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Textile sludge has complex components and certain toxicity, which is in urgent need of resource treatment. The effect of textile sludge replacing cement and aggregates on the properties of polypropylene fiber concrete has been investigated by testing the compressive strength, drying shrinkage, heavy metal leaching concentration, micro morphology, and nanomechanical properties. The results show that the utilization of 10% textile sludge replacing cement increases the later strengths of concrete and decreases the drying shrinkage due to its denser microstructure. With the further content increase of textile sludge replacing cement, the strengths of concrete are reduced and the drying shrinkage is increased. The utilization of textile sludge replacing aggregates increases the compressive strengths of concrete and the drying shrinkage at every age, and among them, the concrete with 15% textile sludge replacing aggregates shows the highest compressive strengths, and the drying shrinkage of concrete increases with the content increase of textile sludge replacing aggregates. The concrete with textile sludge is a good solidification with heavy metal ions. The utilization of 10% textile sludge replacing cement improves the microstructure of concrete and helps to produce more high-density calcium silicate hydrate and reduces the thickness of the interfacial transition zone.

Black-box adversarial attack has attracted a lot of research interests for its practical use in AI safety. Compared with the white-box attack, a black-box setting is more difficult for less available information related to the attacked model and the additional constraint on the query budget. A general way to improve the attack efficiency is to draw support from a pre-trained transferable white-box model. In this paper, we propose a novel setting of transferable black-box attack: attackers may use external information from a pre-trained model with available network parameters, however, different from previous studies, no additional training data is permitted to further change or tune the pre-trained model. To this end, we further propose a new algorithm, EigenBA to tackle this problem. Our method aims to explore more gradient information of the black-box model, and promote the attack efficiency, while keeping the perturbation to the original attacked image small, by leveraging the Jacobian matrix of the pre-trained white-box model. We show the optimal perturbations are closely related to the right singular vectors of the Jacobian matrix. Further experiments on ImageNet and CIFAR-10 show that even the unlearnable pre-trained white-box model could also significantly boost the efficiency of the black-box attack and our proposed method could further improve the attack efficiency.

Now, there is a problem to satisfy the demand of flexible manufacturing systems. Human-computer interaction intelligent assembly technology is more and more attention. Gesture recognition and action understanding is the basis of human-computer interaction assembly. For this reason, the research on hand gesture recognition is carried out. Firstly, the basic according to the features of the hand skin color separation method is described in this paper. The principle of YCrCb color space and the conversion between RGB color space is illustrated. It is described how YCrCb three-channel method eliminates the influence of brightness on color, and how to identify skin color and other colors. Secondly, it is explained that the principle of Markov random field. Markov random field's application in image segmentation problem is shown. Iterative algorithm process based on Markov random field is introduced. Finally, it is described how to combine the two methods, the skin color recognition and iterative algorithm. They are combined with a small area noise removal method to achieve a better image of the hand segmentation. And segmentation results and conclusions are shown.

Shiqiang Yang Xintai Hospital Affiliated to Taishan Medical University, Tai’an City, People’s Republic of China Background: Gastric metastasis from ovarian serous cystadenocarcinoma is extremely rare. Case presentation: We herein report one case of a 45-year-old female with ovarian carcinoma who underwent cytoreductive surgery. Two years later, endoscopic ultrasonography-guided fine needle aspiration revealed gastric metastasis. The patient underwent laparoscopic resection of gastric metastases. She is currently in complete remission. Conclusion: Gastric metastasis from ovarian cancer should not be ignored in the clinic. Keywords: gastric neoplasm, ovarian neoplasm, metastasis

Traditional experiments and EDA technique-based CPLD experiments have their own characteristics in digital logic experiment teaching. Therefore, we try to combine such two kinds of experiments in teaching with the aim of taking full advantage of them. This paper introduces the practice in digital logic experiment teaching based on experimental platform, which is under the guidance of such ideas. We scheduled experimental contents to be performed on the platform developed by us, which considers the ratio of the two kinds of experiments, also arranges the comprehensive experiment with topics selected independently by students. The results prove that the combination of such two kinds of experiments is beneficial to train students in improving their software and hardware application ability, not only in mastering the digital logic course effectively, but also in strengthening the ability of practice.

To provide a live, active and high-quality virtual touring streaming experience, we propose an unmanned drone stereoscopic streaming paradigm using a control and streaming infrastructure of a 2.4GHz Wi-Fi grid. Our system allows users to actively control the streaming captured by a drone, receive and watch the streaming using a head mount display (HMD); a Wi-Fi grid is deployed across the remote scene with multi-channel support to enable high-bitrate stream- ing broadcast from the drones. The system adopt a joint view adaptation and drone control scheme to enable fast viewer movement including both head rotation and touring. We implement the prototype on Dji M100 quadcopter and HTC Vive in a demo scene.

Recent years have witnessed a dramatic increase of user-generated video services. In such user-generated video services, crowdsourced live streaming (e.g., Periscope, Twitch) has significantly challenged today's edge network infrastructure: today's edge networks (e.g., 4G, Wi-Fi) have limited uplink capacity support, making high-bitrate live streaming over such links fundamentally impossible. In this paper, we propose to let broadcasters (i.e., users who generate the video) upload crowdsourced video streams using aggregated network resources from multiple edge networks. There are several challenges in the proposal: First, how to design a framework that aggregates bandwidth from multiple edge networks? Second, how to make this framework transparent to today's crowdsourced live streaming services? Third, how to maximize the streaming quality for the whole system? We design a multi-objective and deployable bandwidth aggregation system BASS to address these challenges: (1) We propose an aggregation framework transparent to today's crowdsourced live streaming services, using an edge proxy box and aggregation cloud paradigm; (2) We dynamically allocate geo-distributed cloud aggregation servers to enable MPTCP (i.e., multi-path TCP), according to location and network characteristics of both broadcasters and the original streaming servers; (3) We maximize the overall performance gain for the whole system, by matching streams with the best aggregation paths.

Most concept recognition in visual multimedia is based on relatively simple concepts, things which are present in the image or video. These usually correspond to objects which can be identified in images or individual frames. Yet there is also a need to recognise semantic con- cepts which have a temporal aspect corresponding to activities or com- plex events. These require some form of time series for recognition and also require some individual concepts to be detected so as to utilise their time-varying features, such as co-occurrence and re-occurrence patterns. While results are reported in the literature of using concept detections which are relatively specific and static, there are research questions which remain unanswered. What concept detection accuracies are satisfactory for time series recognition? Can recognition methods perform equally well across various concept detection performances? What affecting factors need to be taken into account when building concept-based high-level event/activity recognitions? In this paper, we conducted experiments to investigate these questions. Results show that though improving concept detection accuracies can enhance the recognition of time series based concepts, they do not need to be very accurate in order to characterize the dynamic evolution of time series if appropriate methods are used. Experimental results also point out the importance of concept selec- tion for time series recognition, which is usually ignored in the current literature.

Indexing of visual media based on content analysis has now moved beyond using individual concept detectors and there is now a fo- cus on combining concepts or post-processing the outputs of individual concept detection. Due to the limitations and availability of training cor- pora which are usually sparsely and imprecisely labeled, training-based refinement methods for semantic indexing of visual media suffer in cor- rectly capturing relationships between concepts, including co-occurrence and ontological relationships. In contrast to training-dependent methods which dominate this field, this paper presents a training-free refinement (TFR) algorithm for enhancing semantic indexing of visual media based purely on concept detection results, making the refinement of initial con- cept detections based on semantic enhancement, practical and flexible. This is achieved using global and temporal neighbourhood information inferred from the original concept detections in terms of weighted non- negative matrix factorization and neighbourhood-based graph propaga- tion, respectively. Any available ontological concept relationships can also be integrated into this model as an additional source of external a priori knowledge. Experiments on two datasets demonstrate the efficacy of the proposed TFR solution.

In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem. The cost volume is constructed through bitwise operations on a series of binary strings. Then this approach is combined with traditional winner-take-all strategy, resulting in a new local stereo matching algorithm called binary stereo matching (BSM). Since core algorithm of BSM is based on binary and integer computations, it has a higher computational efficiency than previous methods. Experimental results on Middlebury benchmark show that BSM has comparable performance with state-of-the-art local stereo methods in terms of both quality and speed. Furthermore, experiments on images with radiometric differences demonstrate that BSM is more robust than previous methods under these changes, which is common under real illumination., Pattern Recognition (ICPR), 2012 21st International Conference on

We argue that the conventional forward error correcting code (FEC) and retransmission solutions will become ineffective to transmit scalable video coding streaming in wireless mesh network (WMN) for the reason that video frame errors appear in bursts rather than random and the bad channel condition usually persists for a period in wireless channels. A novel SVC streaming cooperative transmission scheme is proposed in this paper. Our cooperative transmission scheme includes two stages among nodes in WMN. The first stage is selecting appropriate paths for the relevant SVC sub-layer streams and the second one is selecting nearby nodes to cache the network coding results of the adjacent sub-layer streams and retransmit the network coded packets to the user terminal for recovering the error packets. Simulation results are given to demonstrate the significant improvement of the SVC streaming reconstructive video quality.

Video transmission over unreliable packet network is in general hampered by the packet losses and constraint by stringent playback deadline. With these two key factors in consideration, multiple description coding (MDC), comprising balanced and unbalanced MDC has been proposed as an error-robust source coding technique. Recently, transmitting multiple descriptions over a single path is interesting due to the unavailability of multiple independent paths. Therefore, in this paper, we investigate the problem of rate allocation for the high-resolution (HR) and low-resolution (LR) descriptions in UMDC transmission over single path. We first propose an approximate but efficient rate allocation model with the aid of two-state Markov link model and a simple distortion model at the sender side. Then we conduct extensive experiments to verity the proposed model and more excitedly the simulation results clearly demonstrate the effectiveness of proposed model

Virtual physics environments are becoming increasingly popular as a teaching tool for grade and high school level mechanical physics. While useful, these tools often offer a complex user interface, lacking the intuitive nature of the traditional whiteboard. Furthermore, the systems are often too advanced to be used by novices for further experimentation. In this paper we describe a physics learning environment using multicolour sketch recognition techniques on digital whiteboards. We argue that the use of coloured pens helps to resolve several ambiguities appearing in single colour sketching interfaces. The recognition system is based on a combination of support vector machines and rule based methods. The system was evaluated using a constructive interaction method, with users completing a set task.