nils-herrmann/clean_scirepeval huggingface dataset:
Supporting Serendipitous Social Interaction Using Human Mobility Prediction

Leveraging the regularities of people's trajectories, mobility prediction can help forecast social interaction opportunities. In this paper, in order to facilitate real-world social interaction, we aim to predict “serendipitous” social interactions, which are defined as unplanned encounters and interaction opportunities and regarded as emerging social interactions. We collected GPS trajectory data from people' daily life on campus and use it as empirical mobility traces to generate decision trees and model trees to predict next venues, arrival times, and user encounter. Mobility regularities are mainly considered in these prediction models, and mobility contexts (e.g., time, location, and speed) act as decision nodes in the classification trees. Experimental results using collected GPS data showed that our system achieves 90% accuracy for predicting a user's next venue using a decision tree algorithm, with minute-level (around 5 min) prediction error for arrival time using the model tree algorithm. Two prototype applications were developed to support serendipitous social interaction on campus, and the feedback from a user study with 25 users demonstrated the usability of these two applications.
Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning. Existing approaches predominantly solve these challenges independently, while neglecting the fact that part localization (e.g., head of a bird) and fine-grained feature learning (e.g., head shape) are mutually correlated. In this paper, we propose a novel part learning approach by a multi-attention convolutional neural network (MA-CNN), where part generation and feature learning can reinforce each other. MA-CNN consists of convolution, channel grouping and part classification sub-networks. The channel grouping network takes as input feature channels from convolutional layers, and generates multiple parts by clustering, weighting and pooling from spatially-correlated channels. The part classification network further classifies an image by each individual part, through which more discriminative fine-grained features can be learned. Two losses are proposed to guide the multi-task learning of channel grouping and part classification, which encourages MA-CNN to generate more discriminative parts from feature channels and learn better fine-grained features from parts in a mutual reinforced way. MA-CNN does not need bounding box/part annotation and can be trained end-to-end. We incorporate the learned parts from MA-CNN with part-CNN for recognition, and show the best performances on three challenging published fine-grained datasets, e.g., CUB-Birds, FGVC-Aircraft and Stanford-Cars.
Joint multi-label multi-instance learning for image classification

In real world, an image is usually associated with multiple labels which are characterized by different regions in the image. Thus image classification is naturally posed as both a multi-label learning and multi-instance learning problem. Different from existing research which has considered these two problems separately, we propose an integrated multi- label multi-instance learning (MLMIL) approach based on hidden conditional random fields (HCRFs), which simultaneously captures both the connections between semantic labels and regions, and the correlations among the labels in a single formulation. We apply this MLMIL framework to image classification and report superior performance compared to key existing approaches over the MSR Cambridge (MSRC) and Corel data sets.
Gait planning and control for biped robots based on modifiable key gait parameters from human motion analysis

In this paper, effective gait planning and control is established for biped robots. First, an experiment of human locomotion is carried out using a motion capture system for analysis of human gait features. We found modifiable key gait parameters affecting the dominant performance of biped robots walking from extracted features. Then, we proposed an effective bio-inspired gait planning (BGP) algorithm using modifiable key gait parameters. In final, the proposed method has been verified through simulations and experiment in a real biped robot DRC-XT.
Application of Multi-sensor Information Fusion in Fault Diagnosis of Rotating Machinery

The typical faults of rotating machinery include unbalance, misalignment, bearing housing looseness, etc. The experimental rotor-bearing system is built and multi-sensor information fusion based on D-S evidence theory is applied in the fault diagnosis of rotating machinery. The fault type is determined through the fusion results and from the research it can be concluded that this approach is more effective, accurate and reliable than that of single sensor
Multi-level Attention Networks for Visual Question Answering

Inspired by the recent success of text-based question answering, visual question answering (VQA) is proposed to automatically answer natural language questions with the reference to a given image. Compared with text-based QA, VQA is more challenging because the reasoning process on visual domain needs both effective semantic embedding and fine-grained visual understanding. Existing approaches predominantly infer answers from the abstract low-level visual features, while neglecting the modeling of high-level image semantics and the rich spatial context of regions. To solve the challenges, we propose a multi-level attention network for visual question answering that can simultaneously reduce the semantic gap by semantic attention and benefit fine-grained spatial inference by visual attention. First, we generate semantic concepts from high-level semantics in convolutional neural networks (CNN) and select those question-related concepts as semantic attention. Second, we encode region-based middle-level outputs from CNN into spatially-embedded representation by a bidirectional recurrent neural network, and further pinpoint the answer-related regions by multiple layer perceptron as visual attention. Third, we jointly optimize semantic attention, visual attention and question embedding by a softmax classifier to infer the final answer. Extensive experiments show the proposed approach outperforms the-state-of-arts on two challenging VQA datasets.
User-curated image collections: Modeling and recommendation

Most state-of-the-art image retrieval and recommendation systems predominantly focus on individual images. In contrast, socially curated image collections, condensing distinctive yet coherent images into one set, are largely overlooked by the research communities. In this paper, we aim to design a novel recommendation system that can provide users with image collections relevant to individual personal preferences and interests. To this end, two key issues need to be addressed, i.e., image collection modeling and similarity measurement. For image collection modeling, we consider each image collection as a whole in a group sparse reconstruction framework and extract concise collection descriptors given the pretrained dictionaries. We then consider image collection recommendation as a dynamic similarity measurement problem in response to user's clicked image set, and employ a metric learner to measure the similarity between the image collection and the clicked image set. As there is no previous work directly comparable to this study, we implement several competitive baselines and related methods for comparison. The evaluations on a large scale Pinterest data set have validated the effectiveness of our proposed methods for modeling and recommending image collections.
Design of Quadruped Robot Based CPG and Fuzzy Neural Network

The paper proposed a method for a quadruped robot control system based central pattern generator (CPG) and fuzzy neural networks (FNN). The common approach for the control of a quadruped robot includes two methods mainly. One is the CPG that is based the bionics, the other is the dynamic control that is based the model of quadruped robot. The control result of CPG is decided by the gait data of the quadruped and the parameters of the CPG are choosing manually. Modeling a quadruped robot is difficult because it is a high nonlinear system. This paper presents a much simpler method for the control of a quadruped robot. A simple CPG is adopted for a timing oscillator; it generates the motion periodic pattern of legs. The FNN is used to control the joint motion in order to get a desired stable trajectory motion.
Design of wireless sensor network node monitoring interface based on Qt

With the rapid development of wireless sensor networks, the monitoring system which is dedicated to monitor wireless sensor networks is paid more and more attention, therefore, the researches of monitoring interface for WSNs also become extremely important. The paper is mainly targeted at the design of monitoring interface for wireless sensor networks node, and then proposes an implementation scheme that is based on Qt Designer in the Linux environment. Migration of Qt/Embedded on Intel PXA270 development platform based on Linux operating system is also presented. And two compile methods of Qt application program based on PC platform and ARM monitoring platform are described in detail.
Adaptive sliding mode control for robots based on fuzzy support vector machines

To improve the control precision of robots, the control method of adaptive sliding mode for robots was presented based on fuzzy support vector machines. The sliding mode control has complete adaptability to system disturbance and siring in sliding mode, which was used to automatically track the uncertainty of system parameters and external disturbance. Fuzzy support vector machines have strong treatment of nonlinear signal and generalization ability, which was used to reduce the chattering in sliding mode control. The FSVM controller parameters were optimized by hybrid learning algorithm, which combines least square algorithm with improved genetic algorithm, to get the optimal control performance with the controlled object. The simulation results of a two-link robotic manipulator demonstrated that the control method designed gets tracking effect with high precision and speed, as well as reduces chattering of control under the condition of existing model error and external disturbance.
Online Condition Monitoring in Micromilling: A Force Waveform Shape Analysis Approach

The cutting force variations have long been studied for machining condition monitoring, due to their sensitivity to the tool conditions. These variations could be the statistics of the amplitude, features extracted from frequency components, wavelet coefficients, etc. Different from most current approaches, this paper introduces a novel online approach that directly correlates tool conditions with the force waveform variations and estimates the tool condition based on their corresponding singularity degree density functions. The singularities of waveforms are measured with Holder exponents (HEs), which are extracted from their wavelet transform modulus maxima. Experimental studies have shown that the HEs reflect the changes of tool conditions. The HE features are found robust to working condition variations, and their probability density functions are discriminate for tool state estimation in micromilling.
Mobile multimedia travelogue generation by exploring geo-locations and image tags

Traveling experience sharing has become pervasive. In this paper, we present a system which automatically generates a multimedia travelogue for mobile users. Multimedia travelogue shows user footprint with photos on the corresponding location on the map, which could offer the space distribution inner or between the locations. Meanwhile, location overviews with representative tags and images show comprehensive knowledge of the landmarks. The key challenges are: 1) when mapping footprint, some photos are without geo-tags; 2)user-contributed photos may be not clear and graceful; 3) to detect which landmark is actually on the photo to offer location overview. We solve these challenges by combining both geo-tags and tags to estimate the location. First we use the relationship of timestamp and geo-tag in the same trip group to estimate the footprints. Second we localize the landmark on the photo by tag match method which matching user tags with representative tags of the landmark. Experimental results on a Flickr image collection of nearly 2 million images of 6,581 users demonstrate the effectiveness of our approach.
A Localization Algorithm Based on Discrete Imprecision Range Measurement in Wireless Sensor Networks

Considering on the insufficiency of DV-HOP algorithm which doesn't use any range information in the localization of wireless sensor networks, we proposed a localization algorithm based on discrete imprecision range measurement. We first estimated the ranges between nodes using the different level of output powers. Second, the algorithm replaced the hop number in DV-HOP algorithm with the estimated range, and replaced the sum of hop with the sum of range measurement. Finally, we proposed a new algorithm to get comparatively precise localization. It has been shown that the localization error of the proposed algorithm in the paper is smaller than that of DV-HOP algorithm whenever in regular or irregular areas.
Effects of no-tillage practice on Atrazine loss on sloping cropland

Atrazine is an herbicide which is widely used in the cornfield to remove annual gramineous weeds and broad-leaved weeds. However, Atrazine and its degradation products have high toxicity to animals and human beings. The lost of Atrazine from farmland with runoff and sediment will cause serious environmental problems. This study was conducted by simulated rainfall experiment to evaluate the effects of no-tillage practice on Atrazine loss on sloping cropland. The results showed that no-tillage practice could significantly reduce runoff and Atrazine loss. As compared to conventional tillage, the runoff and Atrazine loss with no-tillage were decreased by 59.4% and 55.9%, respectively. Meanwhile, the results also showed that there was a negative correlation between slope gradient and the effects of no-tillage practice in preventing runoff and Atrazine loss. The effectiveness of no-tillage practice gradually decreased with the increasing slope gradient.
Application of self-tuning fuzzy PI+PD controller in joint DC servo motors of a four-leg robot

This paper introduces a self-tuning fuzzy PI+PD algorithm for processing the DC servo system of a four leg robot. The control process is online, of which the fuzzy rules are generated analytically. The inference and defuzzification of this fuzzy control method are based on phase plane and centroid, thus it costs less processing time. Through self-tuning gain and self-optimizing in the control process, the servo system of the robot is robust, and its control performance is also good.
An iteration method for inverse kinematics of redundancy robot

A generalized iteration method for the inverse kinematics of the redundancy robot is proposed in this paper. As compared to the traditional method, it can improve time complexity dramatically for the inverse kinematics of redundancy robot and the solution procedure of the inverse kinematics is also simplified simultaneously. The experiment results show that this method can be used to solve the non-uniqueness problem of the solution which exists in the inverse kinematics of redundancy robot. Moreover, this method is an attractive strategy for the inverse kinematics in industrial redundancy robot which performs repetitive tasks because of the limit of computing power and memory space for their controllers.
Tap-to-search: Interactive and contextual visual search on mobile devices

Mobile visual search has been an emerging topic for both researching and industrial communities. Among various methods, visual search has its merit in providing an alternative solution, where text and voice searches are not applicable. This paper proposes an interactive “tap-to-search” approach utilizing both individual's intention in selecting interested regions via “tap” actions on the mobile touch screen, as well as a visual recognition by search mechanism in a large-scale image database. Automatic image segmentation technique is applied in order to provide region candidates. Visual vocabulary tree based search is adopted by incorporating rich contextual information which are collected from mobile sensors. The proposed approach has been conducted on an image dataset with the scale of two million. We demonstrated that using GPS contextual information, such an approach can further achieve satisfactory results with the standard information retrieval evaluation.
Contextual Internet Multimedia Advertising

The advent of media-sharing sites has led to the unprecedented Internet delivery of community-contributed media like images and videos. Those visual contents have become the primary sources for online advertising. Conventional advertising treats multimedia advertising as general text advertising by displaying advertisements either relevant to the queries or the Web pages, without considering the potential advantages which could be brought by media contents. In this paper, we summarize the trend of Internet multimedia advertising and conduct a broad survey on the methodologies for advertising which are driven by the rich contents of images and videos. We discuss three key problems in a generic multimedia advertising framework. These problems are: contextual relevance that determines the selection of relevant advertisements, contextual intrusiveness which is the key to detect appropriate ad insertion positions within an image or video, and insertion optimization that achieves the best association between the advertisements and insertion positions so that the effectiveness of advertising can be maximized in terms of both contextual relevance and contextual intrusiveness. We show recently developed MediaSense which consists of image, video, and game advertising as an exemplary application of contextual multimedia advertising. In the MediaSense, the most contextually relevant ads are embedded at the most appropriate positions within images or videos. To this end, techniques in computer vision, multimedia retrieval, and computer human interaction are leveraged. We also envision that the next trend of multimedia advertising would be game-like advertising which is more impressionative and thus can promote advertising in an interactive, as well as more compelling and effective way. We conclude this survey with a brief outlook on open research directions.
Concurrent Multiple Instance Learning for Image Categorization

We propose a new multiple instance learning (MIL) algorithm to learn image categories. Unlike existing MIL algorithms, in which the individual instances in a bag are assumed to be independent with each other, we develop concurrent tensors to explicitly model the inter-dependency between the instances to better capture image's inherent semantics. Rank-1 tensor factorization is then applied to obtain the label of each instance. Furthermore, we formulate the classification problem in the reproducing kernel Hilbert space (RKHS) to extend instance label prediction to the whole feature space. Finally, a regularizer is introduced, which avoids overfitting and significantly improves learning machine's generalization capability, similar to that in SVMs. We report superior categorization performances compared with key existing approaches on both the COREL and the Caltech datasets.
Typicality-Based Visual Search Reranking

Most existing approaches to visual search reranking predominantly focus on mining information only from the initial ranking order on the basis of pseudo-relevance feedback. However, the initial ranking order cannot always provide enough cues for reranking by itself due to an unsatisfying visual search performance. This letter presents a novel approach to visual search reranking by selecting typical examples to build the reranking model. Observing that typical examples are mostly clearly visible, fill the majority of the visual documents or appear in one of several common poses, by using these examples informed classifiers would generally be more robust to noisy testing cases that may include occlusions, illumination changes or other factors. We first define the typicality on the basis of data distribution, and then theoretically formalize the example selection as an optimization problem on the basis of the example typicality and propose a close-form solution. Based on the selected examples, we build the reranking model by using a support vector machine. Empirically, we conduct extensive experiments on a real-world image set and a benchmark video set, and shows significant and consistent improvements over the state-of-the-art works.
Multigranular Event Recognition of Personal Photo Albums

People are taking more photos than ever before in recent years. To effectively organize these personal photos, the photos are usually assigned to albums according to their events. An efficient way to manage our photos would be if we could recognize the events of the albums automatically. In this paper, we study the problem of recognizing events in personal photo albums. Recognizing events in photo albums is a new challenge since the contents of photos in albums are more complicated than in traditional single-photo tasks, since not all photos in an album are relevant to the event and a single photo in an album often fails to convey the meaningful event semantic behind the album. To solve this problem, we introduce an attention network to learn the representations of photo albums. Then, we adopt a hierarchical model to recognize events from coarse to fine using multigranular features. We evaluate our model on two real-world datasets consisting of personal albums; we find that our model achieves promising results.
A MEMS sensor array for explosive particle detection

In this work, a micro-electro-mechanical system (MEMS) sensor array which is capable of detecting explosive particles has been designed and fabricated. The array consists of 12 silicon beams, on each beam there are a heater and a temperature sensor. AH silicon beams can be heated up to melting point of particular kind of explosive by the heaters, while each sensor is used to measure the temperature change of each beam caused by heat absorption of the explosive particles when they melt Experimental results show that the array is capable of detecting trinitrotoluene (TNT) and cyclotrimethylenetrinitramine (RDX) samples as little as 1/spl mu/g.
Driving principle and dynamic analysis of a micro swimming robot

In this paper, an underwater microrobot driven by external energy field is developed, FMP (Ferromagnetic polymer) actuator is utilized. The size of the underwater microrobot prototype is 3 mm in length, 2 mm in width, and 0.4 mm in thickness. The robot can swim when the magnetic intensity is higher than 8 mT and the frequency is about 10 Hz, with the speed of 0.3 mm/s/spl sim/1 mm/s. The paper emphasizes on the driving principle and the dynamic analysis of the microrobot, and the model accords with the experiment result significantly well. The experimental results indicate that the swimming speed can be controlled by changing the frequency and the intensity of the magnetic field.