Benign Strategy for Recommended Location Service Based on Trajectory Data

A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information.

Interest-Forgetting Markov Model for Next-Basket Recommendation

Recommendation systems provide users with ranked items based on individual’s preferences. Two types of preferences are commonly used to generate ranking lists: long-term preferences which are relatively stable and short-term preferences which are constantly changeable. But short-term preferences have an important real-time impact on individual’s current preferences. In order to predict personalized sequential patterns, the long-term user preferences and the short-term variations in preference need to be jointly considered for both personalization and sequential transitions. In this paper, a IFNR model is proposed to leverage long-term and short-term preferences for Next-Basket recommendation. In IFNR, similarity was used to represent long-term preferences. Personalized Markov model was exploited to mine short-term preferences based on individual’s behavior sequences. Personalized Markov transition matrix is generally very sparse, and thus it integrated Interest-Forgetting attribute, social trust relation and item similarity into personalized Markov model. Experimental results are on two real data sets, and show that this approach can improve the quality of recommendations compared with the existed methods.

Fast and Secure Transmission Method Based on Optocoupler for Mobile Storage

This paper presents a one-way data transmission method in order to ensure the safety of data transmission from mobile storage to secure PC.First,an optocoupler is used to achieve the one-way transmission of physical channel,so that data can only be transmitted from mobile storage to secure PC,while the opposite direction is no physical channel.Then,a safe and reliable software system is designed which contains one-way communication protocol,fast CRC check method and packet retransmission algorithm together to ensure the safety of data transmission.After that,to obtain the maximum transmission rate,the frequency of data bus(slwr)and the packet size(num)which effect on transmission rate are detailed analyzed.Experimental results show the proposed method is high-efficiency and safe.

CTS: Combine Temporal Influence and Spatial Influence for Time-Aware POI Recommendation

As location-based social network (LBSN) services become more popular in people’s lives, Point of Interest (POI) recommendation has become an important research topic.POI recommendation is to recommend places where users have not visited before. There are two problems in POI recommendation: sparsity and precision. Most users only check-in a few POIs in an LBSN. To tackle the sparse problem in a certain extent, we compute the similarity between the check-in datasets of different times. For the precision problem, we incorporate temporal information and geographical information. The temporal information will influence how the user chooses and allow the user to visit different distance point on different day. The geographical information is also used as a control for points which are too far away from the user’s check-in data. Our experimental results on real life LBSN datasets show that the proposed approach outperforms the other POI recommendation methods substantially.

Research and Practice on College Students’Innovation and Entrepreneurship Education

Strengthening the education of innovation and entrepreneurship is one of the important tasks of China’s higher education reform and development.Entrepreneurship Education should focus on setting pioneering genetic code for future generations.Essentially,it is an education innovation-oriented entrepreneurial revolution of human resource development.Technological innovation is strategic support to improve social productivity and comprehensive national strength,which should be placed at the core of national overall development.For China’s higher education,it proposes more new requirements.Serious discussion on the innovation education of college students is needed.Through practice,improvements in the quality of innovation and entrepreneurship education will be achieved.

Fine-Grained Access Control for Big Data Based on CP-ABE in Cloud Computing

In Cloud Computing, the application software and the databases are moved to large centralized data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings many new security challenges, which have not been well solved. Data access control is an effective way to ensure the big data security in the cloud. In this paper,we study the problem of fine-grained data access control in cloud computing.Based on CP-ABE scheme,we propose a novel access control policy to achieve fine-grainedness and implement the operation of user revocation effectively.The analysis results indicate that our scheme ensures the data security in cloud computing and reduces the cost of the data owner significantly.

Gene Coding Sequence Identification Using Kernel Fuzzy C-Mean Clustering and Takagi-Sugeno Fuzzy Model

Sequence analysis technology under big data provides unprecedented opportunities for modern life science. A novel gene coding sequence identification method is proposed in this paper. Firstly, an improved short-time Fourier transform algorithm based on Morlet wavelet is applied to extract the power spectrum of DNA sequence. Then, threshold value determination method based on kernel fuzzy C-mean clustering is used to combine Signal to Noise Ratio (SNR) data of exon and intron into a sequence, classify the sequence into two types, calculate the weighted sum of two SNR clustering centers obtained and the discrimination threshold value. Finally, exon interval endpoint identification algorithm based on Takagi-Sugeno fuzzy identification model is presented to train Takagi-Sugeno model, optimize model parameters with Levenberg-Marquardt least square method, complete model and determine fuzzy rule. To verify the effectiveness of the proposed method, example tests are conducted on typical gene sequence sample data.

ECG-Based Arrhythmia Detection Using Attention-Based Convolutional Neural Network

This study constructs a multi-classification model of arrhythmia basedon the dual-channel convolutional neural network with attention mechanism, inorder to make automatic detection of arrhythmias. Firstly, the public arrhythmiadata set and the previous data preprocessing work were introduced. Secondly,the processed data was input into the deep learning model constructed with convolutionalneural network, to automatically extract features of arrhythmia fromelectrocardiogram signals. Thirdly, the designed deep learning model was usedfor classifications and diagnosis of arrhythmias. Then the performance of the proposedmodel and that from other research work were compared. The validation ofthe method is proved with five cross-validation strategy.

Statute Recommendation Based on Word Embedding

The statute recommendation problem is a sub problem of the automated decision system, which can help the legal staff to deal with the process of the case in an intelligent and automated way. In this paper, an improved common word similarity algorithm is proposed for normalization. Meanwhile, word mover’s distance (WMD) algorithm was applied to the similarity measurement and statute recommendation problem, and the problem scene which was originally used for classification was extended. Finally, a variety of recommendation strategies different from traditional collaborative filtering methods were proposed. The experimental results show that it achieves the best value of Fmeasure reaching 0.799. And the comparative experiment shows that WMD algorithm can achieve better results than TF-IDF and LDA algorithm.

Survey of Methods for Time Series Symbolic Aggregate Approximation

Time series analysis is widely used in the fields of finance, medical, and climate monitoring. However, the high dimension characteristic of time series brings a lot of inconvenience to its application. In order to solve the high dimensionality problem of time series, symbolic representation, a method of time series feature representation is proposed, which plays an important role in time series classification and clustering, pattern matching, anomaly detection and others. In this paper, existing symbolization representation methods of time series were reviewed and compared. Firstly, the classical symbolic aggregate approximation (SAX) principle and its deficiencies were analyzed. Then, several SAX improvement methods, including aSAX, SMSAX, ESAX and some others, were introduced and classified;Meanwhile, an experiment evaluation of the existing SAX methods was given. Finally, some unresolved issues of existing SAX methods were summed up for future work.

Adaptive Flower Pollination Algorithm Based on Chaotic Map

Flower pollination algorithm (FPA) is one of the well-known evolutionary techniques used extensively to solve optimization problems. Despite its efficiency and wide use, the identical search behaviors may lead the algorithm to converge to local optima. In this paper, an adaptive FPA based on chaotic map (CAFPA) is proposed. The proposed algorithm first used the ergodicity of the logistic chaos mechanism, and chaotic mapping of the initial population to make the initial iterative population more evenly distributed in the solution space. Then at the self-pollination stage, the over-random condition of the gamete renewal was improved, the traction force of contemporary optimal position was given, and adaptive logarithmic inertia weight was introduced to adjust the proportion between the contemporary pollen position and disturbance to improve the performance of the algorithm. By comparing the new algorithm with three famous optimization algorithms, the accuracy and performance of the proposed approach are evaluated by 14 well-known benchmark functions. Statistical comparisons of experimental results show that CAFPA is superior to FPA, PSO, and BOA in terms of convergence speed and robustness.

License Plate Recognition Model Based on CNN+LSTM+CTC

With the continuous improvement of the social and economic level, the number of vehicles has exploded in the city, and traditional manual identification license plates have been unable to meet the demand. In this paper, a Convolutional neural network (CNN)-based license plate recognition system is designed. The recognition module uses the CNN+LSTM+CTC model to simplify the convolutional layer structure to adapt to the lightweight training mode. The two-way LSTM structure is used to learn from both sides of the license plate to enhance the end-to-end recognition effect. Compared with the traditional scheme, the CTC loss calculation method eliminates the need for character alignment, streamlines the steps, and improves the recognition accuracy. The experiment shows that the license plate recognition software system designed in this paper has a high recognition accuracy rate of 98.59%.

Topic Model Based Text Similarity Measure for Chinese Judgment Document

In the recent informatization of Chinese courts, the huge amount of law cases and judgment documents, which were digital stored,has provided a good foundation for the research of judicial big data and machine learning. In this situation, some ideas about Chinese courts can reach automation or get better result through the research of machine learning, such as similar documents recommendation, workload evaluation based on similarity of judgement documents and prediction of possible relevant statutes. In trying to achieve all above mentioned, and also in face of the characteristics of Chinese judgement document, we propose a topic model based approach to measure the text similarity of Chinese judgement document, which is based on TF-IDF, Latent Dirichlet Allocation (LDA), Labeled Latent Dirichlet Allocation (LLDA) and other treatments. Combining with the characteristics of Chinese judgment document,we focus on the specific steps of approach, the preprocessing of corpus, the parameters choices of training and the evaluation of similarity measure result. Besides, implementing the approach for prediction of possible statutes and regarding the prediction accuracy as the evaluation metric, we designed experiments to demonstrate the reasonability of decisions in the process of design and the high performance of our approach on text similarity measure. The experiments also show the restriction of our approach which need to be focused in future work.

Secret Data-Driven Carrier-Free Secret Sharing Scheme Based on Error Correction Blocks of QR Codes

In this paper,a novel secret data-driven carrier-free(semi structural formula)visual secret sharing(VSS)scheme with(2,2)threshold based on the error correction blocks of QR codes is investigated.The proposed scheme is to search two QR codes that altered to satisfy the secret sharing modules in the error correction mechanism from the large datasets of QR codes according to the secret image,which is to embed the secret image into QR codes based on carrier-free secret sharing.The size of secret image is the same or closest with the region from the coordinate of(7,7)to the lower right corner of QR codes.In this way,we can find the QR codes combination of embedding secret information maximization with secret data-driven based on Big data search.Each output share is a valid QR code which can be decoded correctly utilizing a QR code reader and it may reduce the likelihood of attracting the attention of potential attackers.The proposed scheme can reveal secret image visually with the abilities of stacking and XOR decryptions.The secret image can be recovered by human visual system(HVS)without any computation based on stacking.On the other hand,if the light-weight computation device is available,the secret image can be lossless revealed based on XOR operation.In addition,QR codes could assist alignment for VSS recovery.The experimental results show the effectiveness of our scheme.

An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.

Equal Radial Force Structure Pressure Sensor Data Analysis and Finite Element Analysis

As big data is very important today, we creative a force sensor with the AT-cut quartz crystal resonator and analyze the experimental data. Quartz crystal resonator has the characteristic that the resonance frequency changes by the external force, which has high precision, fast-speed response. Also it has the superior feature in the temperature and frequency stability. But it also has weakness, because of quartz crystal resonator has low degree of mechanical characteristic and weak to stress concentration by bending that the quartz crystal resonator had been hardly applied to the force measurement. The objective of this study is to construct the sensor mechanism that safely maintains the quartz crystal resonator for the external force with flat structure. We using finite element multiphysics simulation software designed and implemented an innovative structure-equal radial force structure, According to the measured data,applied load equivalent radial force structure between size and the frequency of the quartz monitor chip has a good linear relationship. The proposed force sensor is flat, small, and sensitive. It can be applied to several usages such as medical treatment and contact force detection of human.

Design and Implementation of a Project Management System Based on Product Data Management on the Baidu Cloud Computing Platform

Aiming at enterprises without commercial project management systems(PMS)in a product data management(PDM)environment,and using a cloud computing platform,this research analyses the business process and function of complex product project management in PDM,and proposes a PMS-based organizational structure for such a project.This model consists of a task view,user view,role view,and product view.In addition,it designs the function structure,E-R model and logical model of a PMS database,and also presents an architecture based on the Baidu cloud platform,describes the functions of the Baidu App Engine(BAE),establishes the overall PMS software architecture.Finally,it realizes a revised product design project by using EasyUI,J2EE and other related technologies.Practice shows that the PMS designed for PDM has availability,scalability,reliability and security with the help of the Baidu cloud computing platform.It can provide a reference for small-and medium-sized enterprises seeking to implement information systems with high efficiency and at low cost in the age of big data.

People Flow Monitoring Based on Deep Learning

According to the application scenarios of the size of the humanflow in different consumption places,to solve the problem of crowd detection,distance estimation between crowds and the inability to monitor and calculate the humanflow in real time,this paper designs a real-time crowd detection scheme for appli-cation scenarios where consumers pay attention to the size of the humanflow in consumption places.The main use of the YOLO algorithm with the Darknet53 net-work as the main network is to separate pedestrians from the background.Pedes-trians’central two-dimensional coordinates are converted into three-dimensional coordinates,realizing crowd detection and apart from the distance estimation of crowds,real-time monitoring of current regional traffic andflow density,and solv-ing the problem of being unable to monitor and calculate people in real time.It can be applied to many aspects,such as shop rating,traffic control andflow control of scenic spots.Existing monitors are affected by different lights and cannot provide accurate data.In addition,the processing algorithm of this scheme is stable and accurate,and preprocessing is performed before judging the humanflow and the position of the human body to reduce the interference of light.This scheme has the performance of real-time monitoring and calculation through experimental verification.

Multifunctional Sitting Posture Detector Based on Face Tracking

To reduce the vision problems caused by improper sitting posture,the research group used Raspberry Pi as the main controller for a multifunctional sitting posture detector with functions such as sitting posture detection,face positioning,cloud monitoring,etc.UUsing tech-nologies or algorithms such as machine vision and convolutional neural networks,our design can realize the user’s sitting posture error detec-tion,such as left,right,low head position,or forward body position with alarming,so that the user can maintain the appropriate sitting posture.