Reviewerly/exHarmony huggingface dataset:
The Art Foundation of Virtual Reality Interactivity

The maturation of interactive technology derived from the art of imagination, as well as the continuous improvement and iteration of interactive devices, constitute a great driving force for virtual reality to go deeper and deeper. Based on the retrospective of virtual reality interaction technology and theory, this paper further analyzes the basis of virtual reality interaction realization from the perspective of artistic philosophy, and holds that virtual reality conforms to the psychological perception form of art, and that the accompanying aesthetic experience and the existing meaning of continuous display are also the artistic basis of interactive realization.
A Cyberspace Security Knowledge System Based on Knowledge Graph

Knowledge graph plays an important role in semantic search, data analysis and intelligent decision making, and has made remarkable achievements in many fields. However, it is rarely used in the field of network security, which hinders the systematic and structured development of network space security. In order to build a cyberspace security knowledge system to fill the gaps in this field, and to visualize cyberspace security knowledge, this paper proposes a construction method of network space security knowledge map, and uses bottom-up method to construct network security knowledge system. Firstly, Protégé is used to construct ontology of cyberspace security knowledge. Secondly, semantic relations between entities are extracted from cyberspace security data. Finally, the network security knowledge system is stored and displayed by Neo4j graphics database. The experimental results show that the method is effective and has important significance for the development of network space security.
Engineering Design Optimization Using Memorized Differential Evolution

Differential evolution and its diverse variants are prominently inflated by unfitting operators like mutation and crossover. Basically, DE is does not commit to memorize the finest effects attained in early portion of the preceding peers. To resolve this issue, grounded on memory mechanism a new DE (named as mbDE) is offered in this paper to solve a famous engineering design problem, so-called speed reducer design. This contained new mutation and crossover (swarm, mutation and crossover) created by particle swarm optimization (PSO) circumstance. The empirical results confirm the superiority of mbDE over many existing algorithms.
Protection Against SIM Swap Attacks on OTP System

One-time password-based authentication stands out to be the most effective in the cluster of password-less authentication systems. It is possible to use it as an authentication factor for login rather than an account recovery mechanism. Recent studies show that attacks like SIM swap and device theft raise a significant threat for the system. In this paper, a new security system is proposed to prevent attacks like SIM swap on OTP systems, the system contains a risk engine made up of supervised and unsupervised machine learning model blocks trained using genuine user data space, and the final decision of the system is subject to a decision block that works on the principles of voting and logic of an AND gate. The proposed system performed well in detecting fraud users, proving the system’s significance in solving the problems faced by an OTP system.
The Exploratory Factor Analysis of Illusion in the Virtual Character System

This study reviews related works in regard to the embodiment of virtual character and the illusion in the virtual reality (VR). There are five types of illusion shown in the result according to the characteristics of place illusion, plausibility illusion, body illusion, partial awareness of illusion, and illusion of movement. In order to analyze possible factors of illusion in VR, the meaning and interaction behavior of illusion is defined. Furthermore, the questionnaire items are joined based on the meaning as well as the implicit and explicit interaction of users from the five constructs of illusion.
Anti-Semitism and the Overlooked Benefits of Allowing 'Hate Speech'

Recent increases in anti-Semitic speech and violence have led to calls for censorship of such “hate speech.” Unfortunately, discussions regarding the tradeoff between censorship and free speech often focus solely on the costs and benefits of censorship, omitting the numerous benefits of allowing “hate speech.” This incomplete analysis results in an unjustified bias toward censorship. This Article chronicles the often-overlooked benefits of allowing “hate speech,” thus providing a more accurate assessment of the tradeoffs that need to be considered. The Article concludes by explaining how it is strategically advantageous for free-speech advocates to emphasize the benefits of allowing “hate speech” rather than simply warning of the harms of censorship.
HRSE: Reframing the Human Resource Management with MBSE

According to the ISO/IEC/IEEE 15288 standard, human resource management process is an essential part of organizational project-enabling processes, becoming more and more important during the process of science, technological and industrial innovation. However, many enterprises get lost when they confront overlapped complexity such as confusion of terminology systems, adaption of interwoven development pipeline system, and optimization of workforce topology. Therefore, this article presents a model-based approach for human resource management in enterprises, providing improved cognition, analytics and implementation. In particular, a comprehensive MBSE toolbox and a set of models based on KARMA language are constructed for effective human resource management. Moreover, within the specific domain of human resource management, a system of systems is built up through a strategic initiative, guiding tens of thousands of employees and dozens of organizations towards long-term goals of high-quality development. The case study and measurement results demonstrate that human resource systems engineering (HRSE) conforms to the equivalent applicability of MBSE methods.
Deep Layout Extraction Applied to Historical Postcards

We describe an experimental study on the layout extraction problem applied to circulated old postcards. This type of historical documents presents many challenging aspects related with their automatic analysis as images. For example, their degradation due to passing of time or the possible overlapping of different elements in a reduced space. Postcard layout extraction consists in segmenting in regions the various contained information types present on these images. For the proposed task, we have used semantic segmentation deep neural networks which learn to classify the document image pixels into the different considered class categories in postcards (e.g., stamps, postmarks, handwritten text or illustrations, among others). Our experiments on an annotated dataset of 100 postcards produced respective global F1-score, Jaccard and pixel accuracy metrics values of 0.92, 0.85 and 0.92, which endorses the feasibility of the proposed method. Additionally, to the best of our knowledge, this paper is one of the first investigation in this problem applied to historical postcards.
Video Based Graphical Password Authentication System

Text-based secret phrase is the ordinary strategy utilized for client verification. It stays as the most broadly utilized strategy since it is straightforward, modest contrasted with different strategies, and simple to carry out. Clients will in general choose basic and short passwords to recollect without any problem. It is exceptionally simple to crack the password for intruders, whereas arbitrary and extensive passwords are difficult to recollect. The primary issue with customary literary strategy is that passwords chosen for some, applications are either powerless or critical or secure however hard to recall. A few clients even utilize the name of the framework as a secret phrase. The extensive passwords give greater security at the same time; it is hard to recall a few such long passwords. Graphical passwords can be considered as the best solution where the user selects one picture as a password from several pictures and researchers have proved that graphical passwords are easy to remember as compared to textual passwords. In this paper, another incorporated arrangement of token and video-based confirmation has been proposed in which two-level of authentication is done. In the first phase, the digital image displayed on any token like mobile is used for authentication. In the second phase, frames of some random video are chosen as passwords. The information was tried for 50 clients which are attempting to figure out the password of another client. The chance of guessing the correct video and correct frame is almost negligible. However, there may be a chance that the selected token picture is available at any social networking site so, guessing possibility of picture is becomes 2 out of 50 users. At last, the chance of guessing both correct videos as well as correct token image is zero.
Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

Internet of Things (IoT) and blockchain receive significant interest owing to their applicability in different application areas such as healthcare, finance, transportation, etc. Medical image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public networks. This paper presents a new wind driven optimization algorithm based medical image encryption (WDOA-MIE) technique for blockchain enabled IoT environments. The WDOA-MIE model involves three major processes namely data collection, image encryption, optimal key generation, and data transmission. Initially, the medical images were captured from the patient using IoT devices. Then, the captured images are encrypted using signcryption technique. In addition, for improving the performance of the signcryption technique, the optimal key generation procedure was applied by WDOA algorithm. The goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio (PSNR). Upon successful encryption of images, the IoT devices transmit to the closest server for storing it in the blockchain securely. The performance of the presented method was analyzed utilizing the benchmark medical image dataset. The security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.
Execution of College Enquiry Chatbot using IBM virtual Assistant

From Pre-Primary schools to public and private universities, there are more choices now, than earlier for seeking education. Choosing best one to opt completely depends on certain entities of Organization like its Infrastructure, Faculty and its performance in previous academic years. In the present generation, students have many doubts regarding which college should be opted by them so that their career will be on a positive note and many more doubts once the college is opted regarding exams, schedule, contact details etc, and the selection of the college is done by verifying and validating the college entities. To know about the College, student adopts methods like approaching the college or enquiring on call or through emails. This can be useful for some particular queries but not worth for general queries. So in order to save time and to get relevant answers through less effort College Enquiry Chatbot can be implemented. Chatbot, a recent innovation, are reflections which can understand Natural Language of humans, process it and reply back by question answer capability and reduce time and effort for the questioning people and serve round the clock, on a simple note Chatbot are the replacement of humans to continue generalized quessianire through online. In our Paper we try to implement Chatbot which are developed and implemented using IBM virtual Assistant which is also called as IBM Watson.
An Empirical Investigation in Analysing the Proactive Approach of Artificial Intelligence in Regulating the Financial Sector

The use of artificial intelligence (AI) within the finance industry can be considered as a transformative approach as it enables the financial institutions to enhance their performance capacity. The use of artificial intelligence within the finance sector helps the industries to streamline the processes and optimise their management efficiently for various types of operations pertaining to credit decisions-making, financial risk assessment and management and quantitative trading. The paper aims at analysing the proactive approach that can be taken with the use of AI in order to enhance effective management within the financial sector. The empirical study conducted in the paper utilizes various types of secondary materials with a qualitative approach. The findings of the study demonstrate the enhanced capacity of AI that can be used for a proactive approach, utilised for the assessment of risks or threats prior to any mismanagement incident. In this regard, fintech companies such as Enova, Ocrolus, ZestFinance, and DataRobot and so on have taken a predominant position in aiding the financial industries to use AI-based systems that aids the management process. However, the inclusion of AI within the financial sector is faced with certain challenges such as lack of knowledge regarding technological infrastructure, poor financial investment especially for government aided banks, unawareness of the employees and weak collaboration with the IT industry. Regardless, AI technologies in recent years have achieved great advancement, leading to the enhancement of its capacity to assist the effective management within the financial sector.
Linear Time Edge-Aware Filtering on Complementary Tree Structures

Typical non-local edge-aware filtering methods build long-range connections by deriving a minimum spanning tree (MST) from input image, and the structure of MST completely depends on the intensity difference between neighboring pixels. Each pixel on the MST only connects to a sub-set of pixels in 8-neighborhood, resulting in unbalanced information propagation along eight directions. Thus they tend to generate piece-wise constant filtering output with fake edges among sub-trees. In this paper, we propose two complementary trees to aggregate information from entire image. The structure of each tree depends on the spatial relationships of neighboring pixels. The distances between any two pixels in both spatial space and intensity space are the shortest distance on each tree. Novel algorithms are presented to recursively compute output and normalization constant on each tree in linear time. For each pixel, we first compute the outputs along eight sub-trees, namely sub-trees in left, top, right, bottom, top-left, top-right, bottom-left and bottom-right of the root node, and then fuse the outputs to obtain filtering output on each tree. The final filtering output of our method is simply averaging the outputs of two complementary trees. Moreover, we present a distance mapping scheme in range space to map the intensity difference between neighboring pixels to a new value, which enable our method to manageably filter out low-amplitude structures while sharpening major edges. Extensive experiments in graphic applications, such as image denoising, JPEG artifacts removal, and colorization, to demonstrate the effectiveness and versatility of our method.
Soft BIBD and Product Gradient Codes

Gradient coding is a coding theoretic framework to provide robustness against slow or unresponsive machines, known as stragglers, in distributed machine learning applications. Recently, Kadhe et al. (2019) proposed a gradient code based on a combinatorial design, called balanced incomplete block design (BIBD), which is shown to outperform many existing gradient codes in worst-case adversarial straggling scenarios. However, parameters for which such BIBD constructions exist are very limited (Colbourn and Dinitz, 2006). In this paper, we aim to overcome such limitations and construct gradient codes which exist for a wide range of system parameters while retaining the superior performance of BIBD gradient codes. Two such constructions are proposed, one based on a probabilistic construction that relax the stringent BIBD gradient code constraints, and the other based on taking the Kronecker product of existing gradient codes. The proposed gradient codes allow flexible choices of system parameters while retaining comparable error performance.
Autonomous Ground Vehicle Status Monitoring System Using Interacting Multiple Model Filter: Design and Experimental Validation

The main concern of remote control system for autonomous ground vehicle (AGV) is to perform the given mission according to the purpose of the operator. Although most of the remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate the AGV status monitoring system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to the change of the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but it is a index that can be used to infer the current status of the AGV and the driving environment. The index can help to detect both a ridiculous behavior of the AGV such as skidding or sliding, and areas with low friction road conditions that are not confirmed by an image from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to image-only method. The overall procedure of the proposed method was experimentally validated via a real military AGV on the rough unpaved proving ground.
SNITCH: A Platform for Information Flow Control

Data confidentiality is critical. Existing approaches to data confidentiality often see their use restricted. To tackle the issue, we present a framework for hybrid value-dependent information flow control in low-level representations to boost the adoption of formal methods for certifying data confidentiality. We pair our framework with a concolic test generator to ensure significant coverage. We aim to support major frameworks such as Spring to ease the certification of web applications.
Residual in Residual Cascade Network for Single-Image Super Resolution

Digital images are of great significance in today’s scenario, where almost every single domain relies on them from agriculture to businesses to the military for their specific purposes. To convey the context, a more precise image gives more idealization and personalization to the aspect one wants to build. The precise images connote high clarity and distinct characteristics which is represented by a parameter known as resolution of image. This paper focuses on upscaling the resolution of image using a fast, lightweight artificial neural network (ANN)—RRCasN. The proposed model surpasses the traditional methods and competes with modern ANN-based architectures in terms of size of the model and quality of the high-resolution image produced. This paper also introduces a novel approach for normalization in place of batch normalization.
The Institute of Professional Associations of Preschool Specialists in the System of Vocational Education

The article reveals the features of the models of teacher education in the professional training system. The author considers the specifics of creating an institution of professional associations in the field of scientific research for the purpose of scientific and methodological cooperation of the professional activities of an educator.
Improved Multi-party Quantum Key Agreement with Four-qubit Cluster States

Quantum key agreement is a promising key establishing protocol that can play a significant role in securing 5G/6G communication networks. Recently, Liu et al. (Quantum Information Processing 18(8):1-10, 2019) proposed a multi-party quantum key agreement protocol based on four-qubit cluster states was proposed. The aim of their protocol is to agree on a shared secret key among multiple remote participants. Liu et al. employed four-qubit cluster states to be the quantum resources and the X operation to securely share a secret key. In addition, Liu et al.'s protocol guarantees that each participant makes an equal contribution to the final key. The authors also claimed that the proposed protocol is secure against participant attack and dishonest participants cannot generate the final shared key alone. However, we show here that Liu et al. protocol is insecure against a collusive attack, where dishonest participants can retrieve the private inputs of a trustworthy participant without being caught. Additionally, the corresponding modifications are presented to address these security flaws in Liu et al.'s protocol.
Development of an Invisible Human Experience System Using Diminished Reality

In this study, we have been developing a diminished reality system that allows users to experience becoming invisible human in order to reduce self-awareness and to improve their self-esteem. The system employs a camera to capture the user’s view and replaces his/her body images with background images in real time. These processed images are shown with a head mounted display to realize the immersive experience of becoming invisible human. The image inpainting is performed by a deep learning network. We also created a training and validation datasets and compared three networks. These networks are designed for image inpainting in this study. Moreover, we have made a hypothetical model of how psychological states and self-awareness will change when experiencing the developed system. In the future work, we are planning to conduct an experiment and confirm whether use of the system improves self-esteem. Also, we will investigate the process of changing the psychological state based on the hypothetical model by questionnaire surveys.
An Evaluation Framework for Modeling Languages Supporting Predictable Vehicular Software Systems

Handling the software complexity of modern vehicular systems has become very challenging due to their non-centralized nature and real-time requirements that they impose. Among many software development paradigms for these systems, model-based development excels for several reasons including its ability to verify timing predictability of software architectures of these systems using pre-runtime timing analysis techniques. In this work, we propose a comprehensive framework that captures the timing related information needed for the modeling languages to facilitate these timing analyses. We validate the applicability of the framework by comparing two modeling languages and their respective tool-chains, Rubus-ICE and APP4MC, that are used for software development in the vehicle industry. Based on our results, both modeling languages support the design and analysis of vehicle software, but with different. Both modeling languages support time-, event- and data-driven activation of software components and modeling of single- and multi-rate transactions. Amalthea targets applications on single nodes with multi-core architectures while RCM focuses on single-core single-node and distributed embedded systems with ongoing work for supporting single-node multi-core architectures. In comparison to Amalthea, RCM provides a generic message model which can easily be re-modeled according to protocol-specific properties.