Reviewerly/exHarmony huggingface dataset:
Tactile temporal order judgment during rubber hand illusion: Distinct modulation of the point of subjective simultaneity and temporal resolution

During the rubber hand illusion (RHI), individuals feel a fake hand as their own (ownership) and a perceived position of their real hand shifts toward the fake hand (proprioceptive drift; PD), which represents updating of multisensory hand representations. Bimanual tactile temporal order judgment (TOJ) includes processes of localizing tactile stimuli in space, for which multisensory hand representations are essential. According to the common processes, we examined tactile TOJ performance during the RHI and non-RHI. Temporal resolution (TR) as TOJ accuracy worsened during the non-RHI compared to the RHI. Additionally, a significant correlation between TR and PD was observed only in the non-RHI condition. However, the point of subjective simultaneity (PSS), which offers relative weighting of tactile inputs from the right and left hands, was correlated with illusory hand ownership. These results suggest that PSS and TR from tactile TOJ during RHI relate to self-attribution and localization of the hand, respectively.
Hybrid-MELAu: A Hybrid Mixing Engineered Linguistic Features Based on Autoencoder for Social Bot Detection.

Social bots are defined as computer algorithms that generate massive amounts of obnoxious or meaningful information. Most bot detection methods leverage multitudinous characteristics, from network features, temporal dynamics features, activities features, and sentiment features. However, there has been fairly lower work exploring lexicon measurement and linguistic indicators to detect bots. The main purpose of this research is to recognize the social bots through their writing style. Thus, we carried out an exploratory study on the effectiveness of only a set of linguistic features (17 features) ex- ploitable for bot detection, without the need to resort to other types of features. And we develop a novel framework in a hybrid fashion of Mixing Engineered Linguistic features based on Autoencoders (Hybrid-MELAu). The semi-supervised Hybrid-MELAu frame- work is composed of two essential constituents: the features learner and the predictors. We establish the features learner innovated on two powerful structures: a) the first is a Deep dense Autoencoder fed by the Lexical and the Syntactic content (DALS) that represents the high order lexical and syntactic features in latent space, b) the second one is a Glove-BiLSTM autoencoder, which sculpts the semantic features; subsequently, we generate elite elements from the pre-trained encoder part from each latent space with transfer learning. We consider a sample of 1 Million from Cresci datasets to conduct our linguistic analysis comparison between the writing style of humans and bots. With this dataset, we observe that the bot’s textual lexical diversity median is greater than the human one and the syntactic analysis based on speech-tagging shows a creative behavior in human writing style. Finally, we test the model’s robustness on several public dataset (celebrity, pronbots-2019, and political bots). The proposed framework achieves a good accuracy of 92.22%. Overall, the results shown in this paper, and the related discussion, argue that it is possible to discern the differences between humans’ and bots’ writing styles based on an efficient linguistic deep framework.
THE COGNITIVE PROCESSES OF READING SKILLS

Three processes (phonological, syntactic, and working memory) show a significant increase in development during the period of rapid reading skill acquisition. Students with reading disabilities experience significant disruptions in these processes, but not to the same extent in semantic or orthographic processes. However, reading is hampered by the underutilization of phonological processing and the near-total reliance on semantics and orthographic or other cognitive processes (sustained attention, sequential processing, visual processes, comprehension, etc.). The core traits of reading disability are deficits in three basic cognitive functions: phonological processing, syntactic awareness, and working memory. It is crucial that assessments for learning disabilities take into account and methodically measure these processes. All rights reserved. (Siegel&amp; Mazabel, 2014).
DOCUMENT ANALYSIS TRAIL OF ENGLISH READING TEACHING MODELS: IMPLICATIONS FOR SECOND LANGUAGE TEACHER

The purpose of this document analysis was to chronicle some of the theories of teaching vocabulary and reading of English as a second language. This was prompted by the observation that the majority of rural primary school learners had low English reading abilities. Teachers have forgotten theories which they read and discussed during their pre-service education. Their practice is now governed by pragmatic (what works) methods to get the learners a pass mark. Historical research philosophy which sought to understand the present from the past guided the study. Research design was mainly document analysis. Researchers identified documents by using Google search engines. Critical identifier words were: English reading theories, Second language teaching approaches. Inclusion criterion was based on English as a second language, being available and accessible to researchers. Since the documents were already in circulation in academic circles, there was no need to carry out serious validation. Copies of the English reading models were e-mailed to a purposive sample of 20 teachers of English language. These were selected on being online, teachers of English as a second language, available and willing to participate. Principle researchers presented the theorems to participants who had read the theorems. Focus group discussions on ZOOM and BIG/BLUE BUTTON platforms were held. The study found that, English reading theorems implied that: teachers can author reading materials locally to provide the context which provoke learners to read. Teaching requires content to be presented from simple to complex, starting from letter naming, vowels, phonic sounds and word meanings. Use of educational audio-visual materials can improve the application of behaviourist theories in the classroom. Acculturation can be implemented by requiring learners to read every time, as a school culture. This can be done by having a day or week in which all activities are done by reading instructions
Expectation-based blindness: Predictions about object categories gate awareness of focally attended objects

Selective attention gates access to conscious awareness, resulting in surprising failures to notice clearly visible but unattended objects (“inattentional blindness”). Here, we demonstrate that expectations can have a similar effect, even for fully attended objects (“expectation-based blindness”). In three experiments, participants (N=613) were presented with rapid serial visual presentation (RSVP) streams at fixation and had to identify a target object indicated by a cue. Target category was repeated for the first 19 trials but unexpectedly changed on trial 20. The probability of correct target reports on this surprise trial was substantially lower than on preceding and subsequent trials. This impairment was present for switches between target letters and digits, and also for changes between human and animal face images. In contrast, no drop in accuracy was observed for novel target objects from the same category as previous targets. These results demonstrates that predictions about object categories affect visual awareness. Objects that are task-relevant and focally attended often fail to get noticed when their category changes unexpectedly.
Improving the Reading Habits of Class III Students Studying at Patshaling Primary School, Tsirang

The Reading habit is an essential and important aspect for creating the literate society in this world. It shapes the personality of an individual and helps them to develop the proper thinking methods and create new ideas. At present, due to the influence of the mass media, people do not show much interest in reading books, magazines and journals. Therefore, there is an urgent need to embed and develop the reading habit in the minds of our young children. This study was conducted to assess and determine some appropriate measures and methods to improve the reading habits of Class III students of Patshaling Primary school. Reading habits are best formed at a young impressionable age in school, but once formed it can last one’s lifetime (Green, 2001). The mixed, observational descriptive study of longitudinal design used instruments like observation, diaries and interviews to collect both qualitative and quantitative data. In this paper, the researcher also discussed various methods and benefits in improving reading habits. Findings from this study concluded that the intervention program implemented on 14 students of Class III students could significantly improve the reading habits and develop a positive attitude towards reading. This research comes as the solution to both the teachers and students of lower primary school students who are struggling to improve their reading habits. The study provides some useful insight into developing reading habits and how reading habits can be enhanced to achieve maximum development in our young children.
Sistem Pendukung Keputusan dengan Metode Profile Matching dalam Mengidentifikasi Gejala Awal Penderita COVID-19

The COVID-19 pandemic has not yet subsided, this outbreak has spread to almost all countries in the world, the initial symptoms of sufferers caused by acute respiratory distress coronavirus 2 (SARS-CoV-2). Symptoms of COVID-19 that can be transmitted from human to human, when one person is exposed to signs of a contagious COVID-19 occurring in the community is not the right attitude and action but panic and worry. The initial symptoms of COVID-19 have criteria that identify the initial symptoms of COVID-19, which are 10 (ten) criteria consisting of fever, cough and sore throat, fatigue, loss of smell and taste, joint and muscle pain, headache, diarrhea, shortness of breath. breath, nausea, vomiting, and nasal congestion. The purpose of this study was to identify the early symptoms of COVID-19 sufferers. This research was conducted through processing data on COVID-19 patients sourced from UPT Puskemas VI Koto Selatan, based on the results of the identification of symptoms of COVID-19 in patients carried out by health workers on duty at UPT Puskesmas VI Koto Selatan, then the data was processed using Support System Software. The decision to know the early symptoms of COVID-19. Furthermore, mathematical calculation techniques are also used to see the accuracy results. The method used to determine the initial symptoms in patients with COVID-19 is the Profile Matching method. The results of this study there were 6 patients from 8 test data that had the same decisions generated by the system, therefore the conclusion from this study was the results of the Decision Support System testing that had been carried out in identifying the initial symptoms of COVID-19 sufferers at UPT Puskemas VI South Koto overall there are 75% of patient data indicated by COVID-19 and 25% of patient data not indicated by COVID-19
Effects of Utilization of Mass Media (Video) in the Academic Performance Children At Risk Due To Pandemic in Grade 2 Pupils of Tayuman Elementary School

Mass media denotes a section of the media designed to reach a large audience. It can be referred to as a means of public communication such as television, radio and widely circulated newspapers that tend to reach a large audience with similar social characteristics (Mintz, 2015). Thompson (2014), defined mass media as books, magazines, newspapers, radio, television, cinemas, records, tapes and videos. Mass media is as communication media that reach a large audience, especially television, radio and newspaper. Media can be defined broadly as channels of communication. It is considered in terms of its software and hardware (Ogunmilade, 2014).&#x0D; This study therefore hopes to explore the effects of the mass media specifically video lessons on the performance level of the children at risk in Grade 2 coming from different sections of Tayuman Elementary School (TES)&#x0D; &#x0D; This study has 23 respondents who are the children at risk of dropping out from all the sections of Grade Two. These pupils may fail or drop from the school due to their reasons some of which are uncontrolled. The researcher used the tabulated individual average results of the LOA for the second quarter as the initial basis of the study.&#x0D; The second question was taken from the LOA average results of the 23 pupils who were given the intensive intervention using the video lessons apart from the SLMs and other tasks which they accomplished for the Third quarter.&#x0D; The researcher then compared the LOA results after the intervention sessions using video lessons vis a vis before where the pupils did not undergo any intervention.&#x0D; Then the results were tabulated by the researcher to identify the effects of the intervention administered to the pupils.&#x0D; &#x0D; Whether the variable of stopping or dropping out from the school in controllable or not, the researcher used one of the mass media which is video lessons in conducting intervention to these pupils. The video lessons are from the Marungko booklet which is modified by the Grade One teachers of Tayuman Elementary School. This experimental study was carried out to show the effects of watching videos/video lessons in the performance of the selected Grade 2 pupils at risk of dropping out and failure.&#x0D; The finding gave positive results and the videos found out to be beneficial in child’s learning to read and comprehend. Videos help learners put together the ideas of every watching experience.
Certificateless Authentication Scheme Based on Blockchain in Smart Home Network

The traditional centralized intelligent home network security authentication schemes have security problems such as integrity and confidentiality, while the distributed schemes have the problem of delayed authentication. These schemes are not suitable for the intelligent home environment of edge computing. To solve these problems, a certificateless smart home network authentication scheme based on blockchain and certificateless cryptosystem is proposed to realize mutual authentication among users, intelligent terminals (ITs), and intelligent gateways (IGs). The aggregation signature scheme of certificateless identity is introduced into the authentication of intelligent terminals in smart home network. The IG only needs to generate a signature to complete the identity authentication of multiple ITs. Compared with other authentication schemes, the security verification and performance analysis show that the proposed scheme uses less computation overhead and achieves more security features.
Novel Deep Learning Technique to Improve Resolution of Low-Quality Finger Print Image for Bigdata Applications

High-resolution images are highly in demand when they are utilized for different analysis purposes and obviously due to their quality aesthetic visual impact.The objective of image super-resolution (SR) is to reconstruct a high-resolution (HR) image from a low-resolution (LR)image.Storing, transferring and processing of high-resolution (HR) images have got many practical issues in big data domain.In the case of finger print images, the data is huge because of the huge number of populations.So instead of transferring or storing these finger print images in its original form (HR images), it cost very low if we choose its low-resolution form.By using sampling technique, we can easily generate LR images, but the main problem is to regenerate HR image from these LR images.So, this paper addresses this problem, a novel method for enhancing resolution of low-resolution fingerprint images of size 50 x 50 to a highresolution image of size 400 x 400 using convolutional neural network (CNN) architecture followed by sub pixel convolution operation for up sampling with no loss of promising features available in low-resolution image has been proposed.The proposed model contains five convolutional layers, each of which has an appropriate number of filter channels, activation functions, and optimization functions.The proposed model was trained using three publicly accessible fingerprint datasets FVC 2004 DB1, DB2, and DB3 after being validation and testing were done using 10 percent of these fingerprint data sets.In terms of performance measures like Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM) and loss functions, the quantitative and qualitative results show that the proposed model greatly outperformed existing state-ofthe-art techniques like Enhanced deep residual network (EDSR), wide activation for image and video SR (WDSR), Generative adversarial network(GAN) based models and Auto-encoder-based models.
Letter-of-Credit Chain: Cross-Border Exchange based on Blockchain and Smart Contracts

The exchange of goods between countries is growing, contributing to the promotion of logistics-related technologies.More and more systems are adopting advances in science and engineering to reduce manual handling steps, thereby reducing transit time.Letter-of-Credit (LOC) is a standard method where the parties involved will enter into agreements for the sale and exchange of goods.Specifically, each party will receive a set of original documents and does not need to meet face-to-face under the bank's witness.The process brings many benefits in terms of time and reduces records processing.However, the system faces a lot of risks when one of the parties is dishonest.On the other hand, the traditional LOC systems face a lot of risks related to the transparency of information about the goods, and also the supplier may lose the goods (e.g., 4/100 Vietnamese cashew nut containers are lost.stuck in Italy) or deposits in the hands of shipping companies (e.g., GNN Express -Vietnam) and many more.To this end, many research directions have exploited blockchain technology and smart contracts.Specifically, all information related to the transaction between the supplier and the demander including package, time, and delivery location.However, there needs to be a mechanism to ensure the smooth implementation of smart contracts, specifically for sanctioning when there is a conflict between a supplier and a demander.This role should be considered for the transaction manager, who directly designs and is responsible for their smart contracts.Currently, there is no mechanism to guarantee all interests of the parties involved in non-bank transactions.To increase the processing capacity and integrate with the Blockchain system, we propose the Letter-of-credit Chain that defines the agreements between the parties in international trade.We also deploy the proof-of-concept of the Letter-of-credit Chain on the three EVMsupported platforms (i.e., under ERC20), namely, Ethereum, Binance Smart Chain, and Fantom.By evaluating the actual execution of Gas for each platform, we found that our proposed model had the cheapest fee when deployed on the Fantom platform.Finally, we share the deployment/implementation of these platforms' proof-of-concept to encourage further future research.
Cybersecurity Risk Assessment: Modeling Factors Associated with Higher Education Institutions

Most universities rely heavily on Information Technology (IT) to process their information and support their vision and mission.This rapid advancement in internet technology leads to increased cyberattacks in Higher Education Institutions (HEIs).To secure their infrastructure from cyberattacks, they must implement the best cybersecurity risk management approach, which involves technological and education-based solutions, to safeguard their environment.However, the main challenges in existing cybersecurity risk management approaches are limited knowledge of how organizations can determine or minimize the significance of risks.As a result, this research seeks to advance understanding to establish a risk assessment model for universities to measure and evaluate the risk in HEIs.The proposed model is based on theoretical aspects that we organized as follows: First, we review the existing cybersecurity frameworks to identify the suitability and limitation of each model.Next, we review current works on cybersecurity risk assessment in HEIs to evaluate the proposed risk assessment approaches, scope and steps.Based on the information gathered, we developed a risk assessment model.Finally, we conclude the study with directions for future research.The result presented from this study may give an insig1ht for HEIs staff to analyze what is to be assessed, how to measure the severity of the risk, and determine the level of risk acceptance, improving their decision-making on risk management.
MFCC and Texture Descriptors based Stuttering Dysfluencies Classification using Extreme Learning Machine

Stuttering is a type of speech disorder which results in disrupted flow of speech in the form of unintentional repetitions and prolongation of sounds.Stuttering classification is important for speech pathology treatment and speech therapy techniques which decreases speech disfluency to some extent.In this article, a method for prolongation and repetition classification is presented based on Mel-frequency cepstral coefficients (MFCC) and texture descriptors.Initially, MFCC and filter bank energy (FBE) matrix are computed.Gray level co-occurrence matrix (GLCM) and Gray level run length matrix (GLRLM) textural features are extracted from these matrices.Laplacian score-based feature selection approach is employed to choose relevant features.Finally, extreme learning machine (ELM) is utilized to classify the speech audio event as repetition or prolongation.The algorithm is evaluated using UCLASS database and has achieved improved performance with classification accuracy of 96.36%.
Recognition of Odia Character in an Image by Dividing the Image into Four Quadrants

This paper deals with optical character recognition of Odia characters written in a particular font family 'AkrutiOriAshok-99' with different font sizes 18, 20, 22, 24, 26, 28, 36, 48 and 72 in Bold style.The font 'AkrutiOriAshok-99' is a font from the typing software 'Akruti'.The basic idea behind the approach followed in this paper is the character decomposition into four quadrants and then extracting features from each quadrant.The image processing techniques like converting the image to gray, resizing of image and converting gray image to binary are used in this approach.The system explained in this paper has two major parts: DictionaryBuilding and FindingMatch.For DictionaryBuilding, dictionary of images which are created either by scanning a document or a document converted to image, both written in same font family in different sizes.The features are extracted from each image in any font size in the 'Dictionary' using Preprocessing, FindPath, GettingFeaturesLeft or GettingFeaturesRight, VisitSubQuad, RemainingSubQuad, WriteToExcel and CommonFeature modules.The part FindingMatch is responsible for finding a correct match in the dictionary for the input image.For this, FeatureExtraction and Recognition modules have been used.Longest Common Subsequence (LCS) has been used for finding the common feature in DictionaryBuilding as well as finding the correct match.A total of 1800 characters, 200 characters of each font size have been tested and 98.1% of correctness has been achieved.