Reviewerly/exHarmony huggingface dataset:
Age and Gender Impact on Password Hygiene

Password hygiene plays an essential part in securing systems protected with single-factor authentication. A significant fraction of security incidents happen due to weak or reused passwords. The reasons behind differences in security vulnerable behaviour between various user groups remains an active research topic. The paper aims to identify the impact of age and gender on password strength using a large password dataset. We recovered previously hashed passwords of 102,120 users from a leaked customer database of a car-sharing company. Although the measured effect size was small, males significantly had stronger passwords than females for all age groups. Males aged 26–45 were also significantly different from all other groups, and password complexity decreased with age for both genders equally. Overall, very weak password hygiene was observed, 72% of users based their password on a word or used a simple sequence of digits, and passwords of over 39% of users were found in word lists of previous leaks.
Enhanced Annotation Framework for Activity Recognition Through Change Point Detection

The task of Activity Recognition (AR) on ubiquitous sensor data is traditionally performed on annotated datasets where manually identified change points denoting the start and the end of the activities are present. However, the majority of the real-world smart home applications generate un-annotated data streams, where such change points are not known in prior. In this paper, we address this problem by proposing a real-time annotation framework for Activity Recognition based on Change Point Detection (CPD). First, we investigate the components of feature extraction, data augmentation, noise handling, and classification to propose an optimal AR framework for the chosen datasets. We then propose S-CPD, a novel transfer learning based CPD algorithm, which uses similarities of the output probability distributions in order to generate a change point index (CPI) corresponding to each of the sensor readings in the data stream. Based on this calculated CPI, we segment the data stream and allows us to perform enhanced annotations. To test the efficiency of this proposed annotation framework, we perform extensive experimentation on 4 real-world smart home datasets. Our proposed solutions outperform the existing state-of-the-art AR and annotation frameworks on these datasets by around 1.6% and 14% respectively, while providing comparable performance with that of the state-of-the-art CPD algorithm. In particular, we achieve an average AR and annotation accuracy of 96.64 % and 94.15% respectively, with an average sensor distance error of 1.1 across the 4 datasets.
Model review on the proposed new smart campus framework in achieving industry 4.0

Information technology has penetrated in various aspects of the fields of economy, industry, institutions, and even education. All directions of information technology are more encouraging towards a goal called a smart city. This paper will discuss how the pattern of revolutionary readiness in the field of education will lead to the era of intelligence or what is called a smart campus, this is an important part of supporting smart cities in the field of education. Starting in 2010 until now education has undergone many rapid evolutions. From 2010 to 2015, education began to apply information technology including many media such as LMS, e-Learning, Mooc, and similar applications. Then from 2013 to 2019 the world of education began to develop patterns systematically, including the use of AI, IoT infrastructure, Cloud, AR, and the like. Then in 2018 until now, research in educational technology tends to discuss more about the patterns, models, learning styles, and evaluations presented. However, no one has discussed how to make patterns, models, even a suitable framework for the application of educational technology to achieve its goal, which is what is called a Smart Campus. In this paper will discuss how the smart pattern in an educational institution itself by looking at some of the references obtained. Then combine the patterns with each other so that at the end of the day the Smart Campus pattern can be realized.
Understanding Code Fragments with Issue Reports

Code comments are vital for software development and maintenance. To supplement the code comments, researchers proposed various approach that generate code comments. The prior approaches take three sources: (1) programming experience, (2) code-comment pairs in source files, and (3) comments of similar code snippets. Most of their generated comments explain code functionalities, but programmers also need comments that explain why a code fragment was developed as it is. To meet the timely needs, in this paper, we introduce a new source, issue reports (e.g. maintenance types, symptoms, and purposes of modifications), to generate code comments. Issue reports contain rich information on how code was maintained. The valuable details of issue reports are useful to understand source code, especially when programmers learn why code was developed in a specific way. Towards this research direction, we propose the first approach, called ISSUECOMM, that builds the links between code fragments and issue reports. Our results show that it links more than 70% issue numbers that are written by programmers in code comments. Furthermore, the links built by our tool covers 4× bugs, and 10× other issues than the links written in manual comments. We present samples of our built links, and explain why our links are useful to describe the functionalities and the purpose of code.
Senior High School Students' Knowledge and Attitudes Toward Information on Their Health in the Kumasi Metropolis

The study examines senior high school students' understanding and attitudes toward information on their health in the Kumasi Metropolis. Multiple sampling techniques (convenient and simple random sampling techniques) were used in the study. A questionnaire was used to collect data from 391 respondents for the study. Frequencies and percentages were used to analyze the sociodemographic data. Again, the study used Pearson's correlation coefficient to show the degree of relationship between the level of knowledge of health information and attitudes toward seeking and sharing health information. The study found students' knowledge of the causes and symptoms of malaria, cholera, and Sexually Transmitted Infections (STIs) to be appreciably high as a result of readings from textbooks and health professionals. Again, the study found that the students preferred sharing their health information with friends than their parents and schools' authorities. The study further found that the major sources of students' health information included health professionals and textbooks. Lastly, even though some of the students claimed internet sources to their health information, it was not a major source to the student body at large. The study recommends strong health systems on the campuses of senior high schools as they have become communities on their own as a result of the emergence of the free senior high school program. The monitored positive peer-counseling group should also be encouraged by the schools' management and by extension the counseling units for the students to share views on themselves, particularly on health issues where they deem fit.
DeepEverest

We design, implement, and evaluate DeepEverest, a system for the efficient execution of interpretation by example queries over the activation values of a deep neural network. DeepEverest consists of an efficient indexing technique and a query execution algorithm with various optimizations. We prove that the proposed query execution algorithm is instance optimal. Experiments with our prototype show that DeepEverest, using less than 20% of the storage of full materialization, significantly accelerates individual queries by up to 63X and consistently outperforms other methods on multi-query workloads that simulate DNN interpretation processes.
Innovative Approach to Onboard Media Forensic of a Drone

Like the origin of aviation by enthusiasts, drone flying also started as a hobby. But unlike aviation which took a long time to mature, drone flying has proliferated very quickly across the globe. Large-scale use of drones for authorized military application and illegal use by non-state actors has blurred the distinction between them, thereby leading to an increased number of violations at vital areas/vital points. Expanded use of recreational UAVs/drones has now turned into a big security concern worldwide. Drones are being intentionally used for committing crimes and, therefore, in-depth knowledge of the ways to collect digital evidence from captured drones is a much-needed facet. In order to collect the forensic evidence, it is important to carry out forensics of onboard storage media present in the drone. This storage media which contains different artifacts like pictures, videos, log files, etc., is analyzed to gather the digital evidence to resolve a crime. This research brings out the information gathered from drone storage device.
Research and Implementation of English Grammar Check and Error Correction Based on Deep Learning

English as a universal language in the world will get more and more attention, but English is not our mother tongue, and there exist differences in culture and thinking. English grammar is the most difficult problem to solve. There are many English learners, and the number of English teachers is limited, and it is inevitable to use Internet technology to solve the problem of lack of resources. The article uses deep learning technology to propose an ASS grammar detection model, which can quickly and efficiently detect grammatical errors. The research results show the following. (1) This study selects data from the GEC evaluation task and analyzes the four modules of article, noun, verb, and preposition through algorithms under different models. The results indicate the accuracy of the four modules. The recall rate has been improved to a certain extent, the accuracy rate of nouns is the highest, which can reach 63.99%, the accuracy rate of prepositions is improved to a lesser extent, and the inspection accuracy rate after improvement is 12.79%. (2) In the experiment to verify the effectiveness of the ASS grammar detection model, compared with the detection effect of the ordinary model, the accuracy of the ASS comprehensive inspection has been greatly improved. The comprehensive accuracy of the ordinary detection model is 28.01%, and the ASS model’s comprehensive accuracy rate of the inspection was 82.82%, and the accuracy rate was increased by 54.81%. The result shows that the performance of the ASS inspection model has been improved by leaps and bounds compared with the traditional model. (3) After transforming and upgrading the ASS model, the three models and other models obtained were run on the test set and the mixed test set, respectively. The results show that the accuracy, precision, recall, and F1 score of ASS model are the highest in the test set, which are 98.71%, 98.83%, 98.64%, and 98.73%, respectively, the Bayesian network check model has the lowest accuracy rate of 51.74%, and the ROC curve value and AUC value of the ASS model are both the largest. The accuracy of the ASS model on the mixed test set is also the highest, reaching 98.01%. The JaSt model on the mixed test set has a significant downward trend, with the accuracy rate dropping from 92.16% to 56.68%. It can be concluded that the ASS model can accurately and efficiently monitor grammatical errors.
A Systematic Mapping Study on Ensemble-Based Classifier

The performance of classifiers is essential for the outcome of machine learning algorithms. A practical approach to significantly increase the performance of a classifier is through an ensemble-based classifier. Therefore, ensemble of classifiers must be examined. The purpose of this study is to provide an overview of recent machine learning research (2016–2020) in ensemble-based classifiers, with the primary goal of understanding what research has been carried out among the machine learning community that implements ensemble-based classifiers in their studies. We performed a systematic mapping study to investigate ensemble-based classifiers in machine learning research. A total of 149 prominent papers published from 2016 to 2020 implement this method using various dataset types, and each of these papers addresses the issues encountered with the ensembled classifier. These studies were classified according to the ensemble-based classifier techniques, issues and types of datasets. As a result, the most favorable ensemble technique used by many researchers is the hard-level approach. However, there is a lack of research that addresses the issue of multi-class imbalanced data. Thus, indicates a need for more studies in handling multi-class imbal-anced data. We also propose a decision making flowchart which may aid researcher in making informed decisions on how best to analyze data. The results suggest that researchers should give more attention to the combining techniques of ensemble-based classifiers in order to achieve an ample overall performance, and clearly there is a need for more studies on the issue of multi-class imbalanced data.
Ambiguous and Incomplete: Natural Language Processing Reveals Problematic Reporting Styles in Thyroid Ultrasound Reports

Natural language processing (NLP) systems convert unstructured text into analyzable data. Here, we describe the performance measures of NLP to capture granular details on nodules from thyroid ultrasound (US) reports and reveal critical issues with reporting language.We iteratively developed NLP tools using clinical Text Analysis and Knowledge Extraction System (cTAKES) and thyroid US reports from 2007 to 2013. We incorporated nine nodule features for NLP extraction. Next, we evaluated the precision, recall, and accuracy of our NLP tools using a separate set of US reports from an academic medical center (A) and a regional health care system (B) during the same period. Two physicians manually annotated each test-set report. A third physician then adjudicated discrepancies. The adjudicated "gold standard" was then used to evaluate NLP performance on the test-set.A total of 243 thyroid US reports contained 6,405 data elements. Inter-annotator agreement for all elements was 91.3%. Compared with the gold standard, overall recall of the NLP tool was 90%. NLP recall for thyroid lobe or isthmus characteristics was: laterality 96% and size 95%. NLP accuracy for nodule characteristics was: laterality 92%, size 92%, calcifications 76%, vascularity 65%, echogenicity 62%, contents 76%, and borders 40%. NLP recall for presence or absence of lymphadenopathy was 61%. Reporting style accounted for 18% errors. For example, the word "heterogeneous" interchangeably referred to nodule contents or echogenicity. While nodule dimensions and laterality were often described, US reports only described contents, echogenicity, vascularity, calcifications, borders, and lymphadenopathy, 46, 41, 17, 15, 9, and 41% of the time, respectively. Most nodule characteristics were equally likely to be described at hospital A compared with hospital B.NLP can automate extraction of critical information from thyroid US reports. However, ambiguous and incomplete reporting language hinders performance of NLP systems regardless of institutional setting. Standardized or synoptic thyroid US reports could improve NLP performance.
Genetic Algorithm-Based Pseudo Random Number Generation for Cloud Security

Cloud computing has globally turned out to be one of the emerging techniques in the modern world that provides various services over the inter-networking systems. Among many of its applications, information storage over the Internet has gained considerable demand and immense popularity, and dependency on such service usage has increased over a wide range of users. We aim to develop a cloud infrastructure to ensure the confidentiality of any kind of information residing within its infrastructure to gain its legitimate user’s trust. To fulfill the objective of our research, we propose an innovative, intelligent framework to spawn a pseudo-random number secret key utilizing the combination of the feedback shift register and a genetic algorithm to intensify the key’s strength in terms of its length. The proposed system presented in this artifact the efforts to further enhance the sequence’s length by the able utilization of the genetic algorithm. We have carried out various statistical tests to uphold the strength of the generated key sequences. Amongst them, hybrid model generated a higher range of 13,425 subsequences, a chi-square value of 2.862 which is way better than a critical standard value of 7.81. We have also tested our system by using runs up down, runs below and above the mean test. As per the outcomes of the test, our hybrid model has achieved a hypothesis acceptance region for a run length of 317 and 241 with mean values of 333 and 244.1, respectively. The genetic algorithm usage further enhances the random keys’ strength by generating a long series of random numbers. Hence, the amalgamation of the feedback shift register and the genetic algorithm engenders a series of pseudo-random numbers that gratify randomness and the extension of the series’ length beyond the limit.
Generating Longitudinal Synthetic EHR Data with Recurrent Autoencoders and Generative Adversarial Networks

Synthetic electronic health records (EHR) can facilitate effective use of clinical data in software development, medical education, and medical research without the concerns of data privacy. We propose a novel Generative Adversarial Network (GAN) approach, called Longitudinal GAN (LongGAN), that can generate synthetic longitudinal EHR data. LongGAN employs a recurrent autoencoder and the Wasserstein GAN Gradient Penalty (WGAN-GP) architecture with conditional inputs. We evaluate LongGAN with the task of generating training data for machine/deep learning methods. Our experiments show that predictive models trained with synthetic data from LongGAN achieve comparable performance to those trained with real data. Moreover, these models have up to 0.27 higher AUROC and up to 0.21 higher AUPRC values than models trained with synthetic data from RCGAN and TimeGAN, the two most relevant methods for longitudinal data generation. We also demonstrate that LongGAN is able to preserve patient privacy in a given attribute disclosure attack setting.
Expectation maximization algorithm over Fourier series (EMoFS)

The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior information on parametrized probability distribution function (pdf) and its family. For the highly dynamic environments, however, there is a high potential of mismatch between the domain characteristics and the pre-assumed distribution. The observed data may have been drawn based on an unknown pdf, which can additionally be combination of discontinuous functions with different types. For such cases, it is very likely that an arbitrary selection of a mixture distribution would yield worse performance. Even if the domain characteristics are captured correctly, i.e. the pdf family is known, another complexity may arise due to the fact that a tractable and a closed form cannot always be obtained. Addressing these two problems, we present the EM over Fourier Series (EMoFS) approach for univariate problems to be solved with EM. Our solution produces the true pdf approximately; thus sidesteps the necessity of a prior assumption. Additionally it guarantees a tractable and closed form for E-step. We verify and evaluate our model via comparison with state of the art solutions, theoretical experiments and real world problems.
An Unsupervised Graph-Based Hybrid Approach for Opinion Summarization

Huge volume of opinions is posted daily on the internet due to the rapid growth of merchant sites, social media sites and other discussion forums. This contribution creates an information overload scenario forcing analysts and bosses to look for efficient opinion summarization software but developing an optimal summarization system is an uphill task. In this paper, a hybrid approach towards opinion summarization is investigated. Extractive and abstractive summarization techniques are merged with structured summarization approaches to produce an informative, concise and readable summary of the opinionated text. An unsupervised graph-based approach is used for extractive and abstractive summarization. The results are compared with existing state-of-the-art summarization approaches using ROUGE-N. It is concluded that the proposed system produced more ideal summaries than the existing traditional approaches.
ORR

The time-specific applications are assigned to Central Processing Unit (CPU) of the system and one of the most promising functions of the time-sharing operating systems is to schedule the process in such a way that it gets executed in minimal time. At present, the Round Robin Scheduling Algorithm (RRSA) is the most widely used technique in a timesharing operating system because it gives better performance than other scheduling techniques, namely, First Come First Serve (FCFS), Shortest Job First (SJF), and Priority scheduling. The major challenge in RRSA is the static value of Time Quantum (TQ) which have plays a pivotal to decrease or increase the performance of the system. In existing literature, many statistical techniques are used for identifying efficient time quantum for RRSA. However, there is limited exposure in existing literature on generating a learning model for identifying optimized TQ. In this research work, a new research direction is given for identifying Optimized TQ by training a learning model and predicting optimum TQ value.
A Platform To Enable Algorithms As Service Model Aimed at Digital Health Service Delivery

In this paper we provide a framework for Algorithm As a Service Model which goes beyond Machine Learning as a Service (MLAS) model, by supporting extended category of algorithms including but not limited to measurement, calibration, physics and statistics based models. These algorithms when used for screening of health care data, are termed as Software As Medical Device - SaMD. We present a platform that can act as an accelerator for clinical trials of these SaMD devices. We also go a step ahead from DevOPS model by semi-automating the algorithm deployment process. We have deployed a few algorithms into our platform and in this paper we discuss the case-studies and illustrate the advantages of our approach over conventional methods.
“YouTube’s predator problem”: Platform moderation as governance-washing, and user resistance

YouTube experienced large-scale criticism in early 2019 for predatory behavior toward children on the platform. To address concerns about children’s safety, YouTube acted quickly by demonetizing and deactivating comments on videos featuring minors. In this paper, we analyze both the company’s response to this scandal and how users received that response. We argue that YouTube’s reaction was governance-washing, which presents the appearance of vigorous platform moderation and leverages popular perceptions of technology to create the look of authority while deflecting questions about substance. While YouTubers and users did not dispute that the pedophilic comments were heinous, they questioned the effectiveness of the company’s solutions, arguing that YouTube’s platform governance actions did not solve the problem. Ultimately, we show that users have cogent critiques of governance policies that pretend to be comprehensive but fail to solve what they purport to address, and offer up the term “governance-washing” as a useful framework to make sense of such cases.