BorderPatrol: Securing BYOD using Fine-Grained Contextual Information

Abstract. Emerging traffic management technologies, smart parking applications, together with transport researchers and urban planners are interested in fine-grained data on parking space in cities. However, there are no standardized, complete and up-to-date databases for many urban areas. Moreover, manual data collection is expensive and time-consuming. Aerial imagery of entire cities can be used to inventory not only publicly accessible and dedicated parking lots, but also roadside parking areas and those on private property. For a realistic estimation of the total parking space, the observed use of multi-functional traffic areas is taken into account by segmenting not only parking areas but also roads according to their purpose. In this paper, different U-Net based architectures are tested for detecting all these types of visible traffic areas. A new large-scale, high-quality dataset of manual annotations is used in combination with selected contextual information from OpenStreetMap (OSM) to depict the actual use as parking space. Our models achieve a good performance on parking area segmentation, and we show the significant impact of OSM data fusion in deep neural networks on the simultaneous extraction of multiple traffic areas compared to using aerial imagery alone.

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Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018778 ◽

2019 ◽

Vol 33 ◽

pp. 8778-8785 ◽

Cited By ~ 31

Author(s):

Xinhai Liu ◽

Zhizhong Han ◽

Yu-Shen Liu ◽

Matthias Zwicker

Keyword(s):

Shape Representation ◽

Contextual Information ◽

Point Clouds ◽

Learning Model ◽

Local Region ◽

Discriminative Ability ◽

Fine Grained ◽

Multi Scale ◽

3D Point Clouds ◽

Deep Learning Model

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.

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Does the passé composé influence L2 learners’ use of English past tenses?

International Journal of Learner Corpus Research ◽

10.1075/ijlcr.17007.des ◽

2018 ◽

Vol 4 (1) ◽

pp. 23-53 ◽

Cited By ~ 5

Author(s):

Sandra C. Deshors

Keyword(s):

Contextual Information ◽

Present Perfect ◽

Advanced Learners ◽

Fine Grained ◽

Proficiency Level ◽

Form Function ◽

Usage Patterns ◽

Advanced Proficiency ◽

Past Tenses ◽

Passe Compose

Abstract This study explores the uses of the present perfect (PP) and simple past (SP) by French learners of English and assesses how those uses differ from those in native English and those of the passé composé (PC) in native French which, semantically, overlaps with PP and SP. Methodologically, the study is based on over 3,000 contextualized occurrences of PP, SP and PC, and includes cluster and collostructional analyses. Overall, relatively native-like form-function mappings in interlanguage emerge from the analyses, suggesting that, semantically, advanced learners have integrated the uses of past tenses and that the influence of the PC is relatively weak. Further, at an upper-intermediate to advanced proficiency level, learners have integrated the fine-grained contextual information characteristic of the use of English past tenses. Ultimately, the study shows how different methodological designs can lead to varying conclusions on the (non-)nativelike usage patterns of PP in interlanguage.

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Correlation Matters: Multi-scale Fine-Grained Contextual Information Extraction for Hepatic Tumor Segmentation

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-030-47426-3_36 ◽

2020 ◽

pp. 462-474 ◽

Cited By ~ 1

Author(s):

Shuchao Pang ◽

Anan Du ◽

Zhenmei Yu ◽

Mehmet A. Orgun

Keyword(s):

Information Extraction ◽

Contextual Information ◽

Hepatic Tumor ◽

Tumor Segmentation ◽

Fine Grained ◽

Multi Scale

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Corpus-level Fine-grained Entity Typing Using Contextual Information

10.18653/v1/d15-1083 ◽

2015 ◽

Cited By ~ 10

Author(s):

Yadollah Yaghoobzadeh ◽

Hinrich Schütze

Keyword(s):

Contextual Information ◽

Fine Grained

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Hybrid models for sense guessing of Chinese unknown words

International Journal of Corpus Linguistics ◽

10.1075/ijcl.13.1.06lu ◽

2008 ◽

Vol 13 (1) ◽

pp. 99-128 ◽

Cited By ~ 1

Author(s):

Xiaofei Lu

Keyword(s):

Contextual Information ◽

Hybrid Models ◽

Improve Performance ◽

Unknown Word ◽

Fine Grained ◽

Semantic Categories ◽

Knowledge Based ◽

Unknown Words ◽

The Relationship

This paper addresses the problem of classifying Chinese unknown words into fine-grained semantic categories defined in a Chinese thesaurus, Cilin (Mei et al. 1984). We present three novel knowledge-based models that capture the relationship between the semantic categories of an unknown word and those of its component characters in three different ways, and combine two of them with a corpus-based model that uses contextual information to classify unknown words. Experiments show that the combined knowledge-based model outperforms previous methods on the same task, but the use of contextual information does not further improve performance.

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Multilingual Review-aware Deep Recommender System via Aspect-based Sentiment Analysis

ACM Transactions on Information Systems ◽

10.1145/3432049 ◽

2021 ◽

Vol 39 (2) ◽

pp. 1-33

Author(s):

Peng Liu ◽

Lemei Zhang ◽

Jon Atle Gulla

Keyword(s):

Sentiment Analysis ◽

Contextual Information ◽

International Markets ◽

Superior Performance ◽

Fine Grained ◽

Learning Techniques ◽

Rating Prediction ◽

Real World Datasets ◽

Multiple Languages ◽

Inherent Ambiguity

With the dramatic expansion of international markets, consumers write reviews in different languages, which poses a new challenge for Recommender Systems (RSs) dealing with this increasing amount of multilingual information. Recent studies that leverage deep-learning techniques for review-aware RSs have demonstrated their effectiveness in modelling fine-grained user-item interactions through the aspects of reviews. However, most of these models can neither take full advantage of the contextual information from multilingual reviews nor discriminate the inherent ambiguity of words originated from the user’s different tendency in writing. To this end, we propose a novel Multilingual Review-aware Deep Recommendation Model (MrRec) for rating prediction tasks. MrRec mainly consists of two parts: (1) Multilingual aspect-based sentiment analysis module (MABSA), which aims to jointly extract aligned aspects and their associated sentiments in different languages simultaneously with only requiring overall review ratings. (2) Multilingual recommendation module that learns aspect importances of both the user and item with considering different contributions of multiple languages and estimates aspect utility via a dual interactive attention mechanism integrated with aspect-specific sentiments from MABSA. Finally, overall ratings can be inferred by a prediction layer adopting the aspect utility value and aspect importance as inputs. Extensive experimental results on nine real-world datasets demonstrate the superior performance and interpretability of our model.

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Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks

Sensors ◽

10.3390/s21196393 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6393

Author(s):

Hyejoo Kim ◽

Hyeon-Joo Kim ◽

Jinyoon Park ◽

Jeh-Kwang Ryu ◽

Seung-Chan Kim

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Contextual Information ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Wearable System ◽

Fine Grained ◽

Feature Based ◽

Recent Attention

Generally, people do various things while walking. For example, people frequently walk while looking at their smartphones. Sometimes we walk differently than usual; for example, when walking on ice or snow, we tend to waddle. Understanding walking patterns could provide users with contextual information tailored to the current situation. To formulate this as a machine-learning problem, we defined 18 different everyday walking styles. Noting that walking strategies significantly affect the spatiotemporal features of hand motions, e.g., the speed and intensity of the swinging arm, we propose a smartwatch-based wearable system that can recognize these predefined walking styles. We developed a wearable system, suitable for use with a commercial smartwatch, that can capture hand motions in the form of multivariate timeseries (MTS) signals. Then, we employed a set of machine learning algorithms, including feature-based and recent deep learning algorithms, to learn the MTS data in a supervised fashion. Experimental results demonstrated that, with recent deep learning algorithms, the proposed approach successfully recognized a variety of walking patterns, using the smartwatch measurements. We analyzed the results with recent attention-based recurrent neural networks to understand the relative contributions of the MTS signals in the classification process.

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Application of an image management system in microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100129899 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1052-1053

Author(s):

Richard S. Chemock

Keyword(s):

Data Storage ◽

Cost Savings ◽

Image Data ◽

Analytical Techniques ◽

Operational Mode ◽

Fine Grained ◽

Image Recording ◽

Primary Output ◽

Capacity Data ◽

Image Management System

One of the most common tasks in a typical analysis lab is the recording of images. Many analytical techniques (TEM, SEM, and metallography for example) produce images as their primary output. Until recently, the most common method of recording images was by using film. Current PS/2R systems offer very large capacity data storage devices and high resolution displays, making it practical to work with analytical images on PS/2s, thereby sidestepping the traditional film and darkroom steps. This change in operational mode offers many benefits: cost savings, throughput, archiving and searching capabilities as well as direct incorporation of the image data into reports.The conventional way to record images involves film, either sheet film (with its associated wet chemistry) for TEM or PolaroidR film for SEM and light microscopy. Although film is inconvenient, it does have the highest quality of all available image recording techniques. The fine grained film used for TEM has a resolution that would exceed a 4096x4096x16 bit digital image.

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Use of fractals to describe microstructures of porous thick-film platinum electrodes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100121971 ◽

1992 ◽

Vol 50 (1) ◽

pp. 314-315

Author(s):

Steven D. Toteda

Keyword(s):

Fractal Dimension ◽

Power Plants ◽

Electrical Response ◽

Cubic Phase ◽

Platinum Electrodes ◽

Fine Grained ◽

Impedance Response ◽

Platinum Particles ◽

Energy Surfaces ◽

Highly Porous

Zirconia oxygen sensors, in such applications as power plants and automobiles, generally utilize platinum electrodes for the catalytic reaction of dissociating O2 at the surface. The microstructure of the platinum electrode defines the resulting electrical response. The electrode must be porous enough to allow the oxygen to reach the zirconia surface while still remaining electrically continuous. At low sintering temperatures, the platinum is highly porous and fine grained. The platinum particles sinter together as the firing temperatures are increased. As the sintering temperatures are raised even further, the surface of the platinum begins to facet with lower energy surfaces. These microstructural changes can be seen in Figures 1 and 2, but the goal of the work is to characterize the microstructure by its fractal dimension and then relate the fractal dimension to the electrical response. The sensors were fabricated from zirconia powder stabilized in the cubic phase with 8 mol% percent yttria. Each substrate was sintered for 14 hours at 1200°C. The resulting zirconia pellets, 13mm in diameter and 2mm in thickness, were roughly 97 to 98 percent of theoretical density. The Engelhard #6082 platinum paste was applied to the zirconia disks after they were mechanically polished ( diamond). The electrodes were then sintered at temperatures ranging from 600°C to 1000°C. Each sensor was tested to determine the impedance response from 1Hz to 5,000Hz. These frequencies correspond to the electrode at the test temperature of 600°C.

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