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IEEE 2018-2019 PYTHON BASED PROJECTS

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TEP001
Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

ABSTRACT - Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that an early reviewer tends to assign a higher average rating score and an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines. Contact:
 +91-9008001602
 080-40969981

TEP002
Machine Learning and Deep Learning Methods for Cybersecurity

ABSTRACTWith the development of the Internet, cyber-attacks are changing rapidly and the cyber security situation is not optimistic. This survey report describes key literature surveys on machine learning (ML) and deep learning (DL) methods for network analysis of intrusion detection and provides a brief tutorial description of each ML / DL method. Papers representing each method were indexed, read, and summarized based on their temporal or thermal correlations. Because data are so important in ML / DL methods, we describe some of the commonly used network datasets used in ML / DL, discuss the challenges of using ML / DL for cybersecurity and provide suggestions for research directions. Contact:
 +91-9008001602
 080-40969981

TEP003
Predicting the Top-N Popular Videos via a Cross-Domain Hybrid Model

ABSTRACT Predicting the top-N popular videos and their future views for a large batch of newly uploaded videos is of great commercial value to online video services (OVSs). Although many attempts have been made on video popularity prediction, the existing models has a much lower performance in predicting the top-N popular videos than that of the entire video set. The reason for this phenomenon is that most videos in an OVS system are unpopular, so models preferentially learn the popularity trends of unpopular videos to improve their performance on the entire video set. However, in most cases, it is critical to predict the performance on the top-N popular videos which is the focus of this study. The challenge for the task are as follows. First, popular and unpopular videos may have similar early view patterns. Second, prediction models that are overly dependent on early view patterns limit the effects of other features. To address these challenges, we propose a novel multifactor differential influence (MFDI) prediction model based on multivariate linear regression (MLR). The model is designed to improve the discovery of popular videos and their popularity trends are learnt by enhancing the discriminative power of early patterns for different popularity trends and by optimizing the utilization of multi-source data. We evaluate the proposed model using real-world YouTube data, and extensive experiments have demonstrated the effectiveness of our model.Contact:
 +91-9008001602
 080-40969981

TEP004
A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security

ABSTRACT Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classication problems. However, to establish an effective SVM, the user needs to dene the proper SVM conguration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. In this paper, we formulate the SVM conguration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conicting objectives.We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the rst time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM conguration. The low-level heuristics each use different rules to effectively explore the SVM conguration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Paretobased approaches to approximate the Pareto set of SVM congurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classication and anomaly intrusion detection. The obtained results demonstrate that the proposed framework is very effective, if not superior, compared with its counterparts and other algorithms.Contact:
 +91-9008001602
 080-40969981

TEP005
A Data Analytics Approach to the Cybercrime Underground Economy

ABSTRACTDespite the rapid escalation of cyber threats, there has still been little research into the foundations of the subject or methodologies that could serve to guide Information Systems researchers and practitioners who deal with cybersecurity. In addition, little is known about Crime-as-a-Service (CaaS), a criminal business model that underpins the cybercrime underground. This research gap and the practical cybercrime problems we face have motivated us to investigate the cybercrime underground economy by taking a data analytics approach from a design science perspective. To achieve this goal, we propose (1) a data analysis framework for analyzing the cybercrime underground, (2) CaaS and crimeware definitions, and (3) an associated classification model. In addition, we (4) develop an example application to demonstrate how the proposed framework and classification model could be implemented in practice. We then use this application to investigate the cybercrime underground economy by analyzing a large dataset obtained from the online hacking community. By taking a design science research approach, this study contributes to the design artifacts, foundations, and methodologies in this area. Moreover, it provides useful practical insights to practitioners by suggesting guidelines as to how governments and organizations in all industries can prepare for attacks by the cybercrime underground. Contact:
 +91-9008001602
 080-40969981

TEP006
Achieving Data Truthfulness and Privacy Preservation in Data Markets

ABSTRACTAs a significant business paradigm, many online information platforms have emerged to satisfy society’s needs for person-specific data, where a service provider collects raw data from data contributors, and then offers value-added data services to data consumers. However, in the data trading layer, the data consumers face a pressing problem, i.e., how to verify whether the service provider has truthfully collected and processed data? Furthermore, the data contributors are usually unwilling to reveal their sensitive personal data and real identities to the data consumers. In this paper, we propose TPDM, which efficiently integrates Truthfulness and Privacy preservation in Data Markets. TPDM is structured internally in an Encrypt-then-Sign fashion, using partially homomorphic encryption and identity-based signature. It simultaneously facilitates batch verification, data processing, and outcome verification, while maintaining identity preservation and data confidentiality. We also instantiate TPDM with a profile matching service and a data distribution service, and extensively evaluate their performances on Yahoo! Music ratings dataset and 2009 RECS dataset, respectively. Our analysis and evaluation results reveal that TPDM achieves several desirable properties, while incurring low computation and communication overheads when supporting large-scale data markets. Contact:
 +91-9008001602
 080-40969981

TEP007
Data-Driven Design of Fog Computing aided Process Monitoring System for Large-Scale Industrial Processes

ABSTRACT Stimulated by the recent development of fog computing technology, in this paper, a fog computing aided process monitoring and control architecture is proposed for large-scale industrial processes, which enables reliable and efficient online performance optimization in each fog computing node without modifying pre-designed control subsystems. Moreover, a closedloop data-driven method is developed for the process monitoring system design and an adaptive configuration approach is proposed to deal with the problems caused by the changes of process parameters and operating points. The feasibility and effectiveness of the proposed design approaches are verified and demonstrated through the case study on the Tennessee Eastman (TE) benchmark system. Contact:
 +91-9008001602
 080-40969981

TEP008
Designing Cyber Insurance Policies: The Role of Pre-Screening and Security Interdependence

ABSTRACT - Cyber insurance is a viable method for cyber risk transfer. However, it has been shown that depending on the features of the underlying environment, it may or may not improve the state of network security. In this paper, we consider a single profit-maximizing insurer (principal) with voluntarily participating insureds/clients (agents). We are particularly interested in two distinct features of cybersecurity and their impact on the contract design problem. The first is the interdependent nature of cybersecurity, whereby one entity’s state of security depends not only on its own investment and effort, but also the efforts of others’ in the same eco-system (i.e. externalities). The second is the fact that recent advances in Internet measurement combined with machine learning techniques now allow us to perform accurate quantitative assessments of security posture at a firm level. This can be used as a tool to perform an initial security audit, or prescreening, of a prospective client to better enable premium discrimination and the design of customized policies. We show that security interdependency leads to a “profit opportunity” for the insurer, created by the inefficient effort levels exerted by interdependent agents who do not account for the risk externalities when insurance is not available; this is in addition to risk transfer that an insurer typically profits from. Security pre-screening then allows the insurer to take advantage of this additional profit opportunity by designing the appropriate contracts which incentivize agents to increase their effort levels, allowing the insurer to “sell commitment” to interdependent agents, in addition to insuring their risks. We identify conditions under which this type of contracts lead to not only increased profit for the principal, but also an improved state of network security. Contact:
 +91-9008001602
 080-40969981

TEP009
Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds

ABSTRACT - Mining High Average-Utility Itemsets (HAUIs) in a quantitative database is an extension of the traditional problem of frequent itemset mining, having several practical applications. Discovering HAUIs is more challenging than mining frequent itemsets using the traditional support model since the average-utilities of itemsets do not satisfy the downward-closure property. To design algorithms for mining HAUIs that reduce the search space of itemsets, prior studies have proposed various upper-bounds on the average-utilities of itemsets. However, these algorithms can generate a huge amount of unpromising HAUI candidates, which result in high memory consumption and long runtimes. To address this problem, this paper proposes four tight average-utility upper-bounds, based on a vertical database representation, and three efficient pruning strategies. Furthermore, a novel generic framework for comparing average-utility upper-bounds is presented. Based on these theoretical results, an efficient algorithm named dHAUIM is introduced for mining the complete set of HAUIs. dHAUIM represents the search space and quickly compute upper-bounds using a novel IDUL structure. Extensive experiments show that dHAUIM outperforms three state-of-the-art algorithms for mining HAUIs in terms of runtime on both real-life and synthetic databases. Moreover, results show that the proposed pruning strategies dramatically reduce the number of candidate HAUIs. Contact:
 +91-9008001602
 080-40969981

TEP010
Exploratory Visual Sequence Mining Based on Pattern-Growth

ABSTRACT - Sequential pattern mining finds applications in numerous diverging fields. Due to the problem’s combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user’s perspective. Second, as datasets grow, mining large numbers of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a “transparent box” execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users’ capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals’ activity sequences. Contact:
 +91-9008001602
 080-40969981

TEP011
Fog-Aided Verifiable Privacy Preserving Access Control for Latency-Sensitive Data Sharing in Vehicular Cloud Computing

ABSTRACT -VCC is an emerging computing paradigm developed for providing various services to vehicle drivers, and has attracted more and more attention from researchers and practitioners over the last few years. However, privacy preserving and secure data sharing has become a very challenging and important issue in VCC. Unfortunately, existing secure access control schemes consume too many computation resources, which prevents them from being performed on computing resource constrained vehicle onboard devices. Also, these cloud-based schemes suffer large latency and jitter due to their centralized resource management, and thus may not be suitable for real-time applications in VANETs. In this article, we thus propose a novel fog-to-cloud-based architecture for data sharing in VCC. Our scheme is a cryptography-based mechanism that conducts fine-grained access control. In our design, the complicated computation burden is securely outsourced to fog and cloud servers with confidentiality and privacy preservation. Meanwhile, with the prediction of a vehicle’s mobility, pre-pushing data to specific fog servers can further reduce response latency with no need to consume more resources of the fog server. In addition, with the assumption of no collusion between different providers for the cloud and fog servers, our scheme can provide verifiable auditing of fog servers’ reports. The scheme is proved secure against existing adversaries and newborn security threats. Experimental test shows significant performance improvement in edge devices’ overhead saving and response delay reduction. Contact:
 +91-9008001602
 080-40969981

TEP012
How Data-Driven Entrepreneur Analyzes Imperfect Information for Business Opportunity Evaluation

ABSTRACT -High market uncertainty impedes an entrepreneur’s ability to evaluate the state of the market for a business opportunity. For many entrepreneurial ventures, data collection and analysis techniques and technologies are becoming an important source to manage uncertainty. This trend is often referred to as “datadriven entrepreneurship.”We consider a dynamic approach using data to overcome market uncertainty for business opportunityrelated evaluations. In particular, we examine the entrepreneur’s investment portfolio in which each investment generates expected returns and some information about a specific aspect of the market for a single business opportunity.We develop a model that analyzes imperfect market data (e.g., financial, social, regulatory), while factoring in the entrepreneur’s risk preference and operational shortages of resources, routines, reputation, and regulations. Our numerical findings show that, rather than pursuing the highest expected returns, an entrepreneur may choose perfect information, risk hedging, or market-controlling investments based on his/her cash level and risk preference. Hence, the entrepreneur, fueled by the availability of data analysis, could overcome uncertainties and obtain better insights for business opportunity decisions. Contact:
 +91-9008001602
 080-40969981

TEP013
Image Reconstruction Is a New Frontier of Machine Learning

ABSTRACT -Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective, and organized this special issue dedicated to the theme of “Machine Learning for Image Reconstruction”. This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme “Deep Learning in Medical Imaging”. While the previous special issue targeted medical image processing/analysis, this special issue focuses on data-driven tomographic reconstruction. These two special issues are highly complementary, since image reconstruction and image analysis are two of the main pillars for medical imaging. Together we cover the whole workflow of medical imaging: from tomographic raw data/features to reconstructed images and then extracted diagnostic features/readings. In perspective, computer vision and image analysis are great examples of machine learning, especially deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic reconstruction produces images of internal structures from measurement data which are various features (line integrals, harmonic components, etc.) of the underlying images (features to images). Recently, machine learning, especially deep learning, techniques are being actively developed worldwide for tomographic reconstruction, as clearly evidenced by the high-quality papers included in this special issue. In addition to wellestablished analytic and iterative methods for tomographic image reconstruction, it is now clear that machine learning is an emerging approach for image reconstruction, and image reconstruction is a new frontier of machine learning. Contact:
 +91-9008001602
 080-40969981

TEP014
Modeling and Predicting Cyber Hacking Breaches

ABSTRACT -Analyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. This is a relatively new research topic, and many studies remain to be done. In this paper, we report a statistical analysis of a breach incident data set corresponding to 12 years (2005–2017) of cyber hacking activities that include malware attacks. We show that, in contrast to the findings reported in the literature, both hacking breach incident inter-arrival times and breach sizes should be modeled by stochastic processes, rather than by distributions because they exhibit autocorrelations. Then, we propose particular stochastic process models to, respectively, fit the inter-arrival times and the breach sizes. We also show that these models can predict the inter-arrival times and the breach sizes. In order to get deeper insights into the evolution of hacking breach incidents, we conduct both qualitative and quantitative trend analyses on the data set. We draw a set of cybersecurity insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency, but not in terms of the magnitude of their damage. Contact:
 +91-9008001602
 080-40969981

TEP015
Price-based Resource Allocation for Edge Computing: A Market Equilibrium Approach

ABSTRACT -The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this paper, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms, which efficiently converge to an ME, are introduced. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques. Contact:
 +91-9008001602
 080-40969981

TEP016
QuantCloud: Enabling Big Data Complex Event Processing for Quantitative Finance through a Data-Driven Execution

ABSTRACT -Quantitative Finance (QF) utilizes increasingly sophisticated mathematic models and advanced computer techniques to predict the movement of global markets, and price the derivatives and other assets. Being able to react quickly and intelligently to fast-changing markets is a decisive success factor for trading companies. To date, the rise of QF requires an integrated toolchain of enabling technologies to carry out complex event processing on the explosive growth and diversified forms of market metadata, in pursuit of a microsecond latency on an Exabyte-level dataset. Inspired by this, we present a data-driven execution paradigm that untangles the dependencies of complex processing events and integrate the paradigm with a big data infrastructure that streams time series data. This integrated platform is termed as the QuantCloud platform. Essentially, QuantCloud executes the complex event processing in a data-driven mode and manages large amounts of diversified market data in a data-parallel mode. To show its practicability and performance, we develop a prototype and benchmark by applying real-world QF research models on the New York Stock Exchange (NYSE) data. Using this prototype, we demonstrate this platform with an application to: (i) data cleaning and aggregating (including the computing of logarithmic returns from tick data and the finding the medians of grouped data) and (ii) data modeling: the autoregressive-moving average (ARMA) model. The performance results show that (a) this platform obtains a high throughput (usually in the order of millions of tick messages per second) and a sub-microsecond latency; (b) it fully executes data-dependent tasks through a data-driven execution; and (c) it implements a modular design approach for rapidly developing these data-crunching methods and QF research models. This platform resulting from an aggregated effort of the data-driven execution and big data infrastructure, offers the financial engineers with new insights and enhanced capabilities for effective and efficient incorporation of big data complex event processing technologies in their workflow. Contact:
 +91-9008001602
 080-40969981

TEP017
Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning

ABSTRACT -Internet of Things (IoT) in military settings generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices and wearable combat uniforms). These IoT devices and nodes are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device’s Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. to facilitate evaluation of future malware detection approaches). Contact:
 +91-9008001602
 080-40969981

TEP018
RTSense: Providing Reliable Trust-Based Crowdsensing Services in CVCC

ABSTRACT -CVCC has garnered significant attention in recent years as a special cloud computing platform capable of broadening network service provisioning in mobile computing. Vehicular crowdsensing is a prime candidate for CVCC applications as connected vehicles can provide tremendous sensing, computing, and storage resources. Truthfulness of sensing data is very important, as malicious vehicles may create inaccuracy in sensing results. In this work, we propose RTSense, which enables trust-based crowdsensing services in CVCC. The architecture divides the system into control and data planes, where the trust authority and service providers sit in the control plane, and vehicles and fogs exist in the data plane. We provide solutions for anonymous vehicle authentication, interactive filtering truth discovery, and trust management for reliable crowdsensing. The experimental analysis shows that RTSense can effectively segregate malicious and trustworthy vehicles. We also identify interesting future directions along with possible solutions. Contact:
 +91-9008001602
 080-40969981

TEP019
Toward Better Statistical Validation of Machine Learning-Based Multimedia Quality Estimators

ABSTRACT Objective assessment of multimedia quality using machine learning (ML) has been gaining popularity especially in the context of both traditional (e.g., terrestrial and satellite broadcast) and advance (such as over-the-top media services, IPTV) broadcast services. Being data-driven, these methods obviously rely on training to find the optimal model parameters. Therefore, to statistically compare and validate such ML-based quality predictors, the current approach randomly splits the given data into training and test sets and obtains a performance measure (for instance mean squared error, correlation coefficient etc.). The process is repeated a large number of times and parametric tests (e.g., t test) are then employed to statistically compare mean (or median) prediction accuracies. However, the current approach suffers from a few limitations (related to the qualitative aspects of training and testing data, the use of improper sample size for statistical testing, possibly dependent sample observations, and a lack of focus on quantifying the learning ability of the ML-based objective quality predictor) which have not been addressed in literature. Therefore, the main goal of this paper is to shed light on the said limitations both from practical and theoretical perspectives wherever applicable, and in the process propose an alternate approach to overcome some of them. As a major advantage, the proposed guidelines not only help in a theoretically more grounded statistical comparison but also provide useful insights into how well the ML-based objective quality predictors exploit data structure for learning. We demonstrate the added value of the proposed set of guidelines on standard datasets by comparing the performance of few existing ML-based quality estimators. A software implementation of the presented guidelines is also made publicly available to enable researchers and developers to test and compare different models in a repeatable manner. . Contact:
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 080-40969981

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