Reviewerly/exHarmony huggingface dataset:
A Health Belief Model Approach to Evaluating Maternal Health Behaviors among Africans - Design Implications for Personalized Persuasive Technologies

We investigate the health belief and its impact on maternal health behaviors among African women with a view to offering guidelines for designing personalized persuasive technologies to encourage appropriate maternal health and lifestyles amongst women in the Global South. The results from the analysis of our qualitative data uncover key themes from the interview responses (of 47 stakeholders) associated with our research questions. They are: monitoring of fetus development and ensuring health and wellbeing of expectant mother and unborn baby, inaccessibility of the health facilities and long waiting times (queues) at the health facilities, poverty, perception about Caesarian Session (CS), ignorance of the consequences of inappropriate health behaviors, and low tech skills. Others include traditional and religious beliefs and constant health crises during pregnancies, delivery of newborn babies with serious health problems and deformities; and increase in pregnancy and childbirth-related deaths. Specifically, we uncovered that socio-cultural factors may or may not support appropriate maternal health behaviors. The socio-cultural factors include ethnic practices, traditional beliefs, and religious inclinations of the pregnant mothers and their families. For instance, the belief that “safe delivery is only a function of belief in God”, intake of untested concoctions, and stigmatization due to inability to deliver her baby by herself at home and/or submission to Caesarian Session (CS), are unsupportive religious and traditional beliefs and practices that promote inappropriate maternal health behaviors amongst the people. Subsequently, the themes were categorized based on their associated health belief factors such as perceived benefits, perceived barriers, perceived threat, and socio-cultural factors. We mapped them to their corresponding persuasive design strategies. Finally and based on the outcome of our studies, we propose culturally appropriate guidelines for developing personalized persuasive technologies for maternal health, which will be potentially effective in motivating healthy behaviors and lifestyles amongst expectant and nursing mothers across African communities.
Analysis on the influencing factors of multiple indicators in the United States based on multiple linear regression model

Abstract The US election will have a certain impact on the US economy. In this paper, different index systems are established to analyze the influence of different factors and American economy. After establishing the index system, this paper will use machine learning regression algorithm to fit the data in recent decades. We choose a variety of models, including ordinary linear regression model, Bayesian ridge regression model, elastic network regression model, support vector machine regression model and gradient enhanced regression model. In this paper, a prediction model based on machine learning is established without considering the epidemic situation. First of all, preprocess the data; secondly, determine the index system; then, based on the regression algorithm of machine learning, compare and select the optimal model for data fitting and prediction; finally, use linear regression to predict the indicators of the next four years.
Expected likelihood based query for active learning of Gaussian mixture models based classifiers

Abstract Typically, a supervised machine learning requires sufficient labelled data to achieve satisfying performance. In many domains, however, the labelled data are often expensive to obtain and requires laborious human effort. In order to minimize the labelled data, we propose an active learning method by introducing the expected likelihood as a criterion in the query system, especially for the Gaussian mixture models based classifiers. In this scenario, the expected log-likelihood is calculated for all unlabelled examples in a pool data set, then an instant with the lowest value is chosen to query the object of the label. Experiments were performed to evaluate the effectiveness of the method. Empirically, using the two class artificial data set we observed that the proposed method can reduce significantly the number of labelled examples required to achieve a certain performance compared with passive learning.
Blockchain for Embedded System Accountability

We present, in the form of a proof of concept, a permissioned blockhain framework that attains accountability within a system containing embedded devices. Accountability is a desirable property of distributed systems that enables the detection, identification, and removal of faulty or malicious behavior. It is a complementary approach to Byzantine fault tolerance, which is concerned with ensuring continued functioning of the system in the presence of Byzantine faults. Our proof of concept consists of a Raspberry Pi acting as a human interface; the blockchain is implemented in Hyperledger Fabric and the Raspberry Pi runs a lightweight blockchain client to minimize computational burden. The application shows that we are able to use smart contracts to detect and identify faulty or malicious hardware, and the permissioning framework to remove it.
Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models

Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.
A Virtual Mentor to Support Question-Writing on Stack Overflow

Recent studies on Stack Overflow show that question-related factors, such as conveyed sentiment and presentation quality, can significantly influence the probability of obtaining a useful answer. At the same time, the mentorship provided by human experts was proven effective in support novice users in effective question-writing. In line with previous empirical findings, we developed QAvMentor, a tool capable of providing online, real-time, automated mentorship during question-writing in Stack Overflow.
FLeCCS: A Technique for Suggesting Fragment-Level Similar Co-change Candidates

When a programmer changes a particular code fragment, the other similar code fragments in the code-base may also need to be changed together (i.e., co-changed) consistently to ensure that the software system remains consistent. Existing studies and tools apply clone detectors to identify these similar co-change candidates for a target code fragment. However, clone detectors suffer from a confounding configuration choice problem and it affects their accuracy in retrieving co-change candidates.In our research, we propose and empirically evaluate a lightweight co-change suggestion technique that can automatically suggest fragment level similar co-change candidates for a target code fragment using WA-DiSC (Weighted Average Dice-Sørensen Co-efficient) through a context-sensitive mining of the entire code-base. We apply our technique, FLeCCS (Fragment Level Co-change Candidate Suggester), on six subject systems written in three different programming languages (Java, C, and C#) and compare its performance with the existing state-of-the-art techniques. According to our experiment, our technique outperforms not only the existing code clone based techniques but also the association rule mining based techniques in detecting co-change candidates with a significantly higher accuracy (precision and recall). We also find that File Proximity Ranking performs significantly better than Similarity Extent Ranking when ranking the co-change candidates suggested by our proposed technique.
Multi-behavior Recommendation Based on Simplified Graph Convolutional Networks

There are a large amount of user behavior data in the recommendation system. The types of user behaviors are various, such as click, favorite, like, purchase and so on. However, most of the existing recommendation methods only consider single user behavior, such as purchase, ignoring the influence of auxiliary behavior data on target behavior. In order to make full use of multi-behavior data and further improve the recommendation performance, multi-behavior recommendation based on simplified graph convolutional networks(SGCNMB) is proposed in this paper. The SGCNMB model firstly initializes the embedding vectors for users and items in the shared embedding layer; then, in the embedding propagation layer, the simplified graph convolutional network is exploited to learn the user representations and item representations on a single behavior, the users' preferences on different single behavior are calculated respectively; finally, through the fusion prediction layer, the users' preferences on various behaviors are fused to get the top-n recommendation results of users on the target behavior. Extensive experiments were conducted on two real-world multi-behavior datasets. The experimental results demonstrate that SGCNMB can effectively take advantage of multi-behavior interaction data to further improve the recommendation performance.
Scattershot multiboson correlation sampling with random photonic inner-mode multiplexing

Abstract Multiphoton interference is an essential phenomenon at the very heart not only of fundamental quantum optics and applications in quantum information processing and sensing but also of demonstrations of quantum computational supremacy in boson sampling experiments relying only on linear optical interferometers. However, scalable boson sampling experiments with either photon number states or squeezed states are challenged by the need to generate a large number of photons with fixed temporal and frequency spectra from one experimental run to another. Unfortunately, even the well-established standard multiplexing techniques employed to generate photons with fixed spectral properties are affected by the detrimental effects of losses, spectral distorsions and reduction in purity. Here, we employ sampling correlation measurements in the photonic inner modes, time and frequency, at the interferometer input and output to ensure the occurrence of multiphoton interference even with pure states of input photons with random spectral overlap from one sample to another. Indeed, by introducing a random multiplexing technique where photons are generated with random inner-mode parameters, it is possible to substantially enhance the probability to successfully generate samples and overcome the typical drawbacks in standard multiplexing. We also demonstrate the classical hardness of the resulting problem of scattershot multiboson correlation sampling based on this technique. Therefore, these results not only shed new light in the computational complexity of multiboson interference but also allow us to enhance the experimental scalability of boson sampling schemes. Furthermore, this research provides a new exciting route toward future demonstrations of quantum computational supremacy with scalable experimental resources as well as future applications in quantum information processing and sensing beyond boson sampling.
Learning Organology in Virtual Environments: A Pilot Study for Virtual Music Education

A new perspective on using media in the classroom that started to rise in the past few years is immersive learning. This paper considers the learning success of children on the subject of organology and assesses the question, if a virtual environment on the tale “Peter and the Wolf” can support knowledge retention of primary school students relating to various instruments' sound, appearance, hearing impression and groups. In a first pilot study for evaluating this educational virtual environment, eight children filled out a questionnaire before and after the immersive experience. The findings indicate that the environment on the topic of “Peter and the Wolf” could have a positive effect on knowledge retention for fundamental aspects of organology. These first findings for using virtual environments in Music Education encourage the further investigation of using immersive media for teaching and learning in the subject Music.
Introduction of Artificial Intelligence as the Basis of Modern Online Education on the Example of Higher Education

The article deals with the process of awareness and acceptance of artificial intelligence in the educational process of higher education during the quarantine period. The state of the teacher is considered through the prism of the model of stages of experiencing grief by E. Kubler-Ross. This demonstrates the diversity and complexity of the transition to distance (online) learning. The genesis of the development and the role of information technologies based on artificial intelligence in education at the present stage is determined. Based on research by D. Tatar, J. Roschelle, P. Vahey, W. R. Penuel determined that the highest peak of the development of artificial intelligence and its active implementation in educational technologies refers to the period from 1999 to 2019. The authors highlight the approaches of informatization of the university. The Abai KazNPU has defined a way to build up the integrated digital management system of the university through the univer system. Artificial intelligence programs are highlighted: adaptive and personalized learning, automatic assessment, intermediate learning interval, chatbot, virtual assistants, proctoring and gamification. The authors analyze their own experience of implementing and further actively using artificial intelligence techniques and technologies using the Google Classroom, Kahoot, and Lingualeo platforms in the teaching process. The article presents the results of a survey among the university teaching staff to identify the degree of job satisfaction in the conditions of distance learning. The results of the survey for the autumn half-year of 2020-2021 showed that artificial intelligence is becoming a full-fledged participant in the educational process and occupies a central place in the process of intellectualization of society and the development of the education system as a whole. The progress and technological effectiveness of this process cannot be stopped. It was decided to introduce the elective discipline "Artificial Intelligence" at the bachelor's and master's level, with the continuity of the formed competencies for advanced training in artificial intelligence in the field of educational technologies.
Business Analytics for Yelp Reviews using R

Now-a-days online reviews plays a very important role before taking any decision about having a lunch or dinner at Restaurants or planning any trip. In this paper, we have investigated the dataset from Yelp.com. Yelp has become a very important site, particularly for small businesses who can achieve success or close down, based on their online reviews. We have summarized all the effects of reviews on the restaurants using sentiment mining and have provided with the statistical Insights. Our approach is to create a study on the business which yielded the worst and best ratings and determine the users who gave the worst and best ratings by using R software which is a statistical tool. The model has been created and the results for the same are obtained.
Green Efficiency for Quality Models in the Field of Cryptocurrency; IOTA Green Efficiency

In the last few years, cryptocurrencies have found a special place in the free economy. In addition to the importance and economic features of cryptocurrencies, the technical perspective on this area is also significant. If we want to use cryptocurrencies in the future as a global technology with everyday use, then this field needs to be optimized. In addition to issues such as security, scalability, speed, etc., energy efficiency and sustainability should also be considered. In this paper, the proposed “green efficiency” characteristic is added to the quality model for the field of cryptocurrency. This characteristic consists of four units that have independent tasks, the overall process of which seeks to design an optimal quality model in terms of energy consumption in cryptocurrency. The central unit of this proposal is the G-ECC, which controls other units. The unit makes the final decision to reduce energy consumption and trade-offs between features by reviewing and evaluating reports received from other units. At the end of this article, the green efficiency of IOTA cryptocurrency is reviewed in the proposed model.
Message passing based extended objects tracking with measurement rate and extension estimation

This paper addresses the problem of multiple extended objects (EOs) tracking, which has attracted increasing attention recently due to the developments of high-resolution sensors. Specifically, the state of an EO is modeled by the Gamma-Gaussian-inverse-Wishart (GGIW) distribution, and the message passing inference method, which provides a high efficient implementation for multi-target tracking, is adopted to propagate the posterior of the distributions of multiple EOs. The performance of proposed method is verified via simulation experiments.
Cluster Application with K-Means Algorithm on the Population of Trade and Accommodation Facilities in Indonesia

Abstract The aim of this study is to develop a grouping model in order to determine the means of trade and accommodation according to the regions in Indonesia. Research can be a reference for the government to increasing the income of each region in Indonesia equally. Research data were taken from a website that provides government statistical data, namely BPS ( Badan Pusat Statistik )-www.bps.go.id The solution is to use data mining techniques with clustering methods. The data test process uses the Rapid Miner software. Three clusters of mapping labels are used, namely the high cluster (K1), the normal cluster (K2) and the low cluster (K3). The results of the rapidminer processing were obtained from the centroid data for high clusters, namely ((1527), (810.4), (5865), (6655.3), (323), (315.1); the medium cluster, namely ((286), (199.591), (1327), (2240.227), (93.227), (140.955)); and the low cluster, namely (139.25), (122.5), (508.833), (919.222), (64.417), (94.444)). The cluster results show that 5 provinces are classified as high in clusters; 13 provinces are classified as medium clusters; and 16 provinces are classified as low clusters. Out of the results of the study, some 47% of areas in Indonesia still have low trade and housing facilities. With this analysis, it is hoped that the government will be able to pay more attention to regions whose revenues are still below average.
Convolutional Neuronal Networks for Tumor Regions Detection in Histopathology Images

Recent advances in deep learning and image sensors present the opportunity to unify these two complementary fields of research for a better resolution of the image classification problem. Deep learning provides image processing with the representational power necessary to improve the performance of image classification methods. The deep networks are considered as the most powerful tool in visual recognition, notably on the automated analysis of histological slides using Whole Slide Image (WSI) scanning techniques. In this paper, we proposed a comparative study analysis for efficient localization of tumor regions in kidney histopathology WSIs by employing state-of-the-art convolutional deep learning networks such as ResNet and VGGNet which can be fine-tuned to the target problem and exploit the powerful transfer learning techniques that save low computational costs. Experimental results conducted on an independent set of 17.300 images derived from 12 WSIs show that ResNet50 and VGG19 combined with VGG16 achieve almost 97% and 96% of accuracy respectively, for classifying tumor and normal tissues to identify relevant regions of interest (ROIs).
Reinforcement Learning: An Industrial Perspective

In this chapter, we discuss potential opportunities and challenges associated with applications of reinforcement learning (RL) in the aerospace domain. In particular, we focus on problems related to sensor resource management, autonomous navigation, advanced manufacturing, maintenance, repair and overhaul operations, and human-machine collaboration. We present two detailed RL case studies related to sensor tasking for aerial surveillance and robot control in an additive manufacturing application which utilizes different flavors of RL including the more recent deep RL framework. Finally, we highlight some ongoing research developments which could address key challenges in deploying RL in the aerospace domain.
High-Throughput Cuckoo Hashing Accelerator on FPGA Using One-Step BFS

Cuckoo hashing [1] is an efficient hash method with high space occupancy. For improved latency and throughput of cuckoo hashing, FPGA is used to accelerate basic operations of the hash table based on cuckoo hashing recently. However, the frequent kick out and reinsert operations degrade the throughput of cuckoo hashing implementation on FPGAs, especially under high table occupancy. In this paper, we propose a cuckoo hashing accelerator on FPGAs that uses one-step BFS (breadth-first search) to reduce the number of reinsert operations when the table occupancy becomes high. The average throughput of the accelerator achieves 185 million requests per second (MRPS) when the table occupancy reaches 92% at 200MHz. And the throughput at 90% table occupancy is 30% higher than the work in [2].
Hand Gesture Recognition for Sign Language Using Convolutional Neural Network

Speech disorder is a condition which affects one's ability to speak and hear. Those people use the separate language that is sign language, using that they can communicate with normal people. In this paper we developed a system which can translate sign language to text and then to audio, thus can improve communication with sign language. The system takes image data using the webcam of the computer, then preprocessing of the image is done using masking technique where the hand is masked to recognize the signed alphabet. Using Convolutional neural network algorithm features are mapped and classification is performed to classify the images accordingly, now image that is given as input is predicted and the translation is done in the form of text, then converted to audio. English alphabet are used as the data set for this system that is all the 26 alphabet are taken in the form of masked images. We've used 45500 images for training and 6500 images for testing.