BEST PRACTICES TO PROTECT YOUR PRIVACY AGAINST SEARCH ENGINES DATA MINING - A REVIEW
Franck Seigneur Nininahazwe, Nanjing University of Information Science and Technology, Nanjing, China
Search engines are great tools which help us when we are surfing on the internet. Most people rely on them for directions to links needed for any kind of search. The issue is that while they help you, they also learn about you, and that isn’t bad as long as it is not too invasive, the real issue is these systems gather every bit of information from you such as identity, location and so on. In this review therefore we dive in to discuss some of these protection methods and privacy techniques, we also have done some comparisons between them to give you a detailed understanding to enable one have a clear idea. We also suggest after comparisons and conclude on the best way to protect your privacy while using these internet based search engines.
INCENTIVE COOPERATION ENFORCEMENT BASED ON OVERLAPPING COALITION FORMATION GAME FRAMEWORK FOR AD HOC NETWORKS
Bo Wang, Lihong Wang, Weiling Chang, Jie Xu
CNCERT/CC, Beijing, China, 100029
In this paper, we study the problem of incentive cooperation to encourage cooperative packet forwarding among selfish nodes. Unlike existing works which assume that only disjoint groups of cooperative each node can emerge, we formulate the problem as an overlapping coalition formation game. In this game, each node can choose to participate in one or more cooperative coalitions simultaneously, so as to maximize its utility associated with cooperation. Then, we propose a distributed coalition formation algorithm (OCF) using three move rules to find a stable coalition structure. Simulation results show that the proposed algorithm achieves a better performance than the non-cooperative scheme based on AODV and the classical algorithms for coalitional games with disjoint coalition formation (DCF), in terms of the packet delivery ratio, average end-to-end delay and total payoff of all the nodes.
INTERNET OF THINGS IN SMART SHIITAKE MUSHROOM FARM MANAGEMENT SYSTEM USING WIRELESS SENSOR NETWORKS
Mohamed Rawidean Mohd Kassim & Ahmad Nizar Harun
Product & Systems Architecture MIMOS, Ministry of Science, Technology and Innovation, Kuala Lumpur, Malaysia
Most of the mushroom cultivation around the world are being done in a very primitive way. Due to the high demand for mushrooms, it is very crucial to upgrade the current practice in order to increase the yield through automation and utilizing optimum resources. Wireless Sensor Network (WSN) and related technologies are widely used to build decision support systems to solve many real-world problems especially in agricultural environment. Using the basic principles of WSN and Mobile Computing technology, a smart system for Shiitake (Lentinula Edodes) mushroom farm management was developed in Sabah, Malaysia. The wireless temperature, humidity and CO2 sensors are used to collect data in this system. The real-time data is monitored and control devices are activated based on pre-defined threshold values. Finally, the implementation of this system have optimized the usage of resources such as water and fertilizer and also maximized the quality and productivity of the mushroom.
BIT RATE AND TASK SCHEDULING IN MOBILE CLOUD COMPUTING FOR MULTIMEDIA BIG DATA
Byeongok Choi and Chae Y. Lee
Department of Industrial and System Engineering, KAIST, Taejon, Korea
As video traffic increases with plentiful multimedia services and the proliferation of mobile devices such as smartphones, stream mining to extract valuable information out of multimedia big data is garnering attention. By applying cloud computing to stream mining, resource-scarce mobile devices can offload the workloads of heavy applications to a remote cloud. However, resource provisioning for task scheduling is an inherent challenge of stream mining in cloud computing. In this paper we consider problem of resource provisioning and bit rate scaling for multimedia big data processing. We aim to minimize the virtual machine (VM) leasing cost and the classification error cost while satisfying the deadline constraints of workloads which is formulated as a mixed integer nonlinear programming. Deadline based task scheduling and bit rate scaling are developed to find near optimal solution of the NP-hard problem. The upper and lower bounds of the required number of VMs are obtained for infeasible and feasible schedules respectively. Scaling down the highest bit rate first in the bit rate set of a workload is suggested to guarantee the minimum increase of error cost. Our simulation results show the efficiency of bit rate scaling in task scheduling. 5-10% cost reduction is achieved by bit rate scaling in a cloud computing
IOT-BASED HOME APPLIANCE SYSTEM (SMART FAN)
1 Mehran Ektesabi, Saman A. Gorji, Amir Moradi, 2Suchart Yammen, V. Mahesh K. Reddy,3 Sureerat Tang. 1Swinburne University of Technology, Victoria, Australia.2Naresuan University, Thailand. 3TJ Supply Limited Partnership, Thailand
Smart home appliances such as smart fridge, smart lighting, and smart air conditioner are getting popular for home end users. Smart fans as one of those smart devices are a part of a smart home that can be assumed as a factor of comfort, which may also reduce the electricity cost due to its high efficiency. Hence, this project aims to develop an alternative smart fan tackled from a comfort and cost perspectives. This project is done using as minimum budget as possible by using a combination of the already-available parts of the market. It is expected to develop a prototype of a cheap smart fan, which in turn becomes the starting point to allow further development of other smart home appliances
TIME-DOMAIN SIGNAL MANAGEMENT FOR OFDM SIGNALS
Takuya Kazama1, Kazuki Miyazawa2 and Masahiro Muraguchi3. 1,2Faculty of Engineering, Tokyo University of Science, Tokyo, Japan.3Tokyo University of Science, Tokyo, Japan
We have found out that the CAZAC- OFDM accords the amplitude of IFFT output signal with the amplitude of input DATA and the time ordering of IFFT output signal is unambiguously determined. That is, the OFDMtime-domain signals, which are composed of many sinewaves, can be shaped by CAZAC precoder.As one application example that can use this characteristic of CAZAC precoder, we propose a newtechnique of symbol timing estimation, which enable to avoid the use of the preambles and guard-intervals.Conventional OFDM systems introduce guard-intervals for symbol timing estimation and reduction of multipath effect. Visible light communications (VLCs), which are one kind of line-of–sight communications, does not require consideration of the multipath channel. Therefore, ifwe embed null data at a fixed position in time-domain, we will easily estimate the symbol timing in the receiver side.
A PAPR REDUCTION TECHNIQUE IN OFDM SYSTEMS WITH A LARGE NUMBER OF SUBCARRIERS
Yasuhiro Shimazu1, Yushi Shirato2 and Masahiro Muraguchi3. 1,3Department of Electrical Engineering, Tokyo University of Science
6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-0051, Japan. 2NTT, Japan
A major drawback of orthogonal frequency division multiplexing (OFDM) signals is extremely high peak-to-average power ratio (PAPR). Signals with high PAPR lead to a lowering of the energy efficiency of power amplifiers and the shortened operation time causes a serious problem in battery-powered wireless terminals. We have found the CAZAC precoding makes the PAPR of M-array quadrature amplitude modulation (M-QAM) OFDM signals into the PAPR of M-QAM single-carrier signals. Therefore, it can dramatically improve the PAPR of OFDM signals. However, to satisfy the 3GPP-LTE specification of frequency spectrum, severe bandpass filtering of CAZAC-OFDM signal lead to unacceptable regrowth of the PAPR. The paper provides available control procedure for PAPR and spectrum managements. It is confirmed that the CAZAC-OFDM signal controlled by our procedure maintains enough low PAPR and can provide comparable spectral specifications in the downlink channel of 3GPP-LTE standard.
CONTENT-DEFINED CLUSTERING IN WIRELESS NETWORKS
Bo Yang, Department of Computer Science, Bowie State University, MD, USA
For a wireless ad hoc network comprising autonomous and self-organizing data sources, efficient similarity-based search for semantic-rich resources (such as multi-media data) is a challenging task due to the lack of infra-structures and the multiple limitations (such as band-width, storage, and energy). While the past research discussed much on routing protocols for ad hoc networks, few works have been reported on effective data retrieval with respect to optimized search cost and fair across various environment setups. This paper presents the design of progressive content prediction approaches to facilitate efficient similarity-based search in wireless ad hoc networks. The proposed approaches are fully dynamic, hierarchy-free, and non-flooding, and do not add much system overhead. We verified the performance with experimental analysis.
SPATIAL QUERY RESOLUTION IN VEHICULAR WIRELESS NETWORKS
Bo Yang, Department of Computer Science, Bowie State University, MD, USA
Caching has been widely used in vehicular networks to improve system performance. However, conventional caching methodologies have two major drawbacks in dealing with spatial queries in a dynamic vehicular network: First, the description of cached data is defined based on the query context instead of data content, ignoring the spatial or semantic locality of the data. Meanwhile, the spatial queries issued by the mobile clients are usually similar in content because the movements of clients exhibit high spatial continuity. Second, the description of cached data does not reflect the popularity of the data, making it inefficient in providing QoS-related services. To address these issues, we proposed a location-aware caching model which reflects the distribution of data objects based on the analysis of earlier queries. The novelty of our method stems from several factors including: 1) Describing the data object distribution based on a Hilbert space-filling curve, 2) Optimizing spatial query resolution through efficient exploitation of locally cached data, and 3) Reducing the cost of query resolution with restricted search scope. Through extensive simulations, we show that our model can perform spatial search with less cost. In addition, it is scalable to large vehicular networks and voluminous data.
AN INTELLIGENT BUSINESS INVENTORYMANAGEMENT APPLICATION USINGARTIFICIAL INTELLIGENCE AND VOICERECOGNITION
Howard Li1, Yu Sun2, Fangyan Zhang3
1University High School, Irvine, CA 92612. 2California State Polytechnic University, Pomona, CA 91768. 3Mississippi State University, Mississippi State, MS 39762.
Virtual Enterprise, a class that simulates real-world business world, is designed for high school students to improve their experience in buying and selling. In Virtual Enterprise, various virtual products are created by classes. Each class is trying to sell products to students from different class in exhibitions. However, in trading, thousands of different types of handwritings made the salesperson in exhibitions are too difficult to be organized by administrator. To solve this problem, this paper develops an application, called Easy Exhibition, using Artificial Intelligence technology, which uses voice recognition technology to automatically complete the sales order by voice instead of by handwriting. The experiments show that AI-assisted solution improves both accuracy and efficiency in transaction processing.
ENSEMBLE LEARNING USING FREQUENT ITEMSET MINING FOR ANOMALY DETECTION
Saeid Soheily-Khah and Yiming Wu
SKYLADS Research Team, Paris, France
Anomaly detection is vital for automated data analysis, with speciffc applications spanning almost
every domain. In this paper, we propose a hybrid supervised learning of anomaly detection using
frequent itemset mining and random forest with an ensemble probabilistic voting method, which
outperforms the alternative supervised learning methods through the commonly used measures for
anomaly detection: accuracy, true positive rate (i.e. recall) and false positive rate. To justify our
claim, a benchmark dataset is used to evaluate the esoiency of the proposed approach, where the
results illustrate its beneffits.
IMPROVED BLOCK STAGEWISE REGULARIZED ORTHOGONAL MATCHING PURSUIT IMAGE RECONSTRUCTION METHOD
Xiong-yong Zhu1, Shun-dao Xie2,3, Guo-ming Chen1, Liang Xue1, Wen-fang Wu2, Hong-zhou Tan2,3
1Department of Computer Science Guangdong University of Education Guangzhou, China.
2School of Electronic and Information Engineering Sun Yat-Sen University Guangzhou,China.
3SYSU-CMU Shunde International Joint Research Institute, Foshan, China.
The traditional approaches to signal acquisition need to collect large amounts of redundant data first,then compress them to extract useful information, which is inefficient and requires larger storage spaces.
Compressed sensing (CS) can avoid sampling the redundant data; it obtains the discrete signals at the sampling rate that is lower than the Nyquist sampling rate, and reconstruct the original signal with high
probability. Based on CS, we proposed a method called Block Stagewise Regularized Orthogonal Matching Pursuit (StROMP). Simulation results show that the proposed method can effectively reduce the
storage spaces and computational complexity, which improves the quality of reconstructed images in the premise of ensuring a shorter reconstruction time.
ENSEMBLE LEARNING BASED VOTING MODEL FOR DYNAMIC PROFILE CLASSIFICATION AND PROJECT ALLOTMENT
Suhas Tangadle Gopalakrishna 1 and Vijayaraghavan Varadharajan2
1Infosys Limited, Bengaluru, India,
2Infosys Limited, Bengaluru,India.
Every year, lakhs of students right from college enter professional life through various recruitment activities conducted by the organization. The allotment of projects to the new recruits, carried out by the HR team of the organization is usually a manual affair. It is a time consuming and a tedious process as it involves manually opening each resume and analysing it one by one in order to assign a project. Companies round the globe are leveraging the power of artificial intelligence and machine learning to increase their productivity. In this paper, we present one such use case wherein artificial intelligence is leveraged by the organisation in allotment of projects to the new recruits. Currentmachine learning tools help in the allotment of projects to a few known popular domains on which the classifier has been trained explicitly. We tackle the problem with an ensemble learningbasedvoting classifier consisting of 5 individual machine learning classifiers, voting to classify the profile of the candidate into the relevant domain. The knowledge extracted from the profiles for which there is no majority consensus among the individual classifiersis used to retrain the model. The proposed model achieves a higher accuracy in classifying resumes to proper domains than a standard machine learning classifier which is solely dependent on the training set for classification.Overall, emphasis is laid out on building a dynamic machine learning automation tool which is not solely dependent on the training data in allotment of projects to the new recruits.
OBJECT LOCALIZATION AND ACTIVITIES IDENTIFICATION USING ATTRIBUTE DETAILS IN SMART MEETING ROOMS
Dian Andriana1,2, ArySetijadi Prihatmanto2, Egi Muhammad Idris Hidayat2,and Carmadi Machbub2
1Research Center for Informatics, Indonesian Institute of Sciences, Bandung, Indonesia
2School of Electrical Engineering and Informatics, InstitutTeknologi Bandung, Bandung, Indonesia
This paper is concerned with the development of interactive systems for smart meeting rooms. Automated recognition of video events is an important research area. We present an LTL model of basic objects and activities recognition in smart meeting rooms using object attribute details. There are still problems of misrecognizing objects in existing visual recognition methods because lack of enough feature attributive information details. This paper investigates morphological approach to increase recognition accuracy using variability in a limited area of moving object using object attribute details. The proposed methods are also compared to popular and recent methods of visual object and event recognition.
REUSABLE ASSETS SOFTWARE (RAS) AND FORMAL RULES (FR) IN SOFTWARE DEVELOPMENT AND BUSINESS PROCESS
Javier Darío Fernández-Ledesma, University Pontificia Bolivariana, Medellín, Colombia
Software reuse in the early stages is a key issue in rapid development of applications. This article introduces a metaprocess-oriented methodology based on the model reuse as software assets and formal rules, and starting from the domain specification and analysis phases. The approach includes the definition of a conceptual level to adequately represent the domain, formal rules and a reuse process to specify the metaprocess as software assets. The methodology has been applied successfully in the field of e-health, but our work also describes advances in reuse of models for implementation in other contexts, contributing to improved productivity in software development.
SOFTWARE PRACTITIONERS CHALLENGES IN THE REQUIREMENT ENGINEERING PHASE OF SOFTWARE DEVELOPMENT
Abdool Qaiyum Mohabuth, University of Mauritius, Reduit, Mauritius
Software practitioners have always stressed upon the importance of having a dedicated formal Requirement Engineering (RE) stage in software development. Research has found that RE stage is among those factors that contribute towards project success. Decomposition of this stage in terms of elicitation, analysis, modeling and specification are well defined, but yet software developers claim they face issues as regards to the establishment of proper structural components during the RE stage. Project failures have often been associated with poor requirement definition where the practitioners approach to problem arising during RE phase has been criticised. Ensuring proper interaction among team members and maintaining good communication channel with clients have always been enlisted of being of prime importance in promoting project success in the RE phase. However, findings were mainly based on conventional wisdom. There is still lack of empirical evidence in terms of the identification of other factors that influenced the smooth running of the RE phase. This study aims at identifying the problems faced by practitioners in the RE phase through empirical evidence. An analysis of the influential factors with respect to the criticality for project successes is made. Assessment of the relevance of these factors according to length of projects and size of organisations are also made. A survey questionnaire was designed for identifying the problems faced by practitioners in the RE phase and their frequency of occurrences according to length of project were assessed. The questionnaire was administered to software developers at small, medium and large software enterprises. Semi-structured interviews were also conducted with practitioners who have long years of experience as project leaders. Findings reveal that besides communication flaws there are other influential factors that hinder successful completion of the RE phase. ‘Ti me boxing’, ‘Insufficient support from customer’ and ‘from own management’, ‘Gray areas in requirements’ are among the hindering factors that make the RE phase vulnerable. Besides, it has been observed that there is uniformity across organisations. Small, medium and large development firms face similar issues as regards to the factors that affect the RE phase.