nils-herrmann/clean_scirepeval huggingface dataset:
Weighted Component Hashing of Binary Aggregated Descriptors for Fast Visual Search

Towards low bit rate mobile visual search, recent works have proposed to aggregate the local features and compress the aggregated descriptor (such as Fisher vector, the vector of locally aggregated descriptors) for low latency query delivery as well as moderate search complexity. Even though Hamming distance can be computed very fast, the computational cost of exhaustive linear search over the binary descriptors grows linearly with either the length of a binary descriptor or the number of database images. In this paper, we propose a novel weighted component hashing (WeCoHash) algorithm for long binary aggregated descriptors to significantly improve search efficiency over a large scale image database. Accordingly, the proposed WeCoHash has attempted to address two essential issues in Hashing algorithms: “what to hash” and “how to search.” “What to hash” is tackled by a hybrid approach, which utilizes both image-specific component (i.e., visual word) redundancy and bit dependency within each component of a binary aggregated descriptor to produce discriminative hash values for bucketing. “How to search” is tackled by an adaptive relevance weighting based on the statistics of hash values. Extensive comparison results have shown that WeCoHash is at least 20 times faster than linear search and 10 times faster than local sensitive hash (LSH) when maintaining comparable search accuracy. In particular , the WeCoHash solution has been adopted by the emerging MPEG compact descriptor for visual search (CDVS) standard to significantly speed up the exhaustive search of the binary aggregated descriptors.
Data-Driven Lightweight Interest Point Selection for Large-Scale Visual Search

With the explosive increase of images and videos, visual analysis has become an essential technique in dealing with the big visual data, which utilizes the visual feature descriptors to search or recognize the images or frames with target objects or events. Subject to the constraints of resources (e.g., memory, bandwidth, storage, etc.), interest point selection is crucial to generate robust compact descriptors for high-efficiency visual analysis by selecting and aggregating the most discriminative local feature descriptors, which has been demonstrated in the state-of-the-art low bit rate visual search works. In this paper, we propose a data-driven lightweight interest point selection approach to significantly improve the performance of visual search, while ameliorating the efficiency of extracting feature descriptors. Comprehensive experimental results over benchmarks have shown that the proposed interest point selection algorithm has significantly improved image matching and retrieval performance in the completed MPEG Compact Descriptors for Visual Search (CDVS) standard as well as the emerging MPEG Compact Descriptors for Video Analytics (CDVA) standard, say 20% mAP gain by data-driven selection against random selection of interest points. In particular, the presented data-driven interest point selection has been adopted by MPEG-CDVS and MPEG-CDVA as a normative technique to improve the aggregation of handcrafted features, which has contributed to the combination of handcrafted features and deep learning (CNN) features as well.
An efficient coding framework for compact descriptors extracted from video sequence

Towards effective and efficient image matching or retrieval tasks, the emerging MPEG standard, named Compact Descriptors for Visual Search (CDVS), has fulfilled compact descriptors for still images, consisting of compressed local and global descriptor. Nevertheless, the frame-level coding of CDVS descriptors from a video sequence does not address the inter-frame redundancy issue, which may consume considerable bandwidth and storage resources. In this work, we propose an efficient coding framework of CDVS descriptors to generate compact descriptors for video sequences. For local descriptors, we propose a multiple reference predictive technique to exploit the temporal correlation of local descriptors and location coordinates over a sequence of frames. To further improve the prediction performance, keypoint tracking is applied to identify temporally repeated keypoints. For global descriptors, a propagation coding way is employed to compress the global descriptors of adjacent frames. The empirical evaluation has shown that the proposed coding approach has yielded a low bit rate of less than 40kbps on average, while maintaining comparable matching and retrieval performance. Compared to the sequence of original frame-level CDVS descriptors, the proposed approach has achieved over 25× bit rate reduction.
Improving object detection with region similarity learning

Object detection aims to identify instances of semantic objects of a certain class in images or videos. The success of state-of-the-art approaches is attributed to the significant progress of object proposal and convolutional neural networks (CNNs). Most promising detectors involve multi-task learning with an optimization objective of softmax loss and regression loss. The first is for multi-class categorization, while the latter is for improving localization accuracy. However, few of them attempt to further investigate the hardness of distinguishing different sorts of distracting background regions (i.e., negatives) from true object regions (i.e., positives). To improve the performance of classifying positive object regions vs. a variety of negative background regions, we propose to incorporate triplet embedding into learning objective. The triplet units are formed by assigning each negative region to a meaningful object class and establishing class-specific negatives, followed by triplets construction. Over the benchmark PASCAL VOC 2007, the proposed triplet embedding has improved the performance of well-known Fas-tRCNN model with a mAP gain of 2.1%. In particular, the state-of-the-art approach OHEM can benefit from the triplet embedding and has achieved a mAP improvement of 1.2%.
Weakly supervised topic grouping of YouTube search results

Recent years have witnessed an explosive growth of user contributed videos on websites like YouTube and Metacafe, which usually provide a query-by-keyword functionality to facilitate the user browsing. For a given query, the returned videos typically contain multiple topics that are mixed up to duplicate the user browsing. Therefore, their diversification and grouping are highly demanded to improve the user experiences. However, the tagging and content qualities of user contributed videos are uncontrolled against their precise grouping. In this paper, we present a weakly supervised topic grouping paradigm to diversify the returned videos of a given keyword query. Our grouping is based on the bag-of-words visual signature quantized over the spatiotemporal STIP descriptor  extracted from each returned video. First, we adopt a min-Hashing based visual similarity in combination of the tagging similarity to group the returned videos. Based on the initial grouping configurations, we mine the co-occurred discriminative sub-signatures, based on which we iteratively refine the first step. Such iteration well handles the noise in visual content and tagging, since neither of which is fully trusted during the grouping. We validate our schemes on over 2,000 video clips crawled from a set of YouTube keyword query results. Comparing to alternative approaches, our scheme has shown superior robustness and precision.
A local shape descriptor for mobile linedrawing retrieval

Coming with the rapid spread of Intelligent terminals with camera, mobile visual search techniques have undergone a revolution, where visual information can be easily browsed and retrieved upon simply capturing a query photo. However, most existing work targets at compact description of natural scene image statistics, while dealing with line drawing images retains an open problem. This paper presents a unified framework of line drawing problems in mobile visual search. We propose a compact description of line drawing image named Local Inner-Distance Shape Context (LISC) which is robust to the distortion and occlusion and enjoys scale and rotation invariance. Together with an innovative compression scheme using JBIG2 to reduce query delivery latency, our framework works well on both a self-built dataset and MPEG- 7 CE Shape-1 dataset. Promising results on both datasets show significant improvement over state-of-the-art algorithms.
Query Adaptive Multiview Object Instance Search and Localization Using Sketches

Sketch-based object search is a challenging problem mainly due to three difficulties: 1) how to match the primary sketch query with the colorful image; 2) how to locate the small object in a big image that is similar to the sketch query; and 3) given the large image database, how to ensure an efficient search scheme that is reasonably scalable. To address the above challenges, we propose leveraging object proposals for object search and localization. However, instead of purely relying on sketch features, we propose fully utilizing the appearance features of object proposals to resolve the ambiguities between the matching sketch query and object proposals. Our proposed query adaptive search is formulated as a subgraph selection problem, which can be solved by the maximum flow algorithm. By performing query expansion, it can accurately locate the small target objects in a cluttered background or densely drawn deformation-intensive cartoon (Manga like) images. To improve the computing efficiency of matching proposal candidates, the proposed Multi View Spatially Constrained Proposal Selection encodes each identified object proposal in terms of a small local basis of anchor objects. The results on benchmark datasets validate the advantages of utilizing both the sketch and appearance features for sketch-based search, while ensuring sufficient scalability at the same time.
Selectively Aggregated Fisher Vectors of Query Video for Mobile Visual Search

Mobile visual search has undergone a wide development and gained much progress in recent years thanks to the ever-growing computational power of mobile devices. Most visual search methods take a single image as query and generate an image-level representation to implement image retrieval. To form a compact and discriminative representation for the query image, Fisher vectors (FV) have shown great advantage in both discriminability and computational efficiency. However, single image based visual search sometimes has unsatisfactory performance as a number of quality degeneration situations like limited view, uneven lighting, blur, occlusion and etc. may exist in the query image, while a video clip could overcome these shortcomings and contain more sufficient visual information for better retrieval performance when serving as query. Towards a compact yet discriminative representation of the query in mobile visual search, we propose a temporal-spatial based Fisher Vector (TSFV) for the query video with an equal length to an image based FV. The TSFV introduces a selective local feature aggregation scheme that employs interframe feature matching in temporal terms combined with intraframe feature attributes in spatial terms to evaluate video features' discriminability and select only the discriminative ones for aggregation. Evaluated on a diversified dataset, our proposed TSFV for query video achieves a significant performance improvement compared to typical image based FV with no additional transmission load and query latency.
Topic level sampling towards optimized locality sensitive vocabulary coding

We propose a novel locality sensitive vocabulary coding scheme to extract compact descriptors for low bit rate visual search. We employ Latent Dirichlet Allocation (LDA) to learn the topic vocabularies of lower dimension to generate compact descriptors. To deal with diverse datasets, LDA model is introduced to subdivide a dataset into groups of images with a topic model, where the code word distributions in each group produce coherent statistics from generative learning. Moreover, our empirical study has shown that the original Bag-of-Word (BoW) is sparse, and the occurrences of non-zero words is coherent within a topic. Our proposed topic-wise vocabulary learning yields a more compact yet discriminative codebook to search images. Given a query image, multiple topics are determined, which is fed into the topic vocabularies to generate more compact topical descriptor. Comparison experiments show our topic-wise locality sensitive vocabulary coding produces more compact and discriminative descriptors than the state-of-the-arts.
Compression of Deep Neural Networks for Image Instance Retrieval

Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the dominant approach for generating global image descriptors for the instance retrieval problem. One major drawback of CNN-based global descriptors is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware. In this work, we study the problem of neural network model compression focusing on the image instance retrieval task. We study quantization, coding, pruning and weight sharing techniques for reducing model size for the instance retrieval problem. We provide extensive experimental results on the trade-off between retrieval performance and model size for different types of networks on several data sets providing the most comprehensive study on this topic. We compress models to the order of a few MBs: two orders of magnitude smaller than the uncompressed models while achieving negligible loss in retrieval performance1.
Research of bored pile drilling technique under complicated geological condition

The foundation of super large bridge of Jinghu Express Railway over the Huaihe river is composed of eight bored piles whose diameter is 2.2m and the designed length of piles is 50m. The geological condition of bridge site is complicated and piles have to pass through six-meter thick granite. The drilling platform need to be built firstly at the position of the bored piles and then the air lifting reverse circulation drilling machine is used to the construction of bored piles. It was solved for the large diameter bored piles to be constructed in the deep water and complicated geological condition through the design and construction of deep water drilling platform, the drilling pressure and speed control under the different geological condition and so on. The research showed the bored piles could satisfy the technique request of express railway.
Design of Adaptive Increment Controlled Hybrid-Type Active Power Filter

HAPF (HAPF) is composed of an active power filter (APF) and a passive power filter (PPF), for integrated harmonic compensation of unbalanced or nonlinear loads and compensation of reactive power. The PPF tunes at special harmonics and compensates reactive power to improve the power factor. The APF is composed of two full controlled three-phase PWM inverters. It traces harmonic current on system line, and works as a controlled voltage source, injecting current into system to eliminating harmonic current generated by nonlinear loads. In structure design, the PPF supplies fundamental series resonance brunch for APF to avoid enduring fundamental voltage. It lowers the rated capacity of APF and reduces the volume of APF. The adaptive increment control method is employed, which is simple and fast responding. It enables the control of APF independent of the parameters of PPF. It makes the filter flexible to harmonics, and overcome the effect of input current of APF. Simulation and experiment have been carried out. The result shows that the design achieves expected goals. The harmonic in system is reduced below expect level, and the filter is fast response and adaptive to the harmonic current.
Fuzzy Clustering Approaches Based on AFS Fuzzy Logic I

In this paper, the AFS fuzzy logic clustering algorithm (X.D. Liu, W. Wang and T.Y. Chai,  Transaction on Systems, Man, Cybernetics, 2005) have be studied further by the improvement of the algorithm and the application of the algorithm to iris data (reference ftp://ftp.ics.uci.edu/pub/machine-learning-databases/Iris/). In stead of examples of less than 10 samples, we apply the improved algorithm to iris data which has 150 samples and just the order relationship of the samples on the attributes are used. This study shows that the AFS fuzzy logic clustering algorithm can obtain a high reclassification accuracy according to the order relationship. Thus the algorithm can be applied to the data sets in which the attributes are only described by order relationship
Application of GIS in electronic commerce

In recent years, GIS(Geographic Information System) develops rapidly, and it is applied in various industries. But the application of GIS in electronic commerce is far behind its application in other areas. Using the programmable interfaces provided by Google Maps, we created an online shopping system which has the map functions to probe the application of GIS in electronic commerce. The system adopts the B / S three-tier architecture model and uses the Google Maps API to embed the Google Maps into the system website. It uses Oracle database to save the system data, and the spatial data is used the data of Google maps. The data of the system Oracle database and the spatial data of Google maps are connected through the latitude and longitude. These can improve the system's extensibility greatly and reduce the developing difficulty and cost remarkably.
Research of area coordinate control based on cyber-physical fusion modeling in active distribution network

Cyber-Physical System(CPS) is a new concept of integration of industry and information technology, and it will affect power grid in many aspects. Hybrid system model is a typical modeling method of CPS. This paper has proposed a control method based on cyber-physical fusion modeling of ADN coordinate control model, according to hybrid system model and indicator of feeder control error. Based on the control model, model predictive control theory is used in the optimal control of the grid. Eventually, a simulation is carried out with Matlab, and the result shows the method has a better effect, compared to the PI control.
A Novel CMOS MEMS Accelerometer with Four Sensing Finger Arrays

This study presents a novel CMOS accelerometer with four sensing finger arrays. Moreover, additional springs are also employed for electrical routing. The sensing capacitance is increased for 80%, at the cost of the decreasing of proof mass for 20%. Thus, the sensitivity of the present accelerometer is still increased for near 44%. The measurement results show that the present accelerometer has a gain of 2.94 mV/g.
Node density and connectivity of multi-channel ad hoc cognitive radio networks

Node density and connectivity of large-scale multi-channel cognitive radio networks (CRNs) are studied in this paper. Current existing researches on connectivity of CRNs are mainly conducted under the assumption of one licensed channel and ignore the spectrum sensing errors. Thus, in this paper, practical assumptions such as spatially varying spectrum holes for secondary users (SUs), multiple channels licensed for the primary network, correct detection and false alarm probabilities (Pd and Pf) are considered to derive the diluted density of SUs in closed-form for the secondary network. The sensing errors of SUs and amount of licensed channels of primary users (PUs) are found to have a significant impact on the density of active SUs. Increasing density of PUs considerably reduces the node density of active SUs. Sequentially connectivity of the secondary network is investigated by applying percolation theory and complex network. The critical condition for percolation of the secondary network coexisting with primary network is then estimated analytically. We study the impacts of node density, protection radius, amount of licensed channels of the primary network on connectivity of the secondary network. It is shown that more licensed channels can loosen the condition for SUs percolation. Also, sensing errors significantly affect the connectivity of SUs. However, we found that more accurate sensing carried by SUs (larger Pd and smaller Pf) does not necessarily bring better connectivity of the secondary network.
A novel infrared face recognition method in DCT domain

A novel infrared face recognition method using blood perfusion conversion and DCT is proposed in this paper. Firstly, the blood perfusion image instead of thermal image is used to get the stable biological features. Secondly, due to the low-resolution of the infrared images, the feature extraction method (Linear Discriminative Analysis in DCT domain) is chosen to get the principle features in the blood perfusion image, which is suitable for real-time application. A new feature selection algorithm in DCT domain is applied to lessen the small sample problem of LDA. The experiments illustrate that the feature selection algorithm in DCT domain is effective in getting the discriminant features and the proposed method has better performance compared with the traditional DCT and LDA method.
A Multiple Output Forward Converter Adopting Weighted Time-Sharing Control and Switch-Linear Hybrid Scheme

A double forward multiple output converter with weighted time-sharing control and switch-linear hybrid (WTSC-SLH) technique is presented in this paper. Total regulation of both output voltages is obtained by WTSC. Precise regulation and low ripple multiple output voltage can be achieved by a post regulator with SLH approach. The effects of WTSC-SLH is verified on an experimental two-output double forward converter
Interface toughness characterization in microelectronic packages based on four point bending test and simulation

Microelectronic packaging devices consist of various kinds of materials, such as: die, molding compound, adhesives, copper etc. Due to the mismatch of the materials coefficient of thermal expansion (CTE), thermal stress can accumulate in the device, which would cause the interface delamination of microelectronic packaging devices when ambient temperature changes, and eventually lead to the failure of microelectronic packaging devices. In order to predict delamination, interface properties should be obtained. According to the research, temperature has large effects on the interface toughness. Therefore, interface toughness as function of temperature was studied by experiment and simulation in this paper. The results show that the critical crack energy was affected by temperature largely, the higher the temperature is, the smaller the critical crack energy is. The load speed has no effects on the four-point bending test. Experiments show that the delamination propagation load is stable and the crack propagation load is almost constant after the delamination start. According to the crack propagation load, the interface fracture toughness value (Gc) is obtained from four point bending tests combined with simulation of fitted the crack propagation load. The result of Gc values of formula and simulation can be compared. The critical displacement (Vc) can be estimated by fitting the deformation of the glue between the copper and molding compound with simulations.