nils-herrmann/clean_scirepeval huggingface dataset:
Query difficulty estimation via pseudo relevance feedback for image search

Query difficulty estimation (QDE) attempts to automatically predict the performance of the search results returned for a given query. QDE has been widely investigated in text document retrieval for many years. However, few research works have been explored in image retrieval. State-of-the-art QDE methods in image retrieval mainly investigate the statistical characteristics (coherence, robustness, etc.) of the returned images to derive a value for indicating the query difficulty degree. To the best of our knowledge, little research has been done to directly estimate the real retrieval performance of the search results, such as average precision, instead of only an indicator. In this paper, we propose a novel query difficulty estimation approach which automatically estimate the average precision of the image search results. Specifically, we first select a set of query relevant and query irrelevant images for each query via pseudo relevance feedback. Then an efficient and effective voting scheme is proposed to estimate the relevance label of each image in the search results. Based on the images' relevance labels, the average precision of the search results returned for the given query is derived. The experimental results on a benchmark image search dataset demonstrate the effectiveness of the proposed method.
Monet: A System for Reliving Your Memories by Theme-Based Photo Storytelling

With the ever-increasing use of smartphones and digital cameras, people are now able to take photos anywhere and anytime. Most of these photos simply end up stored in the cloud without further interaction. This occurs because we lack intelligent services to organize these personal photos well. Therefore, there is an urgent need for such a system to enable people to relive their memories by turning their photos into stories. This paper presents a storytelling system named Monet, which automatically creates interesting stories from personal photos by mimicking cinematic knowledge based on a set of predesigned editing styles. The system consists of two stages: photo summarization, which selects a subset of the “best” photos to represent a photo collection, and story remixing, which generates a stylish music video from the selected photos. During photo summarization, photos are grouped into events based on multimodal features (time and location). The “best” photos are then selected according to visual quality, event representativeness, and diversity. The second stage, story remixing, automatically selects an appropriate theme-dependent editing style based on the photo content. Each selected photo is converted to a video clip by applying a virtual camera with appropriate motions. A series of video effects, color filters, shapes, and transitions are then applied to the video clips according to cinematic rules. The generated video is finally multiplexed with a music clip to generate the story. Evaluations show that our system achieves superior performance to state-of-the-art photo event detection and story generation systems.
Detachment Control Analysis for Nanofabricated Adhesive Mimicking Gecko Foot-Hairs

The power assumption is costly to apply external forces with respect to the detachment control of nanofabricated adhesive mimicking gecko foot-hairs. With a purpose of low power consumption and high efficiency, this paper describe an implement in which the array is designed to slant to a certain angle before applying external pull-off force in reference to the detachment mechanism of geckos' toes. In the paper also includes the analysis of the influence on external pull-off force from different parameter settings of the array and different array deformations due to the deformation of functional materials. Finally the paper justifies the approach is totally applicable with the comparison of the pull-off forces in different ways
Social Image Tagging With Diverse Semantics

We have witnessed the popularity of image-sharing websites for sharing personal experiences through photos on the Web. These websites allow users describing the content of their uploaded images with a set of tags. Those user-annotated tags are often noisy and biased. Social image tagging aims at removing noisy tags and suggests new relevant tags. However, most existing tag enrichment approaches predominantly focus on tag relevance and overlook tag diversity problem. How to make the top-ranked tags covering a wide range of semantic is still an opening, yet challenging, issue. In this paper, we propose an approach to retag social images with diverse semantics. Both the relevance of a tag to image as well as its semantic compensations to the already determined tags are fused to determine the final tag list for a given image. Different from existing image tagging approaches, the top-ranked tags are not only highly relevant to the image but also have significant semantic compensations with each other. Experiments show the effectiveness of the proposed approach.
Lane change path planning based on piecewise Bezier curve for autonomous vehicle

Autonomous vehicle is an efficient component in vehicle active system to reduce traffic accidents. The path planning of lane change for autonomous vehicle is an important component in the autonomous vehicle field. In order to generate a path for passing the front vehicle, a new path planning method that is curvature-continuous and satisfies the vehicle nonholonomic constraint is introduced. Firstly, we calculate the safe lane change distance for autonomous vehicle. Secondly, we generate the path based on piecewise quadratic Bezier curve and the maximum curvature of the Bezier curves is calculated to verify whether the autonomous vehicle can follow this path. Then, we present the indicator based on yaw-rate of vehicle to verify the vehicle ride comfort. The experiment results show that the path generated by this method can be successfully executed by autonomous vehicle, the curvature of the path is continuous, meanwhile vehicle nonholonomic and ride comfort indicator constraint condition is met.
Sports Video Mining with Mosaic

Video is an information-intensive media with much redundancy. Therefore, it is desirable to be able to mine structure or semantics of video data for efficient browsing, summarization and highlight extraction. In this paper, we propose a generic approach to key-event as well as structure mining for sports video analysis. Mosaic is generated for each shot as the representative image of shot content. Based on mosaic, sports video is mined by the method with prior knowledge and without prior knowledge. Without prior knowledge, our system may locate plays by discriminating those segments without essential content, such as breaks. If prior knowledge is available, the key-events in plays are detected using robust features extracted from mosaic. Experimental results have demonstrated the effectiveness and robustness of this sports video mining approach.
Predicting Failing Queries in Video Search

The ability to predict when a video search query is not likely to deliver satisfying search results is expected to enable more effective search results optimizations and improved search experience for users. In this paper, we propose a novel context-aware query failure prediction approach that predicts whether a particular query submitted in a user's search session is likely to fail. The approach builds on the well-known concept of query performance prediction introduced in conventional text-based Web search to estimate the query's retrieval performance, but extends this concept with two novel characteristics, user indicators and engine indicators. User indicators are derived from transaction logs, capture the patterns of user interactions with the video search engine, and exploit the context in which a particular query was submitted. Engine indicators are derived from the search results list and measure the consistency of visual search results at the level of visual concepts and textual metadata associated with videos. Extensive evaluation of the approach on a test set containing over one million video search queries shows its effectiveness and demonstrates a significant improvement over traditional and state-of-the-art baseline approaches.
The design and fabrication of a flexible three-dimensional force sensor skin

To obtain shear and normal stress information on non-planar surfaces has long been a significant challenge. This paper described a new version of flexible tactile sensor skin for three-dimensional force measurement. The sensor array has been fabricated by MEMS technology. The sensor skin which can be bended 90/spl deg/ includes 4/spl times/4 force sensor cells. Each cell is hybrid integrated to flexible printing circuit board which consists of a E-shape diaphragm (50 /spl mu/m thick and 4000 /spl mu/m wide). Each cell exhibits a sensitivity of 228 mv/N to normal force and 34 mv/N to shear force in the designed force range of 2N. Design analysis, fabrication processes, and experimental results are presented in this paper.
A leg-wheel wall-climbing robot utilizing bio-inspired spine feet

This paper presents a leg-wheel wall-climbing robot utilizing bio-inspired spine feet, which is called LWbot (a leg-wheel robot). It is a 14 mm long and 57 g weight robot. It is capable of climbing both vertical brick surfaces and penetrable surfaces, such as curtains and polystyrene foam boards. It can climb not only upward, but also downward. The climbing speed on vertical curtain is up to 9.5 cm/s. LWbot consists of a passive wheel and two groups of legs which are equipped with modified Chebyshev four-bar linkages and compliant spine feet. A single motor drives the four-bar linkages to move continuously to achieve the movement of insects' climbing, and the “D” shaped trajectory of foot tip makes the miniature spines easy to penetrate in (or cling on) and pull off the surfaces. Moreover, the compliant foot mechanism inspired by the structure and mechanism of the tarsal chain in the cockroach makes the attachment more reliable.
Evaluation of the effect of spray flow rate and the nozzle velocity on the deposition of droplets

The spray distribution of field operation pesticide spraying machine, not only is affected by the shape of the nozzle, nozzle pressure and wind speed around the crop environment, but also has certain indispensable link to spraying equipment and carrying machine motion state. The robot uses the wheel speed sensor to accurately measure the state of motion sprayer, and uses a real-time sensor to monitor the flow of spray. Based on the spray different motion state settlement sample collection, we analyze the deposition of droplet, and draw the conclusion that the influence of the state of motion on spray droplet deposition can be divided into sensitive areas, the linear region and the weak areas. Compared with other studies, the effects of Spray source status on spray deposition is similar to the impact of local air source settling.
On the selection of trending image from the web

The recommendation of trending images has become a popular feature used by commercial search engines to attract public attention. By browsing through trending images, search engine users can discover trending events at a glance. However, the selection of trending images is very challenging and remains an open issue. Most existing work is highly dependent on editorial efforts, though some preliminarily identify a few plain features for trending images. In this paper, we investigate a set of perceptual factors that can distinguish trending images from common ones. We propose a set of trending-aware features based on several common criteria, which reflect the characteristics of trending images. We further construct a manually labeled dataset based on a commercial search engine's query log over a two-week timespan. We evaluate our proposed method on this dataset and the results demonstrate its effectiveness.
Bayesian Decision-Theoretic Causation Analysis for Gas Explosion

Frequent mine gas incident in China fully shows there must be many undiscovered essential factors in all previous gas incident investigations. Exploring a new analysis approach is vital to prevent and control mine gas emergency. A Bayesian decision-theoretic causation analysis method was introduced to study the mine gas incident. A typical gas explosion case was explained. The interaction law of human, machine, environment, and management factors causing mine gas incident was discussed. The evolution process of the connotative factors from the uncertain state to the certain state was discovered, and the relationships between the external factors and the internal factors were revealed
A catapult robot with chameleon-inspired multi-body elastic nested system

The flexible tongue of chameleon takes advantage of special energy releasing mechanism and performs rapid extension/contraction in longitudinal motion, which provides a new perspective in developing novel continuum robotics. Inspired by the rapid projection mechanism of the chameleon tongue, this paper makes a study of biological structure, regulatory principle, biomechanics of the chameleon tongue to take deep insight into the mechanism of such inherent behaviors. Then a multi-body elastic nested robot is proposed after the elaborate analysis of the biological phenomena of rapid extension/contraction induced by elastic-energy storage/release mechanism. And a cable type external driver is adopted to develop the extension and contraction motions of the projection robot. Finally, via developing physical prototype and carrying out experiments, the motion principle analysis is verified and the function of the tongue-inspired structure is tested with specific control strategy. Generally, this paper will provide a novel theory and technical support to address the problem of high-speed grasping under the unstructured environment.
Video annotation based on temporally consistent Gaussian random field

A novel method for automatically annotating video semantics, called temporally consistent Gaussian random field (TCGRF) is proposed. Since the temporally adjacent video segments (e.g. shots) usually have a similar semantic concept, TCGRF adapts the temporal consistency property of video data into graph-based semi-supervised learning to improve the annotation results. Experiments conducted on the TRECVID data set have demonstrated its effectiveness
The method of coordinate recognition for maize straws under canopy by monocular vision

In large areas of corn, the lane line planning and navigation within the crop rows play a crucial supporting role in intelligent farm machinery operations such as spraying, weeding and testing. Vehicle navigation is mostly by GPS and radar. These two kinds of navigation can't complete the information needed for crop inter-row navigation line segments. Such precision navigation and obstacle avoidance ability is poor. In this paper, morphological filtering method for reducing image weeds and leaves interference straw recognition is proposed. A ridge line extraction algorithm to extract the center line of the straw, finally extracts straw coordinates, and realizes the purposes of recognition straw by monocular visual ultimately. Tested in fields which have not been weeded, the result of those not detected straws accounted for 10.9%. The result of those not detected straws in field after weeded accounted for 8.5%. Results obtained by untreated field and treated field shows that filtration system to weed effect is very obvious.
Query-independent learning for video search

Most of existing learning-based methods for query-by-example take the query examples as ldquopositiverdquo and build a model for each query. These methods, referred to as query-dependent, only achieved limited success as they can hardly be applied to real-world applications, in which an arbitrary query is usually given. To address this problem, we propose to learn a query-independent model by exploiting the relevance information which exists in the pair of query-document. The proposed approach takes a query-document pair as a sample and extracts a set of query-independent textual and visual features from each pair. It is general and suitable for a real-world video search system since the learned relevance relation is independent on any query. We conducted extensive experiments over TRECVID 2005-2007 corpus and shown superior performance (+37% in Mean Average Precision) to the query-dependent learning approaches.
Virtual prototyping of tilting and lifting mechanism in ITER tractor

ITER (international thermonuclear experimental reactor) tractor is an in-cask remote handling equipment, and its tilting and lifting mechanism is very important for the tractor to operate 45t plug in front of the ports of the hot cell and the VV (vacuum vessel) successfully. This paper was aimed at proposing a tilting and lifting mechanism to accurately grasp and operate the heavy-load plug in the ITER cask. The 3D virtual prototype of mechanism is presented and it is simplified as 7-linkage plane structure to reduce the complexity of analysis. The kinematics and dynamics characteristics of this mechanism were studied. To realize bidirectional solution of the movement, the motion parameters relationship between input and output was set up by the kinematics models. Based on Lagrange equation, the driving force of every joint for hydraulic cylinders was calculated. The results of dynamics simulation validated the correctness of calculation. The excellent hydraulic loop model was also built to guarantee the motion stability of system. All the above analytical models can provide intelligent control strategies for hydraulic cylinders of the mechanism to achieve the stable grasping and operation of heavy-load plug.
User specific friend recommendation in social media community

Social networks nowadays have become an important form of communication in which users can post their current status or share their lives by mobile phones or the Web. In this paper, we develop an effective and efficient model to estimate continuous tie strength between users for friend recommendation with the heterogeneous data from social media community. We categorize those multimodal data into two classes: interaction data (e.g., comments, marking favorite photos) and similarity data(e.g., common friends, groups, tags, geo, visual). We propose to use asymmetric relationship in the interaction data for tie strength estimation instead of using the conventional symmetric ones. Furthermore, by exploring the behavior of users in a social media community, we find that the tie strength between users can be approximately modeled as a linear function of their social connections. Based on this observation, we propose an effective and highly efficient user specific linear model for the tie strength estimation. The experiments on a popular social network show promising results and demonstrate the effectiveness of our proposed method.
Interactive mobile visual search for social activities completion using query image contextual model

Mobile devices are ubiquitous. People use their phones as a personal concierge not only discovering information but also searching for particular interest on-the-go and making decisions. This brings a new horizon for multimedia retrieval on mobile. While existing efforts have predominantly focused on understanding textual or a voice query, this paper presents a new perspective which understands visual queries captured by the built-in camera such that mobile-based social activities can be recommended for users to complete. In this work, a query image-based contextual model is proposed for visual search. A mobile user can take a photo and naturally indicate an object-of-interest within the photo via circle based gesture called “O” gesture. Both selected object-of-interest region as well as surrounding visual context in photo are used in achieving a search-based recognition by retrieving similar images based on a large-scale of visual vocabulary tree. Consequently, social activities such as visiting contextually relevant entities (i.e., local businesses) are recommended to the users based on their visual queries and GPS location. Along with the proposed method, an exemplary real application has been developed on Windows Phone 7 devices and evaluated with a wide variety of scenarios on million-scale image database. To test the performance of proposed mobile visual search model, extensive experimentation has been conducted and compared with state-of-the-art algorithms in content-based image retrieval (CBIR) domain.
Landmark Reranking for Smart Travel Guide Systems by Combining and Analyzing Diverse Media

Advanced networking technologies and massive online social media have stimulated a booming growth of travel heterogeneous information in recent years. By employing such information, smart travel guide systems, such as landmark ranking systems, have been proposed to offer diverse online travel services. It is essential for a landmark ranking system to structure, analyze, and search the travel heterogeneous information to produce human-expected results. Therefore, currently the most fundamental yet challenging problems can be concluded: 1) how to fuse heterogeneous tourism information and 2) how to model landmark ranking. In this paper, a novel landmark search system is introduced based on a newly designed heterogeneous information fusion scheme and a query-dependent landmark ranking strategy. Different from the existing travel guide systems, the proposed system can effectively combine the heterogeneous information from multimodality media into a landmark reranking list via a user's query. Experimental results conducted on a large travel information collection illustrate the advantages of the proposed system in terms of both effectiveness and efficiency.
Predicting Image Popularity in an Incomplete Social Media Community by a Weighted Bi-partite Graph

Popularity prediction is a key problem in networks to analyze the information diffusion, especially in social media communities. Recently, there have been some custom-build prediction models in Digg and YouTube. However, these models are hardly transplant to an incomplete social network site (e.g., Flickr) by their unique parameters. In addition, because of the large scale of the network in Flickr, it is difficult to get all of the photos and the whole network. Thus, we are seeking for a method which can be used in such incomplete network. Inspired by a collaborative filtering method-Network-based Inference (NBI), we devise a weighted bipartite graph with undetected users and items to represent the resource allocation process in an incomplete network. Instead of image analysis, we propose a modified interdisciplinary models, called Incomplete Network-based Inference (INI). Using the data from 30 months in Flickr, we show the proposed INI is able to increase prediction accuracy by over 58.1%, compared with traditional NBI. We apply our proposed INI approach to personalized advertising application and show that it is more attractive than traditional Flickr advertising.
A directional adhesion model with hierarchical hair structure for Tokay gecko

This paper proposes a hierarchical model of the Tokay gecko's (Gekko gecko) setal system. Based on Euler's elastica theory, the inextensible fibrillar model of the seta is established, taking the thousands of terminating spatulae into account. The directional adhesion of the seta with different slanted angle of the adhesive pad and spatula pad is studied. The elastic energy and the binding energy at the detachment point are also calculated to draw the influence of the adhesive pad's rotation. Compared with experimental results, the model proves to be effective in dealing with directional adhesion problem of the G. gecko, with potential application to help design high-performance synthetic directional adhesives. Some preliminary conclusions about the G. gecko's locomotion in detachment are made based on the model as a complement to previous models.