Vol.63, No.3, 2020-Table of Contents
  • 3-Dimensional Bag of Visual Words Framework on Action Recognition
  • Abstract Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action encoder, by analyzing the action… More
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  • Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content
  • Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine… More
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  • Computing Topological Invariants of Triangular Chandelier Lattice
  • Abstract A numerical parameter mathematically derived from the graph structure is a topological index. The topological index is the first actual choice in QSAR research and these indices are used to build the correlation model between the chemical structures of various chemicals compounds. Here, we investigate some old degree-based topological indices like Randic index, sum connectivity index, ABC index, GA index, 1st and 2nd Zagreb indices, modified second Zagreb index, redefined version of 1st, 2nd and 3rd Zagreb indices, hyper and augmented Zagreb indices, forgotten index and symmetric division degree index, and some new degree-based indices like SK index, SK1 index,… More
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  • An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach
  • Abstract The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain… More
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  • Exact Solution of Non-Newtonian Blood Flow with Nanoparticles through Porous Arteries: A Comparative Study
  • Abstract In this paper, the mathematical model describing the third-grade nonNewtonian blood flow suspended with nanoparticles through porous arteries is exactly solved. The present physical model was solved in the research literature via the optimal homotopy analysis method and the collocation method, where the obtained solution was compared with the numerical fourth-order Runge-Kutta solution. However, the present paper only introduces a new approach to obtain the exact solution of the concerned system and implements such exact solution as a reference to validate the published approximate solutions. Several remarks on the previously published results are observed and discussed in detail through tables… More
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  • Ant Colony Optimization for Multi-Objective Multicast Routing
  • Abstract In the distributed networks, many applications send information from a source node to multiple destination nodes. To support these applications requirements, the paper presents a multi-objective algorithm based on ant colonies to construct a multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weight (cost, delay and hop) of the multicast tree. Experimental results prove the proposed algorithm outperforms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, it is able to find a better solution with fast convergence speed and high reliability. More
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  • Applying ANN, ANFIS and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2
  • Abstract In the present work, a novel machine learning computational investigation is carried out to accurately predict the solubility of different acids in supercritical carbon dioxide. Four different machine learning algorithms of radial basis function, multi-layer perceptron (MLP), artificial neural networks (ANN), least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are used to model the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and the dissociation constant of acid. To evaluate the proposed models, different graphical and statistical analyses, along with novel sensitivity analysis, are carried out.… More
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  • A MV-Based Steganographic Algorithm for H.264/AVC without Distortion
  • Abstract H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography. In this paper, a novel motion vector (MV) based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion. Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm. In the embedding block, the motion vector space encoding is used to embed a (2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block. Scrambling is adopted to disturb the order of… More
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  • Prediction of Web Services Reliability Based on Decision Tree Classification Method
  • Abstract With the development of the service-oriented computing (SOC), web service has an important and popular solution for the design of the application system to various enterprises. Nowadays, the numerous web services are provided by the service providers on the network, it becomes difficult for users to select the best reliable one from a large number of services with the same function. So it is necessary to design feasible selection strategies to provide users with the reliable services. Most existing methods attempt to select services according to accurate predictions for the quality of service (QoS) values. However, because the network and… More
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  • The Quantum Approximate Algorithm for Solving Traveling Salesman Problem
  • Abstract The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems. It consists of interleaved unitary transformations induced by two operators labelled the mixing and problem Hamiltonians. To fit this framework, one needs to transform the original problem into a suitable form and embed it into these two Hamiltonians. In this paper, for the well-known NP-hard Traveling Salesman Problem (TSP), we encode its constraints into the mixing Hamiltonian rather than the conventional approach of adding penalty terms to the problem Hamiltonian. Moreover, we map edges (routes) connecting each pair of cities to qubits,… More
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  • Microgrids-as-a-Service for Rural Electrification in Sub-Saharan Africa
  • Abstract The majority of the population on the African continent is unable to access basic electricity services, this despite the abundance of renewable energy sources (RESs). The inability to adequately tap into these RESs has led to the continued dependence on nonrenewable energy sources such as coal for electricity generation, and kerosene for cooking and lighting, the resulting use of which is poor health conditions. The use of Microgrids (MGs) is being extensively researched as a feasible means of tackling the challenge of electrification, especially in rural and remote areas. Recent times have seen an increasing number of research works focusing… More
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  • A Polyp Detection Method Based on FBnet
  • Abstract The incidence of colorectal cancer (CRC) in China has increased in recent years. The mortality rate of CRC has become one of the highest among all cancers; CRC increasingly affects the health and quality of people’s lives. However, due to the insufficiency of medical resources in China, the workload on medical doctors has further increased. In the past few decades, the adult CRC mortality and morbidity rate dropped sharply, mainly because of CRC screening and removal of adenomatous polyps. However, due to the differences in polyp itself and the skills of endoscopists, the detection rate of polyps varies greatly. In… More
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  • Power Control and Routing Selection for Throughput Maximization in Energy Harvesting Cognitive Radio Networks
  • Abstract This paper investigates the power control and routing problem in the communication process of an energy harvesting (EH) multi-hop cognitive radio network (CRN). The secondary user (SU) nodes (i.e., source node and relay nodes) harvest energy from the environment and use the energy exclusively for transmitting data. The SU nodes (i.e., relay nodes) on the path, store and forward the received data to the destination node. We consider a real world scenario where the EH-SU node has only local causal knowledge, i.e., at any time, each EH-SU node only has knowledge of its own EH process, channel state and currently… More
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  • Quantum Secure Direct Communication Protocol with Mutual Authentication Based on Single Photons and Bell States
  • Abstract Quantum secure direct communication (QSDC) can transmit secret messages directly from one user to another without first establishing a shared secret key, which is different from quantum key distribution. In this paper, we propose a novel quantum secure direct communication protocol based on signal photons and Bell states. Before the execution of the proposed protocol, two participants Alice and Bob exchange their corresponding identity IDA and IDB through quantum key distribution and keep them secret, respectively. Then the message sender, Alice, encodes each secret message bit into two single photons (| 01〉or|10〉) or a Bell state , and composes… More
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  • Review of Text Classification Methods on Deep Learning
  • Abstract Text classification has always been an increasingly crucial topic in natural language processing. Traditional text classification methods based on machine learning have many disadvantages such as dimension explosion, data sparsity, limited generalization ability and so on. Based on deep learning text classification, this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention Mechanisms-Based and so on. Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets. The main reasons are text classification methods based on deep learning… More
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  • δ-Calculus: A New Approach to Quantifying Location Privacy☆
  • Abstract With the rapid development of mobile wireless Internet and high-precision localization devices, location-based services (LBS) bring more convenience for people over recent years. In LBS, if the original location data are directly provided, serious privacy problems raise. As a response to these problems, a large number of location-privacy protection mechanisms (LPPMs) (including formal LPPMs, FLPPMs, etc.) and their evaluation metrics have been proposed to prevent personal location information from being leakage and quantify privacy leakage. However, existing schemes independently consider FLPPMs and evaluation metrics, without synergizing them into a unifying framework. In this paper, a unified model is proposed to… More
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  • MINE: A Method of Multi-Interaction Heterogeneous Information Network Embedding
  • Abstract Interactivity is the most significant feature of network data, especially in social networks. Existing network embedding methods have achieved remarkable results in learning network structure and node attributes, but do not pay attention to the multiinteraction between nodes, which limits the extraction and mining of potential deep interactions between nodes. To tackle the problem, we propose a method called MultiInteraction heterogeneous information Network Embedding (MINE). Firstly, we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm. Secondly, we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse… More
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  • Crosstalk Aware Register Reallocation Method for Green Compilation
  • Abstract As nanoscale processing becomes the mainstream in IC manufacturing, the crosstalk problem rises as a serious challenge, not only for energy-efficiency and performance but also for security requirements. In this paper, we propose a register reallocation algorithm called Nearby Access based Register Reallocation (NARR) to reduce the crosstalk between instruction buses. The method includes construction of the software Nearby Access Aware Interference Graph (NAIG), using data flow analysis at assembly level, and reallocation of the registers to the software. Experimental results show that the crosstalk could be dramatically minimized, especially for 4C crosstalk, with a reduction of 80.84% in average,… More
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  • Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks
  • Abstract Speaker separation in complex acoustic environment is one of challenging tasks in speech separation. In practice, speakers are very often unmoving or moving slowly in normal communication. In this case, the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information. To fully exploit this information, we design a separation system on Recurrent Neural Network (RNN) with long short-term memory (LSTM) which effectively learns the temporal dynamics of spatial features. In detail, a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency… More
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  • Optimization Scheme of Large Passenger Flow in Huoying Station, Line 13 of Beijing Subway System
  • Abstract This paper focuses on the distribution of passenger flow in Huoying Station, Line 13 of Beijing subway system. The transformation measures taken by Line 13 since operation are firstly summarized. Then the authors elaborate the facilities and equipment of this station, especially the node layout and passenger flow field. An optimization scheme is proposed to rapidly distribute the passenger flow in Huoying Station by adjusting the operation time of the escalator in the direction of Xizhimen. The authors adopt Queuing theory and Anylogic simulation software to simulate the original and the optimized schemes of Huoying Station to distribute the passenger… More
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  • Parallelization and I/O Performance Optimization of a Global Nonhydrostatic Dynamical Core Using MPI
  • Abstract The Global-Regional Integrated forecast System (GRIST) is the nextgeneration weather and climate integrated model dynamic framework developed by Chinese Academy of Meteorological Sciences. In this paper, we present several changes made to the global nonhydrostatic dynamical (GND) core, which is part of the ongoing prototype of GRIST. The changes leveraging MPI and PnetCDF techniques were targeted at the parallelization and performance optimization to the original serial GND core. Meanwhile, some sophisticated data structures and interfaces were designed to adjust flexibly the size of boundary and halo domains according to the variable accuracy in parallel context. In addition, the I/O performance… More
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  • State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries
  • Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More
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  • An Alias Resolution Method Based on Delay Sequence Analysis
  • Abstract Alias resolution, mapping IP addresses to routers, is a critical step in obtaining a network topology. The latest work on alias resolution is based on special fields in the packet, such as IP ID, port number, etc. However, for security reasons, most network devices block packets for setting options, and some related fields exist only in IPv4, so these methods cannot be used for alias resolution of IPv6. In order to solve the above problems, we propose an alias analysis method based on delay sequence analysis. In this article, we present a new model to describe the distribution of Internet… More
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  • GTK: A Hybrid-Search Algorithm of Top-Rank-k Frequent Patterns Based on Greedy Strategy
  • Abstract Currently, the top-rank-k has been widely applied to mine frequent patterns with a rank not exceeding k. In the existing algorithms, although a level-wise-search could fully mine the target patterns, it usually leads to the delay of high rank patterns generation, resulting in the slow growth of the support threshold and the mining efficiency. Aiming at this problem, a greedy-strategy-based top-rank-k frequent patterns hybrid mining algorithm (GTK) is proposed in this paper. In this algorithm, top-rank-k patterns are stored in a static doubly linked list called RSL, and the patterns are divided into short patterns and long patterns. The short… More
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  • Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory
  • Abstract Since the British National Archive put forward the concept of the digital continuity in 2007, several developed countries have worked out their digital continuity action plan. However, the technologies of the digital continuity guarantee are still lacked. At first, this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory, then points out the necessity of data quality guarantee for electronic record. Moreover, we convert the digital continuity guarantee of electronic record to ensure the consistency, completeness and timeliness of electronic record, and construct the first technology framework of the digital continuity guarantee for… More
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  • Joint Deep Matching Model of OCT Retinal Layer Segmentation
  • Abstract Optical Coherence Tomography (OCT) is very important in medicine and provide useful diagnostic information. Measuring retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular conditions. Among the existing retinal layer segmentation approaches, learning or deep learning-based methods belong to the state-of-art. However, most of these techniques rely on manual-marked layers and the performances are limited due to the image quality. In order to overcome this limitation, we build a framework based on gray value curve matching, which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from OCT. The depth convolution network… More
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  • MII: A Novel Text Classification Model Combining Deep Active Learning with BERT
  • Abstract Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an instance selection strategy based on… More
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  • Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning
  • Abstract Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,… More
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  • High Resolution SAR Image Algorithm with Sample Length Constraints for the Range Direction
  • Abstract The traditional Range Doppler (RD) algorithm is unable to meet practical needs owing to the limit of resolution. The order of fractional Fourier Transform (FrFT) and the length of sampling signals affect SAR imaging performance when FrFT is applied to RD algorithm. To overcome the above shortcomings, the purpose of this paper is to propose a high-resolution SAR image algorithm by using the optimal order of FrFT and the sample length constraints for the range direction. The expression of the optimal order of SAR range signals via FrFT is deduced in detail. The initial sample length and its constraints are… More
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  • Hidden Two-Stream Collaborative Learning Network for Action Recognition
  • Abstract The two-stream convolutional neural network exhibits excellent performance in the video action recognition. The crux of the matter is to use the frames already clipped by the videos and the optical flow images pre-extracted by the frames, to train a model each, and to finally integrate the outputs of the two models. Nevertheless, the reliance on the pre-extraction of the optical flow impedes the efficiency of action recognition, and the temporal and the spatial streams are just simply fused at the ends, with one stream failing and the other stream succeeding. We propose a novel hidden twostream collaborative (HTSC) learning… More
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  • Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion
  • Abstract Real-time detection of driver fatigue status is of great significance for road traffic safety. In this paper, a proposed novel driver fatigue detection method is able to detect the driver’s fatigue status around the clock. The driver’s face images were captured by a camera with a colored lens and an infrared lens mounted above the dashboard. The landmarks of the driver’s face were labeled and the eye-area wassegmented. By calculating the aspect ratios of the eyes, the duration of eye closure, frequency of blinks and PERCLOS of both colored and infrared, fatigue can be detected. Based on the change of… More
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  • Visual Relationship Detection with Contextual Information
  • Abstract Understanding an image goes beyond recognizing and locating the objects in it, the relationships between objects also very important in image understanding. Most previous methods have focused on recognizing local predictions of the relationships. But real-world image relationships often determined by the surrounding objects and other contextual information. In this work, we employ this insight to propose a novel framework to deal with the problem of visual relationship detection. The core of the framework is a relationship inference network, which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the… More
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