Reviewerly/exHarmony huggingface dataset:
A Case for Semantic Annotation Of EHR

Today almost all hospitals have adopted Electronic Health Records(EHR), and the digital data that is captured provides an opportunity to improve the patient care. In addition to the EHR in the healthcare system, data such as the readings from smart wearable devices worn by patients also give valuable insight to the patient's health condition. Hence there is a need to consolidate all these heterogeneous information into EHR of patients to get a holistic view of the patient. Currently, different healthcare systems and devices operate in different standards which in turn causes interoperability issues. This paper explores the need to achieve semantic interoperability among healthcare data and proposes a technique to derive inference from semantically annotated EHR data. A proof of concept is done to validate the proposed design.
Towards Knowledge-guided Genetic Improvement

We propose Knowledge-guided Genetic Improvement as a combination of Grammar-guided Genetic Programming with Tree-based Genetic Programming. Instead of utilizing a grammar directly, an operator graph based on that grammar is created, that is responsible for producing abstract syntax trees. Each operator contains knowledge about the grammar symbol it represents and returns only trees valid according to user-defined restrictions such as depth, complexity and approximated run-time performance.
Short-term Wind Speed Prediction Based on Improved CEEMD-FOA-LSSVM

In order to solve the problem caused by the nonstationarity of wind speed sequence in wind speed prediction, an improved CEEMD-FOA-LSSVM model is proposed: Firstly, the standard Complementary ensemble empirical mode decomposition (CEEMD) algorithm is improved to decompose the original wind speed sequence and reduce the end effect. Secondly, calculate the permutation entropy value of each modal component after decomposition, and the sub-sequences are recombined and merged according to the permutation entropy value to obtain multiple new sub-sequences, and the improved Fruit Fly Optimization Algorithm (FOA) algorithm can improve the optimization effect and optimize the Least Squares Support Vector Machine(LSSVM) parameters. Finally, the optimized model is used for predicti on for each new sequence, and the predicted values are combined to complete the model prediction. The simulation results prove that the proposed improved model not only effectively solves the above problems but also improves the prediction accuracy.
SWOT Analysis in the Strategic Planning Process - Meta-modelling Approach

Nowadays enterprises face the problem of achieving their strategic goals. Clear and simple vision is a prerequisite for defining proper and framed strategic goals. Strategies that enterprise has to implement through the set of activities should be defined and analyzed. One of the proven ways of defining the strategies is using the SWOT analysis. This analysis, used in a standard way, does not give sufficient guarantee for success in the strategic planning process. Therefore, this paper gives a meta-model of extended SWOT analysis, as well as a workflow model of its implementation. The meta-model is developed as entity relationship attribute (ERA) diagram, while the workflow model is developed using BPMN notation. These two models, combined together, give us a tool for keeping important data generated in the strategic planning process in a form of unique repository. This repository is a basis for development of a CASE tool that can help managers in managing the enterprise towards achieving its strategic goals.
On the interpretation of surprisingly high variation of soil map diversity in country-wide study of flood-affected agroecosystems using the legacy data in the Czech Republic

Legacy soil data have been produced worldwide to provide maps of soil classes or properties. In this study, we joined the legacy results of conventional soil mapping with the application of the pedodiversity concept and compositional data analysis in a comprehensive spatial analysis of the mapped soil diversity within flood-prone agroecosystems in the Czech Republic. After the statistical differences in Shannon’s entropy (conventional and adjusted with taxonomic distances) over different regional inundation areas had been statistically examined, their interpretation was based on Aitchison’s geometry of simplex, rendering an appropriate statistical framework to avoid methodological bias through the use of raw proportions in soil taxa compositions. Working with tailored orthonormal coordinates enabled us to verify the dependency of the soil taxa composition on the multi-category factors of agroecosystem type and flood-periodicity through a conventional linear modelling. The supervised compositional approach improved interpretability of soil information within the Czech legacy soil maps, and hence general patterns of pedodiversity in floodplains might be effectively described in an informative and quantitative manner. The most obvious differences of the soil map diversity proved the regional inundation areas within the catchments with the enhanced complexity of drainage network that might arise from multiple sources (substrate diversity, climatic zonality, man-induced landscape alteration).
Leadership and Team Dynamics for a Successful Agile Organisational Culture

Agile Manifesto refers to Leadership and Team characteristics in three out of the twelve principles of Agile software. Clearly, the ‘people' in the organisation play a critical role in the success of Agile software development. Many studies have been done on the various aspects of the Leadership and Team characteristics in respect of the role, the behaviour, the process and the structure. While most of these studies are related to the role and behaviour aspects of the leadership and the teams, this chapter attempts to supplement them with those aspects that are related to structure, internal and external interfaces and interactions, to ensure that the Agile engagements are planned and executed successfully and consistently. The emphasis in this chapter is the Organisation Culture that must be conducive for sure success of Agile projects and Scrum teams.
More honour'd in the breach: predicting non-compliant behaviour through individual, situational and habitual factors

A major issue in the digital age is how to safeguard the security of the massive amount of data being stored, processed and transferred through digital channels. Organisational communication research into data security shows that people are the weakest link. Non-compliant behaviour can lead to security breaches. Existing studies have focused above all on how employees can be motivated to comply with data security procedures. However, focusing on desirable behaviour does not explain why people often ignore security regulations. In addition, existing research tends to focus primarily on self-reported attitudes and perceptions, which can give a distorted impression of people’s actual behaviour. In this paper, we address these issues by combining individual, habitual and situational factors to explain non-compliant behaviour in a vignette study, using SmartPLS to analyse survey data from 651 subjects in a large Dutch government organisation. The results indicate that bad habits play a significant role in non-compliant behaviour. This behaviour is fuelled by situational factors like time pressure, while a lack of self-efficacy also increases non-compliant behaviour. Based on these results, a communication strategy that addresses bad habits in a situational context may provide an alternative way to improve people’s compliant behaviour.
Variable-Length Indistinguishable Binary Tree for Keyword Searching Over Encrypted Data

We consider the problem of privacy-preserving keyword search on the clouds. In this paper, we propose a new searchable encryption scheme that satisfies efficient and secure query processing. In particular, our scheme utilizes a novel data structure called Indistinguishable Bloom Filter (IBF) to construct the first variable-length Indistinguishable Binary Tree (vIBtree). Prior IBF based searchable encryption scheme takes tremendous time to construct a huge secure index. Our scheme overcomes these two shortcomings by constructing an inconsiderable secure index within an insignificant time. Meanwhile, we propose a corresponding trapdoor algorithm for vIBtree. Furthermore, we analyze that our scheme is secure under the adaptive \(IND-CKA\) security model. We implemented our scheme in C language. Our experiments show that vIBtree can speedup the secure index construction time and reduce the storage overhead for the data structure over known, state of the art alternatives.
Deep Learning-based Algorithm for Mobile Robot Control in Textureless Environment

For the implementation of stereo image-based visual servoing algorithm in the eye-in-hand robotics applications, one of the main concerns is the accurate point feature detection and matching algorithm. Since the visual servoing is carried out in the textureless environment, the feature detection process is even more challenging. To fulfill the requirement of a robust and reliable point feature detection process, in this paper we present the novel deep learning-based algorithm. The approach based on convolutional neural networks and algorithm for detection of manufacturing entities is proposed and detected regions of interest are utilized for the improvement of the point feature detection algorithm. The proposed algorithm is experimentally evaluated in real-world settings by using wheeled nonholonomic mobile robot RAICO equipped with stereo vision system. The experimental results show the improvement of 58% in the accuracy of matched point features in the images obtained during the visual servoing process. Moreover, with the implementation of the proposed deep learning-based approach, the number of successful experimental runs has increased by 80%.
Competition between Synthetic NN Compounds and NN.<scp>gen</scp>Phrasal Nouns in Polish

This paper discusses synthetic compounds in Polish, focusing on compound nouns which contain either a deverbal suffixal derivative or a nominalized verb stem as their right-hand constituent. Competition is investigated between such right-headed morphological compounds and left-headed phrasal nouns consisting of a head noun followed by its genitive attribute. Furthermore, Polish compound nouns are compared, in respect of their productivity and their semantic interpretation, with Japanese compound nouns headed by nominalized verb stems, and with English synthetic compounds. Examples are provided (on the basis of the National Corpus of Polish and Google searches) of non-institutionalized Polish compound nouns (with agentive or event reading) which can be viewed as forming semantic niches (in the sense of Hüning 2009), extendable through analogy. It is argued that the framework of Construction Morphology (as outlined by Booij 2010), with its postulation of low-level construction schemas, can provide an appropriate account of the process of analogical compound formation in Polish.
Temporal Residual Feature Learning for Efficient 3D Convolutional Neural Network on Action Recognition Task

Deep 3-dimensional (3D) Convolutional Neural Network (ConvNet) has shown promising performance on video classification tasks because of its powerful spatio-temporal feature learning ability. However, the extremely intensive requirements of computing power prohibit 3D ConvNet from being used in resource-constrained scenarios. Therefore, it is vital to design efficient architectures. In this paper, we first evaluate the Fully Separated Block (FSB) in 3D ConvNet, which can significantly compress model size. Nevertheless, it also introduces considerable accuracy loss. In order to further improve the performance of efficient 3D ConvNet on action recognition task, a feature enhancement approach named Temporal Residual Module (TRM) is proposed, which can eliminate the loss brought by FSB and achieve even better performance than original baseline with slight computation overhead. Along with TRM, FSB can be viewed as an integrated module termed Temporal Residual Block (TRB) to replace the basic convolutional module in 3D ConvNet, contributing to less model size and better accuracy. Experiments on the popular action recognition dataset report 3.0% accuracy gain with 7.5 χ compression rate over C3D network and 1.0% accuracy gain with 2.3× compression rate over the state-of-the-art SlowFast ConvNet.
Big Data Analytics in Excelling Health Care: Achievement and Challenges in India

History teaches us that the glorious victory of mankind across the centuries was accomplished through the successful use of information. The gigantic progressions and rapid transformation of human societies have endorsed legitimacy of abundant data, multiple dynamic variables & critical complexities which reinforce the academia and researchers for understanding and pioneering into ‘Big Data Analytics (BDA)’. Health is one of the vibrant socio-economic variables which have correlations with other aspects of life, that is, education, poverty, income, etc. In fact, there are unending debates whether health can be a basic input for a holistic developmental process or it is the outcome of various developmental factors. BDAs are being used across various sectors of the economy. The developed nations have been yielding most feasible solutions using various forms of analysis of big data. Astronomical research has been using a large quantum of data for accomplishing various satellite projects, space technology, and numerous space missions for the astronaut. With the advent of fourth industrial revolution, the world community has been thriving toward a new age technological innovations that include artificial intelligence, machine learning, block chain technology, etc., which act a pivotal tool for BDAs. In the health sector, application of BDAs has been attempted and experimented in the developed nations which have resulted prolific and sustainable solutions to the most typical cumbersome problems. This chapter has demonstrated how BDAs can make progressive reforms in the Indian Health sector outlining the present status and emerging challenges.
Semantic Enhanced Top-k Similarity Search on Heterogeneous Information Networks

Similarity search on heterogeneous information networks has attracted widely attention from both industrial and academic areas in recent years, for example, used as friend detection in social networks and collaborator recommendation in coauthor networks. The structure information on the heterogeneous information network can be captured by multiple meta paths and people usually utilized meta paths to design method for similarity search. The rich semantics in the heterogeneous information networks is not only its structure information, the content stored in nodes is also an important element. However, the content similarity of nodes was usually not valued in the existing methods. Although recently some researchers consider both of information in machine learning-based methods for similarity search, they used structure and content information separately. To address this issue by balancing the influence of structure and content information flexibly in the process of searching, we propose a double channel convolutional neural networks model for top-k similarity search, which uses path instances as model inputs, and generates structure and content embeddings for nodes based on different meta paths. Moreover, we utilize two attention mechanisms to enhance the differences of meta path for each node and combine the content and structure information of nodes for comprehensive representation. The experimental results showed our search algorithm can effectively support top-k similarity search in heterogeneous information networks and achieved higher performance than existing approaches.
In Depth Analysis, Applications and Future Issues of Artificial Neural Network

Artificial Neural Networks (ANNs) can be described as mapping of non-linear structures which are based on the functioning of the human brain. ANN simulates the intuitive way of thinking. It thus differs from the conventional computing methods in the sense that apart from replacing or speeding up the computations of human brain, it focuses on the organization and interconnection of the computing elements. However, the simulation of neural network is very tedious, costly and gross thereby making the whole process challenging. Yet ANN continues to possess its attractive nature in computing due to several reasons. Firstly, it allows computation of complex mathematical problems in a very simple. Secondly, it has a self-organizing nature which makes it feasible to imitate decision making capability of human brain easily. Thirdly, it has vivid properties such as inherent parallelism, distributed storage, robustness, non linear nature, tolerance to failure, handling of fuzzy information and their in and the generalization ability. Thus, ANN being of prime importance in the era of technological advancements, this paper aims at providing an in-depth understanding of the neural network, its modern-day applications and various issues that are needed to be dealt with while we use this technology.
A Novel Superheat Identification of Aluminum Electrolysis with Kernel Semi-supervised Extreme Learning Machine

Abstract In the aluminium reduction production industry, the superheat temperature (ST) is a vital index, which gives the distribution of the current efficiency. Keeping ST in an approximate range improves the lifespan of the electrolysis bath. However, in the practical production process, ST identification result is commonly evaluated by the experimental workers, the real-time measurement of ST is still a challenge that has not been solved. A novel ST measurement method based on kernel extreme learning machine (K-ELM) is studied in this paper. First, a few of input variables are selected according to expert experience. Then, a new activation function (IG kernel function) and regularization term are constructed in ELM to construct IG-SSELM. Finally, the ST model is built with all the dataset samples and further applied to ST real-time detection. The proposed method is applied to ST identification for the first time in the aluminium industrial process which is superior to the existing ST methods in accuracy and robustness.
Author Profiling Using Texts in Social Networks

The automatic identification of an author's demographic traits (e.g., gender, age group) from their written text is termed as author profiling. This problem has become an essential problem in fields like linguistic forensics, marketing, and security. In recent years, online social setups (e.g., Twitter, Facebook, blogs, hotel reviews) have extended remarkably; however, it is easy to provide fake profiles. This research aims to predict the traits of the authors for a benchmark existing corpus, based on Twitter, hotel reviews, social media, and blogs' profiles. In this chapter, the authors have explored four sets of features, including syntactic n-grams of part-of-speech tags, traditional n-grams of part-of-speech tags, combinations of word n-grams, and combinations of character n-grams. They used word unigram and character three-gram as a baseline approach. After analyzing the results, they concluded that the performance improves when the combination of word n-grams is used.
Refactoring Software in the Automotive Domain for Execution on Heterogeneous Platforms

The most important way to achieve higher performance in computer systems is through heterogeneous computing, i.e., by adopting hardware platforms containing more than one type of processor, such as CPUs, GPUs, and FPGAs. Several types of algorithms can be executed significantly faster on a heterogeneous platform. However, migrating CPU-executable software to other types of execution platforms poses a number of challenges to software engineering. Significant efforts are required in such type of migration, particularly for re-architecting and re-implementing the software. Further, optimizing it in terms of performance and other runtime properties can be very challenging, making the process complex, expensive, and error-prone. Therefore, a systematic approach based on explicit and justified architectural decisions is needed for a successful refactoring process from a homogeneous to a heterogeneous platform. In this paper, we propose a decision framework that supports engineers when refactoring software systems to accommodate heterogeneous platforms. It includes the assessment of important factors in order to minimize the risk of recurrent problems in the process. Through a set of questions, practitioners are able to formulate answers that will help in making appropriate architectural decisions to accommodate heterogeneous platforms. The contents of the framework have been developed and evolved based on discussions with architects and developers in the automotive domain.
A Quick No-Reference Quantification of the Overall Contrast of an Image

This study examines the issue of quickly quantifying the overall contrast of the image at the lowest required cost. The main goal of this study is to achieve better the accuracy and reliability of the quick assessment of global contrast for multi-element images. To meet this challenge, in this work, we propose a new approach to quickly quantify the contrast of the image by measuring its incomplete integral contrast. At the heart of the proposed approach is the assessment of contrast for each object in the image relative to the adaptation level given the sizes (area) of these objects. To demonstrate the possibilities of such an approach, new metrics proposed for measuring incomplete integral contrast with the use of different definitions of contrast kernels. Such an approach allows us to a more full and accurate evaluate the impact of objects on forming the estimate of image overall contrast.
A Low-Cost Gaze-Based Arabic Augmentative and Alternative Communication System for People with Severe Speech and Motor Impairments

Interpersonal communication poses a major challenge for individuals with severe speech and motor impairments (SSMI); however, since eye muscle movement control is typically retained, such individuals can alternatively communicate with others by using their gaze to select symbols on electronic communication boards (CBs). Unfortunately, most of the existing systems offering electronic CBs require the purchase of high-cost eye tracking hardware and software components and do not support Arabic language. We have, therefore, developed a low-cost gaze interaction-based Arabic language application to assist pre-literate and early literate individuals with SSMI whose primary language is Arabic to communicate with people in their vicinity. The system utilizes a webcam and open source software for eye tracking, thus, avoiding the need for expensive proprietary eye tracking components. It offers several CBs from which the user can select items to form sentences that are output as speech. The CBs have been carefully designed taking the users’ cognitive and motor capabilities into consideration and several features to facilitate and expedite the communication process have been included. An interface has also been provided for caregivers to customize the application according to the users’ unique needs by adding and editing items, and setting the user’s profile, the background color and the gender for the speech output. The system has been developed in consultation with a speech-language pathologist to ensure that it is compatible with current therapy practices and the actual needs of the targeted users.