Reviewerly/exHarmony huggingface dataset:
N Configuration TRNG based Scrambler Protocol for Secured File Transfer

In the modern era where huge amounts of information are being produced every second, still many loopholes are being found in the secured way of transferring data which risks the privacy of IP. Moreover, access to sensitive/confidential IoT sensor Data is vulnerable while transferring. A way to mitigate the issue that may cause serious data breach opportunities in the transferring of information from the IoT Device to a Remote Desktop/user device is being handled in this work. A new system has been described that uses multiple True Random Number Generators(TRNGâ€™s) designed with the help of Fault-tolerant gates to generate OTP as a single-step authentication, which will then be used to transfer the files. Thus TRNG finds an effective application in the avenue of information security. The original IoT Sensor Data/scrambled IoT Sensor Data will be transferred depending upon the authentication. This ensures that only authorized users are accessible to the original data of the sensor values. The hamming distance of the original information file and the scrambled information file has been determined to verify the same and found to around 50%. Likewise, the time taken during original and scrambled file transfer has also been calculated to analyze the effectiveness of the proposal.
Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments

In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving as noncooperative targets, is considered. The focus is on determining the optimal sensor precisions while simultaneously promoting sparseness in the sensor measurements to guarantee a specified estimation performance. The problem is formulated in the discrete-time centralized Kalman filtering framework. A semidefinite program subject to linear matrix inequalities is solved to minimize the trace of precision matrix which is defined to be the inverse of sensor noise covariance matrix. Simulation results expose a trade-off between sensor precisions and sensing frequency.
Understanding the Variation of Seismicity in the Eastern Himalayas: Using Earthquake Frequency-Magnitude Distribution and Fractal Dimension

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Understanding the Variation of Seismicity in the Eastern Himalayas: Using Earthquake Frequency-Magnitude Distribution and Fractal DimensionAuthorsSauravKumariDAniruddhaSenguptaSee all authors Saurav KumariDCorresponding Author• Submitting AuthorIndian Institute of Technology KharagpuriDhttps://orcid.org/0000-0001-7056-0650view email addressThe email was not providedcopy email addressAniruddha SenguptaIndian Institute of Technology Kharagpurview email addressThe email was not providedcopy email address
Definition and Estimation of Dimension in Facial Expression Space

It has recently been shown that the facial expression space as a psychophysical image space is a Riemann space where the metric tensor is defined by the JND (Just-Noticeable-Difference) discrimination thresholds at every point in the space. The major obstacle to understand this space is how to estimate the metric tensor in high dimensions which requires an inaccessible number of psychophysical experiments. In this paper we address two fundamental issues: methods to estimate the Riemann metric tensor in a high dimensional space and how to define and to determine the effective dimensions of Riemann spaces and psychophysical spaces. We introduce new definitions for these dimensions and novel algorithms for estimating the high dimensional Riemann metric tensor and for dimension reduction to low dimensional subspaces. We then apply these algorithms to the facial expressions space. The Riemann metric tensor of the high dimensional facial expression space is estimated from psychophysical measurements of JND data. We apply these methods to investigate the facial expression space. We describe our experiments to estimate the effective dimension (together with upper and lower bounds) of Riemann manifolds and psychophysical spaces.
Landmark Training Based on Augmented Reality for People with Intellectual Disabilities

People with intellectual disabilities often experience difficulties in wayfinding tasks which obstructs active participation. They typically rely on landmarks but have difficulties in identifying appropriate landmarks on their own. To this aim a concept for an augmented reality application is presented which allows people with intellectual disabilities to train landmark-based navigation based on routes selected together with a guiding person. To enhance training motivation gamification elements are incorporated.
Improving the Imperceptibility of Pixel Value Difference and LSB Substitution Based Steganography Using Modulo Encoding

In the digital era, an image can be used to hide secret message during the communication of two parties, using image steganography. The image steganography method based on the combination of LSB Substitution and PVD method has been introduced for increasing the payload of the stego-image. We proposed an image steganography method based on the modified combination of LSB Substitution and PVD to improve the imperceptibility of the stego-image. We evaluate our method by comparing the performance of the proposed method with Swain’s method. Using the proposed method, the maximum PSNR of the stego-image for 28.75 kB of payload is 50.052 dB, while using Swain’s method is 38.785 dB. However, the proposed method needs more time for executing the process than the other.
Reservoir Computing with an Inertial Form

We propose a framework of a reservoir with an inertial manifold, which is a finite-dimensional object that attracts all trajectories. In addition, based on the theoretical results concerning infinite-dimensional dynamical systems, we demonstrate that this reservoir should have some desirable properties called the echo state property and the separation property. By considering the projection of the system onto the inertial manifold, we describe the behavior of the reservoir as a system of finite-dimensional ordinary differential equations called an inertial form. Next, we present a finite-dimensional model that approximates the inertial form. Finally, we propose a temporally discretized model, which is more suitable for computer computation. We give the theoretical proofs of our theorems herein.
On V-form Firms: Governance, Cryptocurrency, and Internalization on the Blockchain

Although the V-form organization has received attention in light of the integration of blockchain with processes in supply chains, analysis of a V-form organization as a firm in terms of its identity as legal entity has not been considered. Building on the concept of internalization, I reflect upon the efficiency increases the V-form firm can offer to supply chain members. V-form firms can support robust, decentralized markets that use cryptocurrency native to the blockchain with which the supply chain is integrated. Likewise, the V-form firm can provide property within the firm and which the firm is incentivized to protect from predatory intervention. I follow by considering the impact of the V-form firm on institutions adjacent the supply chain. The V-form firm stands to increase the level of competition in the realms of governance and monetary systems, either directly providing alternatives or indirectly influencing existing systems for which it may serve as substitute.
Memorability of Japanese Mnemonic Passwords

Password authentication is the most commonly used mechanism for user authentication. However, its vulnerability to different attacks such as dictionary attacks or brute force attack is well known. The users often use password authentication in insecure ways, such as using weak passwords or reusing passwords, which leads to password crackings. Though these problems are apparent, the trade-offs between password strength and password memorability prevent users from using strong passwords. To realize high password strength and memorability, the use of mnemonic passwords is suggested. However, due to its characteristic that the users must use English sentences, this password-generation strategy is not widely used in countries such as Japan, which do not use English as their native language. Therefore, we introduce Japanese mnemonic passwords, which are passwords using password-generation techniques optimized for Japanese users. We conducted a user study to explore the memorability of Japanese mnemonic passwords. We discuss the types of errors made by the participants and how Japanese mnemonic passwords' usability can be enhanced. We also discuss how this strategy can be used in other non-English-speaking countries.
Generalized Polynomial Chaos-based Ensemble Kalman Filtering for Orbit Estimation

In this paper a novel framework is proposed to carry out state and parameter estimation of a general nonlinear stochastic dynamical system in a prediction-correction fashion. The uncertainties in the initial states and parameters are propagated using generalized polynomial chaos expansion technique to compute the predicted estimates of states and parameters. Once the measurements are available, a nonlinear estimator is developed to update the predicted estimates in ensemble filtering framework. The methodology is then applied to estimate the states and uncertain parameters of a satellite for Low Earth Orbit (LEO) navigation using Global Positioning System (GPS) measurements. The results obtained from generalized polynomial chaos expansion-based ensemble filter are compared with that obtained from the unscented Kalman filter and the particle filter in terms of accuracy and computational efficiency.
Crime Data Analysis Using Association Rule Mining Technique

Law authorization organizations utilize different crime analysis tool to find crimes. However, a major volume of crime data has made the way toward analyzing crime difficulties. Here, proposed technique is for improving the efficiency of the police personnel and police stations. The proposed methodology is used to advancement in the crime type data analysis with apriori algorithm in association rule mining. Techniques should be additionally valuable for police personals proposed a user-oriented crime analysis in Nanded city. The rule obtained in this study helps to identify the location and types wise crime associated cases. In future, it will help to deal with and minimize the most frequent cases.
Real-World Distance Reduction in a Virtual Reality-Based Wheelchair Simulation on Flat Surfaces

A virtual reality (VR)-based wheelchair simulator based on a combination of motions attainable by an electric-powered wheelchair and vection-inducing videos displayed on a head-mounted display has been proposed for patient rehabilitation and training. This simulator requires the actual physical distance traveled by the wheelchair and the distance traveled by the wheelchair in the virtual environment to be the same. This requirement limits the locations where users can use this simulator. We clarify the relationship between these distances and aim to reduce the physical distance required to perform VR-based wheelchair simulations. To this end, we conducted an experiment whereby participants drove a wheelchair on a flat ground at a constant speed and at a decreasing speed for 8 m. The experimental results showed that the required physical distance can be reduced by 30% and 20% using the constant speed and deceleration approaches, respectively. The results of this study are expected to contribute to the development of wheelchair simulators that require less space.
Space Efficient Merging of de Bruijn Graphs and Wheeler Graphs

The merging of succinct data structures is a well established technique for the space efficient construction of large succinct indexes. In the first part of the paper we propose a new algorithm for merging succinct representations of de Bruijn graphs. Our algorithm has the same asymptotic cost of the state of the art algorithm for the same problem but it uses less than half of its working space. A novel important feature of our algorithm, not found in any of the existing tools, is that it can compute the Variable Order succinct representation of the union graph within the same asymptotic time/space bounds. In the second part of the paper we consider the more general problem of merging succinct representations of Wheeler graphs, a recently introduced graph family which includes as special cases de Bruijn graphs and many other known succinct indexes based on the BWT or one of its variants. In this paper we provide a space efficient algorithm for Wheeler graph merging; our algorithm works under the assumption that the union of the input Wheeler graphs has an ordering that satisfies the Wheeler conditions and which is compatible with the ordering of the original graphs.
An Algorithm for Distinguishing Between Speech and Music

In the classification algorithm of speech music, most of the algorithms are to extract feature parameters and use the classifier to distinguish speech and music. On this basis, a speech music classification algorithm is proposed. The algorithm first preprocesses the audio signals before classification, calculates the MEL cepstrum coefficient of audio, then calculates the MEL parameter similarity matrix, then calculates the correlation matrix, and finally judges speech or music by threshold classification. The test results show that compared with other audio classification algorithms, this algorithm does not need a classifier, the classification accuracy is high, and the algorithm is simple.
A new view on parser combinators

Parser combinators offer a concise and fast way to produce reasonably efficient parsers. The combinator libraries themselves can be small and provide an elegant application of functional programming techniques. They are one of the success stories in functional programming that are also ported to many other languages.
Every Action-Based Sensor

In studying robots and planning problems, a basic question is what is the minimal information a robot must obtain to guarantee task completion. Erdmann’s theory of action-based sensors is a classical approach to characterizing fundamental information requirements. That approach uses a plan to derive a type of virtual sensor which prescribes actions that make progress toward a goal. We show that the established theory is incomplete: the previous method for obtaining such sensors, using backchained plans, overlooks some sensors. Furthermore, there are plans, that are guaranteed to achieve goals, where the existing methods are unable to provide any action-based sensor. We identify the underlying feature common to all such plans. Then, we show how to produce action-based sensors even for plans where the existing treatment is inadequate, although for these cases they have no single canonical sensor. Consequently, the approach is generalized to produce sets of sensors. Finally, we show also that this is a complete characterization of action-based sensors for planning problems and discuss how an action-based sensor translates into the traditional conception of a sensor.
Gradient Methods with Dynamic Inexact Oracles

We present a framework for generalizing the primal-dual gradient method, also known as the gradient descent ascent method, for solving convex-concave minimax problems. The framework is based on the observation that the primal-dual gradient method can be viewed as an inexact gradient method applied to the primal problem. Unlike the setting of traditional inexact gradient methods, the inexact gradient is computed by a dynamic inexact oracle, which is a discrete-time dynamical system whose output asymptotically approaches the exact gradient. For minimax problems, dynamic inexact oracles are capable of modeling a range of first-order methods for computing the gradient of the primal objective, which relies on solving the inner maximization problem. We provide a unified convergence analysis of gradient methods with dynamic inexact oracles and demonstrate its use in creating new accelerated primal-dual algorithms.
Discrete stationary wavelet transform and SVD-based digital image watermarking for improved security

Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three level wavelet decomposition and SVD are applied and watermarked image is tested under various attacks such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks and obtained simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.
Inference Variable—A Human Cognition and Design Perspective on Knowledge Graph Visualization

In this paper, we expound our theoretical hypothesis covering effective visualization factors in the area of visual analytics for a knowledge graph (KG) considering human cognition aspects of inferencing. The paper reflects on the first and foremost challenge with visualizations—which is effectively harnessing the potential of a knowledge graph visually. While there is a progress on the technical front in allowing an inference to be queried, there is limited scope to do the same visually. The second challenge identified is when the user has to use his mental space and cognitive reserve to conflate and schematize knowledge to infer. There are certain constraints with this as to how the human attention is efficiently oriented toward the relevant information and the visualized information is not cognitively loaded for the user to infer from it. As far as the visualization aspect of the knowledge graph is concerned, the aim is to reduce the cognitive resources used for building a new schema of the task inside long-term memory. Hence, the proposed inferencing variable in the paper enables the release of space and conserves the cognitive reserve for further higher cognitive process of decision making. It also includes the elements of motivation for the user to visualize knowledge in knowledge graphs and an overview of the existing visualizations of KG.
Scientific libraries: future in the past

The article considers the main problems and challenges facing scientific libraries in the context of the rapid development of information technologies. The article presents the discussion of library specialists who support various positions, the analysis of trends, and possible scenarios for the future existence of scientific libraries. The relations of "letters" and "figures" changing against the background of general digitalization are shown, the possibilities and conditions of their effective interaction are analyzed. The conclusion is made about the expediency of combining the achievements of the past and the future in the knowledge transfer. It reflects the fundamental trends in the needs of users, changes in the services provided and the existing competencies of scientific libraries staff. Attention is drawn to objective and subjective reasons that hinder the innovative development of the library sphere. The information and communication potential and the possibilities of integrating librarians into the process of scientific research are shown on the example of SPSTL SB RAS. The authors have identified and summarized the basic recommendations for optimizing the situation, taking into account the best domestic and foreign experience, with an emphasis on key problems.
Use of Virtual Reality in Participatory Urban Design

This paper focuses on providing a comprehensive look at the use of virtual reality in participatory urban design practices. Immersive virtual environments are used in many areas of design, one of them being participatory urban design. Interactive, multisensory three-dimensional environments prove to be engaging for the public and illuminating for the designer. Presence can be benefitted for several layers of participatory urban design process: understanding the proposed design, commenting on the proposed design, contributing to the design. To this end, a review of the literature on the area is conducted and human-centered design considerations are noted. A human-centered VR experience is not proposed as the only component of the participatory design process, but one of them. The process should be designed by experts such as urban planners and designers with the input of all the stakeholders. This model should be included in the process design and tested in such a process in further studies.
Visual analytics for large networks

Article Visual analytics for large networks: theory, art and practice Share on Authors: Tomasz Bednarz Search about this author , Rowan T. Hughes Search about this author , Alex Mathews View Profile , Dawei Chen View Profile , Liming Zhu View Profile , Daniel Filonik Search about this author Authors Info & Affiliations SIGGRAPH '21: ACM SIGGRAPH 2021 CoursesAugust 2021 Article No.: 15Pages 1–213https://doi.org/10.1145/3450508.3464558Published:09 August 2021 0citation121DownloadsMetricsTotal Citations0Total Downloads121Last 12 Months121Last 6 weeks28 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Smart School Attendance System using Face Recognition with Near Optimal Imaging

This work aimed to develop a students' school attendance system with a web application and IoT devices as the two main parts. The web application was designed to manipulate the student data acquisition while IoT was used for face recognition to detect student attendance. This process applied Haar cascade and EBGM approaches to classify and distinguish faces for the web application. The web application was implemented with PHP language and a MySQL database provided for usage by three user groups: teachers, students and staff. The face recognition system was implemented on Raspberry Pi to connect with web application and record the student time attendance data. This work improves the accuracy of face detection by use of physical factors. We found that the current face recognition algorithm is highly efficient if the face position is arranged for clear image capture. Thus, this work also focuses on finding the right position to capture the user's face. The experimental test provided 100% accuracy of student classifier when they stand in a suitable position for imaging. The results show that the suitable position depends on the student face training count. In addition, the distance between camera and standing student affects the accuracy of face recognition.
An Experimental Study for Tracking Ability of Deep Q-Network under the Multi-Objective Behaviour using a Mobile Robot with LiDAR

The Reinforcement Learning (RL) had been attracting attention for a long time that because it can be easily applied to real robots. On the other hand, in Q-Learning one of RL methods, since it contains the Q-table and grind environment is updated, especially, a large amount of Q-tables are required to express continuous “states,” such as smooth movements of the robot arm. Moreover, there was a disadvantage that calculation could not be performed real-time in case of amount of states and actions.