nils-herrmann/clean_scirepeval huggingface dataset:
Research of service-oriented analysis and design method

In order to solve the problem of rigidity in IT architecture and the existence of heterogeneous information islands within most enterprises, a novel service-oriented IT architecture-SOA (service-oriented architecture)emerges. The realization of SOA is embodied in the accomplishments of various IT applicable functions in the form of service encapsulation and the interconnection and interoperation of services in the form of loosely coupling. Service-oriented Analysis and Design (SOAD) is a specially designed mode of software modeling and developing based on SOA. This paper analyzes the structural model in a service-oriented application system. In accordance with the development code of RUP (Rational unified process), it demonstrates a SOAD mode and an example centered on architecture and activated with business process and use case.
Background-foreground division based search for motion estimation in surveillance video coding

Basically, motion search is very time-consuming in the process of video coding. For surveillance videos, however, there exist a large amount of static background regions whose motion vectors actually are equal to zero. By utilizing the background and foreground information of coding units, this paper proposes a background-foreground division based search algorithm (BFDS) to accelerate the motion search in surveillance video coding. The basic idea of BFDS is to classify a predicting unit (PU) into a background predicting unit (BPU) or a foreground predicting unit (FPU) and then adopt different search strategies respectively for BPUs and FPUs. That is, a zero motion vector biased search strategy is applied in BPUs to reduce the search complexity on a large scale while a precise global search strategy is applied in FPUs to get higher coding performance. Compared with the current TZ search algorithm used in HEVC, the proposed BFDS algorithm can reduce the number of search points by 57.73% while remaining the coding performance almost unchanged.
Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network

Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologicallyinspired deep network, called shuttleNet. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of hidden states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain’s feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.
A Fast and Performance-Maintained Transcoding Method Based on Background Modeling for Surveillance Video

Low-complexity and high-performance surveillance video Transcoding methods play an important role for a wide range of surveillance video transmission and storage applications. Towards this end, the special characteristics of surveillance video should be utilized for Transcoding. In this paper, we propose a fast and performance-maintained Transcoding method. This method firstly divides macro blocks (MBs) into foreground MBs, foreground border MBs and background MBs. Statistics show that the three categories have different distributions of prediction modes, motion vectors and reference frames. Following this, we adopt different Transco ding strategies in terms of removing the redundant prediction modes, narrowing motion search range and reducing reference frames. In particular, we propose an algorithm to exploit the decoded motion vector to adaptively calculate motion search range. Experimental results show that, compared with the recent background modeling based full-decoding-full-encoding, our Transcoding method saves more than 93% time with ignorable quality loss.
A background proportion adaptive Lagrange multiplier selection method for surveillance video on HEVC

In the recent video coding standards, the selection of Lagrange multiplier is crucial to achieve trade-off between the choices of low-distortion and low-bitrate prediction modes. For surveillance video coding, the rate-distortion analysis shows that, a larger Lagrange multiplier should be used if the background in a coding unit took a larger proportion. Therefore, a modified Lagrange multiplier might be better for rate-distortion optimization. To address this problem, we perform an in-depth analysis on the relationship between the optimal Lagrange multiplier and the background proportion, and then propose a Lagrange multiplier selection model to obtain the optimal coding performance for surveillance videos. Following this, we further develop a Lagrange multiplier optimized video coding method. Experimental results show that our coding method can averagely achieve 18.07% bitrate saving on CIF sequences and 11.88% on SD sequences against the background-irrelevant Lagrange multiplier selection method.
Fast Compressed Domain Copy Detection with Motion Vector Imaging

With an increasing number of videos uploaded to the Internet, how to fast detect copy videos in compressed domain has been paid greater attention to. Many researchers have tried using information in motion vector to be the feature. However, in these methods motion vectors are used as histogram, which lacks structural information in detail. To address this problem, in this paper we propose a new way of using Motion Vector Imaging. We first extract motion vector from a compressed video, and then project them onto a canvas to generate a MVI which contains detail motion information. Based on these MVIs, a siamese deep neural network is utilized to train on pairs from dataset and one side of the network is applied to extract features. Finally, a cascade system using MVI model and I frames is used to do fast copy detection. Results on public dataset CC_WEB_VIDEO show that MVI can achieve high recall rate and precision rate at a high speed.
Research on service identification methods based on SOA

In order to solve the problem of rigidity in IT architecture and the existence of heterogeneous information islands within most enterprises, a novel service-oriented IT architecture SOA (service-oriented architecture)emerges. The realization of SOA is embodied in the accomplishments of various IT applicable functions in the form of service encapsulation and the interconnection and interoperation of services in the form of loosely coupling. Based on SOA, Service-oriented Analysis and Design (SOAD) is a specially designed method of software modeling, analyzing and designing. In the whole process of which, service identification is the most significant. This paper analyzes the structural model in a service-oriented architecture, in combination with the development code of RUP (Rational unified process), it demonstrates three most widely used methods service identification in an SOAD procedure and provides with an example.
A refined object detection method based on HTM

Object detection plays a fundamental role in many content-based video systems. Often, it is still challenging to achieve both a reasonable accuracy and a fairly fast processing speed. In this paper, we propose a new object detection framework which utilizes raw RGB data from the pixel domain and some useful coding information from the compressed domain jointly. Firstly, various pixel-level detection algorithms can be embedded in our framework so as to obtain the preliminary results. Then by segmenting the moving regions from the background with the Hit-times Map (HTM), some false results can be removed and meanwhile the detection process can also be accelerated since the search area for sliding the detection window has been restricted to relatively small regions. After that, an additional regulation process is performed to further refine the preliminary detection results by employing both temporal consistency and spatial compactness in the motion vector(MV) field. The experimental results on two benchmark datasets show that the proposed method achieves a remarkable improvement both in detection accuracy and processing speed.
Two-phase Web site classification based on hidden Markov tree models

With the exponential growth of both the amount and diversity of the information that the Web encompasses, automatic classification of topic-specific Web sites is highly desirable. We propose a novel approach for Web site classification based on the content, structure and context information of Web sites. In our approach, the site structure is represented as a two-layered tree in which each page is modeled as a DOM (document object model) tree and a site tree is used to hierarchically link all pages within the site. Two context models are presented to capture the topic dependences in the site. Then the hidden Markov tree (HMT) model is utilized as the statistical model of the site tree and the DOM tree, and an HMT-based classifier is presented for their classification. Moreover, for reducing the download size of Web sites but still keeping high classification accuracy, an entropy-based approach is introduced to dynamically prune the site trees. On these bases, we employ the two-phase classification system for classifying Web sites through a fine-to-coarse recursion. The experiments show our approach is able to offer high accuracy and efficient process performance.
Video picture-in-picture detection using spatio-temporal slicing

For video copy detection and near-duplicate retrieval applications, picture-in-picture (PiP) is one of widely-used but especially difficult transformations to be detected. Traditionally, PiPs in a video are detected by extracting edges within key frames sampled from the video. However, without taking the temporal continuity between frames into account, the performance of these frame-based methods is not that promising. In this paper, we propose a new video PiP detection method by introducing spatio-temporal slicing (STS) to establish the corresponding edge surface probability measurement. An optimization algorithm is then designed to refine vertical and horizontal edge lines by filtering noisy edges. This PiP detection method can be used to improve the performance of video copy detection particularly in the case of the most challenging PiP transformation. The experimental results on the TRECVID-CCD 2010 dataset demonstrate the effectiveness and efficiency of the proposed method.
Rate-Performance-Loss Optimization for Inter-Frame Deep Feature Coding From Videos.

With the explosion in the use of cameras in mobile phones or video surveillance systems, it is impossible to transmit a large amount of videos captured from a wide area into a cloud for big data analysis and retrieval. Instead, a feasible solution is to extract and compress features from videos and then transmit the compact features to the cloud. Meanwhile, many recent studies also indicate that the features extracted from the deep convolutional neural networks will lead to high performance for various analysis and recognition tasks. However, how to compress video deep features meanwhile maintaining the analysis or retrieval performance still remains open. To address this problem, we propose a high-efficiency deep feature coding (DFC) framework in this paper. In the DFC framework, we define three types of features in a group-of-features (GOFs) according to their coding modes (i.e., I-feature, P-feature, and S-feature). We then design two prediction structures for these features in a GOF, including a sequential prediction structure and an adaptive prediction structure. Similar to video coding, it is important for P-feature residual coding optimization to make a tradeoff between feature bitrate and analysis/retrieval performance when encoding residuals. To do so, we propose a rate-performance-loss optimization model. To evaluate various feature coding methods for large-scale video retrieval, we construct a video feature coding data set, called VFC-1M, which consists of uncompressed videos from different scenarios captured from real-world surveillance cameras, with totally 1M visual objects. Extensive experiments show that the proposed DFC can significantly reduce the bitrate of deep features in the videos while maintaining the retrieval accuracy.
Robust and discriminative image authentication based on standard model feature

The goal of image authentication is to accept content-preserving operations and reject content-altering manipulations. So,it is increasingly approached by extracting content-based invariant features from original images and verifying their preservation in received images at later times. Since sparsity usually implies invariance, sparse feature representation has drawn significant attention from the research community. But only if discrimination is also found with a sparse feature, can it be successfully applied in image authentication. This paper proposes a sparse feature for image authentication by exploring the biologically-motivated standard model. Experimental results demonstrate both robustness and discrimination of the feature, and its effectiveness in tamper detection and location as well.
Unsupervised Cross-Dataset Transfer Learning for Person Re-identification

Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative representation. It is unsupervised in the sense that the target dataset is completely unlabelled. Specifically, we present an multi-task dictionary learning method which is able to learn a dataset-shared but target-data-biased representation. Experimental results on five benchmark datasets demonstrate that the method significantly outperforms the state-of-the-art.
Joint Semantic and Latent Attribute Modelling for Cross-Class Transfer Learning

A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task. In this work, we argue that learning the latent attributes jointly with user-defined semantic attributes not only leads to better representation but also helps semantic attribute prediction. A novel dictionary learning model is proposed which decomposes the dictionary space into three parts corresponding to semantic, latent discriminative and latent background attributes respectively. Such a joint attribute learning model is then extended by following a multi-task transfer learning framework to address a more challenging unsupervised domain adaptation problem, where annotations are only available on an auxiliary dataset and the target dataset is completely unlabelled. Extensive experiments show that the proposed models, though being linear and thus extremely efficient to compute, produce state-of-the-art results on both zero-shot learning and person re-identification.
Design and realization of a maze robot

This article gives hardware design concept of a maze robot taking SCM as a core based on research on design of a maze robot' hardware and maze solving algorithm directing at the problem that there are less sensor-based maze algorithm and it is very difficult to find the shortest path. It proposes a deep first search (DFS) algorithm based on left — right hand wall follower rule and in detail analyzes the realizing processes of this algorithm and this algorithm is realized. Experiments show that this system can find a shorter path from the entrance and the exit of a maze which is not too complicated through two times of search and it features smaller spatial complexity.
Multi-view gait recognition with incomplete training data

Changes in the viewing angles pose a major challenge for gait recognition because the human gait silhouettes can be different under the various viewing angles. Recently, View Transformation Model (VTM) was proposed to tackle this problem by transforming gait features from across views to a common viewing angle. However, VTM must use the data of subjects crossing all views to train the pre-constructed model, which might be unsuitable for the real applications. To address this problem, this paper proposes a View Feature Recovering Model (VFRM) to generate the VTM with incomplete training data. In our algorithm, if the gait signature of a pedestrian is missing under a view, it can be recovered from the K-nearest pedestrians whose gait features are available in the same view. Moreover, the Geodesic distance based K-Nearest Neighbor (GKNN) algorithm is adopted in our algorithm to better measure the neighborhood between two pedestrians. Experimental results on a benchmark database has demonstrated the effectiveness of our method.
Quality Assessment for Comparing Image Enhancement Algorithms

As the image enhancement algorithms developed in recent years, how to compare the performances of different image enhancement algorithms becomes a novel task. In this paper, we propose a framework to do quality assessment for comparing image enhancement algorithms. Not like traditional image quality assessment approaches, we focus on the relative quality ranking between enhanced images rather than giving an absolute quality score for a single enhanced image. We construct a dataset which contains source images in bad visibility and their enhanced images processed by different enhancement algorithms, and then do subjective assessment in a pair-wise way to get the relative ranking of these enhanced images. A rank function is trained to fit the subjective assessment results, and can be used to predict ranks of new enhanced images which indicate the relative quality of enhancement algorithms. The experimental results show that our proposed approach statistically outperforms state-of-the-art general-purpose NR-IQA algorithms.
Performance Evaluation for AVS2 Scene Video Coding Techniques

Different from conventional video coding techniques, the emerging AVS2 standard (the second generation of Audio Video coding Standard, AVS2 for short) introduces some new coding techniques mainly focuses on video coding on scene videos. Scene videos are videos with limited scenes, such as surveillance videos, conference videos, etc. For scene videos, due to the limited scenes, great redundancy especially in background region is retained in the video pictures. AVS2 scene video coding techniques aim at significantly reducing the redundancy so as to achieve higher compression performance. This paper introduces the main coding techniques in AVS2 and demonstrates the coding performance. Experimental results show that the coding efficiency has almost doubled over the main group on scene videos which brings new opportunities to the related industries on specific applications.
Representing Visual Objects in HEVC Coding Loop

Different from the previous video coding standards that employ fixed-size coding blocks (and macroblocks), the latest high efficiency video coding (HEVC) introduces a quadtree structure to represent variable-size coding blocks in the coding loop. The main objective of this study is to investigate a novel way to reuse these variable-size blocks to represent the foreground objects in the picture. Towards this end, this paper proposes three methods, i.e., flagging the blocks lying in the object regions flagging compression blocks (FCB), adding an object tree in each Coding Tree Unit to describe the objects' shape in it additional object tree (AOT) and confining the block splitting procedure to fit the object shape confining by shape (CBS). Among them, FCB and CBS add a flag bit in the syntax description of the block to indicate whether it lies in the objects region, while AOT adds a separate quadtree to represent the objects. For all these methods, the additional bits are then fed into the HEVC entropy coding module to compress. As such, the representation of visual objects in the pictures can be implemented in the HEVC coding loop by reusing the variable-size blocks and entropy coding, without additional coding tools. The experiments on six manually-segmented HEVC testing sequences (three in 1080P and three in 720P) demonstrate the feasibility and effectiveness of our proposal. To represent the objects in the 1080P testing sequences, the BD rate increases of FCB, AOT, and CBS over the HEVC anchor are 1.57%, 3.27%, and 5.93% respectively; while for the 720P conference videos, those are 4.57%, 17.23%, and 26.93% (note that the average bitrate of the anchor is only 1009 kb/s).
Agricultural greenhouse environment monitoring system based on Internet of Things

This paper introduces an agricultural greenhouse environmental monitoring system. The system implements the facilities of agricultural data monitoring, the main control system for Internet of Things system, using ARM processor and Linux operating system. In addition, the system uses a series of sensors to collect and transmit the temperature and humidity data in the growth environment of agricultural products, and then process the data in real time, and operates the related electromechanical equipment. At the same time, Sugon school cloud platfrom as the center of the system, by the cloud platform for data collection and distribution work to achieve the agricultural greenhouse WEB system data synchronization, and through the WEB system to complete the greenhouse query and monitoring. The use of the system can provide users with more decision-making information and operational convenience to achieve the entire system of remote management.
The IEEE 1857 Standard: Empowering Smart Video Surveillance Systems

The IEEE 1857 Standard for Advanced Audio and Video Coding was released as IEEE 1857-2013 in June 2013. Despite consisting of several different groups, the most significant feature of IEEE 1857-2013 is its Surveillance Groups, which can not only achieve at least twice the coding efficiency on surveillance videos as H.264/AVC High Profile, but it's the most analysis-friendly video coding standard. This article presents an overview of IEEE 1857 Surveillance Groups, highlighting background model-based coding technology and analysis-friendly functionalities. 2013 will present new opportunities and drive research in smart video surveillance communities and industries.