scholarly journals YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy

2021 ◽  
Vol 5 (3) ◽  
pp. 1-26
Author(s):  
Michalis Pachilakis ◽  
Panagiotis Papadopoulos ◽  
Nikolaos Laoutaris ◽  
Evangelos P. Markatos ◽  
Nicolas Kourtellis
Keyword(s):  
Real Time ◽  
Information Flow ◽  
Personal Information ◽  
End Users ◽  
User Privacy ◽  
Bidding Strategies ◽  
Past Work ◽  
Browser Extension ◽  
In The Wild ◽  
Intuitive Manner

The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flow, and cost of display advertising through RTB. In this paper, we present YourAdvalue, a privacy-preserving tool for displaying to end-users in a simple and intuitive manner their advertising value as seen through RTB. Using YourAdvalue, we measure desktop RTB prices in the wild, and compare them with desktop and mobile RTB prices reported by past work. We present how it estimates ad prices that are encrypted, and how it preserves user privacy while reporting results back to a data-server for analysis. We deployed our system, disseminated its browser extension, and collected data from 200 users, including 12000 ad impressions over 11 months. By analyzing this dataset, we show that desktop RTB prices have grown 4.6x over desktop RTB prices measured in 2013, and 3.8x over mobile RTB prices measured in 2015. We also study how user demographics associate with the intensity of RTB ecosystem tracking, leading to higher ad prices. We find that exchanging data between advertisers and/or data brokers through cookie-synchronization increases the median value of display ads by 19%. We also find that female and younger users are more targeted, suffering more tracking (via cookie synchronization) than male or elder users. As a result of this targeting in our dataset, the advertising value (i) of women is 2.4x higher than that of men, (ii) of 25-34 year-olds is 2.5x higher than that of 35-44 year-olds, (iii) is most expensive on weekends and early mornings.

scholarly journals An IoT and Blockchain Based System for Monitoring and Tracking Real-Time Occupancy for COVID-19 Public Safety

2020 ◽  
Vol 2 (1) ◽  
pp. 67
Author(s):  
Tiago M. Fernández-Caramés ◽  
Iván Froiz-Míguez ◽  
Paula Fraga-Lamas
Keyword(s):  
Real Time ◽  
Public Transportation ◽  
Public Safety ◽  
Personal Information ◽  
Close Contact ◽  
Public Spaces ◽  
User Privacy ◽  
Active Involvement ◽  
The People ◽  
Private And Public

The COVID-19 pandemic has brought several limitations regarding physical distancing in order to reduce the interactions among large groups that could have prolonged close contact. For health reasons, such physical distancing requirements should be guaranteed in private and public spaces. In Spain, occupancy is restricted by law but, in practice, certain spaces may become overcrowded, existing law infringements in places that rely on occupancy estimations that are not accurate enough. For instance, although the number of passengers who enter a public transportation service is known, it is difficult to determine the actual occupancy of such a vehicle, since it is commonly unknown when and where passengers descend. Despite a number of counting systems existing, they are either prone to counting errors in overcrowded scenarios or require the active involvement of the people to be counted (e.g., going through a lathe or tapping a card when entering or exiting a monitored area) or of a person who manages the entering/exit process. This paper presents a novel IoT occupancy system that allows estimating in real time the people occupancy level of public spaces such as buildings, classrooms, businesses or moving transportation vehicles. The proposed system is based on autonomous wireless devices that, after powering them on, do not need active actions from the passengers/users and require a minimum amount of infrastructure. The system does not collect any personal information to ensure user privacy and includes a decentralized traceability subsystem based on blockchain, which guarantees the availability, security and immutability of the collected information. Such data will be shared among smart city stakeholders to ensure public safety and then deliver transparent decision-making based on data-driven analysis and planning.

Phish Detect-Real Time Phish Detecting Browser Extension

2020 ◽  
Vol 17 (4) ◽  
pp. 1675-1681
Author(s):  
Shiva Nandhini ◽  
K. Riya ◽  
P. Divya ◽  
Sahil Khatri ◽  
V. Devi Subadra
Keyword(s):  
Wireless Communication ◽  
Real Time ◽  
Credit Card ◽  
Personal Information ◽  
Social Engineering ◽  
Detection Technique ◽  
Browser Extension ◽  
User Data ◽  
Bank Accounts

Now a days, hacking has become a trend, where the personal information or user data including login credentials, credit card numbers, such as transaction from our bank accounts, key information from government office, defense etc., which threatens the privacy and property security of netizens in wireless communication. This is being done by creating a shadow website which has similar looks and semantics of the legitimate website. So as to overcome these kind of circumstances, the concept of Phishing is introduced. The phishing is a type of social engineering attack to detect false URL’s. So, the paper introduces a new phishing website “PhishDetect” to check for the phishing or fake sites. The paper proposes a detection technique of phishing websites based on checking URL of webpages. The detected attacks are reported for prevention of hacking.

scholarly journals A cloud computing separation model based on information flow

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Wei Ma ◽  
Huanqin Li ◽  
Deden Witarsyah
Keyword(s):  
Cloud Computing ◽  
Information Flow ◽  
End Users ◽  
Separation Mechanism ◽  
Computing Systems ◽  
Cloud Computing Security ◽  
Model Based ◽  
Separation Model

Abstract Separation is the primary consideration in cloud computing security. A series of security and safety problems would arise if a separation mechanism is not deployed appropriately, thus affecting the confidence of cloud end-users. In this paper, together with characteristics of cloud computing, the separation issue in cloud computing has been analyzed from the perspective of information flow. The process of information flow in cloud computing systems is formalized to propose corresponding separation rules. These rules have been verified in this paper and it is shown that the rules conform to non-interference security, thus ensuring the security and practicability of the proposed rules.

scholarly journals Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Filippo Aleotti ◽  
Giulio Zaccaroni ◽  
Luca Bartolomei ◽  
Matteo Poggi ◽  
Fabio Tosi ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Perception ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Handheld Devices ◽  
Single Image ◽  
Handheld Device ◽  
Time Performance ◽  
In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

XERIS/APEX

2021 ◽  
Vol 40 (2) ◽  
pp. 65-69
Author(s):  
Richard Wai
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Distributed System ◽  
Dynamic Scaling ◽  
Distributed Application ◽  
Heavy Weight ◽  
System Models ◽  
In The Wild ◽  
On Line ◽  
Language Technologies

Modern day cloud native applications have become broadly representative of distributed systems in the wild. However, unlike traditional distributed system models with conceptually static designs, cloud-native systems emphasize dynamic scaling and on-line iteration (CI/CD). Cloud-native systems tend to be architected around a networked collection of distinct programs ("microservices") that can be added, removed, and updated in real-time. Typically, distinct containerized programs constitute individual microservices that then communicate among the larger distributed application through heavy-weight protocols. Common communication stacks exchange JSON or XML objects over HTTP, via TCP/TLS, and incur significant overhead, particularly when using small size message sizes. Additionally, interpreted/JIT/VM-based languages such as Javascript (NodeJS/Deno), Java, and Python are dominant in modern microservice programs. These language technologies, along with the high-overhead messaging, can impose superlinear cost increases (hardware demands) on scale-out, particularly towards hyperscale and/or with latency-sensitive workloads.

Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
HyeonJung Park ◽  
Youngki Lee ◽  
JeongGil Ko
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Mobile Platforms ◽  
Depth Cameras ◽  
Language Data ◽  
In The Wild ◽  
Environmental Robustness ◽  
Cloud Servers

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

Emotion prediction errors guide socially adaptive behavior

2021 ◽  
Author(s):  
Joseph Heffner ◽  
Jae-Young Son ◽  
Oriel FeldmanHall
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Adaptive Behavior ◽  
Emotional Response ◽  
New Method ◽  
Prediction Errors ◽  
Emotional Experiences ◽  
Past Work ◽  
Reward Prediction ◽  
Expected Outcomes

People make decisions based on deviations from expected outcomes, known as prediction errors. Past work has focused on reward prediction errors, largely ignoring violations of expected emotional experiences—emotion prediction errors. We leverage a new method to measure real-time fluctuations in emotion as people decide to punish or forgive others. Across four studies (N=1,016), we reveal that emotion and reward prediction errors have distinguishable contributions to choice, such that emotion prediction errors exert the strongest impact during decision-making. We additionally find that a choice to punish or forgive can be decoded in less than a second from an evolving emotional response, suggesting emotions swiftly influence choice. Finally, individuals reporting significant levels of depression exhibit selective impairments in using emotion—but not reward—prediction errors. Evidence for emotion prediction errors potently guiding social behaviors challenge standard decision-making models that have focused solely on reward.

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