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"abstract": "Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges weighted by the association scores of their endpoints. After graph construction, the network may be thresholded such that only the edges with stronger weights remain and the desired sparsity level is achieved. While this approach is feasible for small datasets, its quadratic time complexity does not scale as the individual time series length and the number of compared series increase. Thus, to avoid the costly step of building a fully-connected graph before sparsification, we propose a fast network discovery approach based on probabilistic hashing of randomly selected time series subsequences. Evaluation on real data shows that our methods construct graphs nearly 15 times as fast as baseline methods, while achieving both network structure and accuracy comparable to baselines in task-based evaluation.",
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"abstract": "Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.",
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"content": "Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.",
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"title": "Role discovery based on sociology attributes clustering in Sina Microblog",
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"abstract": "Understanding and mastering users role plays an important part in online public opinions tracking and electronic commerce marketing, etc. Different groups of users have different sociology attributes. Thus, it is very important and interesting to discover user role based on their sociology attributes. The present user role discovery methods are generally based on the structural features or static coarse-grained behavior features. In this paper, by analyzing a large number of real social network data, we propose a novel method for social role discovery based on sociology attributes features: we first mining and define several properties on behalf of sociology attributes; then, to deal with the sociology attributes features clustering, we use Bayesian information criterion as our stopping criterion; at last, the experimental results show that using this method can better understand user role in Sina Microblog. Besides, the methodology in this paper for user role discovery also can be applied to other social network in general.",
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"abstractType": "Regular",
"content": "Understanding and mastering users role plays an important part in online public opinions tracking and electronic commerce marketing, etc. Different groups of users have different sociology attributes. Thus, it is very important and interesting to discover user role based on their sociology attributes. The present user role discovery methods are generally based on the structural features or static coarse-grained behavior features. In this paper, by analyzing a large number of real social network data, we propose a novel method for social role discovery based on sociology attributes features: we first mining and define several properties on behalf of sociology attributes; then, to deal with the sociology attributes features clustering, we use Bayesian information criterion as our stopping criterion; at last, the experimental results show that using this method can better understand user role in Sina Microblog. Besides, the methodology in this paper for user role discovery also can be applied to other social network in general.",
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"normalizedAbstract": "Understanding and mastering users role plays an important part in online public opinions tracking and electronic commerce marketing, etc. Different groups of users have different sociology attributes. Thus, it is very important and interesting to discover user role based on their sociology attributes. The present user role discovery methods are generally based on the structural features or static coarse-grained behavior features. In this paper, by analyzing a large number of real social network data, we propose a novel method for social role discovery based on sociology attributes features: we first mining and define several properties on behalf of sociology attributes; then, to deal with the sociology attributes features clustering, we use Bayesian information criterion as our stopping criterion; at last, the experimental results show that using this method can better understand user role in Sina Microblog. Besides, the methodology in this paper for user role discovery also can be applied to other social network in general.",
"fno": "06785918",
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"Social Network Services",
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"authors": [
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"affiliation": "Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications(BUPT), 100876, China",
"fullName": "Xinglong Yu",
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"affiliation": "Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications(BUPT), 100876, China",
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"abstract": "In recent years, more and more internet companies trend to fuse their products with personal recommendation functions. Although the idea of this business model satisfies characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect is worse than they expected. The quality of recommendation is limited by cold start, data sparse, lack of learning contextual aware, and so on. One of the most important drawbacks of existing recommendation systems is that they usually use only rating matrix as useful information and not fully consider item attributes. In this paper, we proposed an optimized recommendation system, which based on modeling of items in a multidimensional space of item's attribute. The presented approach takes item attributes, user preferences and user context into consideration, and is mainly composed of two modules: (1) attribute-latent factor model, which is used to show user latent preference for each attribute, (2) multi-attribute model, which is used to show user preference between all attributes. The research is conducted in a practical application environment, and the preliminary experimental results show the validity of the recommendation.",
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"content": "In recent years, more and more internet companies trend to fuse their products with personal recommendation functions. Although the idea of this business model satisfies characteristic of Internet industry felicitously, scientists and engineers gradually find out that the effect is worse than they expected. The quality of recommendation is limited by cold start, data sparse, lack of learning contextual aware, and so on. One of the most important drawbacks of existing recommendation systems is that they usually use only rating matrix as useful information and not fully consider item attributes. In this paper, we proposed an optimized recommendation system, which based on modeling of items in a multidimensional space of item's attribute. The presented approach takes item attributes, user preferences and user context into consideration, and is mainly composed of two modules: (1) attribute-latent factor model, which is used to show user latent preference for each attribute, (2) multi-attribute model, which is used to show user preference between all attributes. The research is conducted in a practical application environment, and the preliminary experimental results show the validity of the recommendation.",
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"Matrix Decomposition",
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"abstract": "In this paper, we propose an attribute-based query & retrieval system designed for fashion products. Our system addresses the problem of carrying out fashion searches by the query image and attribute manipulation, e.g. replacing long sleeve attribute of a dress to sleeveless. We present the attributes in two groups: (1) general attributes (category, gender etc.) and (2) special attributes (sleeve length, collar etc.). The special attributes are more suitable for the attribute manipulation and thus conducting searches. In order to solve the mentioned fashion search problem, it is crucial for the deep neural networks to understand attribute similarities. To facilitate more specific similarity learning, clothing items are represented by their structural subcomponents or \"parts\". The parts are estimated using an unsupervised segmentation method and used inside the proposed Convolutional Neural Network (CNN) as an attention mechanism. Meaning, different parts are connected to the special attributes, e.g. sleeve part is connected with sleeve length attribute. With this mechanism, part-based triplet ranking constraint is applied to learn similarity of each special attribute independently from one another in a single network. In the end, the well-defined features are used to conduct the fashion search. Additionally, an adaptive relevance feedback module is used to personalize the fashion search process with the feature descriptions. For our experiments, a new dataset is constructed containing 101,021 images which consist of pure clothing items. Besides achieving decent retrieval results in our dataset, the experiments show that proposed technique outperforms different baselines and is able to adapt towards user's requests.",
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"abstract": "this recommendation algorithm based on User-Item Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item information are analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix (UAIARM). After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.",
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"content": "this recommendation algorithm based on User-Item Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item information are analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix (UAIARM). After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.",
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"abstract": "Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generating a fair consensus ranking is time-consuming and impractical- even for a fairly small number of candidates. While algorithmic approaches for auditing and generating fair consensus rankings have been developed, these have not been operationalized in interactive systems. To bridge this gap, we introduce FairFuse, a visualization system for generating, analyzing, and auditing fair consensus rankings. We construct a data model which includes base rankings entered by rankers, augmented with measures of group fairness, and algorithms for generating consensus rankings with varying degrees of fairness. We design novel visualizations that encode these measures in a parallel-coordinates style rank visualization, with interactions for generating and exploring fair consensus rankings. We describe use cases in which FairFuse supports a decision-maker in ranking scenarios in which fairness is important, and discuss emerging challenges for future efforts supporting fairness-oriented rank analysis. Code and demo videos available at https://osf.io/hd639/.",
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"content": "Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generating a fair consensus ranking is time-consuming and impractical- even for a fairly small number of candidates. While algorithmic approaches for auditing and generating fair consensus rankings have been developed, these have not been operationalized in interactive systems. To bridge this gap, we introduce FairFuse, a visualization system for generating, analyzing, and auditing fair consensus rankings. We construct a data model which includes base rankings entered by rankers, augmented with measures of group fairness, and algorithms for generating consensus rankings with varying degrees of fairness. We design novel visualizations that encode these measures in a parallel-coordinates style rank visualization, with interactions for generating and exploring fair consensus rankings. We describe use cases in which FairFuse supports a decision-maker in ranking scenarios in which fairness is important, and discuss emerging challenges for future efforts supporting fairness-oriented rank analysis. Code and demo videos available at https://osf.io/hd639/.",
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"abstract": "The Delaunay triangulation is well known for its use in geometric design. A derived version of this structure, the Delaunay constrained triangulation, takes into account the triangular mesh problem in presence of rectilinear constraints.The Delaunay constrained triangulation is very useful for CAD, topography and mapping and in finite elements analysis. This technique is still developing. We present a taxonomy of this geometric structure. First we describe the different tools used to introduce the problem. Then we introduce the different approaches highlighting various points of view of the problem.We will focus on the Delaunay stable methods presenting our researches on the subject. A Delaunay stable method preserves the Delaunay nature of the constrained triangulation. Each method is detailed by its algorithms, performances, and properties. For instance we show how these methods approximate the generalized Vorono? diagram of the configuration.Finally, we expose an application of our algorithms in maintaining at lowest cost the DEM realism during the resampling process. The Delaunay stable algorithms are used for 2.5D DEM design. The aim of this work is to demonstrate that the use of topographic constraints in a regular DEM without adding new points preserves the terrain shape. So the resulting DEM can be more easily interpreted because its realism is preserved and the mesh still owns all the Delaunay triangulation properties.",
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"affiliation": "Universit? Joseph Fourier",
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"abstract": "Engineering interactive systems for safety critical applications such as in avionic digital cockpits (and more generally Graphical User interfaces) is a challenge from a dependability viewpoint. The dependability of the user interface and its related hardware and software components must be consistent with the criticality of the functions to be controlled and their required DAL levels. This paper proposes a stepwise refinement approach going from systematic identification of failure modes of these systems to their detection via formally defined assertions. The last steps of the approach present how the assertions can be included into the monitoring part of self-checking interactive components and how they can be deployed on an architecture compliant with the ARINC 653 specification, ensuring temporal and spatial segregation, thus detecting errors and preventing failures due to both physical and transient software faults. We present how these contributions have been applied to the Flight Control Unit Backup interactive application which is available in A380 interactive cockpits.",
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"content": "Engineering interactive systems for safety critical applications such as in avionic digital cockpits (and more generally Graphical User interfaces) is a challenge from a dependability viewpoint. The dependability of the user interface and its related hardware and software components must be consistent with the criticality of the functions to be controlled and their required DAL levels. This paper proposes a stepwise refinement approach going from systematic identification of failure modes of these systems to their detection via formally defined assertions. The last steps of the approach present how the assertions can be included into the monitoring part of self-checking interactive components and how they can be deployed on an architecture compliant with the ARINC 653 specification, ensuring temporal and spatial segregation, thus detecting errors and preventing failures due to both physical and transient software faults. We present how these contributions have been applied to the Flight Control Unit Backup interactive application which is available in A380 interactive cockpits.",
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"abstract": "Securing one's personal space is quite important in leading a comfortable social life. However, it is difficult to maintain an appropriate interpersonal distance all the time. Therefore, we propose an interpersonal distance control system with a video see-through system, consisting of a head-mounted display (HMD), depth sensor, and RGB camera. The proposed system controls the interpersonal distance by changing the size of the person in the HMD view. In this paper, we describe the proposed system and conduct an experiment to confirm the capability of the proposed system. Finally, we show and discuss the results of the experiment.",
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"abstract": "This paper studies the polarization demultiplexing technology in the coherent detection of 100G DP-QPSK optical signals. An Adaptive step size Stokes space method for polarization demultiplexing is proposed, which solves the problem of insufficient convergence caused by the constant step size of the Iterable Stokes space method. The original Iterable Stokes space method has no singularity problem, fast convergence speed and better polarization state tracking performance, but its iterative step length is fixed. When setting the step length, it is necessary to find a balance between the convergence speed and the convergence stability. In this paper, the Iterable Stokes space method is improved. The new algorithm proposed retains the advantages of the Iterable Stokes space method and realizes the adaptive update of the step size. The simulation results show that compared with the Iterable Stokes space method, the proposed new algorithm adds a small amount of operations, but the convergence speed is doubled, the convergence is more stable, and the polarization state tracking performance is also improved.",
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"content": "This paper studies the polarization demultiplexing technology in the coherent detection of 100G DP-QPSK optical signals. An Adaptive step size Stokes space method for polarization demultiplexing is proposed, which solves the problem of insufficient convergence caused by the constant step size of the Iterable Stokes space method. The original Iterable Stokes space method has no singularity problem, fast convergence speed and better polarization state tracking performance, but its iterative step length is fixed. When setting the step length, it is necessary to find a balance between the convergence speed and the convergence stability. In this paper, the Iterable Stokes space method is improved. The new algorithm proposed retains the advantages of the Iterable Stokes space method and realizes the adaptive update of the step size. The simulation results show that compared with the Iterable Stokes space method, the proposed new algorithm adds a small amount of operations, but the convergence speed is doubled, the convergence is more stable, and the polarization state tracking performance is also improved.",
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"title": "2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)",
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"title": "Augmented Reality Map Navigation with Freehand Gestures",
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"abstract": "Freehand gesture interaction has long been proposed as a `natural' input method for Augmented Reality (AR) applications, yet has been little explored for intensive applications like multiscale navigation. In multiscale navigation, such as digital map navigation, pan and zoom are the predominant interactions. A position-based input mapping (e.g. grabbing metaphor) is intuitive for such interactions, but is prone to arm fatigue. This work focuses on improving digital map navigation in AR with mid-air hand gestures, using a horizontal intangible map display. First, we conducted a user study to explore the effects of handedness (unimanual and bimanual) and input mapping (position-based and rate-based). From these findings we designed DiveZoom and TerraceZoom, two novel hybrid techniques that smoothly transition between position- and rate-based mappings. A second user study evaluated these designs. Our results indicate that the introduced input-mapping transitions can reduce perceived arm fatigue with limited impact on performance.",
"abstracts": [
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"content": "Freehand gesture interaction has long been proposed as a `natural' input method for Augmented Reality (AR) applications, yet has been little explored for intensive applications like multiscale navigation. In multiscale navigation, such as digital map navigation, pan and zoom are the predominant interactions. A position-based input mapping (e.g. grabbing metaphor) is intuitive for such interactions, but is prone to arm fatigue. This work focuses on improving digital map navigation in AR with mid-air hand gestures, using a horizontal intangible map display. First, we conducted a user study to explore the effects of handedness (unimanual and bimanual) and input mapping (position-based and rate-based). From these findings we designed DiveZoom and TerraceZoom, two novel hybrid techniques that smoothly transition between position- and rate-based mappings. A second user study evaluated these designs. Our results indicate that the introduced input-mapping transitions can reduce perceived arm fatigue with limited impact on performance.",
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"authors": [
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"affiliation": "Monash University",
"fullName": "Kadek Ananta Satriadi",
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"affiliation": "Monash University",
"fullName": "Barrett Ens",
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"affiliation": "Monash University",
"fullName": "Bernhard Jenny",
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"affiliation": "Monash University",
"fullName": "Tobias Czauderna",
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{
"affiliation": "University of Calgary",
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"abstract": "Developers often tend to search and reuse code snippets from large code repositories to improve their programming skills. To support code reuse, early code search models used information retrieval (IR) techniques to index a large corpus of code and then returned relevant code based on search statements. However, IR-based models can cause information loss due to keyword matching. To solve this problem, developers applied deep learning (DL) techniques to encode search models. However, these models either learn isolated representations of codes and queries or learn interdependent representations of codes and queries in a single way, which both limit the effectiveness of the models.In this study, we propose a code search model MpaCS, which can interact from multiple perspectives to enable cross-language retrieval between code and query statements. MpaCS extracts code and query information from the code textual features (i.e., method name, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). We first implement the alignment between code features and query features at the fragment level. Then we propose a context matching module to achieve a deeper interaction between the code and the query. Finally, we propose a new attention network to explore the maximum alignment relationship between code and query from another perspective. We evaluate the performance of MpaCS on two existing large-scale datasets with 69k and 105k code snippets, respectively. Experimental results show that MpaCS outperforms four state-of-the-art models DeepCS [1],UNIF [2], MPCAT [3] and CARLCS-CNN [4].",
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"abstract": "This paper presents a new meta-heuristic algorithm called Playground algorithm. The Playground algorithm is designed to model social interaction amongst children, and the mechanisms and operators of the algorithm are inspired by the model of child interaction and engagement in games during a child's stay at the playground. In order to evaluate the performance of the algorithm, a series of tests were performed over a class of functions selected so that they possess properties such as: multimodality / unimodality, (non) separability, (non) differentiability, (non) convexity, existence of ridges and valleys and multidimensionality. During testing, the values of the algorithm parameters are varied, in order to determine their recommended values. The analysis was carried out with an overview of the effects of the algorithm parameters on the performance of the algorithm in the problem area, performance in the criterion domain, and the execution time.",
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"affiliation": "University of Sarajevo,Faculty of Electrical Engineering,Sarajevo,Bosnia and Herzegovina",
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"abstract": "Neural Architecture Search (NAS) has achieved great success in image classification task. Some recent works have managed to explore the automatic design of efficient backbone or feature fusion layer for object detection. However, these methods focus on searching only one certain component of object detector while leaving others manually designed. We identify the inconsistency between searched component and manually designed ones would withhold the detector of stronger performance. To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i.e. backbone, neck, and head) of object detector in an end-to-end manner. In addition, we empirically reveal that different parts of the detector prefer different operators. Motivated by this, we employ a novel scheme to automatically screen different sub search spaces for different components so as to perform the end-to-end search for each component on the corresponding sub search space efficiently. Without bells and whistles, our searched architecture, namely Hit-Detector, achieves 41.4% mAP on COCO minival set with 27M parameters. Our implementation is available at https://github.com/ggjy/HitDet.pytorch.",
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"title": "Learning Code-Query Interaction for Enhancing Code Searches",
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"abstract": "Code search plays an important role in software development and maintenance. In recent years, deep learning (DL) has achieved a great success in this domain-several DL-based code search methods, such as DeepCS and UNIF, have been proposed for exploring deep, semantic correlations between code and queries; each method usually embeds source code and natural language queries into real vectors followed by computing their vector distances representing their semantic correlations. Meanwhile, deep learning-based code search still suffers from three main problems, i.e., the OOV (Out of Vocabulary) problem, the independent similarity matching problem, and the small training dataset problem. To tackle the above problems, we propose CQIL, a novel, deep learning-based code search method. CQIL learns code-query interactions and uses a CNN (Convolutional Neural Network) to compute semantic correlations between queries and code snippets. In particular, CQIL employs a hybrid representation to model code-query correlations, which solves the OOV problem. CQIL also deeply learns the code-query interaction for enhancing code searches, which solves the independent similarity matching and the small training dataset problems. We evaluate CQIL on two datasets (CODEnn and CosBench). The evaluation results show the strengths of CQIL-it achieves the MAP@1 values, 0.694 and 0.574, on CODEnn and CosBench, respectively. In particular, it outperforms DeepCS and UNIF, two state-of-the-art code search methods, by 13.6% and 18.1% in MRR, respectively, when the training dataset is insufficient.",
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"content": "Code search plays an important role in software development and maintenance. In recent years, deep learning (DL) has achieved a great success in this domain-several DL-based code search methods, such as DeepCS and UNIF, have been proposed for exploring deep, semantic correlations between code and queries; each method usually embeds source code and natural language queries into real vectors followed by computing their vector distances representing their semantic correlations. Meanwhile, deep learning-based code search still suffers from three main problems, i.e., the OOV (Out of Vocabulary) problem, the independent similarity matching problem, and the small training dataset problem. To tackle the above problems, we propose CQIL, a novel, deep learning-based code search method. CQIL learns code-query interactions and uses a CNN (Convolutional Neural Network) to compute semantic correlations between queries and code snippets. In particular, CQIL employs a hybrid representation to model code-query correlations, which solves the OOV problem. CQIL also deeply learns the code-query interaction for enhancing code searches, which solves the independent similarity matching and the small training dataset problems. We evaluate CQIL on two datasets (CODEnn and CosBench). The evaluation results show the strengths of CQIL-it achieves the MAP@1 values, 0.694 and 0.574, on CODEnn and CosBench, respectively. In particular, it outperforms DeepCS and UNIF, two state-of-the-art code search methods, by 13.6% and 18.1% in MRR, respectively, when the training dataset is insufficient.",
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"abstract": "According to the shortage of analysis software for temperature control while massive concrete structures were widely used, the special temperature control aided analysis software (TCAS) was developed. A mature Fortran program was applied with a precise method for pipe cooling calculation added during development. Friendly graphic user interface was developed, by using Microsoft foundation classes (MFC). Open graphics library (OpenGL) was applied for the display of the model and results. TCAS has strong practicality, pertinence and easy operation, comparing with general commercial finite element analysis software. Theory of temperature control for concrete construction and mechanism of major measures for temperature control were analyzed. In particular, various concrete cooling simulation methods were compared and the cooling pipe discrete element method using in this software was introduced in detail. Details on the development process, software modules, and functions of major modules were introduced in this paper. With the help of TCAS, the choice of concrete temperature control measures and designing of temperature control scheme will become simpler and more convenient.",
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"abstract": "Two important similarity measures between sequences are the longest common subsequence (LCS) and the dynamic time warping distance (DTWD). The computations of these measures for two given sequences are central tasks in a variety of applications. Simple dynamic programming algorithms solve these tasks in O(n2) time, and despite an extensive amount of research, no algorithms with significantly better worst case upper bounds are known. In this paper, we show that for any constant ε>0, an O(n(2-ε)) time algorithm for computing the LCS or the DTWD of two sequences of length n over a constant size alphabet, refutes the popular Strong Exponential Time Hypothesis (SETH).",
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"content": "Two important similarity measures between sequences are the longest common subsequence (LCS) and the dynamic time warping distance (DTWD). The computations of these measures for two given sequences are central tasks in a variety of applications. Simple dynamic programming algorithms solve these tasks in O(n2) time, and despite an extensive amount of research, no algorithms with significantly better worst case upper bounds are known. In this paper, we show that for any constant ε>0, an O(n(2-ε)) time algorithm for computing the LCS or the DTWD of two sequences of length n over a constant size alphabet, refutes the popular Strong Exponential Time Hypothesis (SETH).",
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"abstract": "Halstead Complexity Measures, proposed in 1977, analyze a software system independently of its underlying programming language (technology) based on the measures number of operators and operands. From these two measures, it calculates other measures namely vocabulary, length, volume, difficulty, programming effort, errors, and testing time. The problem, nevertheless, is that since then the Academy and Industry have been coming up with hundreds of new metrics that differ in their assertions and calculations. Therefore, the objective of this paper is to present a correlation analysis between the eleven Halstead measures and other 27 popular measures proposed over the decades (e.g., LOC, cyclomatic complexity, and efferent coupling) through the inspection of 97 open-source Java systems in order to (i) identify redundancy in measures and (ii) minimize the costs of monitoring and diagnosing software projects, facilitating the task of making measurements. As a result, we identified strong correlations between Halstead measures and other measures, mainly related to size such as quantity of methods, packages, attributes, etc. We also identified direct correlation of Halstead measurements with coupling measures named Afferent and Efferent Coupling, with values ranging from 0.802 to 0.931, which are quite close to the maximum value 1 for a correlation. These results demonstrate that-although there is no perfect correlation-there is enough correlation to hypothesize that there is an overlap of measures with different denominations whose measured results are equivalent.",
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"affiliation": "Universidade Federal de Lavras",
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"title": "StoryCake: A Hierarchical Plot Visualization Method for Storytelling in Polar Coordinates",
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"Cyberphysical Systems",
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"abstract": "If sedimentation of constructions exceeds the prescribed limits, it would give rise to huge losses for community and people, so it is significant to establish the effective and practical deformation forecasting model for the safe operation and economic development. With the unique non-linear, non-convexity, non-locality, non-steadiness, adaptability and powerful ability of calculation and information process, BP (Back Propagation) neural network can adapt to the complicated and changeable dynamic characteristics of buildings,that has broad application foreground in deformation prediction. In this paper, on the basis of deformation observation data, a basic algorithm about establishing BP neural network model in sedimentation prediction is presented.at the same time, analysis of examples are given so that the application of neural network for deformation forecasting is studied comprehensively and systemically, the results show that neural network is very effective for Sedimentation prediction and can serve society and people in the future.",
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"fno": "3682a649",
"keywords": [
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"Sedimentation",
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"affiliation": null,
"fullName": "Ting-chen Jiang",
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"title": "Sedimentation Analysis on Padma River Using Machine Learning Techniques",
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"abstract": "This research aims to predict the sedimentation rate in Padma River and to illustrate the rate of accuracy for water and land classification based on various machine learning algorithms. Increased sedimentation can cause utterly problems for the organisms living there. Moreover, the water flow could be changed; the depth of the water can be lessened because of river sediment deposition, making boating and recreational use extra tough. This study comprises sedimentation for the last five years and how it will influence the surrounding environment, ecology, and socio-economy in the upcoming years, predict future areas that are subject to deposition, and show the accuracy of different machine learning algorithms for water and land classification. To conduct this research, we used a dataset consisting of images of Padma River (close to Naria in Shariatpur district of Bangladesh), collected from satellite, and run image processing techniques on it. We have obtained an accuracy of 97.5% for the image of 2018 and 99.17% for the image of 2019 using K-means. On the other hand, we obtained accuracy of 91.67% for images of 2018 and 95% for the image of 2019 using a self-organizing map (SOM). However, random forest (RF) shows the best results, having an accuracy of 97.5% for the image of 2018 and 100% for the image of 2019.",
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"content": "This research aims to predict the sedimentation rate in Padma River and to illustrate the rate of accuracy for water and land classification based on various machine learning algorithms. Increased sedimentation can cause utterly problems for the organisms living there. Moreover, the water flow could be changed; the depth of the water can be lessened because of river sediment deposition, making boating and recreational use extra tough. This study comprises sedimentation for the last five years and how it will influence the surrounding environment, ecology, and socio-economy in the upcoming years, predict future areas that are subject to deposition, and show the accuracy of different machine learning algorithms for water and land classification. To conduct this research, we used a dataset consisting of images of Padma River (close to Naria in Shariatpur district of Bangladesh), collected from satellite, and run image processing techniques on it. We have obtained an accuracy of 97.5% for the image of 2018 and 99.17% for the image of 2019 using K-means. On the other hand, we obtained accuracy of 91.67% for images of 2018 and 95% for the image of 2019 using a self-organizing map (SOM). However, random forest (RF) shows the best results, having an accuracy of 97.5% for the image of 2018 and 100% for the image of 2019.",
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"title": "2019 International Conference on Communications, Information System and Computer Engineering (CISCE)",
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"article": {
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"doi": "10.1109/CISCE.2019.00151",
"title": "The Dynamic Vertical Variation of Cadmium Content Caused by Source and Transport Volume",
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"abstract": "Based on investigation data of Cd in Jiaozhou Bay in May and August of 1991, according to horizontal and vertical variation of Cd content, the vertical distribution and sedimentation region was displayed by the difference in Cd content at surface and bottom in bay mouth. The results showed that Cd content in May reached 0.58μg/L from ships and wharfs in northern bay mouth, and 0.40μg/L in August from atmospheric sedimentation in bay center. In spatial variation, with the increasing distance from source, spatial variation of Cd content was presented. In May, compared with bottom, Cd content at surface was higher in northern bay mouth and lower in bay center, which, whereas, was contrary in August. From the perspective of time, the source of Cd content changed along with time, displaying the variation of Cd content at surface and bottom. From May to August, the source varied from ships and wharfs to atmospheric sedimentation with a decreasing Cd content at surface. Thus, the variation in space and time proved the dynamic vertical variation rule of Cd content proposed by the author. In specific, with the farther source, Cd content at surface could rapidly and constantly sediment to seafloor, vertical disputed amount of Cd content at surface and bottom decreased, then varied to vertical sediment amount, which increased from the lowest value.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Based on investigation data of Cd in Jiaozhou Bay in May and August of 1991, according to horizontal and vertical variation of Cd content, the vertical distribution and sedimentation region was displayed by the difference in Cd content at surface and bottom in bay mouth. The results showed that Cd content in May reached 0.58μg/L from ships and wharfs in northern bay mouth, and 0.40μg/L in August from atmospheric sedimentation in bay center. In spatial variation, with the increasing distance from source, spatial variation of Cd content was presented. In May, compared with bottom, Cd content at surface was higher in northern bay mouth and lower in bay center, which, whereas, was contrary in August. From the perspective of time, the source of Cd content changed along with time, displaying the variation of Cd content at surface and bottom. From May to August, the source varied from ships and wharfs to atmospheric sedimentation with a decreasing Cd content at surface. Thus, the variation in space and time proved the dynamic vertical variation rule of Cd content proposed by the author. In specific, with the farther source, Cd content at surface could rapidly and constantly sediment to seafloor, vertical disputed amount of Cd content at surface and bottom decreased, then varied to vertical sediment amount, which increased from the lowest value.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Based on investigation data of Cd in Jiaozhou Bay in May and August of 1991, according to horizontal and vertical variation of Cd content, the vertical distribution and sedimentation region was displayed by the difference in Cd content at surface and bottom in bay mouth. The results showed that Cd content in May reached 0.58μg/L from ships and wharfs in northern bay mouth, and 0.40μg/L in August from atmospheric sedimentation in bay center. In spatial variation, with the increasing distance from source, spatial variation of Cd content was presented. In May, compared with bottom, Cd content at surface was higher in northern bay mouth and lower in bay center, which, whereas, was contrary in August. From the perspective of time, the source of Cd content changed along with time, displaying the variation of Cd content at surface and bottom. From May to August, the source varied from ships and wharfs to atmospheric sedimentation with a decreasing Cd content at surface. Thus, the variation in space and time proved the dynamic vertical variation rule of Cd content proposed by the author. In specific, with the farther source, Cd content at surface could rapidly and constantly sediment to seafloor, vertical disputed amount of Cd content at surface and bottom decreased, then varied to vertical sediment amount, which increased from the lowest value.",
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"keywords": [
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"authors": [
{
"affiliation": "Guizhou Minzu University",
"fullName": "Dongfang Yang",
"givenName": "Dongfang",
"surname": "Yang",
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{
"affiliation": "Guizhou Minzu University",
"fullName": "Bailing Fan",
"givenName": "Bailing",
"surname": "Fan",
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{
"affiliation": "Guizhou Minzu University",
"fullName": "Chunhua Su",
"givenName": "Chunhua",
"surname": "Su",
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{
"affiliation": "Guizhou Minzu University",
"fullName": "Chengling Huang",
"givenName": "Chengling",
"surname": "Huang",
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},
{
"affiliation": "Guizhou Minzu University",
"fullName": "Sixi Zhu",
"givenName": "Sixi",
"surname": "Zhu",
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"abstract": "With the advancements in spatiotemporal co-occurrence pattern and event sequence mining algorithms, spatiotemporal knowledge discovery from solar event datasets has been prominent in solar data mining. This work presents an efficient and extensible data access mechanism specifically designed for spatiotemporal relationships among the solar event instances. Previous indexing strategies primarily focus on indexing the trajectories of solar event instances. We propose a graph-based indexing structure for spatiotemporal relationships appearing among the event instances. In our graph structure, the vertices correspond to trajectory-based solar event instances, and the edges represent a particular spatiotemporal relationship. Furthermore, it forms the groundwork for semantic representations of solar event instances.",
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"title": "Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
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"abstract": "A spatiotemporal (ST) image cube, created by stacking a temporally dense sequence of images together, is a temporally coherent data representation. Using ST surface flow, i.e., the extension of optical flow to ST surfaces, it is shown how ST flow curves can be recovered and then used to detect groups of flow curves such that each group represents a single object or surface in the scene undergoing motion. The algorithm forms clusters of similar flow curves and is based on constraints called the temporal uniqueness constraints. First, a point in an image can only move to at most one point in the next image. Second, a point in an image can come from at most one point in the previous image. When these constraints are violated, or it appears that they are violated, occlusion or disocclusion has occurred and therefore can also be detected. Successful grouping of coherent regions of the ST cube for two gray-level image sequences is shown.<>",
"abstracts": [
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"abstractType": "Regular",
"content": "A spatiotemporal (ST) image cube, created by stacking a temporally dense sequence of images together, is a temporally coherent data representation. Using ST surface flow, i.e., the extension of optical flow to ST surfaces, it is shown how ST flow curves can be recovered and then used to detect groups of flow curves such that each group represents a single object or surface in the scene undergoing motion. The algorithm forms clusters of similar flow curves and is based on constraints called the temporal uniqueness constraints. First, a point in an image can only move to at most one point in the next image. Second, a point in an image can come from at most one point in the previous image. When these constraints are violated, or it appears that they are violated, occlusion or disocclusion has occurred and therefore can also be detected. Successful grouping of coherent regions of the ST cube for two gray-level image sequences is shown.<>",
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"normalizedAbstract": "A spatiotemporal (ST) image cube, created by stacking a temporally dense sequence of images together, is a temporally coherent data representation. Using ST surface flow, i.e., the extension of optical flow to ST surfaces, it is shown how ST flow curves can be recovered and then used to detect groups of flow curves such that each group represents a single object or surface in the scene undergoing motion. The algorithm forms clusters of similar flow curves and is based on constraints called the temporal uniqueness constraints. First, a point in an image can only move to at most one point in the next image. Second, a point in an image can come from at most one point in the previous image. When these constraints are violated, or it appears that they are violated, occlusion or disocclusion has occurred and therefore can also be detected. Successful grouping of coherent regions of the ST cube for two gray-level image sequences is shown.",
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"affiliation": "Dept. of Comput. Sci., Wisconsin Univ., Madison, WI, USA",
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"abstract": "Research in human action recognition has advanced along multiple fronts in recent years to address various types of actions including simple, isolated actions in staged data (e.g., KTH dataset), complex actions (e.g., Hollywood dataset) and naturally occurring actions in surveillance videos (e.g, VIRAT dataset). Several techniques including those based on gradient, flow and interest-points have been developed for their recognition. Most perform very well in standard action recognition datasets, but fail to produce similar results in more complex, large-scale datasets. Here we analyze the reasons for this less than successful generalization by considering a state-of-the-art technique, histogram of oriented gradients in spatiotemporal volumes as an example. This analysis may prove useful in developing robust and effective techniques for action recognition.",
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"fno": "4797a106",
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"Spatiotemporal Phenomena",
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"abstract": "Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.",
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