Reviewerly/exHarmony huggingface dataset:
Information technology, governance and insurance in Sub-Saharan Africa

Purpose This study aims to investigate the role of information and communication technology (ICT) in modulating the effect of governance on insurance penetration in 42 Sub-Saharan African countries using data for the period 2004-2014. Design/methodology/approach Two insurance indicators are used in the analysis, namely, life insurance and non-life insurance. The three ICT modulating dynamics used include mobile phone penetration, internet penetration and fixed broadband subscriptions. Six governance channels are also considered, namely, political stability, “voice &amp; accountability”, regulation quality, government effectiveness, the rule of law and corruption-control. The empirical evidence is based on generalized method of moments. Findings The following main findings are established. First, mobile phone penetration does not significantly modulate governance channels to positively affect life insurance while it effectively complements “voice &amp; accountability” to induce a positive net effect on non-life insurance. Second, internet penetration complements governance dynamics of political stability, government effectiveness and rule of law to induce positive net effects on life insurance and corruption-control for an overall positive effect on non-life insurance. Third, the relevance of fixed broadband subscriptions in promoting life insurance is apparent via governance channels of regulation quality, government effectiveness and the rule of law while fixed broadband subscriptions do not induce significant overall net effects on non-life insurance though the conditional effects are overwhelmingly significant. Originality/value To the best of the authors’ knowledge, studies on the relevance of ICT in promoting insurance consumption through governance channels are sparse, especially for a region such as Sub-Saharan Africa where insurance penetration is low compared to other regions of the world.
Probabilistic tracking control of dissipated Hamiltonian systems excited by Gaussian white noises

This paper devotes to a feedback control strategy for nonlinear stochastic dynamical system to track a prespecified stationary response probability density. The system description and control design are conducted in Hamiltonian framework, and the excitations are confined to Gaussian white noises. The control design consists of several successive steps: firstly, separating the control into conservative and dissipative components by physical intuition, and expanding two components as polynomials; secondly, deriving the low-dimensional averaged equations of controlled Hamiltonian by stochastic averaging, and obtaining the stationary probability density of controlled responses by solving the associated Fokker-Planck-Kolmogorov (FPK) equation; thirdly and finally, determining the polynomial coefficients by minimising the performance index which balances the tracking performance and control cost. Two examples, i.e. Duffing oscillator and frictional system are adopted to illustrate the application and efficacy of this control strategy to track Gaussian and non-Gaussian response probability density.
Transaction Community Identification in Bitcoin

The emergency of anonymous encrypted digital currency based on blockchain brings the rapid growth of financial crimes simultaneously. However, under the condition of Know Your Customer rules, the traditional rule-based filtering and supervised pattern recognition methods are mainly built, which does not apply to the scenario of anonymous encrypted digital currency. In this paper, we attempt to tackle this problem by constructing user graph from transactions and dividing the whole user graph into tightly connected communities and clustering similar communities into groups. Experimental results on bitcoin transaction datasets show that the proposed approach has higher than 92% precision and higher than 73% recall for identifying gambling and mining pool communities.
Evaluation of Geotagging Twitter Data Using Sentiment Analysis During COVID-19

Late advances in innovation have empowered individuals to add area data to informal communities' geolocations where individuals share their correspondence and whereabouts in their day by day lives, yet in addition during anomalous circumstances, for example, emergency occasions. Be that as it may, since the volume of the information surpasses the limits of human logical capacities, it is nearly inconceivable to play out a direct subjective investigation of the information. The emerging field of visual investigation has been acquainted with tackle such difficulties by integrating the methodologies from factual information examination and human computer into exceptionally intelligent visual situations. The researcher extracts valuable hidden data from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. This paper proposes sentiment analytics of geo-tagged for anomalous social movement during COVID-19 by incorporating a novel classification technique and by a visual analytics approach for forecasting the overall flow of human crowds.
Using Virtual Reality for scenario-based Responsible Research and Innovation approach for Human Robot Co-production

This paper proposes to use Virtual Reality scenarios to explore the reaction of stakeholders within an innovation process in the context of the introduction of robots working in close collaboration with users. The goal is to design the system upfront in such a way, that it is not perceived as a threat to the worker or his/her job. Within the responsible research and innovation approach, the introduction of new technology needs to be accompanied by a careful investigation of the thoughts and feelings of all stakeholders. Especially workers who are currently not working with robots but their workspace is currently undergoing an Industry 4.0 driven transformation, experience fear, that this new technology will make their jobs redundant. On the other hand, it can be observed, that successful robot interaction processes, on the one hand, increase the overall productivity, but also can enhance human well-being. The feeling of “teamwork” with the artificial intelligence entity can develop to be equally positive and motivating. To be able to design future workspaces which will result in a “teamwork” perception instead of the “fear” perception, the use of VR can be applied.
Sentiment Analysis of Mobile Phone Products Reviews Using Classification Algorithms

Sentiment analysis or opinion mining is a process of analyzing opinions and emotions to infer the tendencies and impressions shown on the analyzed data and classify them as positive or negative. Sentiment analysis is extremely important because it helps companies and institutions to measure the scope of their customer's satisfaction with a specific product based on reviews in a very fast way. As the manual analysis of these reviews is very difficult because of the increase in the numbers of reviews on products day after day. This paper proposes a sentiment analysis model to classify product reviews as positive, neutral and negative. It applies five popular machine learning classifiers namely: Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor and Maximum Entropy with the aim to come up with the most efficient classifier. The dataset used consists of 82,815 reviews about mobile phone products, collected from Kaggle website. In order to evaluate the five classifiers, we used recall, precision, F1-mesaure and accuracy to measure the performance of each algorithm. The results show that Maximum Entropy and Naïve Bayes outperforms the other classifiers in term of accuracy in all experiments. Decision Tree algorithm gave the lowest results across all experiments in terms of accuracy.
Comparative Characteristics of Big Data Storage Formats

Abstract One of the most important tasks of any platform for big data processing is the task of the storing data received. Different systems have different requirements for the storage formats of big data, which raises the problem of choosing the optimal data storage format to solve the current problem. This paper describes the five most popular formats for storing big data, presents an experimental evaluation of these formats and a methodology for choosing the format.
An Enhanced Adaptive Histogram Equalization Based Local Contrast Preserving Technique for HDR Images

Abstract To improve the visualization of an image, the image enhancement techniques enhance the assured features of an image. In this work, the algorithm will enhance medical as well as normal or natural images which are captured in low light and day light conditions. An enhanced adaptive histogram equalization based local contrast preserving technique is developed with the help of image processing methods such as changing colour spaces, inverting images, dehazing, increasing saturation etc. The algorithm proposed here is intended to maintain the local image details while attaining the contrast enhancement. To express the performance of this algorithm, the image quality metrics calculated are peak signal to noise ratio and normalized absolute error. This metric parameters show that this model has better performance when compared to other existing methods.
The persistence of global terrorism

This study investigates the persistence of global terrorism in a panel of 163 countries for the period 2010–15. The empirical evidence is based on generalized method of moments. The following findings are established. First, persistence in terrorism is a decreasing function of income levels because it consistently increases from high-income (through upper middle-income) to lower middle-income countries. Second, compared with Christian-oriented countries, terrorism is more persistent in Islam-oriented nations. Third, landlocked countries also reflect a higher level of persistence relative to their coastal counterparts. Fourth, Latin American countries show higher degrees of persistence when compared with Middle East and North African countries. Fifth, the main determinants of the underlying persistence are political instability and weapons import. The results are discussed to provide answers to four main questions that directly pertain to the reported findings. These questions centre on why comparative persistence in terrorism is based on income levels, religious orientation, landlockedness and regions.
Llama - Low Latency Adaptive Media Algorithm

In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.
Cyber-Therapy: The Use of Artificial Intelligence in Psychological Practice

Cyber Therapy is a research project based on the relationship between Computer Science and Psychology. We are working on post-traumatic stress disorder (PTSD) due to severe traffic accidents using a virtual reality driving simulator and ECG, EDA and breathing sensors. With the help of virtual reality (VR) our goal is to build one software that can process the user's biofeedback signals - heart rate, body temperature, state of tension, etc. - in real time, to make the phobic stimulus autonomous. To this purpose, we developed a platform capable of adapting the phobic stimulus based on the user's biofeedback signals. We believe that this human-computer integrated system could be useful to patients as it would allow them to face fear autonomously, and to the psychotherapist, as it would allow a real time - physiologically based - knowledge of fear symptoms severity able to promote a timely and more appropriate program of intervention.
Measuring Engagement Level in Child-Robot Interaction Using Machine Learning Based Data Analysis

In this paper, an approach for measuring the engagement level of a child interacting with a social robot in special education is proposed. The introduced methodology uses visual data gathered during the educational procedure and utilizes known machine learning models for analyzing the data. The examined models were able to estimate the engagement level by at most 14.70% Mean Absolute Error (MAE), with the Multi-Layer Perceptron (MLP) model showing the best performance with 12.70% MAE. Moreover, a Long Short-Term Memory (LSTM) model was able to predict the engagement level by a 10.40% MAE, when handling the problem as a time series prediction task. The overall results were very promising revealing the efficiency of the machine learning models to analyze the visual data describing the complex environment of the child-robot interaction in special education.
Deep learning-based nuclei segmentation and classification in histopathology images with application to imaging genomics

In this chapter, we present a deep learning-based, joint nuclei segmentation and fine-grained classification method in histopathology images, which solves the nuclei segmentation and nuclei classification problems in a unified framework. It consists of a prediction network that outputs the segmentation results of tumor, lymphocyte, and stroma nuclei, as well as a perceptual network that enhances the segmentation of details. The framework is trained by a lung cancer data set with pixel-level annotations and achieves accurate segmentation and classification results. We then briefly introduce two applications of the framework in the context of imaging genomics analysis. The segmentation and classification results can be used to extract imaging features for analyses of intratumor heterogeneity and tumor-infiltrating lymphocytes. A multiscale analysis integrating the imaging features, genomic features, and clinical outcomes will promote understanding of the relationship between genotype and phenotype and help find potential biomarkers that will lead to favorable clinical outcomes.
Comparative Analysis of Feature Detection and Extraction Techniques for Vision-based ISLR system

Sign language recognition is a highly adaptive interface between the deaf-mute community and machines. In India, Indian Sign Language (ISL) plays a significant role in the deaf-mute society, breaking communication distancing. Extracting features from the input image is crucial in vision-based Indian Sign Language Recognition (ISLR). This paper addresses feature detection and extraction techniques used in the ISLR. This paper categorizes existing techniques into three broad groups: scale-based, intensity-based, and hybrid techniques. SIFT (Scale Invariant Feature Transform), SURF (Speeded up Robust Features), FAST (Features from Accelerated Segment Test), BRIEF (Binary Robust Independent Elementary Features), and ORB (Oriented FAST and rotated BRIEF) are the techniques that have been evaluated and compared for intensity scaling, occlusion, orientation, affine transformation, blurring, and illumination. Results were generated in terms of key point detected, time-taken, and the match rate. SIFT is consistent in most circumstances, though it is slow. FAST is the fastest with good performance like ORB, and BRIEF shows its advantages in affine transformation and intensity changes.
Personal Assistance for Alzheimer’s Patient

Alzheimer's disease is a gradual brain disorder and an irremediable disease that slowly destroys memory and thinking skills. The contemporary solution for this disease includes forums for discussion, behavioural monitoring, and path sensing. The proposed solution is to create an application that acts as personal assistance to the patients. The features include face recognition, wandering and fainting detection, assistance to find a way home, reminders to daily chores and past life, organizing , and planning jobs. This can be implemented by using the sensors and GPS in smartphones to sense the actions of the patients. Deep learning and machine learning using python can be applied in android studio along with google maps. The games in the application that are specifically designed for the patient help them in the initial stage of the disease. Memory is improved by learning and making the brain engaged in some activity as it connects brain cells. The new feature in the application is face recognition, where the patient gets a reminder of persons they have met. This helps the patient to overcome the difficulties in recognizing people.
Your car is recording: Metadata-driven dashcam analysis system

Dashcam as an on-board camera is useful as a source of potential digital evidence not only to reveal the truth of a traffic accident but also to explain the situation of a crime scene as a moving surveillance camera. It stores multimedia data as well as a variety of additional information needed for accident investigation including time, location, speed, accelerometer, etc. Under these circumstances, various studies have been conducted for dashcam forensics, but most of them focused mainly on extracting and interpreting visual video frames. In this paper, we identify and classify various metadata generated by 14 dashcam models produced by 11 manufacturers. Furthermore, we develop a normalized database schema to manage multiple different metadata and then discuss several dashcam forensic activities based on it. In addition, a prototype open-source tool is presented to support the proposed metadata-driven dashcam forensics.
Investigating learners’ motivation towards a virtual reality learning environment: a pilot study in vehicle painting

The HandleVR project develops a Virtual Reality (VR) training based on the 4C/ID model [1] to train vocational competencies in the field of vehicle painting. The paper presents the results of a pilot study with fourteen aspirant vehicle painters who tested two prototypical tasks in VR and evaluated its suitability, i.a. regarding their learning motivation. The results indicate that VR training is highly motivating and some aspects (e.g., a virtual trainer) in particular promote motivation. Further research is needed to take advantage of these positive motivational effects to support meaningful learning.
Chatbot for College Website

A Chatbot is a program that reenacts a conversation between a user and a machine. The machine has been embedded knowledge to identify the queries asked by the student and other users and making a decision itself to respond to the queries of the student and other users. The user just needs to ask the queries that are answered by the chatbot. These bots will be totally founded on a book-based UI that permits the client to type orders and get message just as content to discourse response. It can be more made sure about when it is coordinated with well-known web services. The school request chatbots will be fabricated utilizing counterfeit calculations that can undoubtedly comprehend and investigate clients message. The client can pose the inquiries that are any school-related activities through chatbot without genuinely accessible to the school for inquiry. By using Artificial Intelligence, the queries are answered by the system asked by various users. The user simply needs to enlist and login to the system. Natural language handling (NLP) is utilized for tokenizing, stemming, and separating the substance of the objection given by the client.
Mechanism Design with Blockchain Enforcement

We study the design of self-enforcing mechanisms that rely on neither a trusted third party (e.g., court, trusted mechanism designer) nor a long-term relationship. Instead, we use a smart contract written on blockchains as a commitment device. We design the digital court, a smart contract that identifies and punishes agents who reneged on the agreement. The digital court substitutes the role of legal enforcement in the traditional mechanism design paradigm. We show that any agreement that is implementable with legal enforcement can also be implemented with enforcement by the digital court. To pursue a desirable design of the digital court, we study a way to leverage truthful reports made by a small fraction of behavioral agents. Our digital court has a unique equilibrium as long as there is a positive fraction of behavioral agents, and it gives correct judgment in the equilibrium if honest agents are more likely to exist than dishonest agents. The platform for smart contracts is already ready in 2020; thus, self-enforcing mechanisms proposed in this paper can be used practically, even now. As our digital court can be used for implementing general agreements, it does not leak the detailed information about the agreement even if it is deployed on a public blockchain (e.g., Ethereum) as a smart contract.
Incorporating sentimental trend into gated mechanism based transformer network for story ending generation

Story ending generation is a challenging and under-explored task, which aims at generating a coherent, reasonable, and logical story ending given a context. Previous studies mainly focus on utilizing the contextual information and commonsense knowledge to generate story endings. However, there are still some issues must be addressed in the story endings generation processing, such as sentimental consistency and interference from secondary information. In this paper, we propose a Gated Mechanism based Transformer Network (GMTF). The GMTF model utilizes the sentimental trend to make story ending generation more sentimentally consistent with the context. For a given story context, we utilize a sentiment analysis tool VADER to obtain the sentimental trend. Then, the sentimental information and contextual information are input jointly into the transformer network to capture the key clues. Furthermore, the gated mechanism is applied to filter irrelative information and the weights of attention layers for encoder and decoder are shared to make the most of the contextual clues. The experimental results on ROCStories dataset demonstrate that the proposed method achieves 27.03% on BLEU-1, 7.62% on BLEU-2, 1.71 on Grammar, and 1.31 on Logicality, respectively. Specifically, our model outperforms the state-of-the-art model IE+MSA by 0.23%, 0.22%, 1.78%, 5.64%, respectively and the Transformer model by 3.06%, 1.05%, 5.55%, 48.86%, respectively. Both automatic and manual evaluations show that our model can generate more reasonable and appropriate story endings compared with the related well-established approaches.