scholarly journals Constructing Sliding Windows Leak from Noisy Cache Timing Information of OSS-RSA

10.29007/ws8z ◽  
2019 ◽  
Author(s):  
Rei Ueno ◽  
Junko Takahashi ◽  
Yu-Ichi Hayashi ◽  
Naofumi Homma
Keyword(s):  
Sliding Window ◽  
Levenshtein Distance ◽  
Correct Operation ◽  
Sliding Windows ◽  
Operation Sequence ◽  
Multiple Observations ◽  
Window Method ◽  
Secret Keys ◽  
Negligible Probability ◽  
Dynamic Time

This paper presents a method for constructing an operation sequence of sliding window exponentiation from the noisy cache information of RSA, which can be used for a cache attack using sliding window's leak (SWL). SWL, which was reported in CHES 2017, is a kind of cache side-channel leak of a sequence of operations (i.e., multiplication and squaring) from software RSA decryption using the sliding window method for modular exponentiation. It was shown that an SWL attack can retrieve the secret keys of 1,024-bit and 2,048-bit RSA with non-negligible probability if the SWL is correctly captured. How- ever, in practice, it is not always possible for an attacker to acquire a complete and correct operation sequence from cache information observation. In addition, no concrete method for deriving a fully correct operation sequence from a partially acquired operation sequence as been reported in literature. In this paper, we first show that the capture errors in an operation sequence can be evaluated based on the Levenshtein distance between correct and estimated sequences. The dynamic time warping (DTW) algorithm is employed for quantitative evaluation. Then, we present a method of accurately estimating a complete and correct operation sequence from noisy sequences obtained through multiple observations. The basic idea of the proposed method and DTW-based evaluation is to divide the acquired operation sequence into short subsequences referred to as "operation patterns." Furthermore, we show the effectiveness of the proposed method through a set of experiments performed using RSA software in Libgcrypt, which is one of the most common open source software in cryptography.

Development of A Pharmaceutical Early Warning Scoring Model for Cardiac Arrest Using Deep Learning: Retrospective Cohort Study (Preprint)

2020 ◽  
Author(s):  
Hsiao-Ko Chang ◽  
Hui-Chih Wang ◽  
Chih-Fen Huang ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Cardiac Arrest ◽  
Early Warning ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Vital Signs ◽  
Sliding Window ◽  
Early Warning Score ◽  
Series Data ◽  
Dynamic Time

BACKGROUND In most of Taiwan’s medical institutions, congestion is a serious problem for emergency departments. Due to a lack of beds, patients spend more time in emergency retention zones, which make it difficult to detect cardiac arrest (CA). OBJECTIVE We seek to develop a pharmaceutical early warning model to predict cardiac arrest in emergency departments via drug classification and medical expert suggestion. METHODS We propose a new early warning score model for detecting cardiac arrest via pharmaceutical classification and by using a sliding window; we apply learning-based algorithms to time-series data for a Pharmaceutical Early Warning Scoring Model (PEWSM). By treating pharmaceutical features as a dynamic time-series factor for cardiopulmonary resuscitation (CPR) patients, we increase sensitivity, reduce false alarm rates and mortality, and increase the model’s accuracy. To evaluate the proposed model we use the area under the receiver operating characteristic curve (AUROC). RESULTS Four important findings are as follows: (1) We identify the most important drug predictors: bits, and replenishers and regulators of water and electrolytes. The best AUROC of bits is 85%; that of replenishers and regulators of water and electrolytes is 86%. These two features are the most influential of the drug features in the task. (2) We verify feature selection, in which accounting for drugs improve the accuracy: In Task 1, the best AUROC of vital signs is 77%, and that of all features is 86%. In Task 2, the best AUROC of all features is 85%, which demonstrates that thus accounting for the drugs significantly affects prediction. (3) We use a better model: For traditional machine learning, this study adds a new AI technology: the long short-term memory (LSTM) model with the best time-series accuracy, comparable to the traditional random forest (RF) model; the two AUROC measures are 85%. (4) We determine whether the event can be predicted beforehand: The best classifier is still an RF model, in which the observational starting time is 4 hours before the CPR event. Although the accuracy is impaired, the predictive accuracy still reaches 70%. Therefore, we believe that CPR events can be predicted four hours before the event. CONCLUSIONS This paper uses a sliding window to account for dynamic time-series data consisting of the patient’s vital signs and drug injections. In a comparison with NEWS, we improve predictive accuracy via feature selection, which includes drugs as features. In addition, LSTM yields better performance with time-series data. The proposed PEWSM, which offers 4-hour predictions, is better than the National Early Warning Score (NEWS) in the literature. This also confirms that the doctor’s heuristic rules are consistent with the results found by machine learning algorithms.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

scholarly journals The Impact of COVID-19 on the Dependence of Chinese Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianbo Wu ◽  
Xiaofeng Hui
Keyword(s):  
Stock Market ◽  
Strong Dependence ◽  
Sliding Window ◽  
Dependence Structure ◽  
Chinese Stock Market ◽  
Telecommunication Services ◽  
Window Method ◽  
Dependence Relationship ◽  
Stock Indexes ◽  
The Impact

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long.

scholarly journals PRECISION OF VISUAL LOCALIZATION USING DYNAMIC GROUND CONTROL POINTS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 363-370
Author(s):  
P. Trusheim ◽  
C. Heipke
Keyword(s):  
Rapid Development ◽  
Sliding Window ◽  
Bundle Adjustment ◽  
Ground Control ◽  
Visual Localization ◽  
Control Points ◽  
Automated Driving ◽  
Sliding Windows ◽  
Ground Control Points

Abstract. Localization is one of the first steps in navigation. Especially due to the rapid development in automated driving, a precise and reliable localization becomes essential. In this paper, we report an investigation of the usage of dynamic ground control points (GCP) in visual localization in an automotive environment. Instead of having fixed positions, dynamic GCPs move together with the camera. As a measure of quality, we employ the precision of the bundle adjustment results. In our experiments, we simulate and investigate different realistic traffic scenarios. After investigating the role of tie points, we compare an approach using dynamic GCPs to an approach with static GCPs to answer the question how a comparable precision can be reached for visual localization. We show, that in our scenario, where two dynamic GCPs move together with a camera, similar results are indeed obtained to using a number of static GCPs distributed over the whole trajectory. In another experiment, we take a closer look at sliding window bundle adjustments. Sliding windows make it possible to work with an arbitrarily large number of images and to still obtain near real-time results. We investigate this approach in combination with dynamic GCPs and vary the no. of images per window.

Sliding Window Based Fast Corner Matching Palmprint Authentication

2011 ◽  
Vol 1 (3) ◽  
pp. 16-34
Author(s):  
Jyoti Malik ◽  
G. Sainarayanan ◽  
Ratna Dahiya
Keyword(s):  
Response Time ◽  
Hamming Distance ◽  
Recognition Rate ◽  
Sliding Window ◽  
Phase Congruency ◽  
Window Method ◽  
Harris Corner ◽  
Sliding Window Method ◽  
Authentication System ◽  
Harris Corner Detector

Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time (matching time) is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information (features) is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.

scholarly journals Sport-Related Human Activity Detection and Recognition Using a Smartwatch

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5001 ◽  
Author(s):  
Zhendong Zhuang ◽  
Yang Xue
Keyword(s):  
Human Activity ◽  
Recognition Task ◽  
Sliding Window ◽  
Research Field ◽  
Activity Monitoring ◽  
Activity Detection ◽  
Related Activity ◽  
Window Method ◽  
Human Activity Detection ◽  
Detection And Recognition

As an active research field, sport-related activity monitoring plays an important role in people’s lives and health. This is often viewed as a human activity recognition task in which a fixed-length sliding window is used to segment long-term activity signals. However, activities with complex motion states and non-periodicity can be better monitored if the monitoring algorithm is able to accurately detect the duration of meaningful motion states. However, this ability is lacking in the sliding window approach. In this study, we focused on two types of activities for sport-related activity monitoring, which we regard as a human activity detection and recognition task. For non-periodic activities, we propose an interval-based detection and recognition method. The proposed approach can accurately determine the duration of each target motion state by generating candidate intervals. For weak periodic activities, we propose a classification-based periodic matching method that uses periodic matching to segment the motion sate. Experimental results show that the proposed methods performed better than the sliding window method.

Approximate Range Querying over Sliding Windows

2009 ◽  
pp. 2037-2050
Author(s):  
Francesco Buccafurri ◽  
Gianluca Caminiti ◽  
Gianluca Lax
Keyword(s):  
Knowledge Discovery ◽  
Data Reduction ◽  
Experimental Analysis ◽  
Window Size ◽  
Sliding Window ◽  
Knowledge Discovery In Databases ◽  
Flat Structure ◽  
Sliding Windows ◽  
Reduction Techniques ◽  
Processing Step

In the context of Knowledge Discovery in Databases, data reduction is a pre-processing step delivering succinct yet meaningful data to sequent stages. If the target of mining are data streams, then it is crucial to suitably reduce them, since often analyses on such data require multiple scans. In this chapter, we propose a histogram-based approach to reducing sliding windows supporting approximate arbitrary (i.e., non biased) range-sum queries. The histogram is based on a hierarchical structure (as opposed to the flat structure of traditional ones) and it results suitable to directly support hierarchical queries, such as drill-down and roll-up operations. In particular, both sliding window shifting and quick query answering operations are logarithmic in the sliding window size. Experimental analysis shows the superiority of our method in terms of accuracy w.r.t. the state-of-the-art approaches in the context of histogram-based sliding window reduction techniques.

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