Reviewerly/exHarmony huggingface dataset:
Does corporate social responsibility initiative restrain young people from irregular migration in sub-Saharan Africa? Evidence from Nigeria’s oil-producing communities

Purpose The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on irregular migration urge of rural youths in the oil-producing communities. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 2,100 households were sampled across the Niger Delta region of Nigeria. Findings The results from the use of a combined propensity score matching and logit model indicate that GMoU model has made significant impact in dissuading young people from irregular migration drive. Practical implications This implies that if the MOCs increase the CSR intervention on young development initiatives that focus on creation of jobs and provision of financial and other resources that support local entrepreneurs, the push factors that compel youth irregular migration in sub-Saharan Africa would be deterred. Social implications The fight against irregular migration of African youths and subsequent demise by sea, deserts and along the Mediterranean route can only succeed if cluster development boards of GMoUs are able to draw on young people to participate fully in the CSR intervention plans and programmes. Originality/value This research adds to the literature on multinational enterprises’ CSR initiatives in sub-Saharan Africa and rationale for demands for social projects by host communities. It concludes that business has an obligation to help in solving problems of public concern.
Multi-Exemplar Particle Swarm Optimization

PSO and its variants have proven to be useful algorithms for tackling a wide range of optimization problems in recent decades. However, PSO and most of its variants only consider the influences caused by global best position and personal historical best position. Such a single way of influencing often leads to an issue of insufficient diversity of population, and further makes the algorithms be prone to falling into local optimum. In this paper, we propose a multi-exemplar particle swarm optimization (MEPSO) to deal with this issue. Specifically, each particle will choose the global best particle and the best companion particle as its exemplars, which brings more useful knowledge for particle update. To further describe the influences with respect to different exemplars, we define two influence coefficients inspired by mechanics. Such influence coefficients ensure that the best current experience is shared while enrich the diversity of population. Moreover, in the light of the distance between each particle and its best companion particle on each dimension, a variable-scale search is given in this paper to enhance the overall convergence ability. To verify the effectiveness of our algorithm, we conduct abundant experiments on all functions of CEC2013 test suite. The experimental results show that MEPSO performs better than 14 competitors in terms of comprehensive performance and achieves state-of-the-art results.
An Empirical Study on the Phenomenon of Information Narrowing in the Context of Personalized Recommendation

Abstract Personalized recommendation services have been widely applied to improve users’ search efficiency, but when filtering information for users, they also hide the risk of the declining in content diversity, which is information narrowing. In this study, comprehensive news and information platforms in China were taken as the empirical research object. The experimental tracking method was used to collecting the content data of client end recommended news. Simpson and Shannon-Wiener indexes were used to measure the information narrowing phenomenon. Questionnaires were used to collect users’ data of news and information applications usage, the perception of information overload, and the perception of information narrowing. The data of questionnaire was analyzed and processed by MATLAB and SPSS 20.0, and the hypothesizes were tested by statistical methods. The results reveal that there is indeed information narrowing phenomenon in news information personalized recommendation service, but most users are unaware of this.
Natural Language Interfaces to Databases

This chapter consists of an update of a previous publication. Specifically, the chapter aims at describing the most decisive advances in NLIDBs of this decade. Unlike many surveys on NLIDBs, for this chapter, the NLIDBs will be selected according to three relevance criteria: performance (i.e., percentage of correctly answered queries), soundness of the experimental evaluation, and the number of citations. To this end, the chapter will also include a brief review of the most widely used performance measures and query corpora for testing NLIDBs.
Sepsis Deterioration Prediction Using Channelled Long Short-Term Memory Networks

Sepsis is a severe medical condition that results in millions of deaths globally each year. In this paper, we propose a Channelled Long-Short Term Memory Network model tasked with predicting 48-hour mortality in sepsis against the Sequential Organ Failure Assessment (SOFA) score. We use the MIMIC-III critical care database. Our research demonstrates the viability of deep learning in predicting patient outcomes in sepsis. When compared with published literature for similar tasks, our channelled LSTM models demonstrated a comparable AUROC with superior precision score. The results showed that deep learning models outperformed the SOFA score in predicting 48-hour mortality in sepsis in AUROC (0.846–0.896 vs 0.696) and average precision score (0.299–0.485 vs 0.110). Finally, our Fully-Channelled LSTM outperforms a baseline LSTM by \(5.4\%\) in AUROC and \(59.9\%\) in average precision score.
Web Table Column Type Detection Using Deep Learning and Probability Graph Model

The rich knowledge contains on the web plays an important role in the researches and practical applications including web search, multi-question answering, and knowledge base construction. How to correctly detect the semantic types of all the data columns is critical to understand the web table. The traditional methods have the following limitations: (1) Most of them rely on dictionary lookup and regular expression matching, and are generally not robust to dirty data; (2) They only consider character data besides numeric data which accounts for a large proportion; (3) Some models take the characteristics of a single column and do not consider the special organizational structure of the table. In this paper, a column type detection method combining deep learning and probability graph model is proposed, taking the semantic features of a single column and the interaction between multiple columns into account to improve the prediction accuracy. Experimental results show that our method has higher accuracy compared with the state-of-the-art approaches.
Expert System to Manage Parkinson Disease by Identifying Risk Factors: TD-Rules-PD

Advent of modern means of living and busy schedule, a person generally forget to look after the health and thereby prone to some of severe health disorders. From various researches it is clear that Parkinson Disease (PD) basically occur due to negligence of an individual. It is a disorderliness that affects a small part of the brain that manages the gesture and mental order. The syndrome vary from person. Generally PD is identified from a certain age level but recent studies shown that people of any age group can suffer from PD. Considering the extremity, if there is an system to identify the major factors of PD then it would be of great importance. Thus an expert system named Transparent Decision Rules for Parkinson Disease (TD-Rules-PD) is proposed in this paper which finds out the high risk factor of PD. TD-Rules-PD consist of four phases: Rule Production, Rule selection, Rule pruning and Merging and Identify High Risk Factor. Rule Production stage uses Decision tree to produce rules. Rule selection step choose the most efficient rules, from the so collected rules Rule Pruning and Merging step drops the confusing and insignificant rules and then combines the flittered rule set to a single rule and finally Identify High Risk Factor stage finds out the most prevailing factor of PD.
Transformed denoising autoencoder prior for image restoration

Image restoration problem is generally ill-posed, which can be alleviated by learning image prior. Inspired by the considerable performance of utilizing priors in pixel domain and wavelet domain jointly, we propose a novel transformed denoising autoencoder as prior (TDAEP). The core idea behind TDAEP is to enhance the classical denoising autoencoder (DAE) via transform domain, which captures complementary information from multiple views. Specifically, 1-level nonorthogonal wavelet coefficients are used to form 4-channel feature images. Moreover, a 5-channel tensor is obtained by stacking the original image under the pixel domain and 4-channel feature images under the wavelet domain. Then we train the transformed DAE (TDAE) with the 5-channel tensor as the network input. The optimized image prior is obtained based on the trained autoencoder, and it is incorporated into an iterative restoration procedure with the aid of the auxiliary variable technique. The resulting model is affiliationed by proximal gradient descent technique. Numerous experiments demonstrated that the TDAEP outperforms a set of image restoration benchmark algorithms.
Blood cell image segmentation using modified fuzzy divergence with morphological transforms

Blood cell images contain lots of uncertainties due to the randomness and variability of gray values. The contrast between the objects and the background of blood cell images are viewed as fuzzy in nature. So, varieties of fuzzy image thresholding mechanisms have been adopted for removing the grayness and vagueness of blood cell images. In this paper, a noble fuzzy segmentation technique is proposed, which processes fuzzy divergence with the mathematical morphological operation. At first, blood cell images are pre-processed to avoid uneven illumination and to reduce noise by fuzzy image morphology, and in the later stage a suitable threshold point is determined using the modified fuzzy divergence. The quality of the obtained images in the fuzzy divergence with the morphology method is observed for enhancement and better entropy. In this paper, we have used a new fuzzy Lomax-Gumbel membership function with the Fuzzy Divergence algorithm in order to overcome the loopholes of other well-known membership functions. The experimental results tested form the point of view of the robustness of the modified method by comparing with Chaira’s method. Finally, the mean value, entropy, and Standard Deviation have been calculated, which are found as greater values than the values of Chaira’s method.
Want more high-growth entrepreneurs? Then control corruption with less ineffective bureaucracy

For decades, scholars have been concerned with the role of public policy in stimulating entrepreneurial activity. Aside from pro-entrepreneurship policy, governments can also erect barriers to startup activity. Researchers have concluded that the degree of corruption in a country can become a significant deterrent to entrepreneurship, while research on the relationship between bureaucracy and startup rates has been inconclusive. In this study, we apply the theory of planned behaviour – in particular, the perceived behavioural control construct – to clarify the role of corruption and ineffective bureaucracy both independently and jointly in their relationships with entrepreneurship participation rates. Data on individuals from 53 nations for the 2006–2015 period were utilized to test the hypotheses. This research confirms that both are negatively associated with rates of startup activity and that in the context of highly corrupt countries, the two constructs interact to further reduce startup activity.
Adversarial Learning for Recommendation: Applications for Security and Generative Tasks — Concept to Code

Adversarial Machine Learning (AML) has initially emerged as the field of study that investigates security issues of conventional and modern machine learning (ML) models. The objective of this tutorial is to present a comprehensive overview on the application of AML techniques for recommendation in a two-fold categorization: (i) AML for the attack/defense purposes, and (ii) AML to build GAN-based recommender models. A theoretical presentation on the topics is paired with two corresponding hands-on sessions to show the efficacy of AML application and push up novel ideas and advances in recommendation tasks. The tutorial is divided into four parts. We start by introducing a summary on state-of-the-art recommender models, including deep learning ones, and we define the fundamentals of AML. Then, we present the Adversarial Recommendation Framework, to represent attack/defense strategies on RSs, and the GAN-based Recommendation Framework, which is at the basis of novel adversarial-based generative recommenders. The presentation of each framework is followed by a practical session. Finally, we conclude with open challenges and possible future works for both applications.
Development of intelligent model for twitter sentiment analysis

The growth on Digital growth and the Online Shopping is being done virtually, it becomes very difficult for the users to determine the quality of the product. There is no such model existed that can find out the similar or dissimilar community or group of peoples with respect to the tweets (post/messages) with similar textual content, Tweets based on the same location and the tweets based on the invariant time-period for the user timeline. Today as most of the users remain Online, so the requirement of Social Network Analytics arises. Most of the work done in the area of Sentiment Analysis and opinion mining is done using various types of Machine Learning Algorithms. We have taken forward the work of Sentiment Analysis and used the Twitter Sentiment Analysis. As many challenges faced by Twitter Sentiment Analysis and are considered as the target and tried to find out the solution. The user opinion had always played an important role in identifying the user sentiments and the opinion of the user whether it relates to online voting or the user product review. The work done considering the Sentiments of an individual and then mapping done with the Socio-Economic Data.
Boosting Traditional Healthcare-Analytics with Deep Learning AI: Techniques, Frameworks and Challenges

Healthcare Analytics is process of analyzing huge volume of healthcare data such as claims data, Electronic Health Records (EHR) pharmacy data, clinical data, patient behavior data etc. to leverage insights for making more informed decisions, improve patient care and services at various levels of the organization. Traditional Machine Learning techniques are limited in their ability to process and extract feature from raw data. Over the last couple of years this field has seen a huge transformation with advancements in Deep Learning, making it an ideal tool for healthcare analytics, where a huge amount of raw unlabeled and complex healthcare data can be processed faster and with higher accuracy. This chapter focuses on different deep learning applications, techniques, framework and challenges in dealing with Healthcare data.
A Classroom Student Counting System Based on Improved Context-Based Face Detector

Classroom student counting based on surveillance video is the basis for important tasks such as student behavior analysis, resource optimization, school security, and intelligent management. In recent years, with the development of deep learning, the research of face detection has been greatly promoted, but it still cannot solve the problems of difficult recognition of different poses such as tiny faces in the back row, occlusion of students, and head-down in the classroom. This paper designed an intelligent student counting system based on classroom surveillance video to solve the problem of counting people in classroom scenarios. Also the paper proposed a face detector which mixes feature enhancement modules and background-based modules, using Background information including shoulders, bodies and desks, and search for unobstructed, less-occluded, and head-up targets in multiple video frames in the surveillance video based on the relative stillness of the students in the classroom scene, which finally greatly improves the accuracy of the classroom student counting statistics.
Continuous Verification of Machine Learning: a Declarative Programming Approach

In this invited talk, we discuss state of the art in neural network verification. We propose the term continuous verification to characterise the family of methods that explore continuous nature of machine learning algorithms. We argue that methods of continuous verification must rely on robust programming language infrastructure (refinement types, automated proving, type-driven program synthesis), which provides a major opportunity for the declarative programming language community. Keywords: Neural Networks, Verification, AI.
Big Data-Based Approach to Automated Linguistic Analysis Effectiveness

The authors of the finding develop customizable corpus tool to build corpus of historical and religious texts. Big Data approach to Natural Language Processing and Natural Language Understanding was used to achieve the goal of such corpus data platform developing. Calculation of qualitative and quantitative characteristics, building search queries belong to the most important features of the adaptable text corpus effectiveness. Number of computer-based calculations and amount of processing data have been reduced and parallelized to achieve higher performance on the levels of computational methods and implemented system. The higher level of efficiency as a trade-off between effectiveness and computational time has been achieved by choosing proper parameters of computational methods. Latent-Semantic Analysis is used as one of the core methods for making queries. The methods applied are mostly based on Singular Value Decomposition. Parameters of the decomposition are analyzed and justified. Suggested approach has been verified on the test data available in different languages.
Efficient Patient-Friendly Medical Blockchain System Based on Attribute-Based Encryption

In recent years, there have been rapid advances in electronic medical record (EMR) sharing with the fast development of blockchain technology. Using blockchain technology to build the electronic medical record sharing system can effectively solve the problem that medical data is hard to share between different hospitals' databases. However, existing EMR sharing system using blockchain technology is mainly for medical institutions, and the patients as the data owner even can't easily share their own personal EMR when they need to use it. The patient's demand for management of personal EMRs is largely ignored. At the same time, the doctor's longitudinal access to personal EMRs requires a fairly large overhead, which severely causes treatment delay. This paper proposes a patient-friendly blockchain electronic medical data sharing system based on main-sub structured blockchain, which enables patients to manage their personal medical data directly and enhances the authorized users' efficiency of access to personal EMRs significantly. Experimental results demonstrate that our scheme is efficient enough to support the real medical record sharing applications.
Massive-Scale I/Q Datasets for WiFi Radio Fingerprinting

Recent research has proved the effectiveness of neural networks (NNs) in “fingerprinting” (i.e., identifying) wireless radios, by determining the hardware impairments emitted from the transmitter during the waveform transmission process. The artificial neurons of the NN layers are employed to identify and track the radios’ unique impairments by training a large amount of raw data released from these radios. Today, the radio fingerprinting field lacks such a large-scale waveform database that can provide a standard benchmark for researchers working on this field. In this paper, we publicly share 2TB of IEEE 802.11 a/g (WiFi) data obtained from 20 bit-similar Software-Defined-Radios (SDRs).
Link Inference via Heterogeneous Multi-view Graph Neural Networks

Graph Neural Network (GNN) has been attaching great attention along with its successful industry applications, such as social network, recommendation system and so on. Most existing GNN algorithms for link inference tasks mainly concentrate on homogeneous network where single typed nodes and edges are considered. Besides, they are transductive, incapable of handling unseen data, and are difficult to generalize to big graph data. In this paper, we introduce a new idea, i.e. Heterogeneous Multi-view Graph Neural Network (HMGNN), to remedy these problems. A more complex and unstudied heterogeneous network structure where multiple node and edge types co-exist, and each of them also contains specific attributes, is learned in this framework. The proposed HMGNN is end-to-end and two stages are designed: i) The first stage extends the widely-used GraphSAGE model to the studied heterogeneous scenario to generate the vector embedded representations for each type of nodes. ii) The second stage develops a novel and inductive subspace-based strategy for link inference by aggregating multi-typed node and edge feature views. Comprehensive experiments on large-scale spam detection and link prediction applications clearly verify the effectiveness of our model.
Power Delay Analysis of CMOS Reversible Gates for Low Power Application

At present, circuits with low power and less computation time are in high demand in VLSI technology. In this context, the applications of reversible logic are far-reaching. In this paper, the detailed evaluation of certain reversible gates, namely, Feynman gate, Peres Gate, Modified Fredkin Gate, Modified Toffoli Gate, and TS Gate is set forth. Evaluation comprises of transistor implementation, simulation results and average power and delay measurement. The utilization of TSG to form a completely reversible full adder has also been evaluated in details. The implementations are completely reversible in nature and finds their applications in circuits with low power and high speed which are suitable to implement in silicon.
Knowledge Extraction Experiment Based on Tourism Knowledge Graph Q &amp; A Data Set

At present, the most natural language processing tasks use common data sets for experiments. However, as the concept of domain knowledge graphs is proposed, domain-based data sets have gradually become a demand. In this article, we collect data from various travel websites and official websites of tourist attractions, and use this to build a question and answer data set. At the same time, we also introduce the current Bert model with outstanding effect in the nlp field, and use this model to conduct experiments in the travelling question and answer data set. The experimental results not only show the feasibility of the constructed tourism data set, but also lay a foundation for the subsequent construction of a knowledge question answering system for tourism knowledge graph.