[서평]「The Unified Modeling Language User Guide」

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Handbook of Data Mining and Knowledge Discovery

Understanding the sentiment of a comment from a video or an image is an essential task in many applications. Sentiment analysis of a text can be useful for various decision-making processes. One such application is to analyse the popular sentiments of videos on social media based on viewer comments. However, comments from social media do not follow strict rules of grammar, and they contain mixing of more than one language, often written in non-native scripts. Non-availability of annotated code-mixed data for a low-resourced language like Tamil also adds difficulty to this problem. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube. In this paper, we describe the process of creating the corpus and assigning polarities. We present inter-annotator agreement and show the results of sentiment analysis trained on this corpus as a benchmark.

Corpus creation for sentiment analysis in code-mixed Tamil-English text

Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.

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Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

In this paper, we contribute to a recent and successful modelization of uncertainty, which the practitioner often encounters in the formulation of multicriteria group decision making problems. To be precise, in order to approach the uncertainty issue we introduce a novel type of soft rough covering by means of soft neighborhoods, and then we use it to improve decision making in a multicriteria group environment. Our research method is as follows. Firstly we introduce the soft covering upper and lower approximation operators of soft rough coverings. Then its relationships with well-established types of soft rough coverings are analyzed. Secondly, we define and investigate the measure degree of our novel soft rough covering. With this tool we produce a new class of soft rough sets. Finally, we propose an application of such soft rough covering model to multicriteria group decision making by means of an algorithmic solution. A fully developed example supports the implementability of this decision making method.

A novel type of soft rough covering and its application to multicriteria group decision making

This paper proposes Logical Matching Strategy for sequential pattern matching. We show the two real-world applications of the method: (1) locate repeating sequential pattern and (2) alignment-free comparison of sequential pattern of finite length using fuzzy membership values that generate automatically from the number of matches and mismatches. The results show the utility of the method by analyzing DNA sequences taken from the National Center for Biotechnology Information (NCBI) databank. The Logical Matching Strategy can possibly be applied to develop a method of research in sequental pattern matching.

A note on two applications of logical matching strategy

Neurodegenerative diseases are common in the elderly and result from progressive and irreversible neuronal damage (ultimately resulting in the death of nerve cells or neurons). A typical neurodegenerative disease (collectively called degenerative Parkinsonian disorders) mainly affect neurons in the brain regions controlling movements and posture. Parkinsons disease (PD) is the commonest among this group, others being Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP) etc. All these diseases cause abnormalities in body movements and posture. Pathological deposition of iron is seen in the degenerating brain areas in these diseases. The pattern of iron deposition in subcortical brain regions (also called deep gray matter structures) like caudate nucleus, putamen, globus pallidus, red nucleus and substantia nigra could be used in differentiating various neurodegenerative parkinsonian disorders like PD, MSA and PSP. Quantitative Susceptibility Mapping is a Magnetic Resonance Imaging based technique which helps to non-invasively quantify the iron deposition in different brain areas. The technique can be used to quantify the iron deposition in the DGM structures enumerated above. For this, the regions of interest (RoIs) have to be drawn manually by experienced radiologists, which is a time-consuming task and potentially associated with inter-rater variations. The automated segmentation of RoIs (The deep gray matter structures) for QSM analysis has a pivotal role to play in the area of medical imaging pertaining to degenerative Parkinsonian disorders. The proposed model can automatically segment the regions of interest from the brain MRI, based on pixel-label based semantic segmentation. Segnet is a pretrained deep learning model used to perform the pixel-label based semantic segmentation, which can automatically segment the deep gray matter structures of the brain. Thus the amount of iron deposition can be easily calculated form these segmented image using Quantitative Susceptibility Mapping techniques.

A Deep Approach to Quantify Iron Accumulation in the DGM Structures of the Brain in Degenerative Parkinsonian Disorders Using Automated Segmentation Algorithm

This paper presents a deconverting generator for Malayalam language using Universal Networking Language (UNL) for Machine Translation. UNL being an Interlingua representation, conveyed as directed hyper graph with relations and attributes of source language sentence. A set of Universal Words are generated from the source language with its semantic representation, are mapped to UNL features. The work involves identifying the dependent features like syntactic, semantic and lexical features of target language. UNL Relations, UNL Attributes and Universal Word (UW), which are the building blocks of UNL are identified and mapped to the dependent features of Malayalam. Lexical mapping of UWs to root words of Malayalam was done through UNL-Malayalam Word Dictionary. The deconversion is tested against 100 Malayalam Sentences that has achieved an appreciable F-measure score of 0.978. . General Terms Malayalam Deconversion, Universal Networking Language, Interlingua Machine Translation.

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Language Dependent Features for UNL-Malayalam Deconversion

This paper proposes a method for sequential data mining using correlation matrix memory. Here, we use the concept of the Logical Match to mine the indices of the sequential pattern. We demonstrate the uniqueness of the method with both the artificial and the real datum taken from NCBI databank.

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Sequential Data Mining using Correlation Matrix Memory

A prototype model named Shared Security Architecture is introduced, that enables to link relational database management system, distributed DBMS and blockchain technology. Blockchain technology is commonly used in cryptocurrencies but the innovative use of this technology can boost cybersecurity and protect databases more effectively. Three different models have been explored with a combination of traditional data storage (RDBMS), distributed data storage and blockchain technology to construct new security for the data storage system. These models allow direct access to frequently used data, which requires read-write property and restricted access to data which requires write property with immutability without paying much on trust and security.

Elizabeth Sherly

Papers

A note on two applications of logical matching strategy

A Deep Approach to Quantify Iron Accumulation in the DGM Structures of the Brain in Degenerative Parkinsonian Disorders Using Automated Segmentation Algorithm

Language Dependent Features for UNL-Malayalam Deconversion

Sequential Data Mining using Correlation Matrix Memory

Blockchain-Based Shared Security Architecture