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1. A Practical Side-channel Based Intrusion Detection System for Additive Manufacturing Systems

   Sizhuang Liang, Xirui Peng ( August 2021 , ICDCS)

   null (Ed.)

   We propose NSYNC, a practical framework to compare side-channel signals for real-time intrusion detection in Additive Manufacturing (AM) systems. The motivation to develop NSYNC is that we find AM systems are asynchronous in nature and there is random variation in timing in a printing process. Although this random variation, referred to as time noise, is very small compared with the duration of a printing process, it can cause existing Intrusion Detection Systems (IDSs) to fail. To deal with this problem, NSYNC incorporates a dynamic synchronizer to find the timing relationship between two signals. This timing relationship, referred to as the horizontal displacement, can not only be used to mitigate the adverse effect of time noise on calculating the (vertical) distance between signals, but also be used as indicators for intrusion detection. An existing dynamic synchronizer is Dynamic Time Warping (DTW). However, we found in experiments that DTW not only consumes an excessive amount of computational resources but also has limited accuracy for processing side-channel signals. To solve this problem, we propose a novel dynamic synchronizer, called Dynamic Window Matching (DWM), to replace DTW. To compare NSYNC against existing IDSs, we built a data acquisition system that is capable of collecting six different types of side-channel signals and performed a total of 302 benign printing processes and a total of 200 malicious printing processes with two printers. Our experiment results show that existing IDSs leveraging side-channel signals in AM systems can only achieve an accuracy from 0.50 to 0.88, whereas our proposed NSYNC can reach an accuracy of 0.99. 

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2. Path Planning for Within-Hand Manipulation over Learned Representations of Safe States

   Calli, Berk ; Kimmel, Andrew ; Hang, Kaiyu ; Bekris, Kostas ; Dollar, Aaron ( October 2018 , International Symposium on Experimental Robotics (ISER))

   This work proposes a framework for tracking a desired path of an object held by an adaptive hand via within-hand manipulation. Such underactuated hands are able to passively achieve stable contacts with objects. Combined with vision-based control and data-driven state estimation process, they can solve tasks without accurate hand-object models or multi-modal sensory feedback. In particular, a data-driven regression process is used here to estimate the probability of dropping the object for given manipulation states. Then, an optimization-based planner aims to track the desired path while avoiding states that are above a threshold probability of dropping the object. The optimized cost function, based on the principle of Dynamic-Time Warping (DTW), seeks to minimize the area between the desired and the followed path. By adapting the threshold for the probability of dropping the object, the framework can handle objects of different weights without retraining. Experiments involving writing letters with a marker, as well as tracing randomized paths, were conducted on the Yale Model T-42 hand. Results indicate that the framework successfully avoids undesirable states, while minimizing the proposed cost function, thereby producing object paths for within-hand manipulation that closely match the target ones. 

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3. Using dynamic time warping algorithms and spatiotemporal analyses of swine condemnations for syndromic surveillance

   O’Hara, KC ; Liu, X ; Martínez-López, B ( December 2019 , 100th Conference of Research Workers in Animal Diseases)

   Objective: Slaughterhouse data has recently been used to enhance animal disease surveillance in many countries, however has been largely underused for syndromic surveillance in the United States. We characterize spatiotemporal patterns and system dynamics of whole carcass swine condemnations in the US. We illustrate the value of data mining and machine learning approaches to more cost-effectively identify: emerging trends by condemnation reason, areas and time periods with higher than predicted condemnation rates, and regions or time periods with similar trends. Methods: Swine slaughter and condemnation data from 2005-2016 were obtained for slaughterhouses inspected by the Food Safety and Inspection Service (FSIS). Time series of condemnation rates by condemnation reason, type of pig, state and month were generated. Data time warping (DTW) and hierarchical clustering methods were used to identify states with similar patterns in the rate of condemnation cases by cause and type of pig. Spatiotemporal scan statistics were used to identify states and months with significantly higher number of condemnation cases than expected. Clusters were compared to historic infectious disease outbreaks in the swine industry. Results: Between 2005-2016, 1,109,300 whole swine carcasses were condemned. The top causes for condemnation were abscess/pyemia, septicemia, pneumonia, icterus, and peritonitis, respectively. DTW and cluster analysis revealed clear spatiotemporal patterns in the rate of condemnations, many with a strong seasonal component. Several clusters were detected in timeframes where widespread outbreaks had occurred. Conclusions: Timely evaluation of spatiotemporal patterns in swine condemnations may provide critical information in predicting disease outbreaks. Identification of spatiotemporal hot spots can direct investigation of primary on-farm risk factors contributing to condemnation. Risk mitigation through targeted decision-making and improved management practices can minimize carcass condemnations and animal losses, improving economic efficiency, profitability and sustainability of the US swine industry 

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4. Acoustic Span Embeddings for Multilingual Query-by-Example Search

   https://doi.org/10.1109/SLT48900.2021.9383545  

   Hu, Yushi ; Settle, Shane ; Livescu, Karen. ( January 2021 , IEEE Workshop on Spoken Language Technology)

   Query-by-example (QbE) speech search is the task of matching spoken queries to utterances within a search collection. In low- or zero-resource settings, QbE search is often addressed with approaches based on dynamic time warping (DTW). Recent work has found that methods based on acoustic word embeddings (AWEs) can improve both performance and search speed. However, prior work on AWE-based QbE has primarily focused on English data and with single-word queries. In this work, we generalize AWE training to spans of words, producing acoustic span embeddings (ASE), and explore the application of ASE to QbE with arbitrary-length queries in multiple unseen languages. We consider the commonly used setting where we have access to labeled data in other languages (in our case, several low-resource languages) distinct from the unseen test languages. We evaluate our approach on the QUESST 2015 QbE tasks, finding that multilingual ASE-based search is much faster than DTW-based search and outperforms the best previously published results on this task. 

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5. Clustering sequential navigation patterns in multiple‐source reading tasks with dynamic time warping method

   https://doi.org/10.1111/jcal.12748  

   He, Qiwei ; Borgonovi, Francesca ; Suárez‐Álvarez, Javier ( November 2022 , Journal of Computer Assisted Learning)

   Data‐driven investigations of how students transit pages in digital reading tasks and how much time they spend on each transition allow mapping sequences of navigation behaviours into students' navigation reading strategies.

   The purpose of this study is threefold: (1) to identify students' navigation patterns in multiple‐source reading tasks using a sequence clustering approach; (2) to examine how students' navigation patterns are associated with their reading performance and socio‐demographic characteristics; (3) to showcase how the navigation sequences could be clustered on the similarity measure by dynamic time warping (DTW) methods.

   This study draws on process data from a sample of 16,957 students from 69 countries participating in the PISA 2018 study to identify how students navigate through a multiple‐source reading item. Students' navigation sequences were characterized by two indicators: the page sequence that tracks the page transition path and the time sequence that records the time duration on each visited page. K‐medoid partitioning clustering analyses were conducted on pairwise distance similarity measures computed by the DTW method.

   Students' navigation patterns were found moderately associated with their reading proficiency levels. Students who visited all the pages and spent more time reading without rush transitions obtained the highest reading scores. Girls were more likely to achieve higher scores than boys when longer navigation sequences were used with shorter reading time on transited pages. Students who navigated only limited pages and spent shorter reading time were averagely at the lowest rank of socio‐economic status.

   This study provides evidence for the exploration of students' navigation patterns and the examination of associations between navigation patterns and reading scores with the use of process data.

    

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