Reviewerly/exHarmony huggingface dataset:
Global Feature Analysis and Comparative Evaluation of Freestyle In-Air-Handwriting Passcode for User Authentication

Freestyle in-air-handwriting passcode-based user authentication methods address the needs for Virtual Reality (VR) / Augmented Reality (AR) headsets, wearable devices, and game consoles where a physical keyboard cannot be provided for typing a password, but a gesture input interface is readily available. Such an authentication system can capture the hand movement of writing a passcode string in the air and verify the user identity using both the writing content (like a password) and the writing style (like a behavior biometric trait). However, distinguishing handwriting signals from different users is challenging in signal processing, feature extraction, and matching. In this paper, we provide a detailed analysis of the global features of in-air-handwriting signals and a comparative evaluation of such a user authentication framework. Also, we build a prototype system with two different types of hand motion capture devices, collect two datasets, and conduct an extensive evaluation.
Round Robin Scheduling for Virtual Data Hiding and Extraction

Nowadays security of information is highly important. Most of the IT industries have their focus on the security of data either stored on the server system or transmitted on wireless media. The objective of proposed work is to utilize the samples of carrier object so that data Hiding capacity a carrier should be maximized without changing samples the carrier. It also focuses on optimizing utilization of selected samples again and again for data hiding. The main idea of this paper is to concentrate on sample utilization and virtual Data Hiding & extraction. Obtained results from propose concept imply that, with virtual data replacement, there is no change in resultant stego object.
Rider and Sunflower optimization-driven neural network for image classification

Image classification is the classical issue in computer vision, machine learning, and image processing. The image classification is measured by differentiating the image into the prescribed category based on the content of the vision. In this paper, a novel classifier named RideSFO-NN is developed for image classification. The proposed method performs the image classification by undergoing two steps, namely feature extraction and classification. Initially, the images from various sources are provided to the proposed Weighted Shape-Size Pattern Spectra for pattern analysis. From the pattern analysis, the significant features are obtained for the classification. Here, the proposed Weighted Shape-Size Pattern Spectra is designed by modifying the gray-scale decomposition with Weight-Shape decomposition. Then, the classification is done based on Neural Network (NN) classifier, which is trained using an optimization approach. The optimization will be done by the proposed Ride Sunflower optimization (RideSFO) algorithm, which is the integration of Rider optimization algorithm (ROA), and Sunflower optimization algorithm (SFO). Finally, the image classification performance is evaluated using RideSFO-NN based on sensitivity, specificity, and accuracy. The developed RideSFO-NN method achieves the maximal accuracy of 94%, maximal sensitivity of 93.87%, and maximal specificity of 90.52% based on K-Fold.
Assessing the usefulness of empathic design in mobile game-based learning (mGBL)

Mobile games have become an important tool for learning, especially in pandemic contexts when education goes completely remote and digital. In this scenario, using portable devices such as tablets and smartphones is the most popular choice. However, the design of mobile game-based learning (mGBL) solutions is challenging, so new approaches are needed for a higher motivation, enjoyment, and immersion of students. A promising approach among them is the use of empathic design thinking. This paper studies the usefulness of empathic design, by applying assessment techniques like context mapping and activity system modeling to understand how empathic design would focus on the usability and effectiveness of mobile game-based learning solutions for lecturers and learners. The context mapping was done by applying a mixed-research approach with a questionnaire and empathy map-based interviews which revealed that lecturers have a high level of openness to be co-designers of mGBL instruments that would motivate learners to develop problem-solving, creative thinking and collaboration skills. The lecturers consider that adoption of empathic design principles for mGBL creation would be efficient, by engaging them in this process as entities that will impact the usefulness and usability of mGBL products. From these results, the Activity System is presented an mGBL modeling complex design approach that would engage lecturers and learners in the innovative mGBL transformation.
A comparative study of recently deep learning optimizers

Deep learning has achieved great success in computer vision, natural language processing, recommendation systems and other fields. However, the models of deep neural network (DNN) are very complex, which often contain millions of parameters and tens or even hundreds of layers. Optimizing weights of DNNs is easy to fall into local optima, and hard to achieve better performance. Thus, how to choose an effective optimizer which is able to obtain network with higher precision and stronger generalization ability is of great significance. In this article, we make a review of some popular historical and state-of-the-art optimizers, and conclude them into three main streams: first order optimizers that accelerate convergence speed of stochastic gradient descent or/and adaptively adjust learning rates; second order optimizers that can make use of second-order information of loss landscape which helps escape from local optima; proxy optimizers that are able to deal with non-differentiable loss functions through combining with the proxy algorithm. We also summarize the first and second order moment used in different optimizers. Moreover, we provide an insightful comparison on some optimizers through image classification. The results show that first order optimizers like AdaMod and Ranger not only have low computational cost, but also show great convergence speed. Meanwhile, the optimizers that can introduce curvature information such as Adabelief and Apollo, have a better generalization especially when optimizing complex network.
Recaptured images detection algorithm based on frequency domain transformation1

With the raising popularity of digital devices in the current society, the present image detection system is becoming a great threaten. Especially the appearance of the recaptured images. It can be used in traditional invalid digital image detection algorithm. There is a new algorithm in this paper is presented to detect the recaptured and real image. The algorithm obtains low-frequency images, directional filtering images and high-frequency images by multiple application frequency domain filtering. Then the proposed algorithm analyzes the directional filtering images and high-frequency images by means of LBP algorithm to extract features. At last, the recaptured images were classified by the SVM. The experimental results demonstrated the algorithm in this paper could be effectively identify in the recaptured images.
Interactive Learning Application for Indian Sign Language

Sign language is a language used by deaf and dumb people to communicate with one another as well as rest of the world. Since this language is not known to everyone, it poses a barrier for their personal development and also limits their opportunities. We proposed to develop an interactive sign language learning application with embedded sign recognition LSTM model which recognizes the video signs uploaded by the user. Users can either upload pre-recorded videos with the given constraints or capture videos through webcam. We have restricted our domain to emergency-related words only. The proposed model has achieved 88.94% training accuracy and 86.4% testing accuracy using CNN for feature extraction and LSTM for classification.
Flood control management recommendation of Citarum Hilir sub-watersheds using remote sensing and geographic information system approach

Floods that occurred in the Citarum watershed are caused by land degradation and environmental damage following the population and economic pressure on its surrounding. To overcome this problem, proper planning and uninterrupted observation on the physical characteristics of the watershed are needed. This study aims to: 1) identify the physical characteristics of the Citarum Hilir Sub-Watershed that affect the flow coefficient; 2) calculate the flow coefficient by considering the physical characteristics of the sub-watershed; 3) provide management recommendations of the Citarum Hilir Sub-Watershed for flood control. The method to identify the flood characteristics is by calculating the coefficient of river flow using Cook method which incorporates parameters of slope, soil infiltration, land use, and drainage density, deducted from DEM SRTM and Landsat-8. Using Multiparametric GIS Analysis, the result shows that the physical characteristic of land use contributed the most, which is 34.55% to the flow coefficient assessment. The slope parameter has an influence of 25.44%, soil infiltration is 11.42%, and drainage density is 28.60%. The total flow coefficient value is 52.32%, so it is classified as high criteria. Therefore, this area has high flood potential that are prioritized for handling flood control. This area covers a land unit with high to extreme flow coefficient values, which is 73.96% of the total area. Several efforts can be made to follow up the flood control on these priority areas, such as land use management, forest and land rehabilitation, soil and water conservation, and integrated watershed management. Land use planning must refer to this analysis to determine the suitability of land use for water and forest conservation to remediate those high to extreme flow coefficient values. The evaluation on Regional Planning Map of West Java Province in 2009-2029, is needed as a recommendation to control the flood and enhance the Citarum Hilir management.
Optimal Control in Learning Neural Network

We present optimal control approach to improve the neural network learned on a given empirical data (set of observations). Artificial neural networks usually are described as black-boxes and it is difficult to say something about their properties than very general results received from learning data. For many applications, e.g. medicine or embedded system for controlling autonomous vehicles, it is essential to say not only that on training data we get some error but that we will make an error not greater than some $$\varepsilon $$ for every data we can input to our system. To derive required theory we apply an optimal control theory to a certain family of neutral networks, considered as ordinary differential equations, defined by a set of controls and suitable constructed functional. Very often we have additional information or knowledge on the problem the data represent. Our approach allows to include these information and knowledge in the construction of the model. We apply a modification of classical dynamic programming ideas to formulate a new optimization problem. We use it to state and prove sufficient approximate optimality conditions for finding approximate neural network which should work correctly for given $$\varepsilon $$ with respect to built functional, on a data different than the set of observations.
Evidence-Based Immersive Technology Use in Cognitive Assessments and Cognition-Based Interventions

The merging of immersive technologies and cognition has been around for a while. However, it is only in the last decade or so that immersive technologies' contributions in the areas of cognitive assessments and interventions have gathered recognition. This chapter covers findings from published research in cognition-based assessments and interventions using the immersive technologies of virtual reality (VR) and augmented reality (AR). The role of immersive technologies in cognition is critically evaluated to inform all its stakeholders about its potential for use in the future.
Lightweight and Accurate DNN-based Anomaly Detection at Edge

Deep neural networks (DNNs) have been showing significant success in various anomaly detection applications such as smart surveillance and industrial quality control.It is increasingly important to detect anomalies directly on edge devices, because of high responsiveness requirements and tight latency constraints.The accuracy of DNN-based solutions rely on large model capacity and thus long training and inference time, making them inapplicable on resource strenuous edge devices.It is hence imperative to scale DNN model sizes in correspondence to the run-time system requirements, i.e., meeting deadlines with minimal accuracy losses, which are highly dependent on the platforms and real-time system status.Existing scaling techniques either take long training time to pregenerate scaling options or disturb the unsteady training process of anomaly detection DNNs, lacking the adaptability to heterogeneous edge systems and incurring low inference accuracies.In this article, we present LightDNN to scale DNN models for anomaly detection applications at edge, featuring high detection accuracies with lightweight training and inference time.To this end, LightDNN quickly extracts and compresses blocks in a DNN, and provides large scaling space (e.g., 1 million options) by dynamically combining these compressed blocks online.At run-time, LightDNN predicts the DNN's inference latency according to the monitored system status, and optimizes the combination of blocks to maximize its accuracy under deadline constraints.We implement and extensively evaluate LightDNN on both CPU and GPU edge platforms using 8 popular anomaly detection workloads.Comparative experiments with stateof-the-art methods show that our approach provides 145.8 to 0.56 trillion times more scaling options without increasing training and inference overheads, thus achieving as much as 15.05% increase in accuracy under the same deadlines.
Operation record and analysis of interlocking manual test based on input-output model

In order to solve the problems of incomplete interlocking test and incomplete test process, and to make up for the management loophole of completely relying on the laboratory personnel to control the interlocking test quality, the computer interlocking input-output abstract model is established, and the status information of the on-site equipment in the model is input into the variables according to the items to be tested in the interlocking relationship checklist of the station. The test case is constructed by the operation command input variables of the tester and the information display variables of the interlocking interface, and the one-to-one real-time feature matching is carried out by obtaining the relevant data such as the category, time and sequence of the manually clicked buttons, combined with the state change of the signal equipment and the display of the interlocking interface. After matching with the model test case table, the simulation manual automatically marks the matching items in the checklist, so as to achieve the effective supervision of the whole manual test process and improve the reliability of the interlocking test process.
Methodological Features of Distance Education: From the Experience of Teaching at an Economic University

Various methods of distance learning are considered as an alternative to traditional methods of learning. This is due to the fact that recently, for objective reasons, distance learning is increasingly in demand in higher education, which is not an absolutely new form of education, in any case, for modern universities, however, cannot be defined as traditional. Special attention is paid to the organization of various types of classes, monitoring of training results. The experience of using a variety of methods in distance learning at our university has shown that only a skillful combination of these methods allows you to professionally interest a student, encourages him to independently master knowledge under the guidance of a teacher, and ensures a high level of formation of professional competencies. Such training prepares for difficult situations in the profession, develops the speed of professional reaction to economic situations, develops techniques for accumulating the received professional knowledge, removes time and spatial restrictions, expands the communicative sphere of students and teachers, solves the psychological problems of the student.
Reducing Revocation Lists in CS-Based Revocable Group Signature Scheme Using Vector Commitment

In the current ID-based user authentication systems, the user's personal use history can be traced, because the user's ID can be linked. Therefore, this may cause privacy issues. As a solution, group signatures have been researched to realize the anonymous authentication. The group signature requires a revocation function to revoke signatures generated by a user who leaves or is removed from a group. An efficient revocable group signature scheme using a tree structure called the Complete Subtree (CS) method has been proposed. However, the size of the revocation list depends on the number of revoked users. In this paper, we propose an extended scheme where the size of revocation list is reduced, by compressing a number of entries in the list by a vector commitment. We implement the proposed scheme on a PC, and evaluate the effectiveness.
Five Psycholinguistic Characteristics for Better Interaction with Users

When two people pay attention to each other and are interested in what the other has to say or write, they almost instantly adapt their writing/speaking style to match the other. For a successful interaction with a user, chatbots and dialogue systems should be able to do the same. We propose a framework consisting of five psycholinguistic textual characteristics for better interaction with users. We describe the annotation processes used for collecting the data, and benchmark five binary classification tasks, experimenting with different training sizes and model architectures. The best architectures achieve macro-averaged F 1 - scores between 72% and 96% depending on the language and the task. The proposed framework proved to be fairly easy to model even with small amount of manually annotated data if right architectures are used.
Machine Learning with Reconfigurable Privacy on Resource-Limited Computing Devices

Ensuring user privacy while learning from the acquired Internet of Things sensor data, using limited available compute resources on edge devices, is a challenging task. Ideally, it is desirable to make all the features of the collected data private but due to resource limitations, it is not always possible as it may cause overutilization of resources, which in turn affects the performance of the whole system. In this work, we use the generalization techniques for data anonymization and provide customized injective privacy encoder functions to make data features private. Regardless of the resource availability, some data features must be essentially private. All other data features that may pose low privacy threat are termed as nonessential features. We propose Dynamic Iterative Greedy Search (DIGS), a novel approach with corresponding algorithms to select the set of optimal data features to be private for machine learning applications provided device resource constraints. DIGS selects the necessary and the most private version of data for the application, where all essential and a subset of nonessential features are made private on the edge device without resource overutilization. We have implemented DIGS in Python and evaluated it on Raspberry Pi model A (an edge device with limited resources) for an SVM-based classification on real-life health care data. Our evaluation results show that, while providing the required level of privacy, DIGS allows to achieve up to 26.21% memory, 16.67% CPU instructions, and 30.5% of network bandwidth savings as compared to making all the data private. Moreover, our chosen privacy encoding method has a positive impact on the accuracy of the classification model for our chosen application.
Geological hazards and their effect on infrastructure

PreviousNext No AccessSixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021Geological hazards and their effect on infrastructureAuthors: Karim BenhammamKhalid Ali AlHosaniKarim BenhammamMinistry of Energy and InfrastructureSearch for more papers by this author and Khalid Ali AlHosaniMinistry of Energy and InfrastructureSearch for more papers by this authorhttps://doi.org/10.1190/iceg2021-083.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract The Ministry of Energy and Infrastructure mandated the British Geological Survey to conduct a major geological survey of the UAE. The geological hazard was part of the project. This work on the hazards is divided into two parts. The first part is to reviews the seismic hazard of the UAE, and the second part is to evaluate the nature and presence of potential Geological hazards. But the final report of the geological hazards mostly deals with the seismic hazard of the UAE, which is normally considered as a region of low seismicity activities. However, the general zero risk could not be considered especially for public planning and for civil engineers. In the UAE, there are two main major risk possibilities. The first possibility of risk is due to the UAE geographical situation where it is situated almost at edge of the Arabian plate and close to the nor thern shore of the Arabian Gulf, where the seismic events are more active. The most activity is coming from southern of Iran. Recently, during the last decade the earthquakes rate has been increased and begin felt more and more. The second major hazard possibility is the seismicity coming from the Arabian plate itself. However, this second possibility is rare. The effect of the ground shaking due to earthquakes occurred in the south of Iran may generate a large time scale effect and can be felt especially for the tall buildings. Keywords: groundwater, earthquake, geology, sources, riskPermalink: https://doi.org/10.1190/iceg2021-083.1FiguresReferencesRelatedDetails Sixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 341 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 24 Dec 2021 CITATION INFORMATION Karim Benhammam and Khalid Ali AlHosani, (2021), "Geological hazards and their effect on infrastructure," SEG Global Meeting Abstracts : 328-331. https://doi.org/10.1190/iceg2021-083.1 Plain-Language Summary KeywordsgroundwaterearthquakegeologysourcesriskPDF DownloadLoading ...
Study on the characteristics of the new type of plastic water glass gel fire prevention and fire extinguishing material

Water glass material is the main material to prevent and extinguish fire, but ordinary water glass gel is easy to lose water, weak compressive strength, poor fluidity, high cost and so on. In order to solve the above problems, in this paper, the common water glass gel was modified by adding new polymer water absorbing material, plasticizer and sodium bicarbonate. The results show that the optimum ratio of water, sodium bicarbonate, super absorbent resin and β- cyclodextrin is 3000:200:60:5, and the water retention and plastic effect of the gel material is the best. Compared with ordinary water glass gel, the water retention and compressive strength of the new plastic gel are significantly enhanced, which solves the problems existing in common mineral water glass gel.