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Deep-sea organism automatic tracking has rarely been studied because of a lack of training data. However, it is extremely important for underwater robots to recognize and to predict the behavior of organisms. In this paper, we first develop a method for underwater real-time recognition and tracking of multi-objects, which we call “You Only Look Once: YOLO”. This method provides us with a very fast and accurate tracker. At first, we remove the haze, which is caused by the turbidity of the water from a captured image. After that, we apply YOLO to allow recognition and tracking of marine organisms, which include shrimp, squid, crab and shark. The experiments demonstrate that our developed system shows satisfactory performance.

The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. These IoT devices can communicate with others over the Internet and fully integrate into people’s daily life. In recent years, IoT devices still suffer from basic security vulnerabilities making them vulnerable to a variety of threats and malware, especially IoT botnets. Unlike common malware on desktop personal computer and Android, heterogeneous processor architecture issue on IoT devices brings various challenges for researchers. Many studies take advantages of well-known dynamic or static analysis for detecting and classifying botnet on IoT devices. However, almost studies yet cannot address the multi-architecture issue and consume vast computing resources for analyzing. In this paper, we propose a lightweight method for detecting IoT botnet, which based on extracting high-level features from function–call graphs, called PSI-Graph, for each executable file. This feature shows the effectiveness when dealing with the multi-architecture problem while avoiding the complexity of control flow graph analysis that is used by most of the existing methods. The experimental results show that the proposed method achieves an accuracy of 98.7%, with the dataset of 11,200 ELF files consisting of 7199 IoT botnet samples and 4001 benign samples. Additionally, a comparative study with other existing methods demonstrates that our approach delivers better outcome. Lastly, we make the source code of this work available to Github.

Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets.

The text embedded in images provides important information for image understanding. Text segmentation is an essential step for text recognition. It is often difficult to segment text from images at low resolution or with complex background. In this paper, a novel text segmentation framework is proposed to solve the problem. The proposed framework adopts a hybrid strategy integrating two different text segmentation methods to produce text candidates. One segmentation method is designed based on the intensity uniformity of text regions, while the other is developed by integrating the features of intensity and stroke width of text. To separate text pixels from the text candidates, a new non-text pixel filtering method is proposed. In the filtering method, an effective classifier is designed based on the number of breaking elements and the k-means clustering algorithm. The performance of the proposed segmentation framework is tested by the pixel-based and recognition-based evaluation methods. Experimental results show that the F-score of the proposed framework on the video caption dataset and born-digital dataset of ICDAR2013 are 95.29% and 89.09% respectively, while the correctly recognized character rate and word rate on the German TV public dataset are 91.00% and 72.33%. The experimental results indicate that the proposed text segmentation framework has excellent performance and high robustness in text segmentation and recognition.

We study the barrier coverage problem using relocatable sensor nodes. We assume each sensor can sense an intruder or event inside its sensing range. Sensors are initially located at arbitrary positions on the barrier and can move along the barrier. The goal is to find final positions for sensors so that the entire barrier is covered. In recent years, the problem has been studied extensively in the centralized setting. In this paper, we study a barrier coverage problem in the distributed and discrete setting. We assume that we have n identical sensors located at grid positions on the barrier, and that each sensor repeatedly executes a Look-Compute-Move cycle: based on what it sees in its vicinity, it makes a decision on where to move, and moves to its next position. We make two strong but realistic restrictions on the capabilities of sensors: they have a constant visibility range and can move only a constant distance in every cycle. In this model, we give the first two distributed algorithms that achieve barrier coverage for a line segment barrier when there are enough nodes in the network to cover the entire barrier. Our algorithms are synchronous, and local in the sense that sensors make their decisions independently based only on what they see within their constant visibility range. One of our algorithms is oblivious whereas the other uses two bits of memory at each sensor to store the type of move made in the previous step. We show that our oblivious algorithm terminates within Θ ( n 2 ) steps with the barrier fully covered, while the constant-memory algorithm is shown to take Θ ( n ) steps to terminate in the worst case. Since any algorithm in which a sensor can only move a constant distance in one step requires Ω ( n ) steps on some inputs, our second algorithm is asymptotically optimal.

Healthcare Monitoring Systems (HMSs) are promising to monitor patients in hospitals and elderly people living in Ambient Assisted Living environments using Wireless Sensor Networks. HMSs assist in monitoring chronic diseases such as Heart Attacks, High Blood Pressure and other cardiovascular diseases. Wearable and implanted devices are types of Body sensors that collect human health related data. Collected data is sent over Personal Area Networks (PANs). However, PANs are facing the challenge of increasing network traffic due to the increased number of IP-enabled devices connected in Healthcare Monitoring Systems to assist patients. ZigBee technology is an IEEE 802.15.4 standard designed to address network traffic issues in PANs. To route traffic, ZigBee network use ZigBee Tree Routing (ZTR) protocol. ZTR however suffers a challenge of network latency caused by end to end delay during packet forwarding. This paper is proposing a New Tree Routing Protocol (NTRP) for Healthcare Monitoring Systems to collect Heart Rate signals. NTRP uses Kruskal’s minimum spanning tree to find shortest routes on a ZigBee network which improves ZTR. Neighbor tables are implemented in NTRP instead of parent–child mechanism implemented in ZTR. To reduce end to end delay, NTRP groups’ nodes into clusters and the cluster heads use neighbor tables to forward heart rate data to the destination node. NS-2 simulation tool is used to evaluate NTRP performance.

The Quality of Service (QoS) management within a multiple-traffic Wi-Fi MultiMedia (WMM) ad hoc network is a tedious task, since each traffic type requires a well determined QoS-level. For this reason, the IEEE Working Group has proposed the IEEE 802.11e Enhanced Distributed Channel Access (EDCA) protocol at the MAC layer of WMM ad hoc networks. However, several studies have shown that EDCA must be further improved for three main reasons. The first reason is the poor performance of EDCA under high traffic conditions due to the high collision rate. The second reason is the need to maximize the traffic performance (delay, throughput, etc.) guaranteed by EDCA, seen the rapid evolution of the applications (multimedia, real time, etc.). The third reason is the need to maximize the energy efficiency of the EDCA, seen its use in battery constrained devices (e.g. Laptop, Smart phone, Tablet computers, etc.). For these three reasons, we propose in this paper a Three-in-One solution MAC protocol called QoS Maximization of EDCA (QM-EDCA), which is an enhanced version of EDCA. Based on the fuzzy logic mathematic theory, QM-EDCA incorporates a dynamic MAC parameters fuzzy logic system, in order to adapt dynamically the Arbitration inter frame Spaces according to the network state and remaining energy. Simulation results show that QM-EDCA outperforms EDCA by reducing significantly the collision rate, and maximizing traffic performance and energy-efficiency. In addition our solution is fully distributed.

In this paper, we describe the development of template management technology to build virtual resources environments on OpenStack. In recent days, Cloud computing has been progressed and also open source Cloud software has become widespread. Authors are developing cloud services using OpenStack. There are technologies which deploy a set of virtual resources based on system environmental templates to enable easy building, expansion or migration of cloud resources. OpenStack Heat and Amazon CloudFormation are template deployment technologies and build stacks which are sets of virtual resources based on templates. However, these existing technologies have 4 problems. Heat and CloudFormation transaction managements of stack create or update are insufficient. Heat and CloudFormation do not have sharing mechanism of templates. Heat cannot extract templates from existing virtual environments. Heat does not reflect actual environment changes to stack information. Therefore, we propose a new template management technology with 4 improvements. It has a mechanism of transaction management like roll back or roll forward in case of abnormal failure during stack operations. It shares templates among end users and System Integrators. It extracts templates from existing environments. It reflects actual environment changes to stack information. We implemented the proposed template management server and showed that end users can easily replicate or build virtual resources environments. Furthermore, we measured the performance of template extraction, stack create and update and showed our method could process templates in a sufficient short time.

The IEEE 802.11 DCF and EDCA mechanisms based on CSMA/CA are the most widely used random channel access mechanisms in wireless mesh networks (WMNs), but unfortunately these cannot effectively eliminate hidden terminal and exposed terminal problems in multi-hop scenarios. In this paper, we propose a set of efficient multi-radio multi-channel (MRMC) assignment, scheduling and routing protocols based on Latin squares for WMNs with MRMC communication capabilities, called “ M 4”, i.e., the Multiple access scheduling in Multi-radio Multi-channel Mesh networking. M 4 uses nodal interference information to form cliques for inter-cluster and intra-cluster inWMNs, and then applies Latin squares to map the clique-based clustering structure to radios and channels for communication purposes. Then, M 4 again applies Latin squares to schedule the channel access among nodes within each cluster in a collision-free manner. From a systematic view, we also design the corresponding MRMC routing to support M 4 communication. Extensive simulation results show that M 4 achieves much better performance than IEEE 802.11 standards and other channel access control protocols.

Non-photorealistic rendering (NPR) is a field in computer science which can create effective illustrations and appealing artistic imagery. Some researchers have proposed NPR methods to simulate different artistic illustrations. However, simulating the new art styles remains extremely challenging. National pyrography is a very famous artistic work in China, and few algorithms have been put forward to illustrate this style. Some exist rendering methods can not demonstrate the main characters of the real pyrography, and the rendering speed is time-consuming using texture synthesis technique. This paper proposes a non-photorealistic rendering technique that automatically generates a gourd pyrography style from a 2D photograph. Similar to the existing exemplar-based methods, an input natural image is regarded as the foreground image, and an input gourd image is taken as the background image. To avoid time-consuming methods like texture synthesis or analogy, this paper simulates character of real pyrography through image abstraction and enhancement from the foreground image. The foreground image will be deformed using equilateral triangle mesh to match the gourd image and mapped to this background image. Experimental results demonstrate the effectiveness of our methods in producing gourd pyrography stylistic illustrations. Meanwhile, the proposed method is simple, fast, and easy to implement.