nils-herrmann/clean_scirepeval huggingface dataset:
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge

This paper addresses the problem of joint detection and recounting of abnormal events in videos. Recounting of abnormal events, i.e., explaining why they are judged to be abnormal, is an unexplored but critical task in video surveillance, because it helps human observers quickly judge if they are false alarms or not. To describe the events in the human-understandable form for event recounting, learning generic knowledge about visual concepts (e.g., object and action) is crucial. Although convolutional neural networks (CNNs) have achieved promising results in learning such concepts, it remains an open question as to how to effectively use CNNs for abnormal event detection, mainly due to the environment-dependent nature of the anomaly detection. In this paper, we tackle this problem by integrating a generic CNN model and environment-dependent anomaly detectors. Our approach first learns CNN with multiple visual tasks to exploit semantic information that is useful for detecting and recounting abnormal events. By appropriately plugging the model into anomaly detectors, we can detect and recount abnormal events while taking advantage of the discriminative power of CNNs. Our approach outperforms the state-of-the-art on Avenue and UCSD Ped2 benchmarks for abnormal event detection and also produces promising results of abnormal event recounting.
Coherent image annotation by learning semantic distance

Conventional approaches to automatic image annotation usually suffer from two problems: (1) They cannot guarantee a good semantic coherence of the annotated words for each image, as they treat each word independently without considering the inherent semantic coherence among the words; (2) They heavily rely on visual similarity for judging semantic similarity. To address the above issues, we propose a novel approach to image annotation which simultaneously learns a semantic distance by capturing the prior annotation knowledge and propagates the annotation of an image as a whole entity. Specifically, a semantic distance function (SDF) is learned for each semantic cluster to measure the semantic similarity based on relative comparison relations of prior annotations. To annotate a new image, the training images in each cluster are ranked according to their SDF values with respect to this image and their corresponding annotations are then propagated to this image as a whole entity to ensure semantic coherence. We evaluate the innovative SDF-based approach on Corel images compared with Support Vector Machine-based approach. The experiments show that SDF-based approach outperforms in terms of semantic coherence, especially when each training image is associated with multiple words.
Modeling and Robust Control of Quadruped Robot

The 9-Link dynamic model of a quadruped robot is built with Lagrangian method. Building the model from system view . Simplify the control of a quadruped robot under the four legs full supporting as the problems of tracking control of the truck motion, and the simple model is give. The motion of the robot under four legs full supporting is controlled by robust control. Settling the effect of the external disturbance act on the quadruped robot. Simulation shows that the robust control method is effective when used on the quadruped robot under full supporting.
MoVieUp: Automatic Mobile Video Mashup

With the proliferation of mobile devices, people are taking videos of the same events anytime and anywhere. Even though these crowdsourced videos are uploaded to the cloud and shared, the viewing experience is very limited due to monotonous viewing, visual redundancy, and bad audio-video quality. In this paper, we present a fully automatic mobile video mashup system that works in the cloud to combine recordings captured by multiple devices from different view angles and at different time slots into a single yet enriched and professional looking video-audio stream. We summarize a set of computational filming principles for multicamera settings from a formal focus study. Based on these principles, given a set of recordings of the same event, our system is able to synchronize these recordings with audio fingerprints, assess audio and video quality, detect video cut points, and generate video and audio mashups. The audio mashup is the maximization of audio quality under the less switching principle, while the video mashup is formalized as maximizing video quality and content diversity, constrained by the summarized filming principles. Our system is different from any existing work in this field in three ways: 1) our system is fully automatic; 2) the system incorporates a set of computational domain-specific filming principles summarized from a formal focus study; and 3) in addition to video, we also consider audio mashup that is a key factor of user experience (UX) yet often overlooked in existing research. Evaluations show that our system achieves performance results that are superior to state-of-the-art video mashup techniques, thus providing a better UX.
Notice of RetractionA Dynamic Simulation of Force in the Movement of Ameba Based on Cosmos

Imitation amoeba robot entering the space whose nominal size is smaller than itself can complete can perform many tasks inaccessible to other robots or humans such as surveillance, inspection, repair, cleaning, and exploration. Parameterized solid models of amoeba are established by using Solidworks. The simulation analysis of the movement and the force situation in the movement is completed by using the software of COSMOS. The change of Amoeba entities that based on deformation of the ipmc material in effect of the control unit is researched.
Automatic Video Genre Categorization using Hierarchical SVM

This paper presents an automatic video genre categorization scheme based on the hierarchical ontology on video genres. Ten computable spatio-temporal features are extracted to distinguish the different genres using a hierarchical support vector machines (SVM) classifier built by cross-validation, which consists of a series of SVM classifiers united in a binary-tree form. As the order and genre partition strategy of the SVM classifier series affect the over performance of the united classifier, two optimal SVM binary trees, local and global, are constructed aiming at finding the best categorization orders, i.e., the best tree structure, of the genre ontology. Experimental results show that the proposed scheme outperforms C4.5 decision tree, typical 1-vs-1 SVM scheme, as well as the hierarchical SVM built by K-means.
Analysis on closing bounce of vacuum circuit breaker

First of all, based on the structure of vacuum breaker, the closing bounce model of vacuum circuit breaker is established. Several factors are analyzed with the help of the numerical computing software MATLAB to determine how these factors influence bounce time of moving-contact. Reducing closing speed is a good way to reduce bounce time of moving-contact. Secondly, according to SolidWorks and ADAMS software, a method is found to not only reduce bounce time of moving-contact but also ensure closing successfully. Thirdly, the result of experiment is proved that author's view is correct and helpful.
Application of Intelligent Flexible Skin Sensors for Interfacing with Robotic Pets

This paper describes the application of intelligent flexible skin sensors for interfacing with robotic pets. The new flexible sensor system includes a skin sensor and a fuzzy unit. The skin sensor is fabricated using MEMS technique and can be used to measure normal and shear forces, on curved surfaces due to the flexible structure design of the sensor array. Using these features, the flexible skin sensors are used to cover sensing surfaces of a robotic pet. The fuzzy unit receives the outputs of the sensors and then determines the touching force information applied to the robotic pet. The robotic pet can then recognize the moods, intentions and behaviours of the human host based on the sensed touching force information so as to react to the human host with preprogrammed responses. Analysis and experiments demonstrate that the flexible skin sensor system can serve as an effective interface between robotic pet and their human hosts
An image-based three-camera real-time synchronous skylight polarization pattern measurement system

As a new pattern of navigation approach with the characteristics of high robustness, autonomous characteristics and anti-interference ability, polarization navigation attracts the attention of the experts to perform further researches. A relatively stable skylight polarization pattern exists in a certain time period at a specific place, which acts as the precondition for the completion of the polarization navigation. Therefore, the measurement of skylight polarization is of great importance to the polarization navigation. Various skylight polarization pattern measurement approaches have been performed in the past. However, disadvantages exist in the current measurement instruments, including time delay and non-synchronization. In this paper, a new image-based three-camera skylight polarization pattern measurement system is presented with its real-time and synchronous advantages. The main designing idea of the system is to equip the three cameras with three linear polarizers of different polarization directions, which could perform skylight image acquisition synchronously at the same time. Experiment with this system shows that it is able to obtain the stable skylight polarization pattern with the average time for measurement cycle 1.25s, significantly improving the acquisition time. Validation of the experimental results with theoretical result is also performed, proving the authenticity of measured data.
An Auto-adaptive Routing algorithm for Wireless Sensor Networks

Considering on the insufficiency of using different routing algorithms for different applications, we proposed an auto-adaptive routing algorithm for wireless sensor networks. It firstly proposed two different routing methods using for the sensor node's localization and the for the object's localization respectively. When we had obtained the nodes' positions through some localization algorithms, it used an energy aware geography position based routing method for the object's information acquisition. In order to satisfy this application, it designed an auto-adaptive routing mechanism facilitate the use of the two or more routing methods. It has been shown that the routing algorithm has a high performance in getting a shorter path routing or energy balanced routing by adjusting of its parameters.
Probabilistic Multimodality Fusion for Event based Home Photo Clustering

This paper presents a novel probabilistic approach to fusing multimodal metadata for event based home photo clustering. Photo events are characterized by the coherence of multimodality including time, content and camera settings. We incorporate these multimodal metadata into a unified probabilistic framework, in which event is taken as a latent semantic concept and discovered by fitting a generative model through an expectation-maximization (EM) algorithm. This approach is general and unsupervised, without any training procedure or predefined threshold. The experimental evaluations on 14 k photos taken by 10 amateur photographers have indicated the effectiveness and efficiency of the proposed framework in browsing and searching personal photo collections
Modelling analysis of rough surface contact for biomimetic gecko adhesion array

By combining linear beam theory and the height distribution of rough surface, a theoretical contact model of adhesion force between biomimetic gecko adhesion array and rough surface is developed to obtain a better understanding of the array-surface interaction and to design a better biomimetic gecko adhesion array. Meanwhile the relationship between the elastic energy and adhesion energy is discussed before getting the model. Finally a design of a biomimetic gecko adhesion array with active control ability is proposed in brief.
Research on intellectualization of 126kV SF6 circuit breaker

To realize Intelligent transformation based on the traditional 126kV SF6 circuit breaker, firstly, selection and installation of sensor is discussed, and the rotating shaft which connect the transmission shaft and the angular displacement sensor is improved. Secondly, to determine the heater power, the heater is calculated and studied, based on the temperature changing and spatial size of mechanism box; Finally, how to select the water vapor content transmitter is studied, in order to realize condition monitoring SF6 gas.
Temporally Consistent Gaussian Random Field for Video Semantic Analysis

As a major family of semi-supervised learning, graph based semi-supervised learning methods have attracted lots of interests in the machine learning community as well as many application areas recently. However, for the application of video semantic annotation, these methods only consider the relations among samples in the feature space and neglect an intrinsic property of video data: the temporally adjacent video segments (e.g., shots) usually have similar semantic concept. In this paper, we adapt this temporal consistency property of video data into graph based semi-supervised learning and propose a novel method named temporally consistent Gaussian random field (TCGRF) to improve the annotation results. Experiments conducted on the TREC VID data set have demonstrated its effectiveness.
Viscoelastic analysis of gecko digital peeling by hyperextension

This passage presents the mechanical analysis of the gecko's digital peeling by hyperextension. The structure of Gekko Gecko's digit was studied, and a model of digit II, manus was established. Based on Kendall strip tape theory, we analyzed the peeling process with the Inherent Motion Pattern and the Emergency Motion Pattern. Extracting the biological features of the digit, we then established a Finite Element model for more accurate results. Analysis based on friction adhesion theory of gecko's setal array was done in the Finite Element model. The results suggest that the selection of the two motion patterns of the gecko is dependent of the external normal loading to keep steady adhesion on different inclines, and local tension at the peeling point in the tissue is important for the patterns to work efficiently.
Time Optimal Tracking Control Approach for Suspended Gravity Compensation System

A suspension system for studying the dynamic characteristics of space robots working under microgravity condition is introduced. The system can apply a constant force against the gravity of an operating target. The support point on system's end-effector synchronously tracking the motion of suspension object is expected to obtain long-time zero-gravity of the target satellite. According to dynamic model of suspension module, a "bang-bang" time-optimal programming which is a human simulated conception, and a precision tracking approach through coarse and precise periods is proposed. The control result is presented and verified by using numerical computation and simulation
An improved RRT-based motion planner for autonomous vehicle in cluttered environments

In this paper, we present an improved RRT-based motion planner for autonomous vehicles to effectively navigate in cluttered environments with narrow passages. The planner first presents X-test that can identify passable narrow passages, and then perform an efficient obstacles-based extension operation within passable narrow passages. In order to generate a smooth trajectory for the vehicle to execute, a post-process algorithm with trajectory optimization is proposed. For the purpose of demonstrate benefits of our method, the proposed motion planner is implemented and tested on a real autonomous vehicle in cluttered scenarios with narrow passages. Experimental results show that our planner achieves up to 13.8 times and 7.6 times performance improvements over a basic RRT planner and a Bi-RRT planner respectively. Moreover, the resulting path of our planner is more smooth and reasonable.
Design and characteristics analysis of a novel motion system for validation platform of driverless vehicle

A novel four degree of freedom (four-DOF) motion system for validation platform of driverless vehicle has been proposed. This motion system consists of four primary components-a turn table, a three-DOF parallel manipulator, an active platform and a road surface simulation subsystem-which are described in detail in the following sections. Kinematics solution of this motion system has been derived to construct relationship relating input variables to the output variables. In order to increase in reliability of experimental results, the rollers have been integrated with active platform to simulate the road resistance. The motor control modeling and roller architecture parameters computation have also been given.
Fabrication and characterization of tree-like nanorod arrays for bionic gecko foot-hairs

We report a method to fabricate polymer tree-like arrays for biomimetic gecko foot hairs by using a novel branched anodic aluminum oxide membrane as a template. The novel anodic aluminum oxide template with hierarchically branched nanochannel was fabricated by a modified several steps anodic oxidation process. The polymer tree-like arrays included polystyrene, polymethyl-methacrylat were obtained by a molding method in these branched nanochannel anodic aluminum oxide templates. The number of branching and dimensions can be controlled through branched nanochannel design and assembly. Pull-off force measurements were carried out to characterize the adhesive force of theses tree-like arrays based on the force-distance curves obtained from scanning probe microscope (SPM). These tree-like nanorod arrays will be utilized as novel biomimetic dry adhesives in future space, microelectronics, MEMS devices and robot.
Multi-level constraints of redundant manipulator handled by different trajectory tracking

A new control strategy for redundant robot motion control is proposed in this paper. In this strategy, the robot manipulator can track different trajectory precisely submitting to a multi-level constraints model. The significant physical quantity, namely, torque of the motor, is considered in this multi-level constraints model. Besides, to track the different path effectively, a control law has been set as the system input. In order to verify this model, we simulate this model to resolute the redundant robot kinematics. The simulation results show that this method can realize the trajectory tracking effectively, especially on the aspect of the straight line motion tracking and circular motion tracking.
Virtual Sensor Modeling for Multi-agent Intelligent Virtual Environment

Every agent in the proposed intelligent virtual environment collects information through sensors to percept on environment and to interact with others, which puts emphasis on agent sensing and sensory data representation. The paper focuses on developing agent virtual sensors model to represent agent sensory information. A virtual distance sensor model was realized to navigate unmanned vehicle in virtual mine training environment by analysing the depth buffer information of the rendered scene and a laser range finder. Integrated the virtual entity physical attributes, the virtual environment atmospheric model and the real sensor properties, the virtual sensor model can simulate the high-fidelity sensor functions to provide trainee with sufficient visual information for decision-making training.
Multi-Layer Multi-Instance Learning for Video Concept Detection

This paper presents a novel learning-based method, called ldquomulti-layer multi-instance (MLMI) learning,rdquo for video concept detection. Most of existing methods have treated video as a flat data sequence and have not investigated the intrinsic hierarchy structure of the video content deeply. However, video is essentially a kind of media with ML structure. For example, a video can be represented by a hierarchical structure including, from large to small, shot, frame, and region, where each pair of contiguous layers fits the typical MI setting. We call such a ML structure and the MI relations embedded in the structure as the MLMI setting. In this paper, we systematically study both ML structure and MI relations embedded in video content by formulating video concept detection as a MLMI learning problem. Specifically, we first construct a MLMI kernel to simultaneously model such ML structure and MI relations. To deal with the ambiguity propagation problem which is introduced by weak labeling and ML structure, we then propose a regularization framework which takes hyper-bag prediction error, sublayer prediction error, inter-layer inconsistency measure, and classifier complexity into consideration. We have applied the proposed MLMI learning method to concept detection task over TRECVid 2005 development corpus, and report better performance to vector-based and the state-of-the-art MI learning methods.
Design and implementation of a digital inclinometer

Based on the principle of force sensing, a digital inclinometer has been designed. It not only can check the degree of level, but also can measure the angle of a surface with any degree of inclination from 0/spl deg/ to 360/spl deg/. The resolution of current implementation is 0.1/spl deg/. This type of inclinometer can be used in various situations where there often exists the demanding of measuring an angle under the reference of gravity system.
PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

Compared with person reidentification, which has attracted concentrated attention, vehicle reidentification is an important yet frontier problem in video surveillance and has been neglected by the multimedia and vision communities. Since most existing approaches mainly consider the general vehicle appearance for reidentification while overlooking the distinct vehicle identifier, such as the license plate number, they attain suboptimal performance. In this paper, we propose PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks. In particular, our framework not only utilizes the multimodality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle reidentification in two progressive procedures: coarse-to-fine search in the feature domain, and near-to-distant search in the physical space. Furthermore, to evaluate our progressive search framework and facilitate related research, we construct the VeRi dataset, which is the most comprehensive dataset from real-world surveillance videos. It not only provides large numbers of vehicles with varied labels and sufficient cross-camera recurrences but also contains license plate numbers and contextual information. Extensive experiments on the VeRi dataset demonstrate both the accuracy and efficiency of our progressive vehicle reidentification framework.
Bionic design of the body of tank-like climbing robot

The body of tank-like climbing robot is prone to bend under the continuate action of the preload of treads and the weight of the robot. Increasing the thickness of the body can reduce the bending, but it will also increase the weight of robot, which make against the weight reduction. The elytra of beetles have excellent mechanical properties. In this paper, the unique shape of the elytra of the beetles is extracted and a bionic body for tank-like climbing robot is designed. With the same thickness of the body, the bionic body can reduce the deformation and increase the strength at the same time.