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"abstract": "Previous work on snippet generation focused mainly on how to produce one snippet for an individual search result. This paper aims to generate snippets as a comprehensive overview for an entity query (e.g., flu) in a search-result page. Our approach first extracts the attributes (e.g., Symptom and diagnose) of the categories (e.g., Disease) from a community-based question-answering (CQA) website, and then generates the snippets based on how central a sentence is to the meaning of the query, its category, and how well it diversifies the attributes. Integer Linear Programming (ILP) is adopted to find the optimal sentence set. The experiments are conducted on Wikipedia and Yahoo! Answers. Experimental results demonstrate the effectiveness of our approach, compared to an existing commercial search engine and several summarization baselines.",
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"abstract": "As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single snippet is a lightweight task. However, it will be a heavy workload to generate multiple snippets of multiple documents as the search engines need to process large amount of queries per second, and each result list usually contains several snippets. To deal with this heavy workload, we propose a new high-performance snippet generation approach based on CPU-GPU hybrid system. Our main contribution of this paper is to present a parallel processing stream for large-scale snippet generation tasks using GPU. We adopt a sliding document segmentation method in our approach which costs more computing resources but can avoid the common defect that the high relevant fragment may be cut off. The experimental results show that our approach gains a speedup of nearly 6 times in average process time compared with the baseline approach-Highlighter.",
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"content": "As an important part of searching result presentation, query-biased document snippet generation has become a popular method of search engines that makes the result list more informative to users. Generating a single snippet is a lightweight task. However, it will be a heavy workload to generate multiple snippets of multiple documents as the search engines need to process large amount of queries per second, and each result list usually contains several snippets. To deal with this heavy workload, we propose a new high-performance snippet generation approach based on CPU-GPU hybrid system. Our main contribution of this paper is to present a parallel processing stream for large-scale snippet generation tasks using GPU. We adopt a sliding document segmentation method in our approach which costs more computing resources but can avoid the common defect that the high relevant fragment may be cut off. The experimental results show that our approach gains a speedup of nearly 6 times in average process time compared with the baseline approach-Highlighter.",
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"fullName": "Ruixuan Li",
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"abstract": "Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly present in structured documents, forms, etc. To that end, we ask a mostly unexplored question: \"Can we search for other similar snippets present in a target document page given a single query instance of a document snippet?\". We propose MONOMER to solve this as a one-shot snippet detection task. MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents. We conduct extensive ablations and experiments showing MONOMER outperforms several baselines from one-shot object detection (BHRL), template matching, and document understanding (LayoutLMv3). Due to the scarcity of relevant data for the task at hand, we train MONOMER on programmatically generated data having many visually similar query snippets and target document pairs from two datasets - Flamingo Forms and PubLayNet. We also do a human study to validate the generated data.",
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"content": "Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly present in structured documents, forms, etc. To that end, we ask a mostly unexplored question: \"Can we search for other similar snippets present in a target document page given a single query instance of a document snippet?\". We propose MONOMER to solve this as a one-shot snippet detection task. MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents. We conduct extensive ablations and experiments showing MONOMER outperforms several baselines from one-shot object detection (BHRL), template matching, and document understanding (LayoutLMv3). Due to the scarcity of relevant data for the task at hand, we train MONOMER on programmatically generated data having many visually similar query snippets and target document pairs from two datasets - Flamingo Forms and PubLayNet. We also do a human study to validate the generated data.",
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"normalizedAbstract": "Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layout and visual cues commonly present in structured documents, forms, etc. To that end, we ask a mostly unexplored question: \"Can we search for other similar snippets present in a target document page given a single query instance of a document snippet?\". We propose MONOMER to solve this as a one-shot snippet detection task. MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents. We conduct extensive ablations and experiments showing MONOMER outperforms several baselines from one-shot object detection (BHRL), template matching, and document understanding (LayoutLMv3). Due to the scarcity of relevant data for the task at hand, we train MONOMER on programmatically generated data having many visually similar query snippets and target document pairs from two datasets - Flamingo Forms and PubLayNet. We also do a human study to validate the generated data.",
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"Document Image Processing",
"Natural Language Processing",
"Object Detection",
"Query Processing",
"Text Analysis",
"Active Consumption",
"Digital Documents",
"Document Snippet",
"Document Understanding",
"MONOMER Fuses Context",
"One Shot Snippet Detection Task",
"Powering Search",
"Query Snippet",
"Rich Layout",
"Shot Doc Snippet Detection",
"Similar Snippets Present",
"Single Query Instance",
"Spatial Modalities",
"Structured Documents",
"Target Document Page",
"Target Document Pairs",
"Target Documents",
"Template Matching",
"Text Matching Problem",
"Textual Modalities",
"Unexplored Question",
"Visual Cues",
"Visual Modalities",
"Visually Similar Query Snippets",
"Visualization",
"Computer Vision",
"Fuses",
"Layout",
"Object Detection",
"Search Problems",
"Task Analysis",
"Algorithms Vision Language And Or Other Modalities",
"Image Recognition And Understanding Object Detection",
"Categorization",
"Segmentation",
"Scene Modeling",
"Visual Reasoning",
"Machine Learning Architectures",
"Formulations",
"And Algorithms Including Transfer",
"Low Shot",
"Semi",
"Self",
"And Un Supervised Learning"
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"affiliation": "Adobe Media and Data Science Research Labs,Noida,India",
"fullName": "Milan Aggarwal",
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"surname": "Aggarwal",
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{
"affiliation": "Adobe Media and Data Science Research Labs,Noida,India",
"fullName": "Surgan Jandial",
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"affiliation": "Adobe Media and Data Science Research Labs,Noida,India",
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"affiliation": "Adobe Media and Data Science Research Labs,Noida,India",
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"abstract": "Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the \"localization by classification\" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet (or frame) individually and thus overlook the fruitful temporal context relation. Here arises the single snippet cheating issue: \"hard\" snippets are too vague to be classified. In this paper, we argue that learning by comparing helps identify these hard snip-pets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short. Specifically, we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption. Besides, since it is in-feasible to access frame-level annotations, we introduce a Hard Snippet Mining algorithm to locate the potential hard snippets. Substantial analyses verify that this mining strategy efficaciously captures the hard snippets and SniCo Loss leads to more informative feature representation. Extensive experiments show that CoLA achieves state-of-the-art results on THUMOS’14 and ActivityNet v1.2 datasets.",
"abstracts": [
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"content": "Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the \"localization by classification\" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet (or frame) individually and thus overlook the fruitful temporal context relation. Here arises the single snippet cheating issue: \"hard\" snippets are too vague to be classified. In this paper, we argue that learning by comparing helps identify these hard snip-pets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short. Specifically, we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption. Besides, since it is in-feasible to access frame-level annotations, we introduce a Hard Snippet Mining algorithm to locate the potential hard snippets. Substantial analyses verify that this mining strategy efficaciously captures the hard snippets and SniCo Loss leads to more informative feature representation. Extensive experiments show that CoLA achieves state-of-the-art results on THUMOS’14 and ActivityNet v1.2 datasets.",
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"normalizedAbstract": "Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the \"localization by classification\" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet (or frame) individually and thus overlook the fruitful temporal context relation. Here arises the single snippet cheating issue: \"hard\" snippets are too vague to be classified. In this paper, we argue that learning by comparing helps identify these hard snip-pets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short. Specifically, we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption. Besides, since it is in-feasible to access frame-level annotations, we introduce a Hard Snippet Mining algorithm to locate the potential hard snippets. Substantial analyses verify that this mining strategy efficaciously captures the hard snippets and SniCo Loss leads to more informative feature representation. Extensive experiments show that CoLA achieves state-of-the-art results on THUMOS’14 and ActivityNet v1.2 datasets.",
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"affiliation": "School of Electronic and Computer Engineering, Peking University",
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"abstract": "Identifying brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data is an important advanced subject in neuroinformatics in recent years, where the learning method based on bayesian networks (BN) has become a new hot topic in the field. This paper proposes a new method to learn the brain effective connectivity network structure by combining ant colony optimization (ACO) with BN method, named as ACOEC. In the proposed algorithm, a brain effective connectivity network is first mapped onto an ant, and then the ant colony optimization by simulating real ants looking for food is employed to construct network structures and finally an ant with the highest score is obtained as the optimal solution. The experimental results on simulated and real fMRI data sets show that the new method can not only accurately identify the connections and directions of the brain networks, but also quantitatively describe the connection strength of the brain networks, which has a good clinical application prospects.",
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"content": "An extension of two key robust statistics to fuzzy sets is presented and applied to the fuzzy c-means clustering algorithm. Examples of a robust statistic are the median and the median absolute deviation from the median or MAD, a robust estimate of dispersion. These extensions are derived, and the fuzzy median is applied to the fuzzy c-means clustering algorithm. The modified clustering algorithm shows improved performance in clustering data sets generated by heavy-tailed distributions like the Cauchy distribution.",
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"abstract": "A computer graphics object reconstructed from real-world data often contains undesirable noise and small-scale oscillations. An important problem is how to remove the noise and oscillations while preserving desirable geometric features of the object. We develops methods for polyhedral surface smoothing and denoising with simultaneous increasing mesh regularity. We also propose an adaptive smoothing method allowing to reduce possible oversmoothing. Roughly speaking, our smoothing schemes consist of moving every vertex in the direction defined by the Laplacian flow with speed equal to a properly chosen function of the mean curvature at the vertex.",
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"abstract": "This paper proposes a novel approach for smoothing surfaces represented by triangular meshes. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by vertex position update based on the least square error criteria. This paper applies the fuzzy ordering theory to the vector based data and introduces the fuzzy vector median. The application of FVM filters to surface normal smoothing yields improved results over previously introduced normal smoothing algorithms. The improved filtering results, coupled with LSE vertex position update, produces surface smoothing that minimizes the effects of noise while simultaneously preserving detail features. Simulation results are presented showing the performance of the proposed method and its advantages over commonly used surface smoothing algorithms. Additionally, optimization procedures for FVM are derived and evaluated.",
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"content": "This paper proposes a novel approach for smoothing surfaces represented by triangular meshes. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by vertex position update based on the least square error criteria. This paper applies the fuzzy ordering theory to the vector based data and introduces the fuzzy vector median. The application of FVM filters to surface normal smoothing yields improved results over previously introduced normal smoothing algorithms. The improved filtering results, coupled with LSE vertex position update, produces surface smoothing that minimizes the effects of noise while simultaneously preserving detail features. Simulation results are presented showing the performance of the proposed method and its advantages over commonly used surface smoothing algorithms. Additionally, optimization procedures for FVM are derived and evaluated.",
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"affiliation": "Dept. of Electr. & Comput. Eng., Delaware Univ., Newark, DE, USA",
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"abstract": "This paper proposes a novel approach for smoothing surfaces represented by triangular meshes in a haptic environment. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by integration of surface normals for vertex position update based on the least absolute error (LAE) criteria. The median and Order Statistic based filters are extensively used in signal processing, especially image processing due to their ability to reject outliers and preserve features such as edges and monotonic regions. More recently, fuzzy ordering theory has been introduced to allow averaging amongst similarly valued samples. This paper extends the fuzzy ordering concept to vector based data and introduces the fuzzy vector median filter. The application of FVM filters to surface normal smoothing yields improved results over previously introduced normal smoothing algorithms. The improved filtering results, coupled with LAE vertex position update, produces surface smoothing that minimizes the effects of noise while simultaneously preserving detail features. Simulation results are presented showing the performance of the proposed method and its advantages over commonly used surface smoothing algorithms. Additionally, optimization procedures for FVM are derived and evaluated.",
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"abstract": "A smoothing parametric addendum surface design method has been developed to improve the quality and efficiency of mold industrial design. This paper proposes to establish the section curve types database based on the user-modified characteristic parameters, and presents the method based on thin-plate-under-tension model to generate addendum surface by the section curves and connect-lines. There has the second-order continues between surface-patchs, thus it ensures the good smoothing of the overall surface. The relevant parametric design module is independently developed in Visual C++ based on software KMAS. We use an auto panel addendum surface successfully design process as an example to illustrate the application and effectiveness of the method.",
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"abstract": "An O(n log n)-time online algorithm is given for packing items i.i.d. uniform on (0, 1) into bins of size 1 with expected wasted space Theta (n/sup 1/2/ log /sup 1/2/n). This matches the lowest bound that no online algorithm can achieve O(n/sup 1/2/ log /sup 1/2/ n) wasted space. It is done by analyzing another algorithm which involves putting balls into buckets online. The analysis of this second algorithm also gives bound on the stochastic rightward matching problem, which arises in analyzing not only the above online bin packing problem, but also a 2-D problem of packing rectangles into a half-infinite strip. The bounds on rightward matching thus give good bounds for the 2-D strip packing problem.",
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"abstract": "Given a set of rectangular pieces and a container of fixed width and variable height, the 2D rectangular strip packing problem consists of orthogonally placing all the pieces within the container, without overlapping, such that the overall height of the layout is minimized. It has many industrial applications such as wood or glass cutting, very large scale integration design, etc. To solve this problem, many algorithms based on different strategies have been proposed. According to the wisdom and experience of human being, a quasi-human heuristic algorithm for the 2D rectangular strip packing problem is recommended based on the existing research, some important packing strategies are presented in this paper. Twenty-one rectangle-packing, 13 random strip-packing and 10 classes strip-packing instances are used to test the performance of the algorithm developed. All of 21 rectangle-packing instances and three of 13 strip-packing instances are achieved optimal solutions, and the average relative distance of 10 classes strip-packing instances obtained by the developed algorithm is 2.28%. Experimental results demonstrate that the algorithm developed is fairly efficient in solving the 2D rectangular strip packing problem.",
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"abstract": "Anatomy-based facial tissue modeling for surgical simulation is a field whose time has come. Real-time facial animation has been created in the last few years using models based on the anatomical structure of the human skin. Anatomy-based models are also under development in the field of medical visualization, with which facial surgery can be realistically simulated. In this article we present an anatomy-based 3D finite element tissue model. Integrated into a computer-aided surgical planning system this model allows the precise prediction of soft tissue changes resulting from the realignment of the underlying bone structure. The model has already been used in our Department of Oral and Maxillofacial Surgery and has improved craniofacial surgical planning procedures. The model is described in detail and surgical simulation results are shown and discussed.",
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"content": "Anatomy-based facial tissue modeling for surgical simulation is a field whose time has come. Real-time facial animation has been created in the last few years using models based on the anatomical structure of the human skin. Anatomy-based models are also under development in the field of medical visualization, with which facial surgery can be realistically simulated. In this article we present an anatomy-based 3D finite element tissue model. Integrated into a computer-aided surgical planning system this model allows the precise prediction of soft tissue changes resulting from the realignment of the underlying bone structure. The model has already been used in our Department of Oral and Maxillofacial Surgery and has improved craniofacial surgical planning procedures. The model is described in detail and surgical simulation results are shown and discussed.",
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"abstract": "Robot-assisted Minimally Invasive Surgery (MIS) requires the surgeon to alternatively control both the surgical instruments and the endoscopic camera, or to leave this burden to an assistant. This increases the cognitive load and interrupts the workflow of the operation. Camera motion automation has been examined in the literature to mitigate these aspects, but still lacks situation awareness, a key factor for camera navigation enhancement. This paper presents the development of a phase-specific camera motion automation, implemented in Virtual Reality (VR) during a suturing task. A user study involving 10 users was carried out using the master console of the da Vinci Research Kit. Each subject performed the suturing task undergoing both the proposed autonomous camera motion and the traditional manual camera control. Results show that the proposed system can reduce operational time, decreasing both the user's mental and physical demand. Situational awareness is shown to be fundamental in exploiting the benefits introduced by camera motion automation.",
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"title": "“Surgical Gps” Proof of Concept for Scoliosis Surgery",
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"abstract": "Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time.",
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"content": "Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time.",
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"abstract": "Abstract: In this study, we propose a physically-based approach based on anatomical knowledge for real-time facial expression animation. The facial model incorporates a physically-based approximation to facial skin and a set of anatomically-motivated facial muscles. The skin is modeled by a mass-spring system with nonlinear springs which have biphasic stress-strain relationship to simulate the elastic dynamics of the real facial skin. Facial muscles are modeled as forces deforming the spring mesh. Based on the Action Units (AUs) of the Facial Action Coding System (FACS), various expressions can be generated by the combination of the contractions of a set of facial muscles. Lagrangian mechanics governs the dynamics, dictating the deformation of facial surface in response to muscle forces. Simulation results show the real-time facial deformation as well as realistic expression animation.",
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"abstract": "This paper presents a new method for reconstructing animated, anatomy-based facial models of individuals from range data with minimal manual intervention. A prototype model with a multi-layer skin-muscle-skull structure serves as the starting point for our method. After the global adaptation, the skin mesh of the prototype model is represented as a dynamic deformable model which is deformed to fit scanned data according to internal force stemming from the elastic properties of the surface and external forces produced from the scanned data points and features. The underlying muscle layer that consists of three types of facial muscles is automatically adapted. According to the adapted skin and muscle structures, a set of automatically generated skull feature points is transformed to drive a volume morphing of the template skull model for skull fitting. The reconstructed model realistically reproduces the shape and features of a specific person and can be animated instantly.",
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"abstract": "The classification and identification technology plays an important role in the research of Brain-computer interface (BCI) systems. In this paper, we do fuzzy clustering disposal for the multi-channel electroencephalogram (EEG) during finger movement at first according to event-related desynchronization phenomena (ERD) in the event-related EEG. Then we classify signal-trial EEG with the feature extracted from EEG based on common spatial subspace decomposition (CSSD) algorithm. The averaged classification accuracy achieves 94.58% in the course of testing on the data from four subjects. Experiment results show that the combination of fuzzy clustering and neural network can greatly improve the rate of identification for finger movement.",
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"abstract": "Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this demonstration, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geogrids. Then we mine significant places from the geo-grids by considering their significance.",
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"content": "Mining the frequently visited places of single mobile users, i.e., significant places, is crucial for supporting personalized location-based services. Most of existing works for significance place mining have a need to take advantage the GPS trajectories of users. However, it is difficult to encourage mobile users to contribute GPS trajectories because of the high power consumption of GPS. In this demonstration, we propose a geo-grid based approach for mining significant places from cell ID trajectories. In our approach, the mined significant places are represented as sets of geo-grids which are much smaller than the coverage areas of cell-sites. To be specific, we firstly extract the stay areas where the mobile user used to stay and map them to many geogrids. Then we mine significant places from the geo-grids by considering their significance.",
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"fullName": "Tengfei Bao",
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"abstract": "Mobile devices provide the availability of tracking and collecting trajectories of moving objects such as vehicles, people or animals. There exists a well-known collection of patterns which can occur for a subset of trajectories. Specifically we study the so-called Popular Places, that is regions that are visited by many distinct moving objects.We propose algorithms to efficiently compute different forms of reporting Popular Places, that take benefit of the Graphics Processing Unit parallelism capabilities. We also describe how to visualize the reported solutions. Finally we present and discuss experimentalresults obtained with the implementation of our algorithms.",
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"title": "The Ninth International Conference on Mobile Data Management (mdm 2008)",
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"doi": "10.1109/MDM.2008.29",
"title": "Privacy Preservation in the Publication of Trajectories",
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"abstract": "We study the problem of protecting privacy in the publication of location sequences. Consider a database of trajectories, corresponding to movements of people, captured by their transactions when they use credit or RFID debit cards. We show that, if such trajectories are published exactly (by only hiding the identities of persons that followed them),there is a high risk of privacy breach by adversaries who hold partial information about them (e.g., shop owners). In particular, we show that one can use partial trajectory knowledge as a quasi-identifier for the remaining locations in the sequence. We device a data suppression technique, which prevents this type of breach, while keeping the posted data as accurate as possible.",
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{
"abstractType": "Regular",
"content": "We study the problem of protecting privacy in the publication of location sequences. Consider a database of trajectories, corresponding to movements of people, captured by their transactions when they use credit or RFID debit cards. We show that, if such trajectories are published exactly (by only hiding the identities of persons that followed them),there is a high risk of privacy breach by adversaries who hold partial information about them (e.g., shop owners). In particular, we show that one can use partial trajectory knowledge as a quasi-identifier for the remaining locations in the sequence. We device a data suppression technique, which prevents this type of breach, while keeping the posted data as accurate as possible.",
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"normalizedAbstract": "We study the problem of protecting privacy in the publication of location sequences. Consider a database of trajectories, corresponding to movements of people, captured by their transactions when they use credit or RFID debit cards. We show that, if such trajectories are published exactly (by only hiding the identities of persons that followed them),there is a high risk of privacy breach by adversaries who hold partial information about them (e.g., shop owners). In particular, we show that one can use partial trajectory knowledge as a quasi-identifier for the remaining locations in the sequence. We device a data suppression technique, which prevents this type of breach, while keeping the posted data as accurate as possible.",
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"Spatiotemporal",
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"affiliation": null,
"fullName": "Manolis Terrovitis",
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"abstract": "The psychological concept of resilience has drawn attention in recent years, but there are few studies focusing on resilience in China. This study conducted the questionnaire using the Japanese version of the Ten Item Personality Inventory (TIPI-J) and the Bidimensional Resilence Scale (BRS) in order to examine the relationship between individual personality traits (extraversion, agreeableness, conscientiousness, neuroticism and openness) and innate or acquired resilience factors in Chinese university students by means of correlation analysis. As a result, the marginally significant positive correlation was found between the innate resilience factor and the outgoing and sociable personality traits, extraversion <tex>Z_$(r=.355,p <. 10)$_Z</tex>. No statistical association was seen between resilience and the other factors of agreeableness, conscientiousness, neuroticism and openness. The results suggest that recuperative ability is not only influenced by personality traits but also by other factors and that the acquisition of resilience may be determined by the mutual interaction with other factors.",
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"title": "2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)",
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"abstract": "Although an evaluation index of participants’ communication skills in group dialog would be useful for improving each participant’s ability to interact in a group, conventional methods of assessing personality traits are required lots of labor- and time-consuming for participants when conducted frequently, so automation of these methods is preferred. In this study, we developed a method to estimate personality traits of each participant based on multimodal dialog information in knowledge-transfer-type discussions. To achieve it, we created a corpus of knowledge-transfer-type dialogs including participants’ multimodal information and personality assessments of the BigFive and Locus of Control and constructed statistical models to classify high and low degree of the personality traits based on participants’ multimodal information. The evaluation results showed that the model was able to estimate degree of each factor of the BigFive with accuracies in the range of 0.87-1.00, and to estimate degree of Locus of Control with accuracies in the range of 0.87-1.00. The most useful features to estimate personality traits were combinations of acoustic features, head movement features, turn-taking features, and linguistic features. It was also found that the personality traits of participants could be estimated with high accuracy even when using data from the first 5 minutes of the discussion session.",
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"content": "Although an evaluation index of participants’ communication skills in group dialog would be useful for improving each participant’s ability to interact in a group, conventional methods of assessing personality traits are required lots of labor- and time-consuming for participants when conducted frequently, so automation of these methods is preferred. In this study, we developed a method to estimate personality traits of each participant based on multimodal dialog information in knowledge-transfer-type discussions. To achieve it, we created a corpus of knowledge-transfer-type dialogs including participants’ multimodal information and personality assessments of the BigFive and Locus of Control and constructed statistical models to classify high and low degree of the personality traits based on participants’ multimodal information. The evaluation results showed that the model was able to estimate degree of each factor of the BigFive with accuracies in the range of 0.87-1.00, and to estimate degree of Locus of Control with accuracies in the range of 0.87-1.00. The most useful features to estimate personality traits were combinations of acoustic features, head movement features, turn-taking features, and linguistic features. It was also found that the personality traits of participants could be estimated with high accuracy even when using data from the first 5 minutes of the discussion session.",
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"normalizedAbstract": "Although an evaluation index of participants’ communication skills in group dialog would be useful for improving each participant’s ability to interact in a group, conventional methods of assessing personality traits are required lots of labor- and time-consuming for participants when conducted frequently, so automation of these methods is preferred. In this study, we developed a method to estimate personality traits of each participant based on multimodal dialog information in knowledge-transfer-type discussions. To achieve it, we created a corpus of knowledge-transfer-type dialogs including participants’ multimodal information and personality assessments of the BigFive and Locus of Control and constructed statistical models to classify high and low degree of the personality traits based on participants’ multimodal information. The evaluation results showed that the model was able to estimate degree of each factor of the BigFive with accuracies in the range of 0.87-1.00, and to estimate degree of Locus of Control with accuracies in the range of 0.87-1.00. The most useful features to estimate personality traits were combinations of acoustic features, head movement features, turn-taking features, and linguistic features. It was also found that the personality traits of participants could be estimated with high accuracy even when using data from the first 5 minutes of the discussion session.",
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"authors": [
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"affiliation": "Nagaoka University of Technology,Dept. of Information and Management Systems Engineering,Niigata,Japan",
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"affiliation": "Nagaoka University of Technology,Dept. of Information and Management Systems Engineering,Niigata,Japan",
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"proceeding": {
"id": "1grOKVFffCo",
"title": "2019 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"title": "Influence of Personality Traits and Body Awareness on the Sense of Embodiment in Virtual Reality",
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"abstract": "With the increasing use of avatars (i.e. the virtual representation of the user in a virtual environment) in virtual reality, it is important to identify the factors eliciting the sense of embodiment or the factors that can disrupt this feeling. This paper reports an exploratory study aiming at identifying internal factors (personality traits and body awareness) that might cause either a resistance or a predisposition to feel a sense of embodiment towards a virtual avatar. To this purpose, we conducted an experiment (n=123) in which participants were immersed in a virtual environment and embodied in a gender-matched generic virtual avatar through a head-mounted display. After an exposure phase in which they had to perform a number of visuomotor tasks (during 2 minutes) a virtual character entered the virtual scene and stabbed the participants' virtual hand with a knife. The participants' sense of embodiment was measured, as well as several personality traits (Big Five traits and locus of control) and body awareness, to evaluate the influence of participants' personality on the acceptance of the virtual body. The major finding of the experiment is that the locus of control is linked to several components of embodiment: the sense of agency is positively correlated with an internal locus of control and the sense of body ownership is positively correlated with an external locus of control. Interestingly, both components are not influenced by the same traits, which confirms that they can appear independently. Taken together our results suggest that the locus of control could be a good predictor of the sense of embodiment when the user embodies an avatar with a similar physical appearance. Yet, further studies are required to confirm these results.",
"abstracts": [
{
"abstractType": "Regular",
"content": "With the increasing use of avatars (i.e. the virtual representation of the user in a virtual environment) in virtual reality, it is important to identify the factors eliciting the sense of embodiment or the factors that can disrupt this feeling. This paper reports an exploratory study aiming at identifying internal factors (personality traits and body awareness) that might cause either a resistance or a predisposition to feel a sense of embodiment towards a virtual avatar. To this purpose, we conducted an experiment (n=123) in which participants were immersed in a virtual environment and embodied in a gender-matched generic virtual avatar through a head-mounted display. After an exposure phase in which they had to perform a number of visuomotor tasks (during 2 minutes) a virtual character entered the virtual scene and stabbed the participants' virtual hand with a knife. The participants' sense of embodiment was measured, as well as several personality traits (Big Five traits and locus of control) and body awareness, to evaluate the influence of participants' personality on the acceptance of the virtual body. The major finding of the experiment is that the locus of control is linked to several components of embodiment: the sense of agency is positively correlated with an internal locus of control and the sense of body ownership is positively correlated with an external locus of control. Interestingly, both components are not influenced by the same traits, which confirms that they can appear independently. Taken together our results suggest that the locus of control could be a good predictor of the sense of embodiment when the user embodies an avatar with a similar physical appearance. Yet, further studies are required to confirm these results.",
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"normalizedAbstract": "With the increasing use of avatars (i.e. the virtual representation of the user in a virtual environment) in virtual reality, it is important to identify the factors eliciting the sense of embodiment or the factors that can disrupt this feeling. This paper reports an exploratory study aiming at identifying internal factors (personality traits and body awareness) that might cause either a resistance or a predisposition to feel a sense of embodiment towards a virtual avatar. To this purpose, we conducted an experiment (n=123) in which participants were immersed in a virtual environment and embodied in a gender-matched generic virtual avatar through a head-mounted display. After an exposure phase in which they had to perform a number of visuomotor tasks (during 2 minutes) a virtual character entered the virtual scene and stabbed the participants' virtual hand with a knife. The participants' sense of embodiment was measured, as well as several personality traits (Big Five traits and locus of control) and body awareness, to evaluate the influence of participants' personality on the acceptance of the virtual body. The major finding of the experiment is that the locus of control is linked to several components of embodiment: the sense of agency is positively correlated with an internal locus of control and the sense of body ownership is positively correlated with an external locus of control. Interestingly, both components are not influenced by the same traits, which confirms that they can appear independently. Taken together our results suggest that the locus of control could be a good predictor of the sense of embodiment when the user embodies an avatar with a similar physical appearance. Yet, further studies are required to confirm these results.",
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"abstract": "Recent research on information visualization has shown how individual differences act as a mediator on how users interact with visualization systems. We focus our exploratory study on whether personality has an effect on user preferences regarding idioms used for hierarchy, evolution over time, and comparison contexts. Specifically, we leverage all personality variables from the Five-Factor Model and the three dimensions from Locus of Control (LoC) with correlation and clustering approaches. The correlation-based method suggested that Neuroticism, Openness to Experience, Agreeableness, several facets from each trait, and the External dimensions from LoC mediate how much individuals prefer certain idioms. In addition, our results from the cluster-based analysis showed that Neuroticism, Extraversion, Conscientiousness, and all dimensions from LoC have an effect on preferences for idioms in hierarchy and evolution contexts. Our results support the incorporation of in-depth personality synergies with InfoVis into the design pipeline of visualization systems.",
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"abstract": "This paper addresses the problem of how to extract, describe, and evaluate handwriting deformation from the deterministic viewpoint for improving recognition accuracy. The key ideas are threefold. The first is to extract handwriting deformation vector field (DVF) between a pair of input and target images by 2D warping. The second is to hierarchically decompose the DVF by a parametric deformation model of global/local affine transformation, where local affine transformation is iteratively applied to the DVF by decreasing window sizes. The third is to accept only low-order deformation components as natural, within-class handwriting deformation. Experiments using the handwritten numeral database IPTP CDROM1B show that correlation-based matching absorbing components of global affine transformation and local affine transformation up to the 3rd order achieved a higher recognition rate of 92.1% than that of 87.0% obtained by original 2D warping.",
"abstracts": [
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