visualization.
Individual retrieval based on oral cavity point cloud data and correntropy‐based registration algorithm

In this study, the authors present a novel individual retrieval method based on oral cavity point cloud data and correntropy-based registration algorithm. Since the three-dimensional oral cavity data contains a large amount of noise and outliers, it may lead to a decrease in registration accuracy, which affects the accuracy of retrieval rate. Therefore, the authors introduce the correntropy into the rigid registration algorithm to solve this problem. Then, they filter the matched point cloud data and then use the mean squared error to judge the individual differences of the model data. Finally, the accurate retrieval of the oral cavity data is realised. Experimental results demonstrate the proposed retrieval three-dimensional model algorithm can be successfully searched under different model data, which can help forensics use the characteristics of biological individuals to accurately search and identify, and improve recognition efficiency.
An Augmented Reality Assistant to Support Button Selection for Patients with Age-related Macular Degeneration

Age-related macular degeneration (AMD) is the most common cause for acquired blindness in industrialized countries. It mainly affects sensory receptors of the central visual field. Individuals with AMD have difficulty recognizing faces and reading or distinguishing the keys on a keyboard. However, their peripheral vision is not affected, so patients can still recognize colors and shapes in the periphery. With the rise of head-mounted displays (HMD), new possibilities have emerged to create novel visual aids. We present a visual assistant that magnifies a captured video stream and presents the magnified image to the user within a section of the screen. In this work, we examine how different sizes of this magnification section as well as magnification factors may affect the precision, accuracy and speed of inputting words on a keyboard for patients with AMD, which we simulated with eye-tracking. For this, we conducted a quantitative experiment with 16 healthy participants and a qualitative case study with a patient. Finally, we conclude with results and future work.
Experience with a Trans-Pacific Collaborative Mixed Reality Plant Walk

In this paper, we share our experiences with a Trans-Pacific, collaborative mixed-reality (MR) based plant walk tour involving three geographical locations: (i) Dallas, Texas; (ii) Denver, Colorado; and (iii) Yokohama, Japan. The Wide Area Network (WAN) from Japan to Denver and from Denver to Dallas was through 100 Gbps optical links. These links were a mixture of leased and dedicated infrastructure. The average observed latency was around 131 ms and the maximum available bandwidth was 26.6 Gbps. The tour developed using a component-based framework, provided group travel with a leader-follower criterion involving a semi-active travel technique. By applying a “modified Virtual Reality” (mVR) triangle paradigm, we examined the user experience of this collaborative tour. We considered Visualization, Synchronization, and Exploration design choices as the three key aspects of the mVR triangle. We assessed the user experience of the tour through a Gibbs triangle, with each of the composition of the Gibbs triangle being a key aspect of the mVR triangle. We obtained users’ scores on the three aspects of the mVR using a seven-point, Likert scale questionnaire. We conducted 14 sessions of group travel. Overall, we found the user experience of the trans-pacific plant walk tour to be in the Balanced Experience Region (BER) region or the center part of the Gibbs triangle, which conveys that the majority of the participants experienced a balanced influence of mVR aspects throughout the tour.
Exploring Communication Modalities to Support Collaborative Guidance in Virtual Reality

Collaborative virtual environments provide the ability for collocated and remote participants to communicate and share information with each other. For example, immersive technologies can be used to facilitate collaborative guidance during navigation of an unfamiliar environment. However, the design space of 3D user interfaces for supporting collaborative guidance tasks, along with the advantages and disadvantages of different immersive communication modalities to support these tasks, are not well understood. In this paper, we investigate three different methods for providing assistance (visual-only, audio-only, and combined audio/visual cues) using an asymmetric collaborative guidance task. We developed a novel experimental design and virtual reality scenario to evaluate task performance during navigation of a complex and dynamic environment while simultaneously avoiding observation by patrolling sentries. Two experiments were conducted: a dyadic study conducted at a large public event and a controlled lab study using a confederate. Combined audio/visual guidance cues were rated easier to use and more effectively facilitated the avoidance of sentries compared with the audio-only condition. The presented work has the potential to inform the design of future experiments and applications that involve communication modalities to support collaborative guidance tasks with immersive technologies.
Investigation of Museum Exhibition Display in Terms of Inclusive Information for Blind Visitors.

Abstract A museum is a place where we can learn about the culture of a place. Museums rely on their exhibition display to communicate its content. Museum exhibition displays mostly use visual media in communicating its information; they store their collections inside glass boxes, use LED screens, visual projections, and so on. Then how museums can be adapted to be more inclusive to blind visitors who lost their sight? This paper investigates the museum exhibition display, that took place in Bank Indonesia Museum, in its ability to give information to blind visitors. The author observed the existing of Bank Indonesia Museum Exhibition to know what kind of display that they use. Furthermore, to acknowledge if the exhibition can give information for blind visitors, the author experienced visiting the exhibition as a blind visitor. Lastly, the author analysed if it is inclusive and informative or not based on the theories. Based on the studies that have been done by the author, museum exhibition display has to provide medias that can help blind visitors get the information provided such as audio and touch medias, considering the ability of blind visitor to get information from hearing and touching.
Effects of Egocentric Versus Exocentric Virtual Object Storage Technique on Cognition in Virtual Environments

As tasks in Virtual and Augmented Reality applications become increasingly complex, there is a need to better organize information or virtual objects within the virtual environment. For example, 3D Widget or menus may need to hide information from the user until it is needed. Users may need to organize their own virtual objects or tools by storing them until usage is needed or carry with them to another location. For these scenarios and others, there may be benefits to associating or storing virtual objects in relation to one’s own avatar (Egocentric) or in relation to the other virtual objects in the environment (Exocentric). This paper presents the results of a user study comparing the effects of Egocentric and Exocentric Virtual Object Storage Techniques on cognition in a virtual environment. Participants performed a task that enabled them to store virtual objects using different Virtual Object Storage Techniques. As participants completed their tasks, we measured task and cognition performance. We found that the egocentric technique improves task performance, increases accuracy, and reduces cognitive workload during memory task for virtual object storage in comparison to the exocentric technique in a virtual environment.