Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.

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Machine Learning and Integrative Analysis of Biomedical Big Data.

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are often deployed in open environments to provide innovative services in various application domains such as smart cities, smart health, and smart communities. These IoT devices produce a massive amount of confidentiality and security-sensitive data. Thus, security of these devices is very important in order to ensure the safety and effectiveness of the system. In this paper, a decentralized authentication and access control mechanism is proposed for lightweight IoT devices and is applicable to a large number of scenarios. The mechanism is based on the technology of the fog computing and the concept of the public blockchain. The results gained from the experiments demonstrate a superior performance of the proposed mechanism when compared to a state-of-the-art blockchain-based authentication technique.

A decentralized lightweight blockchain-based authentication mechanism for IoT systems

Since the 1G of mobile technology, mobile wireless communication systems have continued to evolve, bringing into the network architecture new interfaces and protocols, as well as unified services, high data capacity of data transmission, and packet-based transmission (4G). This evolution has also introduced new vulnerabilities and threats, which can be used to launch attacks on different network components, such as the access network and the core network. These drawbacks stand as a major concern for the security and the performance of mobile networks, since various types of attacks can take down the whole network and cause a denial of service, or perform malicious activities. In this survey, we review the main security issues in the access and core network (vulnerabilities and threats) and provide a classification and categorization of attacks in mobile network. In addition, we analyze major attacks on 4G mobile networks and corresponding countermeasures and current mitigation solutions, discuss limits of current solutions, and highlight open research areas.

Survey on Threats and Attacks on Mobile Networks

Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid

Hotel selection driven by online textual reviews: Applying a semantic partitioned sentiment dictionary and evidence theory

In real world data are often plagued by missing values which adversely affects the final outcome of the analysis based on such data. The missing values can be handled using various techniques like deletion or imputation. Of late, R has become one of the most preferred platform for carrying out data analysis, and its popularity is growing further. R provides various packages for handling missing values through imputation. The presence of multiple packages however, calls for an analysis of their comparative performance and examine their suitability for handling a given set of data. The performance of different R packages may differ for different datasets and may depend on the size of the dataset and richness of the missing values in the datasets. In this paper, the authors perform comparative study of the performance of the common R packages, namely VIM, MICE, MissForest, and HMISC, used for missing value imputation. The authors measured the performances of the said packages in terms of their imputation time, imputation efficiency and the effect on the variance. The imputation efficiency was measured in terms of the difference in predictive performance of a model built using original dataset vis-a-vis a dataset with imputed values. Similarly, the variance of the variables in the original dataset was compared that of corresponding variables in the imputed dataset. A missing value imputation package can be considered to be better if it consumes less imputation time and provides high imputation accuracy. Also in terms of variance, one would like to have the imputation package maintain the original variance of the variables. On analysing the four imputation packages on two datasets over three predictive algorithms–Logistic Regression, Support Vector Machines, and Artificial Neural Networks–it was observed that the performances varies depending on the size of the dataset, and the missing values present in them. The study highlights that certain missing value package used in conjunction with a given predictive algorithm provides better performance, which is again a function of the dataset characteristics.

Handling missing values: A study of popular imputation packages in R

Identity privacy is a security issue that is crucial for the users of a cellular network. Knowledge of the permanent identity of a user may allow an adversary to track and amass comprehensive profiles about individuals. Such profiling may expose an individual to various kind of unanticipated risks, and above all may deprive an individual of his right to privacy. With the introduction of sensitive services like online banking, shopping, etc. through cellular phones, identity privacy has now become a bigger security issue. In GSM and UMTS, the problem of user identity privacy vulnerability is proven to exist. In both these systems, there are situations where the permanent identity of a subscriber may get compromised. Long Term Evolution (LTE), which evolved from GSM and UMTS, is proposed by 3GPP for inclusion into the fourth generation of cellular networks. Although security of LTE has evolved from the security of GSM and UMTS, due to different architectural and business requirements of fourth generation systems, LTE security is substantially different and improved compared to its predecessors. However, the issue of identity privacy vulnerability continue to exist in LTE. In this paper, we discuss how the security architecture of LTE deals with identity privacy. We also discuss a possible solution that may be utilised to overcome the problem of user identity privacy in LTE.

Enhancing User Identity Privacy in LTE

Effect of trip mode on opinion about hotel aspects: a social media analysis approach.

Identity resolution of a person using various online social networks can enable an interested party to have a better and holistic understanding of former’s behavior and personality. Major challenges in developing a reliable and scalable matching scheme for online identities include non-availability of required information or having contradictory information for the same user across these networks. In this study, we present a scheme for identity matching which utilizes important features extracted from contents generated by or shared with users across one’s online social networks. With the help of natural language processing and text mining techniques, we extract and process parts-of-speech, symbols, emoticons, numbers, and high frequency words in user’s posts, tweets, retweets, and URLs. On the basis of experiments with ground truth Twitter–Facebook real datasets, this method achieved 91.2 percent accuracy in matching user’s identity across the user’s profiles. The main contribution of this paper is that this proposes a novel method for identity matching, which utilizes only the publicly available content information of online social network users. This method can be used alone for identity matching, or can be used along with other identity resolution frameworks to enhance their accuracy.

Basav Roychoudhury

Papers

Handling missing values: A study of popular imputation packages in R

Enhancing User Identity Privacy in LTE

Effect of trip mode on opinion about hotel aspects: a social media analysis approach.

Words are important: A textual content based identity resolution scheme across multiple online social networks

Provably secure group authentication and key agreement for machine type communication using Chebyshev’s polynomial