Andres hernandez ai_machine_learning_london_nov2017Andres Hernandez
My slides from the AI & Machine Learning in Quantitative Finance conference in London. I train a neural network to train another neural network to optimize particular black boxes
RESOLVING CYCLIC AMBIGUITIES AND INCREASING ACCURACY AND RESOLUTION IN DOA ES...csandit
A method to resolve cyclic ambiguities and increase the accuracy and the resolution in the
direction-of-arrival (DOA) estimation using the Estimation of Signal Parameters via Rotational
Invariance Technique (ESPRIT)algorithm is proposed. It is based on rotating the array and
sampling the received signal at multiple positions. Using this approach, the gain in accuracy
and resolution is addressed as function of the mean and variance of the DOA. Simulations
results are provided as a means of verifying this analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
4 matched filters and ambiguity functions for radar signals-2Solo Hermelin
Matched filters (Part 2of 2) maximizes the output signal-to-noise ratio for a known radar signal at a predefined time.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
Andres hernandez ai_machine_learning_london_nov2017Andres Hernandez
My slides from the AI & Machine Learning in Quantitative Finance conference in London. I train a neural network to train another neural network to optimize particular black boxes
RESOLVING CYCLIC AMBIGUITIES AND INCREASING ACCURACY AND RESOLUTION IN DOA ES...csandit
A method to resolve cyclic ambiguities and increase the accuracy and the resolution in the
direction-of-arrival (DOA) estimation using the Estimation of Signal Parameters via Rotational
Invariance Technique (ESPRIT)algorithm is proposed. It is based on rotating the array and
sampling the received signal at multiple positions. Using this approach, the gain in accuracy
and resolution is addressed as function of the mean and variance of the DOA. Simulations
results are provided as a means of verifying this analysis.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
4 matched filters and ambiguity functions for radar signals-2Solo Hermelin
Matched filters (Part 2of 2) maximizes the output signal-to-noise ratio for a known radar signal at a predefined time.
For comments please contact me at solo.hermelin@gmail.com.
For more presentations on different subjects visit my website at http://www.solohermelin.com.
Model Predictive Control based on Reduced-Order ModelsPantelis Sopasakis
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control.
In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.
Low rank tensor approximation of probability density and characteristic funct...Alexander Litvinenko
Very often one has to deal with high-dimensional random variables (RVs). A high-dimensional RV can be described by its probability density (\pdf) and/or by the corresponding probability characteristic functions (\pcf), or by a function representation. Here the interest is mainly to compute characterisations like the entropy, or
relations between two distributions, like their Kullback-Leibler divergence, or more general measures such as $f$-divergences,
among others. These are all computed from the \pdf, which is often not available directly, and it is a computational challenge to even represent it in a numerically feasible fashion in case the dimension $d$ is even moderately large. It is an even stronger numerical challenge to then actually compute said characterisations in the high-dimensional case.
In this regard, in order to achieve a computationally feasible task, we propose to represent the density by a high order tensor product, and approximate this in a low-rank format.
Probabilistic Matrix Factorization (PMF)
Bayesian Probabilistic Matrix Factorization (BPMF) using
Markov Chain Monte Carlo (MCMC)
BPMF using MCMC – Overall Model
BPMF using MCMC – Gibbs Sampling
MSc Seminar by Yazan Safadi, Subject: "Optimal Integrated Routing and Signal Control in Urban Traffic Networks"
For more info please do not hesitate to contact me at safadiyazan@gmail.com.
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...Geoffrey Négiar
We propose a novel Stochastic Frank-Wolfe (a.k.a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. This class of problems includes empirical risk minimization with sparse, low-rank, or other structured constraints. The proposed method is simple to implement, does not require step-size tuning, and has a constant per-iteration cost that is independent of the dataset size. Furthermore, as a byproduct of the method we obtain a stochastic estimator of the Frank-Wolfe gap that can be used as a stopping criterion. Depending on the setting, the proposed method matches or improves on the best computational guarantees for Stochastic Frank-Wolfe algorithms. Benchmarks on several datasets highlight different regimes in which the proposed method exhibits a faster empirical convergence than related methods. Finally, we provide an implementation of all considered methods in an open-source package.
In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems.
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Shota Yamanaka and Homei Miyashita. Scale Effects in the Steering Time Difference between Narrowing and Widening Linear Tunnels. In Proceedings of NordiCHI 2016.
Platoon Control of Nonholonomic Robots using Quintic Bezier SplinesKaustav Mondal
In this project, quintic polynomials were used to perform platooning in nonholonomic robots. Both hardware and simulations results have been presented.
Model Predictive Control based on Reduced-Order ModelsPantelis Sopasakis
The need for reduced-order approximations of dynamical systems emerges naturally in model-based control of very large-scale systems, such as those arising from the discretisation of partial differential equation models. The controller based on the reduced-order model, when in closed-loop with the large-scale system, ought to endow certain properties, in primis stability, but also satisfaction of state constraints and recursive computability of the control law in the case of constrained control.
In this paper we introduce a new approach to the design of model predictive controllers to meet the aforementioned requirements while the on-line complexity is essentially tantamount to the one that corresponds to the low-dimensional approximate model.
Low rank tensor approximation of probability density and characteristic funct...Alexander Litvinenko
Very often one has to deal with high-dimensional random variables (RVs). A high-dimensional RV can be described by its probability density (\pdf) and/or by the corresponding probability characteristic functions (\pcf), or by a function representation. Here the interest is mainly to compute characterisations like the entropy, or
relations between two distributions, like their Kullback-Leibler divergence, or more general measures such as $f$-divergences,
among others. These are all computed from the \pdf, which is often not available directly, and it is a computational challenge to even represent it in a numerically feasible fashion in case the dimension $d$ is even moderately large. It is an even stronger numerical challenge to then actually compute said characterisations in the high-dimensional case.
In this regard, in order to achieve a computationally feasible task, we propose to represent the density by a high order tensor product, and approximate this in a low-rank format.
Probabilistic Matrix Factorization (PMF)
Bayesian Probabilistic Matrix Factorization (BPMF) using
Markov Chain Monte Carlo (MCMC)
BPMF using MCMC – Overall Model
BPMF using MCMC – Gibbs Sampling
MSc Seminar by Yazan Safadi, Subject: "Optimal Integrated Routing and Signal Control in Urban Traffic Networks"
For more info please do not hesitate to contact me at safadiyazan@gmail.com.
Stochastic Frank-Wolfe for Constrained Finite Sum Minimization @ Montreal Opt...Geoffrey Négiar
We propose a novel Stochastic Frank-Wolfe (a.k.a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. This class of problems includes empirical risk minimization with sparse, low-rank, or other structured constraints. The proposed method is simple to implement, does not require step-size tuning, and has a constant per-iteration cost that is independent of the dataset size. Furthermore, as a byproduct of the method we obtain a stochastic estimator of the Frank-Wolfe gap that can be used as a stopping criterion. Depending on the setting, the proposed method matches or improves on the best computational guarantees for Stochastic Frank-Wolfe algorithms. Benchmarks on several datasets highlight different regimes in which the proposed method exhibits a faster empirical convergence than related methods. Finally, we provide an implementation of all considered methods in an open-source package.
In this tutorial session we will discuss how dynamical modeling combined with time-series analysis and optimization can lead to an efficient management of complex water systems. We will introduce key performance indicators to evaluate the performance of the controlled system and formulate an economic model predictive control (EMPC) scheme to address the prescribed control objectives. We will also see how we can harness the computational power of graphics cards to accelerate complex computations involved in our control problems.
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Shota Yamanaka and Homei Miyashita. Scale Effects in the Steering Time Difference between Narrowing and Widening Linear Tunnels. In Proceedings of NordiCHI 2016.
Platoon Control of Nonholonomic Robots using Quintic Bezier SplinesKaustav Mondal
In this project, quintic polynomials were used to perform platooning in nonholonomic robots. Both hardware and simulations results have been presented.
Accelerating Dynamic Time Warping Subsequence Search with GPUDavide Nardone
Many time series data mining problems require
subsequence similarity search as a subroutine. While this can
be performed with any distance measure, and dozens of
distance measures have been proposed in the last decade, there
is increasing evidence that Dynamic Time Warping (DTW) is
the best measure across a wide range of domains. Given
DTW’s usefulness and ubiquity, there has been a large
community-wide effort to mitigate its relative lethargy.
Proposed speedup techniques include early abandoning
strategies, lower-bound based pruning, indexing and
embedding. In this work we argue that we are now close to
exhausting all possible speedup from software, and that we
must turn to hardware-based solutions if we are to tackle the
many problems that are currently untenable even with stateof-
the-art algorithms running on high-end desktops. With this
motivation, we investigate both GPU (Graphics Processing
Unit) and FPGA (Field Programmable Gate Array) based
acceleration of subsequence similarity search under the DTW
measure. As we shall show, our novel algorithms allow GPUs,
which are typically bundled with standard desktops, to achieve
two orders of magnitude speedup. For problem domains which
require even greater scale up, we show that FPGAs costing just
a few thousand dollars can be used to produce four orders of
magnitude speedup. We conduct detailed case studies on the
classification of astronomical observations and similarity
search in commercial agriculture, and demonstrate that our
ideas allow us to tackle problems that would be simply
untenable otherwise.
Normalization Cross Correlation Value of Rotational Attack on Digital Image W...Komal Goyal
Volume-3, Issue-8, and Publishes on August 2014 of IJRET
(International Journal of Research in Engineering and Technology) eISSN: 2319-1163 | pISSN: 2321-7308, Impact Factor (by ISRA): 1.962
Action Trajectory Reconstruction for Controlling of Vehicle Using SensorsIOSR Journals
Abstract: Inertial sensors, such as accelerometers and gyro-scopes, are rarely used by themselves to compute
velocity and position as each requires the integration of very noisy data. The variance and bias in the resulting
position and velocity estimates grow un-bounded in time. This paper proposes a solution to provide a de-biased
and de-noised estimation of position and velocity of moving vehicle actions from accelerometer measurements.
The method uses a continuous wavelet transform applied to the measurements recursively to provide reliable
action trajectory reconstruction. The results are presented from experiments performed with a MEMS accelerometer
and gyroscope.
Keywords: Action trajectory, continuous wavelet transform, inertial measurement unit.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Wise Sliding Window Segmentation: A classification-aided approach for traject...Mohammad Etemad
Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for trajectory data mining is segmentation.
This task can be seen as a pre-processing step in which a trajectory is divided into several meaningful consecutive sub-sequences. This process is necessary because trajectory patterns may not hold in the entire trajectory but on trajectory parts.
In this work we propose a supervised trajectory segmentation algorithm, called Wise Sliding Window Segmentation (WS-II).
It processes the trajectory coordinates to find behavioral changes in space and time, generating an error signal that is further used to train a binary classifier for segmenting trajectory data.
This algorithm is flexible and can be used in different domains. We evaluate our method over three real datasets from different domains (meteorology, fishing, and individuals movements), and compare it with four other trajectory segmentation algorithms: OWS, GRASP-UTS, CB-SMoT, and SPD.
We observed that the proposed algorithm achieves the highest performance for all datasets with statistically significant differences in terms of the harmonic mean of purity and coverage.
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFEIOSR Journals
Abstract: The performance of WCDMA system deteriorates in the presence of multipath fading environment. Fading destroys the orthogonality and is responsible for multiple access interference (MAI). Though conventional rake receiver provides reasonable performance in the WCDMA downlink system due to path diversity, but it does not restores the orthogonality. Linear equalizer restores orthogonality and suppresses MAI, but it is not efficient, since its performance depends on the spectral characteristics of the channel. To overcome this, Minimum Mean Square Error- Decision Feedback Equalizer (MMSE-DFE) with a linear, anticausal feedforward filter, causal feedback filter and simple detector is proposed in this paper. The filter taps of finite length DFE is derived using the cholesky factorization theory, capable of suppressing noise, Intersymbol Interference (ISI) and MAI. This paper describes the WCDMA downlink system using finite length MMSE-DFE and takes into consideration the effects of interference which includes additive white gaussian noise, multipath fading, ISI and MAI. Furthermore, the performance is compared with conventional rake receiver and MMSE and the simulation results are shown. Keywords – MMSE, MMSE-DFE, rake receiver, WCDMA
Channel Equalization of WCDMA Downlink System Using Finite Length MMSE-DFEIOSR Journals
The performance of WCDMA system deteriorates in the presence of multipath fading environment.
Fading destroys the orthogonality and is responsible for multiple access interference (MAI). Though
conventional rake receiver provides reasonable performance in the WCDMA downlink system due to path
diversity, but it does not restores the orthogonality. Linear equalizer restores orthogonality and suppresses
MAI, but it is not efficient, since its performance depends on the spectral characteristics of the channel. To
overcome this, Minimum Mean Square Error- Decision Feedback Equalizer (MMSE-DFE) with a linear,
anticausal feedforward filter, causal feedback filter and simple detector is proposed in this paper. The filter taps
of finite length DFE is derived using the cholesky factorization theory, capable of suppressing noise,
Intersymbol Interference (ISI) and MAI. This paper describes the WCDMA downlink system using finite length
MMSE-DFE and takes into consideration the effects of interference which includes additive white gaussian
noise, multipath fading, ISI and MAI. Furthermore, the performance is compared with conventional rake
receiver and MMSE and the simulation results are shown.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Chi2016slide yamanaka novideo_0508
1. Modeling the Steering Time Difference
between Narrowing and Widening Tunnels
Shota Yamanaka (Meiji University & JSPS)
Homei Miyashita (Meiji University)
10, May, 2016
Meiji University Japan Society for the
Promotion of Science
Session: Quantify efficiency of input method 1
2. Demo Video of the Experimental Task
Narrowing tunnel vs. Widening tunnel
2
3. Demo Video of the Experimental Task
Narrowing tunnel vs. Widening tunnel
3
4. Demo Video of the Experimental Task
Movement time comparison
4
5. Demo Video of the Experimental Task
Movement time comparison
5
6. Observation and Goals
6
(MT & ID)
Movement time (MT) for a Narrowing Tunnel is longer than a Widening Tunnel
MTNT > MTWT
Their indexes of difficulty (IDs) of the steering law will be:
IDNT > IDWT
Narrowing Widening
(1) To prove that there is a time difference
(2) To model the ID for the time difference
Our goals
8. Steering Law [Accot and Zhai 1997]
Steering law: 𝑀𝑇 = 𝑎 + 𝑏
𝐴
𝑊
• a and b: empirically determined constants
•
𝐴
𝑊
is called Index of Difficulty (ID)
e.g., a narrower or longer path has a higher ID
that requires a longer MT
8
When navigating a tunnel of width W and amplitude A,
the movement time MT has a linear relationship to A/W
W
A
9. 𝐼𝐷 =
𝐴
𝑊
is Held to Constant-width Tunnels
9
Constant-width circle
[Accot and Zhai 1999, 2001]
A
WW
A
Constant-width straight tunnel
[Accot and Zhai 1997, 1999, 2001]
𝐼𝐷 =
𝐴
𝑊
𝐼𝐷 =
𝐴
𝑊
10. Steering Law: Various Devices/Environments
10
Racing wheel controller
for driving simulator [Zhai+ 2004]
Direct-input stylus
[Kulikov+ 2005]
Mouse, touchpad, trackpoint, trackball, indirect-input stylus
[Accot+ 1999]
3D controller for ball+tube and
ring+thread tasks [Casiez+ 2004]
11. Other Tunnel Shapes: Different ID formulae
11
Narrowing straight tunnel
[Accot and Zhai 1997]
Widening spiral tunnel
[Accot and Zhai 1997]
𝐼𝐷NT =
𝐴
𝑊𝑅 − 𝑊𝐿
ln
𝑊𝑅
𝑊𝐿
𝐼𝐷ST =
2𝜋
2𝜋 𝑛+1
𝜃 + 𝜔 6 + 9 𝜃 + 𝜔 4
𝜃 + 2𝜋 + 𝜔 3 − 𝜃 + 𝜔 3
𝑑𝜃
n : the number of turns
θ : current position (in angle)
ω : width-increasing parameter
WL : left width (start side)
WR : right width (end side)
WL
A
WR
12. Consistency of Narrowing and Constant-width Tunnels
12
WL
A
WR
When WL → WR , IDNT is matched to IDConstant
lim
𝑊 𝐿→𝑊 𝑅
𝐴
𝑊𝑅 − 𝑊𝐿
ln
𝑊𝑅
𝑊𝐿
= lim
𝑟→1
𝐴
𝑥 − 𝑥𝑟
ln
𝑥
𝑟𝑥
where 𝑊𝑅 = 𝑥, 𝑊𝐿 = 𝑟 × 𝑊𝑅
=
𝐴
𝑥
lim
𝑟→1
1
1 − 𝑟
ln
1
𝑟
=
𝐴
𝑥
× 1
=
𝐴
𝑊𝑅
IDNT includes IDConstant
WL
A
WR
Shrink
14. Observation of the Pilot Study
Tunnel type seems to be a significant reason behind the MT difference
14
① For narrowing, left/right directions did not affect the MT
② For widening, left/right directions did not affect the MT
③ Narrowing/widening type always affected the MT
Narrowing, to left Widening, to rightNarrowing, to right
(MT) (MT)
Widening, to left
(MT)
(Narrowing or Widening)
≈≈
① ②
③
①ー③ will be checked also
in the main experiment
15. Revisiting ID for a Narrowing Straight Tunnel [Accot and Zhai 1997]
Navigating a narrowing tunnel can be converted to
navigating the infinite number of constant-width infinitesimal-length tunnels
𝐼𝐷NT =
0
𝐴
𝑑𝑥
𝑊 𝑥
=
0
𝐴
𝑑𝑥
𝑊𝐿 +
𝑥
𝐴
𝑊𝑅 − 𝑊𝐿
=
𝐴
𝑊𝑅 − 𝑊𝐿
ln
𝑊𝑅
𝑊𝐿
W
A
𝐼𝐷 =
𝐴
𝑊
WR
Start line
End line
WL
A
W(x)
dx
x
constant-width linear tunnel
15
16. ID for a Widening Straight Tunnel
• Integration does not take account of the left/right direction
• The same calculation of IDNT can be used to derive IDWT
𝐼𝐷NT = 𝐼𝐷WT =
𝐴
𝑊𝑅 − 𝑊𝐿
ln
𝑊𝑅
𝑊𝐿
WR
Start line
End line
WL
A
W(x)
dx
x
WR
End line
Start line
WL
A
W(x)
dx
x
Narrowing direction Widening direction
16
Our first derivation
This does not reflect our observation:
MTNT > MTWT
17. Our Hypothesis of the Time Difference
Users perform a limited number of movement corrections
Users repeatedly determine the current movement (distance×angle)
with getting visual feedback
WR
Start line
End line
WL
A
W(x)
dx
x
The conventional steering law calculates dx → 0,
which means that movements are continuous
(infinite number of re-aiming)
17
18. Difficulty of One Movement
Our model
Acceptable slippage in y-axis is affected by the goal-side width
The current strategy is limited by the width at a little forward
Narrowing tunnel: users cannot use
the wider (start) side efficiently
Widening tunnel: users can use the
full width of the wider (end) side
18
Speed-down Speed-up
19. Deriving IDNT Based on Our Hypothesis
For simplicity, we assume that there are three movement corrections at
regular distance intervals
WR
Start line
End line
WL
A/3 A/3 A/3
e.g.) ID for ① is
𝐴/3
𝑊1
=
𝐴/3
(2𝑊 𝐿+𝑊 𝑅)/3
=
𝐴
2𝑊 𝐿+𝑊 𝑅
𝐼𝐷NT(3) =
𝐴
2𝑊𝐿 + 𝑊𝑅
+
𝐴
𝑊𝐿 + 2𝑊𝑅
+
𝐴
3𝑊𝑅
Narrowing direction
19
W1 W2 W3
① ② ③
① ② ③
20. Deriving IDWT Based on Our Hypothesis
As the same manner, IDWT(3) can be derived:
WR
End line
Start line
WL
A/3 A/3 A/3
Widening direction
20
W1 W2 W3
𝐼𝐷WT(3) =
𝐴
3𝑊𝐿
+
𝐴
2𝑊𝐿 + 𝑊𝑅
+
𝐴
𝑊𝐿 + 2𝑊𝑅
21. Deriving the ID Difference (IDGap)
𝐼𝐷 )Gap(3 = 𝐼𝐷 )NT(3 − 𝐼𝐷WT 3
=
𝐴
3𝑊𝑅
−
𝐴
3𝑊𝐿
=
𝐴(𝑊𝐿 − 𝑊𝑅)
3𝑊𝐿 𝑊𝑅
=
𝐴
2𝑊𝐿 + 𝑊𝑅
+
𝐴
𝑊𝐿 + 2𝑊𝑅
+
𝐴
3𝑊𝑅
−
𝐴
3𝑊𝐿
+
𝐴
2𝑊𝐿 + 𝑊𝑅
+
𝐴
𝑊𝐿 + 2𝑊𝑅
21
WR
Start line
End line
WL
A/3 A/3 A/3
Narrowing direction
W1 W2 W3
① ② ③
WR
End line
Start line
WL
A/3 A/3 A/3
Widening direction
W1 W2 W3
3 → N to generalize
22. Generalizing IDGap
Users’ strategies may be affected by some conditions:
• Tunnel parameters: A, WL, WR, and the degree of change of W
• Current width: one movement becomes shorter under a narrower W
• Current speed: the lower speed is, the more re-thinking occurs in a certain distance
22
WRWL
A
N
A
N
A
N
A
N
A
N
WRWL
𝑎% 𝑏% 𝑐% 𝑑% 𝑒%
𝐼𝐷Gap(𝑁)=
𝐴(𝑊𝐿 − 𝑊𝑅)
𝑁𝑊𝐿 𝑊𝑅
If users perform movement corrections N times at regular distance intervals,
23. Our model IDGap
Replacing the number of equal partitions N with a free weight k
that reflects the experimental conditions and tunnel parameters
23
𝐼𝐷Gap(𝑘)=
𝐴(𝑊𝐿 − 𝑊𝑅)
𝑘𝑊𝐿 𝑊𝑅
Our model:
IDGap(k)
Narrowing Widening
Consistency of our model and the constant-width model: When WL → WR, IDGap(k) → 0
“If the width becomes constant, the time difference between MTNT and MTWT becomes 0”
✔
25. Experiment (Goals, Task)
Goals: testing (1) the MT difference, and (2) the validity of IDGap
Task: Navigating non-constant-width straight tunnels
Start area End area
Start line
End line
WL
WRA
Direction of movement
Path
Outside of path region
Cursor
25
26. Experiment (Design)
A 300, 600 pixels
(= 61.2, 122.4 mm)
WL & WR 11, 31, 51 pixels
( = 2.2, 6.3, 10.4 mm)
Dir Left, Right
2 (A) ×6 (W) × 2 (Dir) = 24 conditions = 1 block
Only WL ≠ WR (not constant-width) conditions were selected
3 (WL) × 3 (WR) - 3 (WL = WR) = 6 (W)
Tunnel type (narrowing/widening) was defined by the combination of {WL , WR , Dir}
26
27. Experiment (Device, Participant, Procedure, Data)
Device: direct-input 13.3-inch pen-tablet
Wacom Cintiq 12WX, 261.1 × 163.2 mm, 1280 × 800 pixels
Participant: eleven local university students (within-participant)
11 males, all right-handed, Mage = 21.9 years, SDage = 2.27 years
Each participant performed 1 warm-up and 5 actual blocks
24 conditions × 5 blocks × 11 participants = 1320 trials
Collected data
MT, error rate, time-stamped cursor trajectory
27
28. General Results (repeated measures ANOVA and the Bonferroni post hoc test)
main effects: ID (F5, 50 = 29.449, p < .001) and
tunnel type (F1, 10 = 23.667, p < .01)
post hoc test: widening is faster than narrowing
(p < .01; 826 ms vs. 1233 ms)
28
0
0.05
0.1
0.15
0.2
0 10 20 30 40
Errorrate
ID [bits]
y = 80.4x - 154
R² = 0.961
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
Widening
Narrowing
Accot’s ID model Accot’s ID model
main effects: ID (F5, 50 = 4.204, p < .01) and
tunnel type (F1, 10 = 12.111, p < .01)
post hoc test: widening produces less errors
than narrowing (p < .01; 1.05% vs. 6.78%)
Left/right directions had no significant effects on MT (F1, 10 = 0.083, p = .780) and error rate (F1, 10 = 0.040, p = .846)
Widening
Narrowing
Error rateMovement time
29. Without the separation, steering law shows a bad fit
The regression expression predicts nothing
Model Fitness of Conventional Steering Law
Steering law shows good fits for the both tunnel types
Users can predict MTNT and MTWT at high
accuracy when the tunnel types are separated
29
y = 80.4x - 154
R² = 0.961
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
y = 68.1x - 146
R² = 0.826
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
✔
The center line between the two tunnel data
✘
Conventional model does not take account of this
Widening
Narrowing
Accot’s ID model
Accot’s ID model
30. Converting IDNT to “IDWT + IDGap”
𝐼𝐷Gap = 𝐼𝐷NT − 𝐼𝐷WT =
𝐴(𝑊𝐿 − 𝑊𝑅)
𝑘𝑊𝐿 𝑊𝑅
30
Original results of regression expression:
Narrowing: 𝑀𝑇 = −154 + 80.4 × 𝐼𝐷 ・・・①
Widening: 𝑀𝑇 = −138 + 55.9 × 𝐼𝐷 ・・・②
By using IDGap model,
regression expression for narrowing is onto ②
Narrowing: 𝑀𝑇 = −138 + 55.9 × 𝐼𝐷 +
𝐴(𝑊 𝐿−𝑊 𝑅)
𝑘𝑊 𝐿 𝑊 𝑅
y = 80.4x - 154
R² = 0.961
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
y = 55.3x - 113
R² = 0.991
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
Our IDGap model
with k = 3.14
Widening
Narrowing
Accot’s ID model
31. Predicting MTNT from MTWT
Measurement:
step 1) measure MTWT at 6 IDs
step 2) measure MTNT at the lowest ID
step 3) calculate k from MTWT and MTNT
at the lowest ID (→ k = 2.22)
31
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
Widening
Narrowing
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
Prediction:
step 4) predict the regression line
of MTNT using IDGap(2.22)
step 5) predict MTNT at the other five IDs
32. Predicting MTNT from MTWT
32
y = 0.833x + 104
R² = 0.971
0
1000
2000
3000
0 1000 2000 3000
Observedtime[ms]
Predicted time [ms]
Prediction accuracy
The correlation coefficient between
predicted MTNT and observed MTNT shows good fit
Usefulness of IDGap model:
Prediction of MTNT at high IDs
Measurement of MTNT requires a long time and many errors
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
33. Speed Profiles: Velocity × Elapsed Time
Some characteristic peaks appear at regular time intervals, but sometimes not
33
0
1
2
3
4
8
48
88
128
168
208
248
288
328
368
408
448
488
528
568
608
648
688
728
768
808
848
888
928
968
1008
1048
1088
1128
Velocity[pixels/ms]
Elapsed Time [ms]
0 160 320 480 640 800 960 1120
Narrowing
Widening
Single raw speed profiles for narrowing and widening directions by participant B
A = 600, WL = 11, WR = 51
34. Speed Profiles: Velocity × Progress in the X-axis
Narrowing has more peaks (speed-ups/downs) than widening
34
0
1
2
3
4
1 101 201 301 401 501
Velocity[pixels/ms]
Progress in the x-axis [pixels]
Narrowing
Widening
0 100 200 300 400 500 600
A = 600, WL = 11, WR = 51
0
1
2
3
4
(Start) (End)
Single raw speed profiles for narrowing and widening directions by participant B
Movement corrections are affected by the tunnel type
35. Speed Profile of a Narrowing Tunnel
Movement corrections are affected by the current width
They are more often and low peaks where the current width is narrow
35
0
1
2
3
4
1 101 201 301 401 501
Velocity[pixels/ms]
Progress in the x-axis [pixels]
Narrowing
0 100 200 300 400 500 600
0
1
2
3
4
Wide = less & high peaks
Narrow = many & low peaks
(Start) (End)
WRWL
𝑎% 𝑏% 𝑐% 𝑑% 𝑒%
36. 0
0.3
0.6
0.9
1.2
1.5
1 101 201 301 401 501
Velocity[pixels/ms]
Average Speed Profiles
Speed profiles of all the strokes filtered by a seven-point simple moving average
“Turnovers” of the speed appeared at 25ー30% of the tunnel length A
36
0 100 200 300 400 500 600
Narrowing
Widening
A = 600
0
0.3
0.6
0.9
1.2
1.5
1 51 101 151 201 251
Velocity[pixels/ms]
0 100 200 300
1.5
1.2
0.9
0.6
0.3
0
Narrowing
Widening
A = 300
Progress in the x-axis [pixels] Progress in the x-axis [pixels]
(Start) (End)
1.5
1.2
0.9
0.6
0.3
0
(Start) (End)
Velocity[pixels/ms]
Velocity[pixels/ms]
This is in contrast of the conventional steering law’s local form
37. Local Form of Steering Law
37
“The speed at any point is proportional
to the permitted variability at that point” 𝑣 𝑠 =
𝑊 𝑠
𝜏
s
W(s)
velocity at the
current position
empirically determined
time constant
width of the current position
This suggests “turnovers” would appear
at 50% of A (where their widths are the same)
Another finding: Local form is also affected by the tunnel type:
whether the tunnel width will be narrowing or widening
38. Validity of IDGap
Can IDGap always be applied to narrowing and widening tunnels?
38
Steering law is held to circular tunnels
[Accot and Zhai 1999]
Steering law is held to A = 3.7-237 mm
[Accot and Zhai 2001]
Our model was tested under a limited condition (tablet size, tunnel parameters, etc.)
How about other sizes?How about other shapes?
39. Narrowing and Widening Circular Tunnels
Conventional steering law showed good fits without separating the tunnel type
39
y = 122x - 96.9
R² = 0.985
0
1000
2000
3000
4000
5000
0 10 20 30 40
MT[ms]
ID [bits]
y = 126x - 93.3
R² = 0.989
y = 119x - 101
R² = 0.991
0
1000
2000
3000
4000
5000
0 10 20 30 40
MT[ms]
ID [bits]
Narrowing
Widening
Accot’s model
(separated)
Accot’s model
(NOT separated)
IDGap model is not required for circular tunnels
Shota Yamanaka and Homei Miyashita. A Study of the Steering Time Difference between Narrowing and Widening
Circular Tunnels. Information and Media Technologies, 2016. (In Press)
>>> 3x
40. 0
0.1
0.2
0.3
0.4
0.5
0.6
1
27
53
79
105
131
157
183
209
235
261
287
313
339
Velocity[pixels/ms]
Progress in angle [degrees]
0 90 180 270 360
(c) A = 600
Narrowing
Widening
0
0.1
0.2
0.3
0.4
0.5
0.6
1
27
53
79
105
131
157
183
209
235
261
287
313
339
Velocity[pixels/ms]
Progress in angle [degrees]
(b) A = 450
0 90 180 270 360
Narrowing
Widening
0
0.1
0.2
0.3
0.4
0.5
0.6
1
27
53
79
105
131
157
183
209
235
261
287
313
339
Velocity[pixels/ms]
Progress in angle [degrees]
Narrowing
Widening
0 90 180 270 360
0.5
0.4
0.3
0.2
0.1
0
0.6
(a) A = 300
Narrowing and Widening Circular Tunnels
“Turnovers” appeared at 45-50% in all As
40
✘
Shota Yamanaka and Homei Miyashita. A Study of the Steering Time Difference between Narrowing and Widening
Circular Tunnels. Information and Media Technologies, 2016. (In Press)
Users cannot aim
the goal at early phase
Users can aim
the goal at early phase
✔
41. Scale Effects in Narrowing and Widening Tunnels
41
In submission.
1/1 (48×27 cm) 1/2 (24×13 cm) 1/4 (12×6.7 cm) 1/9 (5.2×3.0 cm) 1/12 (4.0×2.2 cm)
42. Scale Effects in Narrowing and Widening Tunnels
There are always the MT difference in each scale
IDGap model always improve the fitness, and k varied from 3 to 6
42
In submission.
separated
not separated
using IDGap
1/1 1/2 1/4 1/9 1/12
43. Future Work
Other devices/environments Other tunnel shapes
43
Where does the MT difference disappear?
Identifying the role of k
We want to know what k mainly reflects (A, WL, WR, device, etc.)
The results of the three experiments show that there is no optimal k value
k must be calculated in each conditions (scale, tunnel shape, device, etc.)
We have tested only a direct-input pen tablet
44. Summary
Additional results
• Circular tunnels did not require the IDGap model (A Study of the Steering Time Difference between
Narrowing and Widening Circular Tunnels, Information and Media Technologies, 2016.)
• The IDGap relationships were observed in very large to very small scales
44
𝐼𝐷Gap(𝑘)=
𝐴(𝑊𝐿 − 𝑊𝑅)
𝑘𝑊𝐿 𝑊𝑅
y = 55.3x - 113
R² = 0.991
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]
Our IDGap model
with k = 3.14
We derived IDGap model based on “a limited number of movement corrections” hypothesis
We confirmed that MTNT was longer than MTWT (p < .01; 1233 ms vs. 826 ms)
The data supported IDGap as a relationship between IDNT and IDWT
y = 80.4x - 154
R² = 0.961
y = 55.9x - 138
R² = 0.993
0
500
1000
1500
2000
2500
0 10 20 30 40
MT[ms]
ID [bits]WT
NT
Accot’s ID model
45. Same ID but Different MTs
View-point of racing games
[Bateman+ 2011]
Tunnel A & W ×Cursor size [Naito+ 2004]
Tunnel with a corner [Pastel 2006]
45
(ID)
(ID)
Cut-off
46. Consistency of IDGap and Accot’s Model
46
Our 𝐼𝐷Gap(𝑘) =
𝐴(𝑊𝐿 − 𝑊𝑅)
𝑘𝑊𝐿 𝑊𝑅
Accot′s 𝐼𝐷Narrowing =
𝐴
𝑊𝑅 − 𝑊𝐿
ln
𝑊𝑅
𝑊𝐿
• When WL → WR, IDGap(k) → 0
If the width becomes constant, the time difference between MTNT and MTWT becomes 0
• When k → 0, IDGap → ∞: not suitable for ID difference
ー no movement correction
ー users decide the cursor movement when start (only one time)
• When k → ∞, IDGap → 0 : confirming Accot and Zhai’s model
ー tunnel type (narrowing or widening) does not affect the degree of difficulty
ー assuming infinite number of (or continuous) movement corrections
47. Scale Effects in Narrowing and Widening Tunnels
47
In submission.
No significant “U-shaped” function of motor/visual scales
middle scale (≈A5) is the best: see Accot and Zhai's scale effects paper at CHI ’01
The smaller scale was, the worse performance became