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NETSPAM- Spam Review Detection Using The Linguistic And Spammer Behavioral Methods

Online Reviews Regarding Different Products Or Services Have Become The Main Source To Determine Public Opinions. Unfortunately, To Gain Profits Or Fame, Spam Reviews Are Written To Promote Or Demote Targeted Products Or Services. This Practice Is Known As Review Spamming. This Can Help To Analyze The Impact Of Widespread Opinion Spam In Online Reviews. In This Work, Two Different Spam Review Detection Methods Have Been Proposed: (1) Spam Review Detection Using Behavioral Method (SRD-BM) Utilizes Thirteen Different Spammer’s Behavioral Features To Calculate The Review Spam Score Which Is Then Used To Identify Spammers And Spam Reviews, And (2) Spam Review Detection Using Linguistic Method (SRD-LM) Works On The Content Of The Reviews And Utilizes Transformation, Feature Selection And Classification To Identify The Spam Reviews. Experimental Evaluations Are Conducted On A Real-world Amazon Review Dataset Which Analyze 26.7 Million Reviews And 15.4 Million Reviewers. The Evaluations Show That Both Proposed Models Have Significantly Improved The Detection Process Of Spam Reviews. Comparatively, SRD-BM Achieved Better Accuracy Because It Works On Utilizing Rich Set Of Spammers Behavioral Features Of Review Dataset Which Provides In-depth Analysis Of Spammer Behavior. To The Best Of Our Knowledge, This Is The First Study Of Its Kind Which Uses Large-scale Review Dataset To Analyze Different Spammers’ Behavioral Features And Linguistic Method Utilizing Different Available Classifiers.

DEEP REPRESENTATION BASED FEATURE EXTRACTION AND RECOVERING- Efficient Finger Vein Technology Based On Fast Binary Robust Independent Elementary Feature Combined With Multi-Image Quality Assessment Verification

Finger-vein Biometrics Has Been Extensively Investigated For Personal Verification. Despite Recent Advances In Fingervein Verification, Current Solutions Completely Depend On Domain Knowledge And Still Lack The Robustness To Extract Finger-vein Features From Raw Images. This Paper Proposes A Deep Learning Model To Extract And Recover Vein Features Using Limited A Priori Knowledge. Firstly, Based On A Combination Of Known State Of The Art Handcrafted Finger-vein Image Segmentation Techniques, We Automatically Identify Two Regions: A Clear Region With High Separability Between Finger-vein Patterns And Background, And An Ambiguous Region With Low Separability Between Them. The First Is Associated With Pixels On Which All The Segmentation Techniques Above Assign The Same Segmentation Label (either Foreground Or Background), While The Second Corresponds To All The Remaining Pixels. This Scheme Is Used To Automatically Discard The Ambiguous Region And To Label The Pixels Of The Clear Region As Foreground Or Background. A Training Dataset Is Constructed Based On The Patches Centered On The Labeled Pixels. Secondly, A Convolutional Neural Network (CNN) Is Trained On The Resulting Dataset To Predict The Probability Of Each Pixel Of Being Foreground (i.e. Vein Pixel) Given A Patch Centered On It. The CNN Learns What A Fingervein Pattern Is By Learning The Difference Between Vein Patterns And Background Ones. The Pixels In Any Region Of A Test Image Can Then Be Classified Effectively. Thirdly, We Propose Another New And Original Contribution By Developing And Investigating A Fully Convolutional Network (FCN) To Recover Missing Fingervein Patterns In The Segmented Image. The Experimental Results On Two Public Finger-vein Databases Show A Significant Improvement In Terms Of Finger-vein Verification Accuracy.

OUTSOURCED DATA STREAMS UNDER MULTIPLE KEYS- KV-Fresh Freshness Authentication For Outsourced Multi-Version Key-Value Stores

Data Outsourcing Is A Promising Technical Paradigm To Facilitate Cost-effective Real-time Data Storage, Processing, And Dissemination. In Such A System, A Data Owner Proactively Pushes A Stream Of Data Records To A Third-party Cloud Server For Storage, Which In Turn Processes Various Types Of Queries From End Users On The Data Owner’s Behalf. This Paper Considers Outsourced Multi-version Key-value Stores That Have Gained Increasing Popularity In Recent Years, Where A Critical Security Challenge Is To Ensure That The Cloud Server Returns Both Authentic And Fresh Data In Response To End Users’ Queries. Despite Several Recent Attempts On Authenticating Data Freshness In Outsourced Key-value Stores, They Either Incur Excessively High Communication Cost Or Can Only Offer Very Limited Real-time Guarantee. To Fill This Gap, This Paper Introduces KV-Fresh, A Novel Freshness Authentication Scheme For Outsourced Key-value Stores That Offers Strong Real-time Guarantee. KV-Fresh Is Designed Based On A Novel Data Structure, Linked Key Span Merkle Hash Tree, Which Enables Highly Efficient Freshness Proof By Embedding Chaining Relationship Among Records Generated At Different Time. Detailed Simulation Studies Using A Synthetic Dataset Generated From Real Data Confirm The Efficacy And Efficiency Of KV-Fresh.