Reviewerly/exHarmony huggingface dataset:
Static Keystroke Dynamic Authentication (SKDA) Model to Authenticate User during Password Change

Keystroke dynamics is considered as a supporting factor of authentication. Especially in the static keystroke dynamics, the user is identified by using the timing featured, which is captured while the user enters the login ID and password. To achieve this, the user profile needs to be created with timing features. However, in a scenario like a change of password where nearly no keystroke timing data is available, non-conventional features may be helpful. This article focuses on using non-conventional features such as NumLock key, Shift key, CapsLock key, etc for identifying users during a change of password. The paper also details how to capture the non-conventional features in static keystroke dynamics and build a model that can be used in the change of nassword.
An Optimal Visualization of Traffic System by using Augmented Reality and Virtual Reality

This study explains the concept of deadlock by focusing on prevention and avoidance with the help of one of the emerging technologies, Augmented Reality (AR). With the help of AR, people can easily understand this concept. This can be easily explained with the help of real time example of traffic system on the road. To solve this issue, this study has implemented the Bankers Algorithm, which is a Deadlock Prevention Algorithm. The Bankers Algorithm can use its calculation and prevent the occurrence of Deadlock or incase a deadlock is happening it can help to resolve it.
File Encryption using Noise Images as Key

Everything in today's highly connected digital world is increasingly dependent on instantaneous global data transfers. Internet efficiency facilitates our daily lives. Sharing information online presents significant security dangers and difficulties in the modern day. The use of cryptography is the means through which sensitive information can be protected from various threats. Improved cryptosystems technology is a no-brainer for securing communication networks. An improved cryptosystem is the focus of our research, and to that end we have presented a novel method of data encryption and decryption that guarantees increased efficiency over current best practises. This research work has proposed a noise image encryption and decryption scheme, wherein the noise signal is randomly selected to set the initial values for a chaotic system which also enhances the security of the system. for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way. This study has developed a file encryption system, where encrypted file is saved to decrypt and download later. The main difference between the existing and proposed system is the implementation of file encryption system using noise images that store key values inside it.
Cross-Site Request Forgery as an Example of Machine Learning for Web Vulnerability Detection

This paper presents a strategy for discovering flaws in web applications through Machine Learning (ML). Web-based applications are especially troublesome to examine attributed to their variety and extensive usage of custom development methodologies. As little more than a basis, machine learning is extremely useful in website safety: It just might combine cognitive knowledge of web app terminology with automated software approaches based on verbally reported information. Mitch tool is the foremost machine learning strategy towards black-box investigation for Cross-Site Request Forgery (C.S.R.F) problems, was built using these principles. Mitch-helped us find Thirty-five recently developed cross-site request forgeries (C.S.R. Fs) in twenty wide fields, together with 3 main C.S.R. Fs in industry applications.
A Novel Platform with Motion Video Recognition for Intelligent Sport Monitoring Application

A novel platform with motion video recognition for the intelligent sport monitoring application is studied in this manuscript. The action markers of human target action images in sports videos are random. Combining image preprocessing and specific part recognition technology, dynamic tracking and recognition of human target action markers in sports videos is an important application scenario. This paper proposes the novel system of processing, which contains the image preprocessign, arm recognition, whole body recognition and the data storage. The proposed is efficiency in processing the real-time video images and the tests show the performance.
A Systematic Literature Review on Symmetric and Asymmetric Encryption Comparison Key Size

Symmetric and asymmetric encryption methods are used when a web browser is used to reply to emails, submit website forms, and perform similar activities. Key size is essential to enable security, which means the longer a key is, the better security can be ensured. Purpose: This review aims to present an overview of the critical size length, which is significant against different types of cyber attacks. Methodology: This research study is based on systematic literature review and secondary data analysis. Results: The symmetric and asymmetric key sizing participate in enabling data security and protection. Conclusion: Different cryptographic techniques will contribute to highlight the best approach that can be employed to maintain confidential data and address related issues within symmetric and asymmetric encryption key size.
Utilising Deep Learning as a Law Enforcement Ally

Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population density and the size of any nation's landmass. Thus, public participation becomes crucial, revolutionary, and beneficial. This cycle is both times and works seriously. In this paper, we tried to suggest a different framework for criminal Distinguishing & Recognition using Deep learning and Heroku Cloud, i.e. Cloud Computing, which, assuming it is used by our Crime Control Organizations, would help them catch criminals from CCTV images or images uploaded by the public if seen anywhere. This system is in place to assist in capturing criminals and anyone who can upload information indicating that they saw the relevant individual at a specific location and time. In India, where conditions are always changing due to things like light, weather, and specific directions, existing solutions use conventional face acknowledgement computations, which might be problematic because there is no open public contribution. Our research paper employs LBPH, Deep Learning, and Heroku Cloud technologies to construct the system.
Performance Analysis of Network Management System using Bioinspired -Blockchain Techniquefor IP Networks

To mine a blockchain on IP Networks, one must do several tasks related to chain management, rule optimization, verification, and hash generation design. Various consensus model subsets may benefit from the various blockchain mining techniques proposed by researchers. Most of these techniques, however, are rather complicated, which slows down the mining process for large-scale blockchains. Overly simplistic models that include unnecessary redundancies are inefficient and have little practical use. To solve these issues and boost blockchain mining efficiency in large-scale deployments, the authors of this paper propose creating a novel hybrid bioinspired approach. The proposed IP Network model is adaptable to almost all consensus procedures and may be easily combined with dynamic consensus models with few alterations. After collecting performance and context-specific data from the underlying blockchains, the technique uses Genetic Algorithm (GA) that distributes these range sets among miner nodes that support trust, allowing for high-performance mining while maintaining a high degree of trust under actual application situations. The model was tested against Proof-of-Stake (PoS), Proof-of- Work (PoW), Proof-of- Trust (PoT), and Practical Byzantine Fault Tolerance (PBFT) based consensus algorithms to ensure its effectiveness in real-world scenarios. Mining latency, energy consumption, and computational complexity were used as metrics against which this performance was measured. This analysis revealed that the proposed model has the potential to decrease mining latency by 4.5%, energy usage by 3.9%, and compute complexity by 4.1% across a variety of consensus mechanisms, making it suitable for a number of real-time applications.
Cloud Data Security using Hybrid Algorithm

Today's growing research topics include security in cloud computing. Since cloud storage provides easy access to the data whenever you need it. Many companies are switching from Conventional data storage to cloud storage. But data security is the biggest concern that companies face while using cloud computing. In this article, a multilevel cryptography-based safety solution for cloud computing is designed. This paradigm is a combination of asymmetric & symmetric key cryptography techniques. The RSA and Data Encryption Standard (DES) are used in this proposed methodology to provide several levels of encoding and decoding at the sender & recipient side, increasing the safety of cloud storage. This paradigm increases the data security to the highest possible level as compared to the current system.
Comprehensive Research on Speaker Recognition and its Challenges

The study of speech is a subfield of the measurement of uniquely identifying and measurable patterns in human activity. It communicates details about a person's characteristics. As an illustration, consider gender, age, emotion, and health status. Voice Identification is the process of pointing person purely with the help of vocal pulses. Using voice assistants and smart gadgets as a base, several academics are exploring in this field. This article offers detailed information on speaker recognition. Additionally, a different adversarial technique is described in advance of the well-known machine learning methods. This study provides an overview of the speaker recognition topic by covering its system, modelling approaches, applications, and some underlying theories. The strengths of speaker recognition technologies will be discussed.
Voice Flow Control using Artificial Intelligence

In the ultramodern era of fast moving technology, we are able to do actions that were never before possible. However, in order to complete and manage these studies, a platform that can easily and comfortably automate all of our chores is required Therefore, we must create a Special Assistant with excellent deductive skills and the capacity to communicate with the outside world only via one of the materialistic forms of human contact, i.e. HUMANVOICE.[1]There aredifferent ways to develop voice user interfaces. People without programming skills can use this Voice flow to develop their first voice project. Voice flow is suitable for simple and more complicated voice projects. It can beused for Alexia Skills and Google Actions as well.
Detection of Fake Websites using Machine Learning Techniques

Phishing websites are harmful websites that spoof legitimate web pages to get sensitive information from users as login, account, and bank card info. Detecting these hoax websites is a difficult topic since hacking is mostly a semantics-based assault that targets human vulnerabilities rather than network or system flaws. Machine learning systems can identify phishing assaults and have greater adaptability for forms of hack attempts, hence these are widely used. To employ this sort of method, input characteristics should be properly chosen. These aspects determine the overall performance of the solution. In this paper, two techniques Logistic Regression and Multinomial Naïve Bayes are extensively used in detecting these websites using phishing-url datasets. Out of these, Logistic Regression has achieved the highest accuracy results of 97%.
A Real-Time Virtual Yoga Assistant Using Machine Learning

Yoga is a practice that aims to develop an all-around personality by synchronizing the mind, body, and spirit. However, incorrect postures or techniques can cause damage. In ancient times, yoga was performed under the supervision of a teacher, but it is difficult to find a competent guru in today's fast-paced world. The goal of this project is to develop an application that can track and evaluate physical exercise, specifically yoga, through the use of human pose estimation. This application, called “A Real-Time Virtual Yoga Assistant,” uses machine learning methodologies to classify data on yoga positions in both pre-recorded and real-time videos. The research also examines various pose estimation and key point detection approaches and deep learning models used for posture classification.
Decentralized E-Commerce Platform Implemented using Smart Contracts

The internet has evolved a lot in the last 40 years and its early applications has become unrecognizable. Web 1.0 focused on serving static pages is considered as the read-only web, whereas web 2.0 made way for dynamic pages making it as read-write web, which is predominantly experienced. The main problem face here is, as the users of E-commerce applications, is single-point-of-failure (SPOF), which means, the data stored in centralized servers is highly susceptible and vulnerable to attacks. Now, the internet's next evolution enables to develop decentralized applications and adds few other features like trustlessness, distributed, transparent, robust, etc. This also makes web 3.0 as read-write-own web. Now, this evolution wave of decentralization has hit the applications. To overcome this problem, this study has proposed a solution to completely decentralize the E-Commerce platform by using Blockchain in conjunction with smart contracts and utilizes the decentralized storage like IPFS (Inter Planetary File System). This article has developed a system that uses ReactJs as the frontend and the Ethereum blockchain as the backend to execute smart contracts using the EVM (Ethereum Virtual Machine) and to store data in a decentralized way by using IPFS and BigChainDb.
Cryptocurrency Sentiment Analysis using Bidirectional Transformation

This paper predicts sentiments of crypto currency news articles using BERT (Bidirectional Encoder Representation) model, as there is a lack of research in crypto currency price prediction using natural language processing. The text data obtained is unlabeled and it is labelled using a parsimonious rule-based model and then BERT is used to dassify news sentiment as “Positive”, “Negative” or “Neutral” which may be helpful in reading cryptocurrency market movement.
Kinematic Analysis and Robust Control of a Spherical-Motor-Based Visual Tracking System

This article presents the concept of a spherical-motor-based visual tracking system. Unlike conventional gimbal systems consisting of serially articulated motors/gears for achieving multi-DOF negotiation of optical axis of a camera, the spherical motor is capable of providing 3-DOF in one joint, thus greatly reducing the unwanted inertia and frictions of the rotating parts. The kinematic model relating the image projection motion and the spherical motor orientation is established for the omnidirectional visual tracking configuration, based on which an image-based visual servo (IBVS) algorithm is derived. A cascaded visual tracking controller consisting of an IBVS control and a complementary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> ) control is proposed for precisely controlling the spherical motor in the presence of external disturbances. The capability of the proposed system for tracking a flying target is investigated and the performances are compared to a conventional gimbal system. The results demonstrate that the spherical-motor-based tracking system with the proposed controller can avoid singularities usually encountered in conventional articulated gimbals and provide fast and precise visual tracking of flying targets.
The Effect of Nudges, Visual Literacy and Their Interaction on Enhancing the Understanding of Enterprise Process Models

In Enterprise Engineering (EE), like other fields, visual information has several advantages over text and we are used to represent a process model as visual information. However, little is known about the contributing factors that influence the understanding of these process models. Visual literacy and nudges are two potential contributing factors to the ability to understand process models and reduce clutter. In this study, we researched by using an online questionnaire (N = 37) whether we could enhance the understanding of process models with the help of nudges, visual literacy and the interaction effects between the nudges and visual literacy. We executed a univariate ANOVA to compare the effect of the nudges on the understanding of process models with the two nudge conditions, visual literacy and all possible interactions as predictors. The results showed that nudges do not significantly influence the understanding of process models, which was not in line with our expectations. Visual literacy may have a significant influence, which aligns with our expectations. There was one significant interaction between visual literacy and the arrow nudge; however, not in the direction that we expected, therefore, not in line with our expectations. Given our small sample size, our significance could rest on a coincidence. We offer no open-and-shut conclusions about enhancing the understanding of process models with the help of nudges, visual literacy and the interaction effects between the nudges and visual literacy.
Current Challenges and Opportunities in Enterprise Architecture: Insights from 950 + LeanIX Customers

Data-driven architecture is a new paradigm promoted by LeanIX that focuses on making Enterprise Architecture accessible to a wider audience of stakeholders in organizations, to increase data quality and provide transparency when undergoing organizational transformations. This democratization of Enterprise Architecture allows organizations to transform faster and take advantage of trends such as the API Economy, Software-as-a-Service, and Citizen Developers with low-code applications. From this perspective, in this paper, we present the most common challenges that organizations face in their Enterprise Architecture practice due to siloed information and lack of communication between stakeholders. Furthermore, using tools like Excel and PowerPoint to manage the architecture poses challenges due to obsolete data, inability to create meaningful analyses and having multiple sources of truth. In the case of C&A, Helvetia and Marc O’Polo we present how having a modern data-driven Enterprise Architecture has helped these organizations with addressing their challenges and transforming quickly. Finally, we present our ideas on avenues for advancing the field of Enterprise Architecture from a research and practice perspective.
Exploring the Potential and Challenges of Reinforcement Learning for Object Pushing with Robots

This paper describes a 2D simulation environment for reinforcement learning of real robots that incorporates physical simulation using the PyBox2D framework. Using reinforcement learning, we aim to explore different strategies for playing air hockey and non-prehensile object manipulation. To achieve this, we first trained our models in simulation and then transferred the successful ones to a Franka Emika Panda robot for testing on a real system. Our research primarily focused on two main tasks: playing air hockey and non-prehensile object manipulation, with an emphasis on pushing. We present the results obtained from these experiments and discuss the potential future work that can be done to improve the models’ performance. The main contribution of this work is to demonstrate the effectiveness of using reinforcement learning in combination with simulation to train models for complex manipulation tasks. The successful transfer of the models from simulation to the real world highlights the potential of this approach in real-world applications.
A Digital Zen Garden for Mental Health Conditions Treatment

From the beginning of the COVID-19 pandemic, the severity and prevalence of symptoms of psychological distress, fatigue, brain fog, and other conditions have increased considerably, including among people who have not been infected with SARS-CoV-2. Many studies summarize the effect of the pandemic on the availability of mental health services and how this has changed during the pandemic. Concerned that potential increases in mental health conditions, had already prompted 90% of countries surveyed to include mental health and psychosocial support in their post COVID-19 response plans, but major gaps and concerns remain. In this paper we developed a de-stress proposal through a digital zen garden by using an augmented reality sandbox. The system provides patients with flexible interaction and easy control of the scenario, while making real time data recording. Three evaluation methods were developed to review the effectiveness of the therapy. According to the evaluation results of patients’ training, the system is a low cost entertainment tool that augments patients’ motivation, and helps to increase the effectiveness of therapy.
Analyzing Attendance Indicators in Digital Technologies-Mediated Courses in Tertiary Technician Education

The Community of Inquiry (CoI) model has been used as a theoretical framework to analyze online learning, recognizing the importance of having three types of presence: Cognitive, Teaching and Social. In particular, this model gained visibility after the interruption of face-to-face classes due to the COVID-19 pandemic. In this context, higher education institutions adapted their courses to a virtual format to serve students during this period of time. This work addresses a case study in which indicators of the three presences in tertiary technician education courses are analyzed during the years 2020 and 2021 at the Higher Institute of Teacher and Technical Training No. 93 (ISFDyT 93) of the city of San Vicente. To this end, a survey was implemented, which was answered by 119 students. The results obtain indicate that both the Educational and Cognitive presences are highly valued, the Social presence a bit less, as well as the indicators that refer to the dialog between the students.