Reviewerly/exHarmony huggingface dataset:
Do commonly available round facemasks fit near-term and term infants?

With inappropriately large facemasks, it is more difficult to create a seal on the face, potentially leading to ineffective ventilation during neonatal stabilisation. We investigated whether commonly available round facemasks are of appropriate size by measuring facial dimensions in near-term and term infants using two-dimensional (2D) and three-dimensional (3D) images.Prospective single-centre observational study.Infants born in our centre at 34-41 weeks' gestation were eligible.Patients were photographed with 2D and 3D technique.Distances between nasion and gnathion were measured and compared with the outer diameter of various round facemasks.2D and 3D images were performed using standard equipment. Correlations between gestational age and the above-mentioned distances were assessed using Pearson's r.Images were taken from 102 infants with a mean (SD) gestational age of 37.9 (2.3) weeks. Mean distance between nasion and gnathion was 46.9 mm (5.1) in 2D and 49.9 mm (4.1) in 3D images, that is, on average 3 mm smaller in 2D than with 3D (p<0.01). Based on these measurements, round facemasks with an external diameter of 50 mm seemed fitting for most (61%) term infants and 42 mm masks for most (72%) near-term infants (GA 34-36 weeks).Round facemasks with an external diameter of 60 mm are too large for almost all newborn infants, while 42/50 mm round facemasks are well fitting. Important anatomical structures were only visible using 3D images.NCT03369028.
A Typo-Tolerant Password Authentication Scheme with Targeted Error Correction

Password-based authentication is used in almost every computer system. However, users might not always type their passwords correctly. In order to improve the accuracy of password authentication without reducing security, Chatterjee et al. (IEEE S&P'16) and Guan et al. (SecureComm'17) proposed typo-tolerant schemes, respectively. However, these schemes do not consider the impact of personal information on password usages when generating candidate sets, while Wang et al. (NDSS'18) show that 36.95%~51.43% of users employ their personal information to generate passwords. In this paper, we propose a typo-tolerant password authentication scheme with targeted error correction. Our scheme focuses on two aspects: fuzzy judgment and error correction. During the process of password authentication, we first use fuzzy judgment to determine whether the input password contains personal information, and then correct the password according to the result of the fuzzy judgment. The error correction is divided into two types: with personal information and without personal information. Our experimental results show that our solution, when correcting the password entered by the user, is generally able to achieve higher accuracy. When considering the four main errors occurring in the mobile dataset and the general dataset, the average correct rate is 96.29%. The analysis illustrates that the average success proportion of targeted error correction (68.56%) is two times higher than the average success proportion of non-targeted error correction (31.44%), indicating the feasibility of our scheme.
Adaptively Secure Garbling Schemes for Parallel Computations

We construct the first adaptively secure garbling scheme based on standard public-key assumptions for garbling a circuit $$C: \{0, 1\}^n \mapsto \{0, 1\}^m$$ that simultaneously achieves a near-optimal online complexity $$n + m + \textsf {poly} (\lambda , \log |C|)$$ (where $$\lambda $$ is the security parameter) and preserves the parallel efficiency for evaluating the garbled circuit; namely, if the depth of C is d, then the garbled circuit can be evaluated in parallel time $$d \cdot \textsf {poly} (\log |C|, \lambda )$$ . In particular, our construction improves over the recent seminal work of [GS18], which constructs the first adaptively secure garbling scheme with a near-optimal online complexity under the same assumptions, but the garbled circuit can only be evaluated gate by gate in a sequential manner. Our construction combines their novel idea of linearization with several new ideas to achieve parallel efficiency without compromising online complexity. We take one step further to construct the first adaptively secure garbling scheme for parallel RAM (PRAM) programs under standard assumptions that preserves the parallel efficiency. Previous such constructions we are aware of is from strong assumptions like indistinguishability obfuscation. Our construction is based on the work of [GOS18] for adaptively secure garbled RAM, but again introduces several new ideas to handle parallel RAM computation, which may be of independent interests. As an application, this yields the first constant round secure computation protocol for persistent PRAM programs in the malicious settings from standard assumptions.
Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context

Commonly, product review analysis includes extracting sentiment from product documents. The contextual aspect contained in a review document has potential to improve results obtained by the sentiment analysis task. In this regard, this paper proposes an approach that takes into account both local and global context. The main contribution of this work is threefold. Firstly, local context is defined and the graph-based word sense disambiguation (WSD) method is extended to assign the correct sense of a word in the context of a sentence. Secondly, global context is defined for addressing contextual issues related to the specific domain of a review document by using an improved SentiCircle-based method. Thirdly, a weighted mean-based strategy to determine sentiment value at document level is presented. Several experiments were conducted to assess the proposed method. Overall, the proposed method outperformed the baseline method in the metrics of precision, recall, F-measure and accuracy.
Towards a Performance Analysis of Composite Web Services using Petri Nets.

The proliferation of web services-based applications, collaboration, interoperability between companies, and more generally demands of web services over the internet are major challenges in the design of performance evaluation in web services discovery and composition. It is required for the web services providers to analyse the performance related to the satisfaction of clients requests. This paper presents an analytical model based on Petri nets, for evaluating the performance of a web services system where requests and web services follow an exponential server. The arrival process of the users requests and web services follows a Poisson distribution. When the web services finish the service, they join the queue. This model follows a Poisson distribution, it is based on Petri nets. We use an analytical method for solving the model. Furthermore, we compute the system's response time, and the average number of clients in the system in terms of the arrival rate of clients requests. We find the limit number of clients in the system from which it begins to be saturated.
SecP2I A Secure Multi-party Discovery of Personally Identifiable Information (PII) in Structured and Semi-structured Datasets

Personal data governance is became a key issue within organisations. This is mainly due to (i) the strategic value of personal data which provide more insights improving commercial and operational efficiency ; and data security risk issues and privacy regulation restrictions (GDPR, CCPA, . . . ). Creating data catalogs is an important step for setting up a personal data governance. However, it remains a time-consuming task especially because of the absence of naming conventions in database modeling coupled to the heterogeneity of database management systems (DBMS) across Information Systems (IS). The paper presents SecP2I, an efficient data analytics-based approach permitting personal data discovery in structured and semi-structured datasets while guaranteeing end-to-end data confidentiality. The effectiveness of the platform is proven using a real world HR dataset.
A Few Critical Design Parameters Affecting User's Satisfaction in Interaction with Voice User Interface of AI-Infused Systems

Objective: The aim of this study is to identify a few critical design parameters for enhancing user"s satisfaction while interacting with the AI-infused intelligent systems through voice user interface (VUI).<BR><BR>Background: The interaction between the user and the AI-infused system is called as Human-AI Interaction (HAII) and supposed to have different features with respect to the human-computer interaction (HCI). It is therefore necessary to establish new criteria for designing and evaluating HAII in the point of user"s satisfaction. <BR><BR>Method: This study identified 31 user requirements regarding with HAII from previous studies and organized them into 9 secondary and 3 tertiary level user requirement categories. It was investigated and selected 9 design parameters of VUI that might make differences in user"s satisfaction. The priority of each design parameter was calculated using quality function deployment (QFD) technique.<BR><BR>Results: The amount of information, error control, and length of answer were found as the top three critical design parameters among others. They accounted for 51% of the total criticality score. It implies the reliability of information that the AI-infused systems provide during interaction is the most important factor for enhancing user"s satisfaction.<BR><BR>Conclusion: This study suggested theoretically nine critical interaction parameters and their priority in designing VUI embedded in AI-infused systems. <BR><BR>Application: The result of the study can be used to derive various experimental research models and hypothesis in HAII.
Privacy Preservation Mechanism For The Data Used In Image Authentication

With the growth in the demand for data exchange between any electronic system, the need for security of the information exchanged has become a necessity. The protection of the information related to the user from getting exposed to unauthorized person becomes a challenging issue. Hence, there is a requirement to encrypt the private user data to maintain the privacy. The paper focuses on maintaining the privacy of the sender information which is used for authentication of the image transmitted in the communication system. By embedding the information of the sender in the encrypted form, privacy for the sender information is obtained. The encryption of the sender data can be performed by using various encryption algorithms. In this work, encryption of the information of the person sending the image which is taken in the form of text file of varying size is performed using blowfish encryption algorithm which is a symmetric key block cipher. The length of the key used in blowfish for encryption and decryption ranges from 32 to 448 bits and the block size used is 64 bits. The structure of the algorithm is fiestal network. The work is implemented using MATLAB.
Ontology expansion based on UWN reusability

In the present era of big data the demand for developing efficient knowledge representation techniques for different applications is expanding steadily. Knowledge representation is largely based on ontology in the Semantic Web. However, one of the major difficulties to construct a new ontology require users the high cost and long time. Collecting complete knowledge about the domain of opening is time consuming and cannot guarantee expected results too. Therefore, reuse of existing resources can offer a cost and time benefit than building a new one from scratch. Hence, an approach is proposed to reuse the existing ontologies available on the web or online libraries then enrich it. This paper proposed Uyghur domain ontology constructing method based on UWN reusability, which can extract domain ontology factors from UWN and get the initial ontology in which the concepts and relations are already relative standard, then expand it automatically via Word2Vec models, at last construct the final higher quality version after revising and improving. The method is simple, but effective on ontology enrichment. After evaluating the accuracy, the experimental results show that our method is feasible and effective.
Utilizing an AI-Based Grammar Checker in an EFL Writing Classroom

This study explored the effectiveness of using feedback from Grammarly in EFL writing classrooms and students’ perceptions of such feedback. Writing assignments by 31 university students were examined regarding the numbers and types of feedback provided by the online writing support tool Grammarly and the students’ uptake of the feedback in their immediate revisions and subsequent writing. The results show that, of the five types of feedback provided by Grammarly, grammar appeared most frequently, followed by spelling, conciseness, punctuation, and conventions. Participants responded to the feedback approximately 75% of the time, but their response rates declined as they gained more experience in using the program, showing a tendency to evaluate the feedback before initiating corrections to their writing. Also, the analysis revealed that the instant and intensive feedback from Grammarly enhanced students’ awareness and accuracy in their use of determiners. Conversely, the students also used the program to reduce the cognitive load required to complete in-class writing tasks.
A ranking-based approach for supporting the initial selection of primary studies in a Systematic Literature Review

Traditionally most of the steps involved in a Systematic Literature Review (SLR) process are manually executed, causing inconvenience of time and effort, given the massive amount of primary studies available online. This has motivated a lot of research focused on automating the process. Current state-of-the-art methods combine active learning methods and manual selection of primary studies from a smaller set so they can maximize the finding of relevant papers while at the same time minimizing the number of manually reviewed papers. In this work, we propose a novel strategy to further improve these methods whose early success heavily depends on an effective selection of initial papers to be read by researchers using a PCAbased method which combines different document representation and similarity metric approaches to cluster and rank the content within the corpus related to an enriched representation of research questions within the SLR protocol. Validation was carried out over four publicly available data sets corresponding to SLR studies from the Software Engineering domain. The proposed model proved to be more efficient than a BM25 baseline model as a mechanism to select the initial set of relevant primary studies within the top 100 rank, which makes it a promising method to bootstrap an active learning cycle.
ROBOTS IN EDUCATION: A JORDANIAN UNIVERSITY CASE STUDY

This paper adopts a technology acceptance model used for studying Robot’s acceptance and focuses on the acceptance of robotic technologies. Despite a wide range of studies on the acceptance and usage of robotics technologies in different fields, there is lacuna of empirical evidence on the acceptance of robotics technologies in the educational context. We contribute to the scholarship on robotics technologies in an educational context, by using qualitative semi-structured interviews, and proposing a research model to empirically explore the main factors affecting the acceptance of robotics technologies, and particularly among university students. We contribute to practice by offering insights on users' expectations and intentions toward the potential use of robot services to both robot developers, and educational institutions alike. The results revealed a potential impact of effort expectancy, performance expectancy, social influence, and facilitating conditions on the intention behavior towards using robots as academic advisors. Additionally, an emergent dimension (i.e. emotions) was found to have an influence on the behavioral intentions, via its proposed impact on performance and effort expectancies. Overall, social characteristics of robots ought to be considered when investigating their acceptance, specifically when used as social entities in a human environment.
Student Performance Score Prediction Using Artificial Neural Network with the Support of Exploratory Factor Analysis and Clustering

This study proposes a sequence of data analysis techniques for prediction purposes which consists of the use of exploratory factor analysis for classification, clustering of variables in a factor for focusing to a prediction goal and prediction of missing data via an artificial neural network using performances of engineering students' data. Average end-year scores of courses have been used to represent performance of a student. Prediction goal is the forecasting of a course score before the examination. Factor analysis has been used to group courses under individual factors. The factor in which course score to be predicted exists is clustered to group students with similar scores from various courses in that factor, excluding the course score to be predicted. An artificial neural network is trained for course scores in the cluster and target course score of a student is simulated in the artificial neural network for prediction after completion of training with an acceptable training error. The study showed a significant accuracy for prediction. In addition, the realized work has shown that it is possible to re-evaluate an education program, guide a student for better academic performance and provide support for employment via a combination of data mining methods. Python has been used to carry out the analysis.
Effect of Amplitude Mismatch on Entanglement Visibility in Photon-Pair Sources

Entangled photons produced by parametric down-conversion effectively have two down-conversion paths. Ideally, amplitudes of the two paths are matched. We show that the entanglement visibility is, to first order, insensitive to amplitude mismatch.
Employee performance and customer loyalty: mediation effect of customer satisfaction

Customer preferences and behaviours are constantly changing, thus employee adaptation to customer demands in service providing is essential in today's modern business. This study will discuss customer perception of employee performance as well as overall employee performance on customer satisfaction and loyalty. In order to collect data for this study, two different surveys were conducted; delivered to employees of small and medium enterprises and its relevant customers in the Kurdistan Region of Iraq. The results of the study showed that customer perception of employee performance had a positive and significant impact on customer satisfaction and customer loyalty, while overall employee performance did not show any impact. Additionally, customer satisfaction showed positive mediation between customer perception of employee performance and customer loyalty. Having these findings of the paper we can conclude that customer perception of performance is a crucial factor in the long-term effectiveness of the organisations.
Introduction to Learner Corpus Research

Corpus linguistics and Learner Corpus Research (LCR) share a set of common features, among which is the use of corpora and corpus tools to analyze language. A comparison of the oft-cited definitions of LCR reveals that one of the key features of learner corpora is that the language they contain is meant to be as authentic as possible and is often defined as natural. Access to large electronic learner corpora in the 1990s led to a revolution in the way learner language was analyzed and described. The initial criticism levelled against LCR – be it fair or not – proved very fruitful as it prompted learner corpus researchers to explicitly verbalize the numerous advantages of LCR and move the field further. It can be argued that there has been a true qualitative evolution over time in the research methods used in LCR. In corpus-driven approaches, the corpus is viewed as a source of inspiration to formulate hypotheses about language.
Error Estimation for Sketched SVD via the Bootstrap

In order to compute fast approximations to the singular value decompositions (SVD) of very large matrices, randomized sketching algorithms have become a leading approach. However, a key practical difficulty of sketching an SVD is that the user does not know how far the sketched singular vectors/values are from the exact ones. Indeed, the user may be forced to rely on analytical worst-case error bounds, which do not account for the unique structure of a given problem. As a result, the lack of tools for error estimation often leads to much more computation than is really necessary. To overcome these challenges, this paper develops a fully data-driven bootstrap method that numerically estimates the actual error of sketched singular vectors/values. In particular, this allows the user to inspect the quality of a rough initial sketched SVD, and then adaptively predict how much extra work is needed to reach a given error tolerance. Furthermore, the method is computationally inexpensive, because it operates only on sketched objects, and it requires no passes over the full matrix being factored. Lastly, the method is supported by theoretical guarantees and a very encouraging set of experimental results.
Applying a new feature fusion method to classify breast lesions

Developing a computer-aided diagnosis (CAD) scheme to classify between malignant and benign breast lesions can play an important role in improving MRI screening efficacy. This study demonstrates that extracting features from both spatial and frequency domains, and applying an efficient combination of data reduction and classifier methods, had the potential to significantly improve accuracy in classifying between malignant and benign breast masses. By applying our CAD scheme to the testing dataset, we obtained an accuracy of 83.1% for the best combination of data reduction and classification (DNE-SVM).
The method of table tennis players' posture recognition based on a genetic algorithm

In order to overcome the problems of poor denoising effect, low recognition efficiency and low recognition accuracy of the existing methods of table tennis players' posture recognition, this paper proposes a method of table tennis players' posture recognition based on a genetic algorithm. The optimisation model of multi-objective key frame extraction is constructed, and the optimisation model of multi-objective key frame extraction is solved by genetic algorithm to obtain the key frame. Kalman filter is used to remove the noise in the key frame, eliminate the interference of the noise on the recognition result, and shorten the recognition time. According to the results of noise removal, the dynamic time warping algorithm is used to recognise table tennis players' posture. The experimental results show that the proposed method has good denoising effect, high recognition efficiency and high recognition accuracy, with the highest recognition accuracy of 98.7%.