Reviewerly/exHarmony huggingface dataset:
ENHANCING THE SECURITY AND INTEGRITY OF MEDICAL INFORMATION THROUGH WATER MARKING

Ongoing headways in another period of e- wellbeing and telemedicine advancements present more current open doors for sharing clinical information when an open correspondence station is utilized. Notwithstanding, this trade is confronting basic difficulties of copyright insurance, content validation, and respectability check. Hence, because of the popularity for safe control of patient's clinical data, computerized watermarking is expected, as a worth added instrument, for secure clinical information the executives. In this review, another reversible watermarking method for the security of clinical recordings is proposed. The patient personality is inserted, as a watermark, into the host clinical recordings. A calculation is embraced for choosing the ideal casings in every video shot. This approach incorporates the utilization of polynomial change strategies, to recognize the best watermark implanting regions in the ideal casings. Exploratory outcomes exhibit high subtlety of the watermark and solid vigor against various assaults. Thus, our calculation approves the safety efforts that guarantee a successful control of clinical recordings. Software Tools: Image processing Toolbox of MATLAB version 2016a Key Words: Watermarking, Biomedical imaging, Discrete wavelet transform,,PSNR
MULTILITERACY LEARNING IN NOMMENSEN THEATER FILMS IN BATAK

Multiliteracy learning in recent years has become very interesting because by studying two or more literacy materials we can see how they relate to other literacy materials. Especially when it comes to learning literature and language, the film Nommensen in Batak land is a theatrical film that has been accommodated and shown especially for the Batak community in North Sumatra and outside the Tapanuli area. The film is usually only made in memory of Nommensen. However, in this study, the film will be studied so that it can produce a multiliterate assessment contained in the film. The results of this study indicate that complete multiliteracy learning can be learned even in certain drama or cultural context films.
Internet access and COVID-19

With the rapid development in the technology, there has been a greater dependence on the digital information which led to the increase in the communication technology. Further. with the greater dependence there requires a certain understanding of legal and human rights status for internet access as there is astonishing increase in the number of users of the internet. Recently in the case of Faheema Sharini V. State of Kerala, the Hon’ble High Court of Kerala opined that the ‘right to internet access’ should also be a part of Article 21 of the Constitution of India, forming a part of the right to privacy and the right to education. Despite India being considered as one of the largest democracies but the recent internet shutdowns depict a different picture altogether. For instance, the Government while exercising its powers under Section 144 of the Code of Criminal Procedure, 1973, brought the Kashmir valley under complete internet shut down for a period of 5 (five) months.
A Study on the Possibility of New Convergence in Physical Theatre: Theater-Re‘s Nature of Forgetting

Physical theatre is a form of theatrical performance that tells a story primarily through physical movements rather than language. Physical theatre provides a live, human experience, which is very different from any other form, and audiences are affected much more viscerally. The performance, emphasizing the metaphorical aspect, allows the audience to use their imagination to the maximum and induces emotional immersion through the dynamics of mime, gestures, physical movements, modern dance, and acrobatics. Since the 2000s, scientific studies on human behavior and cognitive neuroscience have caught the attention of physical performance researchers and performers, who have started to question the correlation between physical performance and cognitive behavior and neuroscience. The convergence of cognitive science and performance has developed in the direction of reviving “working memory” and “implicit memory” and activating “sensory memory” in people living with dementia in need of behavioral correction. Theatre Re’s The Nature of Forgetting (2017) received the great attention of the 2017 London International Mime Festival with a sell-out run. It is a powerful and explosive physical theatre piece about what is left when memory and recollection are gone. The process of “forgetting” that takes place in the human brain seems to be perfectly realized on stage with the emphasized “physicality”.
Predictive Analysis for daASD Using Population Based Incremental Learning

Autism is a clinically defined behavioral syndrome that initially appears in childhood and reflects the underlying neurodevelopmental irregularities.The main aim is to define the clinical trajectories of dyslipidemia-associated ASD (daASD) is to take the massive data integration has revealed an emerging ASD subtype characterized by dyslipidemia.However, the clinical course of this subtype remains unknown.To analysis of Electronic Health Record (EHR), data identifying the pattern sequence of medical events experienced by people with daASD.Within these data, individuals with daASD will be identified as those having at least one ASD diagnosis and dyslipidemia diagnosis or repeated blood lipid test results that are outside the normal range.Comparisons will be made between individuals with daASD, ASD with no dyslipidemia (ASD+/dyslipidemia-), dyslipidemia with no ASD (ASD-/dyslipidemia+), and individuals with no history of neuro-developmental disorders or abnormal lipid levels (ASD-/dyslipidemia-). Matching will be based on age, gender, ethnicity and address/zip code, as a proxy of socioeconomic status.The parents of each member of the above groups will be identified and their data will be collected.The direct comparisons of daASD with other ASD subtypes are required diagnosis for comorbid epilepsy/recurrent seizures and comorbid Fragile X syndrome using population based incremental learning.In this regard, Random Forest approach is on daASD for classification and semi-supervised learning is a process for daASD.The daASD experimental result is based on Electronic Health Records of Least Square Estimation Regression with Predict the Disease.
PENGEMBANGAN MODUL PEMBUATAN TES ASESMEN KOMPETENSI MINIMUM PADA PLATFORM E-LEARNING BERBASIS MOODLE UNTUK GURU SEKOLAH

One of the objectives of implementing the Minimum Competency Assessment (AKM) is to show how to master the essential competencies needed in various subjects. At the same time, digital formats must be integrated at every level of education, including the assessment process. Responding to the latest regulations regarding assessment and advances in digital technology requires teachers to have a combination of content and support skills. This study aims to develop an integrated media and environment for in-service teachers to improve their skills in creating AKM tests. The resulting test is designed according to variations in Moodle-based e-learning. This platform was chosen because many schools use Moodle as a Learning Management System (LMS). The study was conducted using the research and development method of the Thiagarajan 4D model, with the steps of define, design, develop, and disseminate. The development results include products, skills improvement, and a portfolio of user implementation results.
Development of Digital Competence for Research

Several kinds of research conclude that the level of digital competence of students is mainly oriented to their daily activities. Therefore, we present the current paper which seeks to determine the impact of implementing a blended learning course designed to improve digital competence for research (DCR) among a group of undergraduate engineering students. With this approach, a quasi-experimental explanatory methodology with a causal-comparative scope was applied. For this reason, the results were analyzed before and after applying a specially designed course to the experimental group, comparing it with a passive control group by collecting data using three previously validated instruments. For data analysis, students’ t-tests and two-way ANOVA (Analysis of Variance) were used, estimating the effects with Cohen’s study. Given the results, there was a statistically significant improvement (p ≤ 0.05) in their skills and attitudes, but not in their knowledge, obtaining a significant effect size only in the procedural dimension (f = 0.41 y η2 = 0.142). Therefore, the implementation of the course used in the blended learning modality is considered to significantly improve the DCR of a group of undergraduate engineering students, although the results should be evaluated with due reservations.
Mie-resonant metaphotonics

We introduce the recently emerged field of Mie-resonant metaphotonics and discuss spatial and temporal control of light by employing multipolar electric and magnetic Mie resonances and the physics of bound states in the continuum.
Nonvolatile electrically controllable broadband switches based on phase-change material GST

In this work, we report a nonvolatile electrically programmable broadband silicon photonic switch based on phase-change materials Ge 2 Sb 2 Te 5 with compact coupling length (50 µm), small crosstalk ( &lt; -8 dB) across the entire telecommunication C-band, and record-high endurance (&gt;2,800 cycles).
A Real-Time Robotic System for Sewing Personalized Stent Grafts

This chapter presents a multi-robot system to manufacture personalized product for medical purpose. This is a modularized system with three components: a personalized module, a bimanual module, and a vision module. The personalized module is designed to accommodate for different patients’ anatomy structure, while the bimanual module performs an intricate sewing task. All the robots are coordinated via the vision module, which tracks and guides their motions inreal time. Experiments show that this system can adapt to different personalized designs and achieve good accuracy and robustness. Therefore, this system can be extended to similar manipulation tasks, especially for flexible production, where multi-robot cooperation is required.
Petro-geochemical features of the Bajocian island-arc volcanism in the Lesser Caucasus (Azerbaijan)

&#x0D; &#x0D; &#x0D; This article discusses the petro-geochemical features of the Bajocian volcanism in the Azerbaijan Lesser Caucasus. Jurassic volcanism, manifested with varying intensity in the Lok-Karabakh zone, took place in various geodynamic settings, depending on the alternation of extension and compression processes in the island arc. Under these conditions, in the Lesser Caucasus during Middle Jurassic Epoch, two volcanic associations were formed: the Bajocian basalt-rhyolite and the basalt-andesite-dacite-rhyolite of the Bathonian age. It was found that the volcanic rocks of the Lower Bajocian complex belong to the tholeiitic series, and the Upper Bajocian rocks belong to the calc-alkaline series. In the rocks of the association, light REE slightly prevail over heavy ones and form almost flat spectra, the normalized plots are characterized by the chondritic nature of the distribution of rare earth elements, and the lines are parallel to the spectrum of the distribution of rare earth elements in MORB. In such rocks particularly the europium ratio (Eu / Eu * = 0.81–1.21) approaches 1 and low La / Yb ratios are observed. In some samples of more basic rocks, the content of heavy REE increases. Such a distribution of REE in the studied volcanic rocks is common for basic rocks of the tholeiitic series in typical island arcs. In the analyzed single rhyolite sample, a negative Eu anomaly is observed (Eu / Eu * = 0.56). The volcanic rocks on the primitive mantle normalized multi-element plots are characterized by depletion in Ta, Nb and enrichment in LILE (Rb, Ba, Pb, U, Th), which is characteristic of island arc-related volcanic rocks of supra-subduction zones (SSZ). The rocks are also depleted in titanium, potassium, and phosphorus. It was concluded that, in terms of geochemical features, the Middle Jurassic igneous rocks were formed at the ensimatic island arc, which was the initial stage of the development of the island arc tectonic setting, replaced in the Upper Jurassic by ensialic subduction.&#x0D; &#x0D; &#x0D;
“Have You Been in This Position? Because Your Comment Does Not Make Sense.” Discourse Strategies and Situated Ideals of Interaction on Social Networking Sites for Mothers

I use a Grounded Practical Theory approach to examine the discursive strategies and situated ideals of interactants on social networking sites for mothers (SNSM). Through identifying a problem of practice shaming and analyzing the discourse and metacommunication around the problem, I identified interactants using discursive strategies of antagonistic questions, personal experience, direct insults, and emoji reactions to engage in and respond to practice shaming. An examination of the discursive strategies revealed situated ideals of speaking authority and the place of shaming on SNSM.
Adversarial Examples Generation Through Attacking the 1 × 1 Convolution Operator

Convolution neural networks (CNN) are vulnerable to adversarial attack, which brings security risks to the application of convolutional neural networks in reality. Adversarial attacks are usually done by adding subtle perturbations in images to fool models to output wrong results. In black-box conditions, the transferability of attack method is mostly focused to fool more model. To enhance the transferability of adversarial attack, we proposed an attack method against the commonly used <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> convolution operator (1ConvA, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> Convolution Attack), which exists in many convolutional networks for improving the complexity of the model and the gradient flow. Experiments on many CNNs demonstrate 1ConvA can generate adversarial examples with better transferability.
Intellectual Property Right Confirmation System Oriented to Crowdsourced Testing Services

In the process of crowdsourced testing service, the intellectual property of crowdsourced testing has been faced with problems such as code plagiarism, difficulties in confirming rights and unreliability of data. Blockchain is a decentralized, tamper-proof distributed ledger, which can help solve current problems. This paper proposes an intellectual property right confirmation system oriented to crowdsourced testing services, combined with blockchain, IPFS (Interplanetary file system), digital signature, code similarity detection to realize the confirmation of crowdsourced testing intellectual property. The performance test shows that the system can meet the requirements of normal crowdsourcing business as well as high concurrency situations.
Noise processing and multitask learning for far‐field dialect classification

Summary Deep learning has made great achievements in the field of speech recognition. With the popularization of embedded devices such as intelligent speaker and the demand for dialect interaction scenes, it poses great challenges to far‐field speech recognition and dialect language recognition. In order to solve the dialect language recognition of embedded devices in far‐field speech recognition, we propose a deep learning neural network model with multitask learning. First, the audio is passed through the end‐to‐end noise reduction model to improve the effect of audio recognition. Then we define dialect recognition as the main task and dialect area as the auxiliary task, using the multitask learning method to improve the accuracy of dialect classification. The experimental results show that the end‐to‐end noise reduction model can improve the accuracy of audio recognition, and the best effect can be 7.54% higher than the baseline, and the accuracy of dialect language recognition can be improved by about 5% through multi task learning model.
Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic

Proponents of the Distributed Morphology framework have posited the existence of two levels of morphological word formation: a lower one, leading to loose input-output semantic relationships; and an upper one, leading to tight input-output semantic relationships.In this work, we propose to test the validity of this assumption in the context of Hebrew word embeddings.If the two-level hypothesis is borne out, we expect state-of-the-art Hebrew word embeddings to encode (1) a noun, (2) a denominal derived from it (via an upper-level operation), and (3) a verb related to the noun (via a lower-level operation on the noun's root), in such a way that the denominal (2) should be closer in the embedding space to the noun (1) than the related verb (3) is to the same noun (1).We report that this hypothesis is verified by four embedding models of Hebrew: fastText, GloVe, Word2Vec and AlephBERT.This suggests that word embedding models are able to capture complex and fine-grained semantic properties that are morphologically motivated.
SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification

GNNs have achieved remarkable performance on graph classification tasks. It can be attributed to the accessibility of abundant graph data, which are usually isolated by different data owners. Graph Federated Learning (GraphFL) allows multiple clients to collaboratively build GNN models without explicitly sharing data. However, all existing works assume that all clients have fully labeled data, which is impractical in reality. This work focuses on the graph classification task with partially labeled data. (1) Enhancing the collaboration processes: We propose a new personalized FL framework to deal with Non-IID data. Clients with more similar data have greater mutual influence, where the similarities can be evaluated via unlabeled data. (2) Enhancing the local training process: We introduce auxiliary loss for unlabeled data that restrict the training process. We propose a new pseudo-label strategy for our SemiGraphFL framework to make more effective predictions. Extensive experimental results prove the effectiveness of our design.
Revisiting Attention-Based Graph Neural Networks for Graph Classification

The attention mechanism is widely used in GNNs to improve performances. However, we argue that it breaks the prerequisite for a GNN model to obtain the maximum expressive power of distinguishing different graph structures. This paper performs theoretical analyses of attention-based GNN models' expressive power on graphs with both node and edge features. We propose an enhanced graph attention network (EGAT) framework based on the analysis to deal with this problem. We add a degree-related scale term to the attention coefficients and adjust the message extraction function to enhance the expressive power, which is critical in the graph classification task. Furthermore, we introduce a virtual node connected with all nodes to augment the node representation update process with global information. To prove the effectiveness of our EGAT framework, we first construct synthetic datasets to validate our theoretical proposal, then we apply EGAT to two Open Graph Benchmark (OGB) graph classification tasks to empirically demonstrate that our model also performs well in real applications.
Recall Network: A Simple Brain-Inspired Algorithm for Classification

The latest development of neuroscience has deepened the understanding of the information-processing mechanisms in the human brain and inspired a couple of sophisticated computational methods, such as deep learning, memory networks, and hierarchical temporal memory. However, it remains a challenge to explore simpler models due to the high computational cost of the above-mentioned methods. This paper proposes recall network (RN), an intuitive and simple model, that initializes itself by constructing the network path derived from the correlation of features in the training dataset and then makes classification decisions by recalling the paths that are relevant to the features in the test set. The algorithm has been applied to 263 datasets available from UCI Machine Learning Repository, and the classification results of repeated 10-fold cross-validation experiments on Weka demonstrate its competitive performance with prestigious classification algorithms, such as ANN, J48, and KNN.
Decision making Assessment model for University admission

Educational data mining has the potential to enhance the decision making by providing services to academicians. The students are confused when they aspire to get admission in selected colleges. Depending upon the university ranking the students get admission based on certain parameters. This paper presents a decision making assessment model that relies on combination of Distillation of Data through Human Judgment and fuzzy inference systems (FISs) to accommodate imprecise data using fuzzy rules after applying data attribute reduction to overcome uncertainty and complexity in the assessment model. This model was implemented using Weka and Matlab software
Content Analysis of COVID-19 Vaccine and Infertility Tweets: The Etiology of a Misinformation Pandemic

OBJECTIVE:To understand misinformation surrounding infertility and the COVID19 vaccine on Twitter by analyzing the prevalence and content of this misinformation. DESIGN:This study involves content analysis of tweets from a public dataset to identify the etiology of misinformation. MATERIALS & METHODS:Tweets included were from key points in the COVID19 vaccine discourse (July 2021 and December 2021) and contained at least one word related to COVID19 vaccination and one word related to fertility.Relevant tweets were analyzed for factuality.Descriptive statistics on followers, verification status, and engagement were obtained.Differences between the factual and misinformation groups were examined using ANOVA or Chi-square tests.Rates of factual vs misinformation tweets were compared to US COVID19 case counts and the most common hashtags and words were determined.Sentiment analysis was used to determine if tweets were positive, neutral, or negative. RESULTS:Misinformation tweets rose from 29.9% in July 2021 to 45.1% in December 2021.The proportion of misinformation in July 2021 fell as daily COVID19 cases rose (r=-0.50, p= 0.01), while relevant tweets mirrored cases (r=0.53,p< 0.001).Accounts sharing factual information had more followers (p<0.001) and verified users were more likely to share factual tweets (p=<0.001).Factual and misinformation tweets had similar engagement.Common hashtags in factual tweets included #getvaccinated and #vaccineswork, compared to #vaccinesideeffects and #vaccineskillingunbornbabies in misinformation tweets.Sentiment analysis found factual tweets were more positive and misinformation tweets more negative (p<0.001).CONCLUSION: Misinformation about the COVID19 vaccine and infertility is a threat to vaccine uptake in patients desiring future fertility.Understanding the content, sentiment, and reach of tweets is vital to combat misinformation.