Reviewerly/exHarmony huggingface dataset:
Congenital Central Hypothyroidism Caused by a Novel IGSF1 Variant Identified in a French Family

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; Congenital central hypothyroidism (CCH) is a rare disorder that can be caused by X-linked mutations in the immunoglobulin superfamily member 1 (&lt;i&gt;IGSF1&lt;/i&gt;) gene. Here, we describe four familial cases with a variable presentation due to a novel &lt;i&gt;IGSF1&lt;/i&gt; pathogenic variant. &lt;b&gt;&lt;i&gt;Case Presentation:&lt;/i&gt;&lt;/b&gt; In the index case, an investigation at birth of a suspected brain-lung-thyroid syndrome surprisingly revealed a central hypothyroidism. Next-generation sequencing uncovered a novel &lt;i&gt;IGSF1&lt;/i&gt; pathogenic variant: a hemizygous single base duplication (G) resulting in a premature stop codon (NM_001555.4: c.2485dup, p.Ala829Glyfs*15). Further family investigations revealed missed neonatal CCH for the older brother who presented with prolonged jaundice (thyroid stimulating hormone 3.06 mUI/L, FT4 9.4 pmol/L, FT3 4.2 pmol/L). It also led to the diagnosis of CCH at 11 months of age for the younger brother, whose thyroid function was considered normal at birth. Neuropsychological evaluations showed no cognitive impairment for the eldest two brothers, but a slightly reduced processing-speed index compared with the other parameters for the oldest. Furthermore, a maternal uncle was diagnosed with biochemical CCH at 34 years of age, despite having few symptoms, and a complete workup revealed prolactin deficiency and macroorchidism. &lt;b&gt;&lt;i&gt;Discussion:&lt;/i&gt;&lt;/b&gt; This report of a rare case of neonatal CCH caused by IGSF1 deficiency highlights the importance of recognizing the neonatal signs of hypothyroidism to diagnose CCH as early as possible. Our results also show the importance of performing family genetic screening if a pathogenic variant is identified, to properly monitor carriers as CCH may develop over time. We suggest that these families should be followed up in the long term to better understand the natural history of this syndrome and evaluate the need for hormone substitution.
Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services

In mobile networks, crowdsourcing in Quality of Experience (QoE) assessment phase involves collecting data from the user terminals or dedicated collection devices. A mobile operator or a research group may provide applications that can be run in different mobility test modes such as walk or drive tests. Crowdsourcing using users’ terminals (e.g., a smartphone) is a cheap approach for operators or researchers for addressing large scale area and may help to improve the allocated resources of a given service and/or the network provisioning in some segments. In this work, we first collect a dataset for three popular Internet services: on-demand video streaming, web browsing and file downloading at the user terminal level. We consider two user terminals from two different vendors and many mobility test modes. The dataset contains more than 220,000 measures from one of the major French mobile operators in the Île-de-France region. The measurements are effectuated for six months in 2021. Then, we implement different models from the literature for estimating the QoE in terms of user’s Mean Opinion Score (MOS) for every service using features at radio or application levels. After that, we provide an in-depth analysis of the collected dataset for detecting the root cause of poor performance. We show that the radio provisioning issues is not the only cause of detected anomalies. Finally, we discuss the prediction quality of HD video streaming service (i.e., launch time, the bitrate and the MOS) based only on the physical indicators. Our analysis is applied on both plain-text and encrypted traffic within different mobility modes.
Calibration Method for Line-Structured Light Three-Dimensional Measurement Based on a Simple Target

Line-structured light three-dimensional (3D) measurement is apt for measuring the 3D profile of objects in complex industrial environments. The light-plane calibration, which is a crucial link, greatly affects the accuracy of measurement results. This paper proposes a novel calibration method for line-structured light 3D measurement based on a simple target, i.e., a single cylindrical target (SCT). First, the line-structured light intersects with the target to generate a light stripe. Meanwhile, the camera captures the calibration images, extracts the refined subpixel center points of the light stripe (RSCP) and detects the elliptic profiles of the two ends of the cylindrical target (EPCT). Second, two elliptical cones defined by the EPCT and the camera optical center are determined. Combining the two defined elliptical cones and the known radius of the SCT, we can uniquely solve the axis equation of the SCT. Finally, because the coordinates of RSCP in the camera coordinate system fulfill both the camera model and the cylindrical equation, these coordinates can be solved and used to obtain the optimal solution of the light-plane equation. The results of simulations and experiments verify that our proposed method has higher accuracy and effectiveness.
Toward Open Science: Contributing to Research Culture Change

Advancing open science across the complex and decentralized research ecosystem remains challenging to fully enact for a variety of reasons. Specific open science initiatives, including journal policy changes, preregistration, and Registered Reports represent existing opportunities for publishers, societies, institutions, funders, and researchers to contribute to more coordinated culture change across research communities. In a November 2021 post1 in The Scholarly Kitchen, Roger Schonfeld suggested that the current model for scientific scholarly communication may be ill-suited to improve and sustain public confidence and trust in science as it moves toward greater openness and transparency. The increasing politicization of science,2–4 along with related challenges in effectively managing the public communication of science, intensifies the need to build and sustain trust in the scientific process. After outlining proposed priorities for the scholarly communications community to contribute to trust-building in science, Schonfeld ultimately points to the need for greater coordination and collaboration across stakeholders in the global knowledge system—publishers, senior research officers, policy makers, institutions, funders, and libraries—to sustain a trusted information environment. System-level coordination and collaboration is a convergent theme across the open science reform movement.5–7 The recent adoption8 of UNESCO’s Recommendation of Open Science9 by all 193 member states offers a new signal of intentionality and a normative framework for global system coordination. The COVID-19 pandemic spurred coordination and implementation of open initiatives among various stakeholders across the scholarly communications system who aligned to thwart a global crisis. Specifically, the statement issued in January 2020 by the Wellcome Trust on “Sharing […]
Evaluating Common Reconnaissance Tools and Techniques for Information Gathering

A reconnaissance attack is a commonly overlooked step in penetration testing but a critical step that could help increase the effectiveness of an attack on a target.However, it is a common attack faced by companies and institutions, among others.It enables the attacker or penetration tester to gain valuable information on the target and select the best tools and methods that would make the attack successful.This study aims to identify and review existing state-of-the-art methodology for reconnaissance attacks based on certain techniques and evaluation metrics which will be beneficial to professional, ethical hackers in selecting the bestfit tool for a successful reconnaissance attack and enlighten organizations and the general public of the potential harm of a successful reconnaissance attack.This study explored seven online databases, which include Springer, Elsevier, Wiley, IEEE, ACM, ArXiv and Google Scholar.A total of 1306 publications were retrieved.Several screening criteria were executed to select relevant articles.Finally, 19 articles were identified for in-depth analysis.A quantitative evaluation was conducted on the selected articles using two search strategies: Techniques and source.A Quantitative Analysis (QA) was conducted on the selected articles and the outcome based on existing reconnaissance tools shows that 95.2% of the tools allowed experts to gather information by running their necessary command from the command line.While 4.8% of the tools do not provide a command-line interface allowing users to launch it from the command line interface while specifying some parameters to customize how it runs.61.9% of the tools are network-based as they can be used to gather about the target's network infrastructure such as protocols, ports, DNS, IP address, hosts and the general network architecture.At the same time, 28.5% could be classified as hybrid as they allow the attacker to gather system-based and network-based information.This study concludes with findings that show that some of the tools operate in a different capacity; the best-fit tool is massively dependent on the attacker or penetration tester and the scenario's situations.Therefore, a tool should be selected based on the user's preference and the attack style.
A Proposed Harmony Search Algorithm for Honeyword Generation

The honeyword system is a password cracking detection technique that aims to improve the security of hashed passwords by making password cracking simpler to detect. Many honeywords (false passwords) accompany the sugarword (true password) to form the sweetwords (false and true passwords) for every user. If the attacker signs in using a honeyword, a silent alarm trigger shows that the honeyword system might be compromised. Many honeyword generation techniques are presented; each one has a flaw in the generating process, a lack of support for all honeyword characteristics, and a slew of honeyword problems. The harmony search algorithm (HSA), a metaheuristic intelligence algorithm inspired by music, is used in this article to offer a novel method for generating honeyword. The suggested honeyword generation technique will enhance the generating process, enhance honeyword characteristics, and address prior approaches’ shortcomings. This paper will show several previous honeyword generation techniques, explain the suggested one, discuss the experimental findings, and compare the new honeyword generation method with the previous ones.
EFEKTIVITAS METODE BERNYANYI BERBANTUAN MEDIA AUDIOVISUAL TERHADAP PENGUASAAN KOSAKATA BAHASA INGGRIS ANAK KELOMPOK B

This research was conducted in group B TK Dharma Wanita Tembokrejo Muncar Banyuwangi. The background of this research is because there are still many children who are confused and cannot clearly show the English that has been spoken by the teacher so it is necessary to optimize the mastery of English vocabulary by using audiovisual media in the form of video. This study aims to determine whether the use of audiovisual media is effective or not on the mastery of English vocabulary in group B. The type of research used is a quasi-experimental design with a Non-Equivalent Control Group. The sample in this study were group B children with a total sample of 24 respondents. Methods of data collection using tests, observations with check lists and documentation. The results obtained from the results of the pretest and posttest in the experimental and control groups before and after the treatment there were significant differences. The results of the difference between the pretest and posttest in the experimental group were 34.89, the control group was 25, so that tcount = 2,778 and t table with db = 22 at a significance level of 5%. The value of tcount is greater than ttable (2,778&gt;2,074), it shows that there is a difference in vocabulary mastery between children who are treated using audiovisual media and children who are not treated using audiovisual media.
A load balancing strategy for reducing data loss risk on cloud using remodified throttled algorithm

&lt;p&gt;&lt;span&gt;Cloud computing always deals with new problems to fulfill the demand of the challenging organizations around the whole world. Reducing response time without the risk of data loss is a very critical issue for the user requests on cloud computing. Load balancing ensures quick response of virtual machine (VM), proper usage of VMs, throughput, and minimal cost of VMs. This paper introduces a re-modified throttled algorithm (RTMA) that reduces the risk of data hampering and data loss considering the availability of VM which increases system’s performance. Response time of virtual machines have been considered in our work, so that when migration process is running, data will not be overflowed in the VMs. Thus, the data migration process becomes high and reliable. We have completed the overall simulation of our proposed algorithm on the cloud analyst tool and successfully reduced the risk of data loss as well as maintains the response time.&lt;/span&gt;&lt;/p&gt;
Validation of an Instrument to Measure Students’ Perceptions of Online Learning

In the context of the COVID-19 global pandemic and the health measures derived from it, emergency online learning (EOL) became the only possible alternative to be able to continue the training process in the university context. The general ob-jective of the present research is to know the perceptions and evaluations of stu-dents at a Chilean public university about the "emergency" online learning pro-cess that occurred in the context of the COVID-19 pandemic. Based on several previous works related to online learning (OL), an instrument (questionnaire) was developed and implemented with the participation of 117 students. The re-search is quantitative-descriptive and had the following specific objectives: (1) To determine the attitude of university students towards EOL; (2) To characterize the learning experience of students in online modality; (3) To know about the availa-ble resources of students for EOL; and (4) To validate the developed questionnaire through a factor structure analysis. In general, the results show that students are proficient in the aspects related to self-management of learning, but have a low level of motivation for EOL and suffer a deficit in aspects related to interac-tion with others (teachers and peers). These results provide a first approximation of Chilean students' perceptions of EOL and allow us to identify several aspects that should be improved by professors and university authorities. This research was financially supported by the German Academic Exchange Service (DAAD as part of the project “Praxispartnerschaften zwischen Hochschulen und Un-ternehmen in Deutschland und in Entwicklungsländern ab 2017” (Project Nr. 57334905).
Performance of the K-Nearest Neighbors Method on Identification of Maize Plant Nutrients

Maize is one kind of commodity consumption in domestic as well as export that has high economic value. However, the low productivity is caused by the main factor, namely the decreased level of soil fertility, so that it has the same effect on crop yields. These problems require the application of technology with the K-Nearest Neighbor (KNN) method. The method of study is based on 17 signs of nutrient deficiencies with Minkowski distance calculation process, calculation of deficiency of soil nutrients based on the value of K determined. The test results of the research use K = 75 to get an accuracy of 92.40. Comparative analysis of the K-nearest neighbor (K-NN) and NB methods by looking for the closeness between the criteria for new cases and old case criteria based on the criteria for the closest cases. The results showed that the K-Nearest Neighbor (K-NN) Algorithm had a better accuracy value than NB.
Shape Descriptor Based on Curvature

Nowadays, the applications developed in the imaging area have focused on image measurement and compression, facial recognition, blurring, or image enhancement, to name a few.Among these activities, shape descriptors play an important role in describing the topology of an object and determining its general and local characteristics.Despite the large number of studies carried out, the idea of a robust shape descriptor remains a difficult problem, the challenges that hinder shape recognition are due to transformations such as rotations, scaling, deformations caused by noise or occlusions.This article presents the development of a descriptor based on the use of curvature, with this procedure, the dimensionality of the data is reduced so that the variability of the shapes can be described or explained by means of characteristics, and through neural networks associates these features and thus discriminates one type of object from another.
Distance Learning at the University of Jordan in the Setting of Covid-19 Pandemic from the Students’ Perspectives

The study aimed to investigate the reality of distance learning at the University of Jordan in the setting of Covid-19 pandemic from the students’ point of view. The study relied on the descriptive analytical approach, and used questionnaire, which consisted of 60 items, as its study tool to achieve the objectives of the study. The stability of the study tool was confirmed and it was validated. The data was collected from a random sample of 826 students from the University of Jordan. The results indicated that the degree to which faculty members’ use of e-learning applications in education from the students’ point of view was medium. As for the University of Jordan students’ attitudes towards distance learning, they tended to be negative. The problems, students face during distance learning, came with a medium degree as well. The results showed that there were statistically significant differences at the significance level (α= 0.05) in relation to the attitudes of the University of Jordan students towards distance learning regarding the variable of gender and it came in favor of males. Moreover, there were statistically significant differences at the significance level (α= 0.05) in relation to the problems that faced the students during distance learning regarding the gender, but it comes in favor of females this time. Statistically significant differences at the level of the significance (α= 0.05) appeared in the responses of the study sample to the problems that faced them during distance learning in relation to the faculty and it came in favor of humanities faculty. One of the most important recommendations the study reached to was to train the faculty members on using e-learning applications.
English Text Readability Measurement Based on Convolutional Neural Network: A Hybrid Network Model

Text readability is very important in meeting people's information needs. With the explosive growth of modern information, the measurement demand of text readability is increasing. In view of the text structure of words, sentences, and texts, a hybrid network model based on convolutional neural network is proposed to measure the readability of English texts. The traditional method of English text readability measurement relies too much on the experience of artificial experts to extract features, which limits its practicability. With the increasing variety and quantity of text readability measurement features to be extracted, it is more and more difficult to extract deep features manually, and it is easy to introduce irrelevant features or redundant features, resulting in the decline of model performance. This paper introduces the concept of hybrid network model in deep learning; constructs a hybrid network model suitable for English text readability measurement by combining convolutional neural network, bidirectional long short-term memory network, and attention mechanism network; and replaces manual automatic feature extraction by machine learning, which greatly improves the measurement efficiency and performance of text readability.
A Novel Reformed Reduced Kernel Extreme Learning Machine with RELIEF-F for Classification

With the exponential growth of the Internet population, scientists and researchers face the large-scale data for processing. However, the traditional algorithms, due to their complex computation, are not suitable for the large-scale data, although they play a vital role in dealing with large-scale data for classification and regression. One of these variants, which is called Reduced Kernel Extreme Learning Machine (Reduced-KELM), is widely used in the classification task and attracts attention from researchers due to its superior performance. However, it still has limitations, such as instability of prediction because of the random selection and the redundant training samples and features because of large-scaled input data. This study proposes a novel model called Reformed Reduced Kernel Extreme Learning Machine with RELIEF-F (R-RKELM) for human activity recognition. RELIEF-F is applied to discard the attributes of samples with the negative values in the weights. A new sample selection approach, which is used to further reduce training samples and to replace the random selection part of Reduced-KELM, solves the unstable classification problem in the conventional Reduced-KELM and computation complexity problem. According to experimental results and statistical analysis, our proposed model obtains the best classification performances for human activity data sets than those of the baseline model, with an accuracy of 92.87 % for HAPT, 92.81 % for HARUS, and 86.92 % for Smartphone, respectively.