Reviewerly/exHarmony huggingface dataset:
POSTER: A Tough Nut to Crack: Attempting to Break Modulation Obfuscation

Despite being primarily developed for spectrum management, sharing, and enforcement in civilian and military applications, modulation classification can be exploited by an adversary to threaten user privacy (e.g., via traffic analysis), or launch jamming and spoofing attacks. Several existing works study how an adversary can still classify the user traffic despite obfuscation techniques at upper layers, but little work has been done on how an adversary can classify the "modulation scheme'' when it is obfuscated at the physical layer. In this respect, we aim to study how to break the state-of-the-art modulation obfuscation schemes by applying various machine learning (ML) methods. Our preliminary results show that common ML techniques perform poorly in correctly classifying an obfuscated modulation scheme except for the random forest method (with a score as much as twice the other techniques we consider), providing insights on why other techniques, e.g., deep learning, might be more promising for finding underlying correlations.
Using Performance Predictions to Evaluate Two-factor Authentication Setup Processes

Low user adoption due to usability issues during setup is one of the primary challenges facing two-factor authentication implementations. A method which could quickly provide performance predictions for two-factor authentication setup processes would help create opportunities for iterative improvements during the design phase. These improvements can lead to increased human performance in the two-factor authentication setup processes once implemented and may help address some of the usability challenges that plague two-factor authentication user adoption efforts. Various software-based prediction tools can help simulate human performance and analyze underlying usability issues. The challenge is to evaluate human performance quickly and iteratively in two-factor authentication setup processes to help improve the usability of the processes during the design phase. We demonstrate rapid software-based performance predictions for three two-factor authentication setup processes and compare them to previously published human performance data for insights.
An Image Depth Processing Method Based On Parallel Computing and Multi-GPU

An image depth processing method based on CPU+GPU hybrid heterogeneous programming and Multi-GPU parallel computing is proposed in this paper. With the gradual increase of the image data, image processing algorithms have higher and higher requirements for GPU and CPU. Firstly, this paper introduces the heterogeneous programming system of the combination of CPU and GPU clusters and the technical points of CUDA. Secondly, the technical points based on Multi-GPU parallel computing image depth processing algorithm is introduced. Finally, the effectiveness of this algorithm is verified through experimental simulation.
Relationship between the Line Structured Light Vision Calibration Accuracy and Hardware Parameters

Abstract The line structured light vision has better robustness and higher measurement accuracy as an active vision measurement method, which is widely used in the measurement of component size and shape tolerance. The calibration is a key technology of vision measurement. The calibration accuracy is not only affected by the calibration algorithm, but the parameters of the hardware equipment also have an important impact on the final calibration accuracy. Based on the analysis of the calibration algorithm, this paper conducts a theoretical analysis on the selection of the main hardware parameters, and uses the orthogonal experiment method to determine the relationship between the equipment parameters and the calibration accuracy. In the experiments, the aperture has the greatest impact on the calibration accuracy among the main equipment parameters, and the calibration accuracy is the highest when the camera aperture is between F4-F8.
Comparison Elicitation Techniques in Furniture Manufacture Requirement

The development of the industrial revolution 4.0 in manufacturing has changed conventional business processes to become automated, especially in the furniture sector. The constraints faced by the furniture manufacturing industry are that producers do not have a reporting tool that estimates how many products to sell, goods produced do not match their needs and stock errors. Therefore, the process of extracting needs is an important phase in the development of information systems because it determines the final outcome of the program. The research aims to define system requirements, so that it can help the decision-making process based on real-time data. This study describes the use of system planning techniques in the form of collaborative techniques, observational techniques and contextual techniques, which are combined with the Wellsandt evaluation criteria and measured using the low, medium and high scales. The results of this study are the acquisition of any business process areas that have a high level of difficulty to be prioritized and described in the form of a spider diagram. Implication of this research is that requirements can be used by developers to prioritize areas of business processes that must be improved in making decision support systems.
Enhancing Sense of Attention from a Communication Robot by Drawing the User’s Face on Its Thought Bubble in the Video Conferencing System

In recent years, web conferencing systems have become widely used in non-face-to-face interactions. However, web conferencing systems make difficult to establish eye contact for users and can lead to decrease in non-verbal communication. In this paper, we propose a method that will enhance a user’s feeling of interlocutor’s attention, even when the line of sight does not actually match, by displaying a thought bubble for the interlocutor filled with the user’s in-camera image. Because robots have the same gaze problem as humans we thought it is important to know that this web conference system can represent the attention of a simple moving robot without using complex gaze mechanisms. So, we conducted a within-subject design experiment using a robot whose gaze and movements can be controlled and a participant as interlocutors in a web conference with and without the thought bubble. The experimental results indicate that our proposed method increased the sense of being looked at.
Run Wild: Resource Management System with Generalized Modeling for Microservices on Cloud

Microservice architecture competes with the traditional monolithic design by offering benefits of agility, flexibility, reusability resilience, and ease of use. Nevertheless, due to the increase in internal communication complexity, care must be taken for resource-usage scaling in harmony with placement scheduling, and request balancing to prevent cascading performance degradation across microservices. We prototype Run Wild, a resource management system that controls all mechanisms in the microservice-deployment process covering scaling, scheduling, and balancing to optimize for desirable performance on the dynamic cloud driven by an automatic, united, and consistent deployment plan. In this paper, we also highlight the significance of co-location aware metrics on predicting the resource usage and computing the deployment plan. We conducted experiments with an actual cluster on the IBM Cloud platform. RunWild reduced the 90th percentile response time by 11% and increased average throughput by 10% with more than 30% lower resource usage for widely used autoscaling benchmarks on Kubernetes clusters.
Application of Machine Learning to Resource Modelling of a Marble Quarry with DomainMCF

Machine learning is constantly gaining ground in the mining industry. Machine learning-based systems take advantage of the computing power of personal, embedded and cloud systems of today to rapidly build models of real processes, something that would have been impossible or extremely time-consuming a couple of decades ago. The widespread access to the internet and the availability of cheap and powerful cloud computing systems led to the development and acceptance of tools to automate resource modelling processes or optimise mine scheduling, using machine learning methodologies. The domain modelling system discussed in this paper, called DomainMCF, has been developed by Maptek, using artificial neural network technology. In the application presented in this paper, DomainMCF is used to model the spatial distribution of marble quality categorical parameters, and the results are combined to produce a final marble quality classification using drillhole and quarry face samples from an operational marble quarry in NE Greece. DomainMCF was made available for this study as a cloud processing service through an early access program for individuals or companies interested in testing its capabilities and suitability in various modelling scenarios and geological settings. The resulting marble product classifications are compared with those produced by the already established classification system that is based on a more conventional estimation method. The produced results show that DomainMCF can be effectively applied to the modelling of marble quality spatial distribution and similar domaining problems.
Application of Python Programming and Its Future

Python is a powerful general-purpose, high-level, object-oriented programming language which is created by Guido van Rossum. As it is having simple syntax, it is suitable and is an excellent decision for both the individuals who are eager to learn programming for first time and also for real-world programming. The world is getting advanced and everything is becoming smart by using python. It’s utilized for more number of applications by thanking various libraries which are standard coming alongside it with its ability to incorporate with different dialects and utilize their features. The paper is all about studying the useful programming language which is python. This paper describes about the most features of Python programming. It makes the list having dissimilarity among Python and any other languages with the assistance of some code. Also this paper likewise talks about the purposes for python being credited as the quickest developing programming language lately upheld by research done over the articles secured from different magazines and famous sites. This paper at that point talks about utilizations of Python programming.
Context-Aware Privacy-Optimizing Address Tracing

Application tuning requires a coordinated effort across hardware and software to achieve optimized application performance. Execution traces offer unique insights into a program’s behavior over real inputs and serve as an invaluable resource for hardware and software engineers during the co-optimization process. Unfortunately, these traces are rarely shared between technology partners because even the simplest address traces gathered from applications that utilize private data can divulge sensitive information. Developers must choose between sharing accurate and precise execution information that will lead to the best co-optimization results while protecting sensitive data. This is the fundamental tradeoff between utility and privacy in the context of program traces.Concurrently, global policy is moving in favor of providing users with privacy protections. As a field, we must develop tools, mechanisms, and primitives to uphold these regulatory protections. In this work, we utilize the leading industry standard: the LINDDUN privacy threat modeling method, to model the threats to privacy of traces. We leverage advances in information flow tracking techniques and LINDDUN’s mitigation strategies to prevent inadvertent leakage of information. We introduce multiple classes of privacy-enhancing tracing techniques that allow context-aware differentiation of what information should remain in the trace and in what amounts based on annotations of private user input. To explore how meaningful the privatized traces are, we compare cache simulation and prefetching properties. This new approach leaks as few as zero bits of sensitive information and has an order of magnitude better utility than prior work.
Exploring an Immersive User Interface in Virtual Reality Storytelling

Virtual reality provides a highly immersive experience as the player’s actions interact with the virtual world in real time, and feedback is immediately reflected. We explored a device that could enhance immersion by inducing a natural interaction between the player and the virtual world through VR storytelling. In this study, we mainly concentrated on utilizing affordance in the diegetic UI, which constitutes the virtual world, to increase immersion by inducing natural interaction for the player. To use affordance in VR storytelling, we classified physical affordance, cognitive affordance, sensory affordance, and functional affordance as roles that support diegetic UI. We suggest cognitive affordance and physical affordance to recognize and execute the need for interaction necessary to progress the story. We also suggest sensory affordance and functional affordance to detect the target smoothly and help the player’s intentional sequence of actions from a functional point of view. Finally, we suggest that the player’s immersion can be improved by supporting affordances so that the story can progress through the diegetic UI and natural interaction with the player.
Efficient binary embedding of categorical data using BinSketch

In this work, we present a dimensionality reduction algorithm, aka. sketching, for categorical datasets. Our proposed sketching algorithm Cabin constructs low-dimensional binary sketches from high-dimensional categorical vectors, and our distance estimation algorithm Cham computes a close approximation of the Hamming distance between any two original vectors only from their sketches. The minimum dimension of the sketches required by Cham to ensure a good estimation theoretically depends only on the sparsity of the data points—making it useful for many real-life scenarios involving sparse datasets. We present a rigorous theoretical analysis of our approach and supplement it with extensive experiments on several high-dimensional real-world data sets, including one with over a million dimensions. We show that the Cabin and Cham duo is a significantly fast and accurate approach for tasks such as $$\mathrm {RMSE}$$ , all-pair similarity, and clustering when compared to working with the full dataset and other dimensionality reduction techniques.
Sustainable Development and Education: Challenges and Risks

The article focuses on the main problems and risks associated with the global processes of abrupt transfer of learning processes to a distance format by several countries in the world during the COVID-19 pandemic. The features of the transformation of traditional education, considering new requirements, previously based on the principles of sustainable development, are considered. The problems of introducing online formats, distance technologies, digital educational environment, difficulties, and risks in the forced rapid transition to online learning are identified. The emphasis is placed on the need to identify the problems of digital and social inequality of students. Special attention is paid to measures to mitigate the consequences of distance education for socially unprotected categories of students, since they are the ones who faced serious obstacles of various types and found themselves in the zone of greatest risk.
Smart Health From the Hospital to the City

The COVID-19 pandemic has accelerated the transition of health systems from “hospital” to “city” thanks to smart devices aimed at health promotion through the deployment of ICT in urban contexts. Worldwide, the pandemic has triggered a rethinking of the healthcare system according to the prevention principle, characterized by the usage of advanced technology, which can widely re-orient markets and public action in the field of public and private health services. The chapter will consider the impacts of such innovations and the challenges for the regulatory framework, with a special focus on the Italian context. First, it outlines the main global and EU strategies for digital healthcare. Second, it focuses on the use of smart devices as tools of health prevention. Third, it highlights critical aspects regarding competition and privacy. Finally, the author reflects upon the centrality of the city as a crucial actor for urban health innovation and its upcoming regulatory challenges.
Designing to Support Remote Working Relationships with Indigenous Communities

Linguistic research with speakers of Aboriginal languages usually takes place though face-to-face interaction. The success of these interactions depends on relationships between scholars and Aboriginal people, relationships which are built up over an extended period. During the COVID-19 pandemic, researchers have often been prevented from travelling to remote Aboriginal communities, making it difficult to sustain these relationships and continue the collaboration. We describe an appliance design for supporting consultations between outside scholars and remote community members. Requests are framed as personal invitations via a asynchronous video messaging and delivered to the remote participant via “Lingobox”, a portable appliance akin to a multimedia answering machine. The device is being used with speakers of the Kunwinjku language in an extremely remote region of northern Australia.
Faculty Member Employment of Online Platforms

COVID-19 prompted to transform the traditional education system into a distance education system and using technological tools, including educational platforms. online learning platforms play an important role in modern education. The objective of the research is to explore the degree of using platforms by the faculty members at Al-Aqsa University, and to highlight the difficulties they face when using platforms .To achieve the research objective, ​followed the qualitative and quantitative method. The instruments of the research were a semi-structured interview included the important difficulties the faculty members faced inusing platforms , and a questionnaire included the most important skills to use platforms. The results revealed that participants agreed on the most important obstacle of using educational platforms, the difficulty of access to the internet. The results of the survey indicated that the use of e-platform skills is high. The results of this study help decision-makers to plan, evaluate and implement online learning platforms in their institutions
Virtual Learning Environment for a Multivariate System

This paper focuses on the implementation of a control algorithm based on numerical methods for a system of three tanks connected in series and parallel in a virtual reality environment, with the objective of controlling the liquid level. The application developed is intended to support the learning process of students in the area of engineering, for which a virtual environment is generated with components that simulate a real plant. The modeling process is based on nonlinear equations. These are used to implement the closed-loop control law that shows an optimal behavior of both the control law and the mathematical model obtained. It also allows to simulate the operation of a real industrial process contributing to multivariable system processes.
Incorporating Range Rate Measurements in EKF-based Elliptical Extended Object Tracking

This work deals with the tracking of an elliptical extended object using radar measurements. We model the ellipse using the orientation and semi-axes as individual state parameters. The radar measurements typically consist of range, bearing, and range rate with individual noise. To minimize non-linearities in the measurement equation, we utilize the transformation of the measurement noise from polar to Cartesian coordinates along with the removal of that transformation's bias and we calculate a pseudo range rate by multiplying range and range rate measurement. Using the resulting measurement vector, we define a measurement equation based on multiplicative noise and apply linearization techniques to get a closed-form solution for the update using Kalman filter formulas. We evaluate our approach in simulations, comparing it to the original MEM-EKF* and an approach using linearization to incorporate Doppler range rate into the random matrix framework.
Parallelization of a Neural Network Algorithm for Handwriting Recognition: Can we Increase the Speed, Keeping the Same Accuracy

This paper examines the problem of parallelizing neural network training using the back-propagation neural network, as a breakthrough example in the field of deep learning. The challenge of our solution is to twist the algorithm in such a way so it can be executed in parallel, rather than sequentially. In this paper, we test validity of a research hypothesis if the speed can be increased by parallelizing the back-propagation algorithm and keep the same accuracy. For this purpose, we developed a use-case of a handwriting recognition algorithm and conducted several experiments to test the performance, both in execution speed and accuracy. At the end, we examine just how much it benefits to write a parallel program for a neural network, with regards to the time it takes to train the neural network and the accuracy of the predictions. Our handwriting problem is that of classification, and in order to implement any sort of solution, we must have data. The MNIST dataset of handwritten digits provides necessary data to solve the problem.
A Novel Video Stabilization Model With Motion Morphological Component Priors

Video stabilization is the process of improving the video quality by removing annoying fluctuant motion caused by camera jittering. A key issue of a successful solution is the temporal adaptability to motion and the overall robustness with respect to different motion types. However, most previous methods usually produce non-motion adaptive stabilized videos. In other words, under-smoothing in slow motion segments and over-smoothing in rapid motion segments will be produced for complex shaky videos. To overcome these drawbacks, we propose a novel video stabilization approach using a motion morphological component (MMC) decomposition. Specifically, the observed motion is decomposed into three MMCs: low-frequency smoothed (LFS) motion, high-frequency compensatory (HFC) motion, and shaky motion. LFS motion helps to largely stabilize videos, and HFC motion helps to recover missing motion to deal with over-smoothing. Subsequently, we present an MMC-based model to retrieve the desired smoothed motion, in which weighted nuclear norm and autoregression priors are used for LFS motion, while a sparsity prior is adopted for HFC motion. In addition, we design an adaptive weight setting scheme to detect rapid motions and to calculate the optimal weights. Finally, we develop a stabilization algorithm under the Alternating Direction Method of Multipliers (ADMM) framework. Experimental results demonstrate that our method can achieve high-quality results compared with that of other state-of-the-art stabilization methods in terms of robustness and efficiency, both quantitatively and qualitatively.