Reviewerly/exHarmony huggingface dataset:
BISINDO Alphabet Visualization in Interactive Media

Learning technology continues to develop along with the times. In the implementation of daily learning, we often encounter the utilization of technological developments in the world of education, as is often done by teachers or lecturers, namely combining technological tools in learning processes. The development of science and technology has a positive impact with the increasingly open and spread of information and knowledge from and to the whole world penetrating the boundaries of space and time. One of the technological developments in education is interactive media, with the making of this interactive learning media the teaching and learning process becomes effective, interesting and fun for students. The purpose of this research is to produce BISINDO learning media. The benefits of this learning media are to be a solution to the learning process that is effective, interesting and more fun and facilitates the learning process of students.&#x0D; Keywords: BISINDO; Dale’s Cone Experience; Deaf; Distance Learning Media; Sign Language
The Evaluation System of Digital Textbooks on College Russian

The reform of college Russian teaching has brought new topics and challenges to the construction of textbooks, and the establishment of a set of digital textbooks evaluation system will promote the compilation and production of college Russian textbooks in the new era, and promote the development of college Russian teaching.On the basis of previous studies, this paper briefly introduces the development process of the establishment of the evaluation system of digital textbooks at home and abroad, establishes the theoretical basis for the establishment of digital college Russian textbooks, takes the construction of the school digital loose leaf textbooks of Chinese Culture (Russian Version) as a sample, tracing back a series of teaching experiments and questionnaire surveys conducted for the establishment of the evaluation system of this book, and conducts in-depth analysis and discussion on the survey data.On this basis, a textbook evaluation index system guided by students' opinions is established.
Applicants for KOSEN and Aspiring Engineers

The number of applicants for KOSEN is able to be regarded as an evaluation index of the number of students who want to become engineers. The 2006 PISA survey shows that among OECD countries, Japanese students who have a parent in a science-related career made low scores, and only 8.2% of the 15 year old students aspired to science related career. The applicants for KOSEN have remained almost constant at about 1.6% of the 15-year-old students since 2000. Accordingly, it can be inferred that the ratio of students who aspire to science-related career, including engineers, has not changed. The social status and income of engineers must be improved for increasing the number of students who aspire to engineers.
'Biodynamically cultivated hash, confidentially': information sharing on the dark web

This article analyses the marketing mechanisms of Dark Web drug trade focusing on advertising as information sharing. Key research questions are 1) vendors use to convince the potential customers about the quality and service, 2) what kind of arguments do vendors try to build and how they do it? and 3) what kind of information is shared when illegal goods are advertised. The data were collected from Utopia, which is an archive of the Finnish cryptomarket called Sipulimarket. The sample contained all advertisements from December 6, 2019 to March 19, 2020. All the advertisements were analysed by using a qualitative content analysis. Four main elements on which the marketing speech is based are 1) quality and 2) effects of the drugs (which we discuss here together), 3) price and 4) persuasiveness of the vendor. Together with the name of “drug store”, they make up the image. These elements are the types of information shared mostly when convincing customers about the products. In advertisements both persuasiveness and informative contents were shared. By understanding the mechanisms used in illicit communication happening in the Dark Web, we get a new perspective on sharing information that requires anonymity.
A Convolutional Tsetlin Machine-based Field Programmable Gate Array Accelerator for Image Classification

This paper presents a Field Programmable Gate Array (FPGA) implementation of an image classification accelerator based on the Convolutional Tsetlin Machine (CTM). The work is a concept design, and the solution demonstrates recognition of two classes in 4 × 4 images with a 2 × 2 convolution window. More specifically, there are two sub-Tsetlin Machines (TMs), one per class. A single sub-TM employs 40 clauses, each controlled by 20 Tsetlin Automata. The accelerator features random patch selection, in parallel for all clauses, based on reservoir sampling. The design is implemented in a Xilinx Zync XC7Z020 FPGA. With an operating clock speed of 30 MHz, the accelerator is capable of inferring at the rate of 3.3 million images per second with an additional power consumption of 20 mW from idle mode. The average test accuracy is 96.7% when trained on data with 10% noise. A training session with 100 epochs and 8192 examples takes 1.5 seconds. Due to the limited hardware resources required, the CTM accelerator represents a promising concept for online learning in energy-frugal systems. The solution can be scaled to multi-class systems and larger images.
Working with Strings

Strings are also data – they're arrays of bytes. But unlike simple Data, the String class knows how to interpret that data. To interpret data correctly, we need to know encoding. The most widespread options are UTF-8 and UTF-16, also known as Unicode.
Pengembangan Media Halma Untuk Meningkatkan Kemampuan Berhitung Kelipatan Suatu Bilangan Siswa Kelas IV

The purpose of this study was to develop learning media for the halma game to improve the ability to count multiples of a number for grade 4 SD in Demak Regency. This research method is Research and development. There are 3 stages in this research: the preliminary study stage, the development stage, media validation, and field testing, as well as the dissemination and socialization stage. The results of this study are in the form of manual learning media products in the form of multiples number board games from paper and MMT, and digital, in the form of digital in the form of a game application using articulate storyline 3. Validation from media experts and material experts gave assessments, validators I, II, III, and IV respectively 36, 35, 37, and 36 from a maximum score of 40. The media developed was categorized as very good. The results of the control class posttest t test and the experiment shows the value of t = -336 with a Sig. (2-tailed) value of 0.664 bigger than α = 0.05, so that the media developed is valid.
Level fusion analysis of recurrent audio and video neural network for violence detection in railway

This paper deals with the security improvement of passengers in public transport by automatically processing the audio and video streams of an embedded surveillance system. In this paper we analyse several levels of fusion of two deep audio and video recurrent network models for violent actions recognition. Each audio and video model is based on recent generic feature extractors proposed in the state-of-the-art to benefit of powerful feature representation capabilities. Each level of fusion is trained and evaluated on a new real-world audio-video surveillance streams recorded in a real train with scenes of violence played by actors. The obtained results confirm the interest in seeking to detect violence by jointly using audio and video signal and highlight the difficulty to define the optimal level of fusion.
Convolutions for Spatial Interaction Modeling

In many different fields interactions between objects play a critical role in determining their behavior. Graph neural networks (GNNs) have emerged as a powerful tool for modeling interactions, although often at the cost of adding considerable complexity and latency. In this paper, we consider the problem of spatial interaction modeling in the context of predicting the motion of actors around autonomous vehicles, and investigate alternatives to GNNs. We revisit 2D convolutions and show that they can demonstrate comparable performance to graph networks in modeling spatial interactions with lower latency, thus providing an effective and efficient alternative in time-critical systems. Moreover, we propose a novel interaction loss to further improve the interaction modeling of the considered methods.
The Impact of External Auditors size on Earnings Management: Evidence from Jordan

The current study offers empirical evidence on the association that exists between the audit firm size and earnings management based on a sample of 64 manufacturing firms that are listed with the Amman Stock Exchange (ASE) from 2017 to 2021.From the results of the study, there is a significant negative association between the audit firm size and earnings management.These results have implications for policy makers and regulatory bodies in Jordan since they highlight the need to improve the good corporate governance practices and attempt to constrain the incidence of earnings management in Jordanian firms.
UMixer: A Novel U-shaped Convolutional Mixer for Multi-scale Feature Fusion in Medical Image Segmentation

Medical image segmentation plays a critical role in assisting diagnosis and prognosis. Since the transformer was first introduced to the field, the neural network structure has experienced a transition from ConvNet into Transformer. However, some redesigned ConvNets in recent works show astonishing effects, which outperforms classic elaborate ConvNets, and even complicated transformers. Inspired by these works, we introduced large-kernel convolutions to improve the ConvNets in capturing the long-range dependency. Cooperated with a novel multi-scale feature fusion method, we proposed a U-shaped convolutional structure, dubbed UMixer, which effectively integrates shallow spatial information with deep semantic information and high-resolution detailed information with low-resolution global information. Without any attention mechanism and pre-training on large datasets, UMixer achieves more accurate segmentation results than traditional ConvNets and Transformers on the Synapse dataset. Experiments demonstrate the effectiveness of this multi-scale feature fusion structure and its capability in modeling long-range dependency.
Recognition of typewritten Gurmukhi characters

Today, extensive research has been done on data analysis and text recognition, currently numerous optical character recognition (OCR) frameworks are available by various researchers. Language scripting is considered as a challenging task due to the presence of degraded characters which affects the OCR performance. In Gurumukhi content, old reports are inaccessible because of its delicate condition. For the most part old archives get debased which hampers their lucidness. The utmost work is done by researchers on character recognition of Gurmukhi script dealing with handwritten and printed characters. The aim of this research work is to cover blind spot area of typewritten Gurmukhi script for recognition of character. To achieve this, features are extracted using projection profiles, zoning features, transition features, distance vector and neighbouring pixels and machine learning techniques like SVM (using linear and polynomial kernel) and k-NN (with value of k = 3, 5, 7 and 11) are applied to recognise the characters. It is found that SVM linear approach provides best result in our case.
Introduction to Language Models

One of the biggest developments that made Homo sapiens different from other animal species on this planet was the evolution of language. This allowed us to exchange and communicate ideas and thoughts, which led to so many scientific discoveries including the Internet. This is how important language is.
Linear Modeling and Open-loop Control of a Multi-Axis Piezoelectric Micro-Mirror for Random-Access Laser Scanning

This paper describes early-stage modeling of a thin-film piezoelectric microelectromechanical system (MEMS) scanning mirror capable of random-access scanning over significant range-of-motion. Due to low transduction forces, MEMS mirror technology typically relies on purely resonant operation about a neutral position, or low-bandwidth quasi-static positioning or trajectory following. High energy density of piezoelectric materials can support both high resonant frequencies and large static offsets, allowing scans to be directed in a random-access fashion to specified locations in a large field-of-view. However, this requires coordination of multiple inputs and elastic vibration modes. Here, we identify the linearized modal model for a sample piezoelectric scanner and a simple, open-loop strategy for transitions between full-field scanning and narrow-field scanning in specified regions. Influence of step-wise transition between operating modes on settling time and overshoot is characterized in simulation.
Rendezvous Scheduling for Charging Coordination Between Aerial Robot - Mobile Ground Robot

This paper studies the problem of rendezvous scheduling between an energy-limited aerial robot (AR) and a mobile ground robot (MGR). If the AR cannot complete its mission in a single sortie, it has to recharge at scheduled locations and times with the MGR acting as a mobile charging station. Differing from previous work, the visit order of the waypoints is assumed to be determined a priori using one of the available algorithms for pathfinding or area coverage. We consider two alternative cases depending on whether the AR can land prior to the arrival of the MGR or it has to hover in the air and wait for its arrival. Our approach to each is motivated by the principle of optimality - namely the corresponding constrained optimization problem is decomposed into smaller problems whose solutions are then integrated together. Their solutions are found from a binary search tree of charge selections that evolves iteratively using a tree sort algorithm. The advantage of this approach is that as the focus is on the scheduling aspect, the algorithm becomes relatively more tractable and realistically applicable. Our simulation results demonstrate that the optimal rendezvous schedule can be determined in realizable time even for large-scale missions.
INFLUENCE OF L1 WHILE LEARNING SUBJECT-VERB AGREEMENT OF ENGLISH AS A FOREIGN LANGUAGE

The study aims at investigating the poverty of subject verb agreement of Gujari learners of English at primary level. According to the term subject-verb agreement in English that verb should agree with the noun or pronoun. It is either singular or plural, the verb changes accordingly. In other words, the SVA can be defined as the change of verb according to the subject’s number and person. The study focusses upon the influence of the first language while constructing the English sentences. It investigated the Gujari speakers of English learners at district Kotli at primary level how their writing is packed with already learned languages. The study used the Brown (1994) model to classify the errors. The model categories the errors such as addition, substitution, wrong selection, omission. Learners writing sample were collected to classify the errors. It was found that adding the helping verb or main verb where it is not required in English. Similarly, they omit the verb despite its requisition likewise they substitute it according to the drive of their already learned languages. they were also found committing errors in the use of pronouns that automatically impact the subject-verb agreement. Keywords: Subject Verb Agreement, Errors, Gujari Learners, First Language Influence.
Named Entity Recognition Based on Combining Pretrained Transformer Model and Deep Learning

Named entity recognition (NER) is a critical subtask of information extraction that helps to detect and classify the tag entities in unstructured text with their corresponding type of categories such as: company, people name, organization, money, location, time, and percentage. As of today, there have been a variety of approaches which could perform the task of NER. Among those methods, transformers prove to perform well with high accuracy; therefore, it will be the focus of our research. This paper proposes combining pretrained transformer models: BERT and PhoBERT with deep learning techniques: long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) for named entity recognition in Vietnamese.
HUMAN PRESENCE DETECTION AND FACE MASK DETECTION USING DEEP LEARNING APPROACH

Convolutional Neural Networks have always given promising results in the use-cases of object detection. Due to the learning capabilities of neural networks, they are able to find hidden patterns in the architecture of the data. Using deep learning models in the image recognition field has always given good results. Using this approach we introduce a presence detection model that finds the presence of a person via camera and predicts the their availability from the trained images. The model first checks for mask detection based on Transfer Learning approach and predicts if the present person is wearing mask or not and further marks the presence.
Augmented active acoustics: Recent experiences in acoustical enhancement for modern musical performance

Electronic acoustic enhancement systems allow the designer to implement traits not easily obtained with physical construction, or to provide variable room behavior to accommodate different program needs in a multi-purpose facility. Traditionally, most active acoustic systems have been employed for the enhancement of acoustic music, speech, and other classical program material. Here, we provide an overview of active enhancement methods particularly suited to contemporary, amplified musical performances. By accurately modeling and simulating the geometry of the performance space, naturally occurring reflection paths may be augmented with their synthetic counterparts, each rendered with high spatial fidelity but modified acoustic properties. Material characteristics may then be selected beyond what is practical in the built environment, and room signatures may be modified and changed over time. Real-time signal processing placed in-line with an audio mixing console then renders these sound fields into a collection of loudspeakers throughout the listening space. Examples are provided of auditoria where these systems have been installed, along with measured acoustical behavior demonstrating both the effectiveness of the technique and practical design constraints for implementation.
PERILAKU MENYIMPANG DI DUNIA PENDIDIKAN (Studi Kasus Evaluasi Pembelajaran MA NW Dames)

Deviant behavior is a form of behavior or action that is not in accordance with the norms in the community. While deviant behavior in education Dimiyati (1980: 32) stated that the deviant behavior of children / adolescents in terms of education is that they are considered to interfere with the process of teaching and learning in school, do not obey the rules that have difficulties in association and other aspects that interfere and harm themselves or harm others. As a result, all of these behavioral irregularities were repeated repeatedly by them. The method in this study is a qualitative research method with a descriptive approach. The type of research used in this study is a case study (case study) From the studies conducted, this study resulted in deviant behavior in the world of education that often occurs during evaluation of learning. In the process of evaluating learning deviations carried out by students that is working together in working on exam questions (cheating).
Stage Evolving Graph Neural Network based Dynamic Recommendation with Life Cycles

Existing methods of dynamic recommender systems usually build neural networks or collaborative filtering models according to the absolute timeline, which fails to consider the preferences of users or characteristics of products in their life cycles. With the rise of graph neural networks, researchers have recently proposed methods based on graph neural networks to solve the recommendation problem. However, such models do not consider evolving stages in their life cycles. In this paper, we study the dynamic recommendation problem and propose a novel model named Stage Evolving Graph Neural Network (SEGNN). SEGNN learns the life stage representation by the Gated Stage Attention Unit (GSAU) and then uses a fully connected layer as the rating score predictor. GSAU adopts two types of attention layers to learn the preferences of users and products within one coupled life stage and discover the preferences over life stages, respectively. GSAU also utilizes a memory mechanism to ensure that the evolutionary patterns are maintained in the final representations of users and products. Due to the lack of information caused by data sparsity, SEGNN designs a masked loss function to train the model. Extensive experiments are done on real-world datasets, and the experimental results show the effectiveness of our proposed model.
Efficient Prediction of Sales during Festival Times in an Electronic Showroom Using Novel Deep Belief Network Compared Over Alexnet with Improved Accuracy

Aim:The aim of this paper is the efficient prediction of sales during festival times in an electronic showroom using a Deep Belief Network compared to AlexNet with improved accuracy.Materials and Methods: Deep Belief Network (N=10) and AlexNet algorithm (N=10) is the iteration for different times in predicting the accuracy percentage for accidents that happened.Two sample groups are considered and tested, G-power is a calculation that contains two different groups, alpha (0.05), and power (80%).Results: It was observed that the Deep Belief Network algorithm obtains an accuracy of 83.63% and the Novel Deep Belief Network has 74.12%.This NDBN appears to have a better significance of p=0.035 than the ResNet, that is p<0.05 using independent T-test analysis.Conclusion: The result proves that the Novel Deep Belief Network approaches to predicting the best retail sales store prediction have higher accuracy than the AlexNet algorithm.
An Efficient Facial Expression Recognition System Using Novel Image Classification by Comparing CNN over Res Net

Aim: The aim of this research article is classification of facial expression recognition Convolutional Neural Network (CNN) compared with ResNet yielded better accuracy.Materials and Methods: The data of the facial expressions is taken from the FER2013 available on kaggle.The convolutional neural network (CNN) is the most widely used algorithm for image analysis with best accuracy and ResNet is also an algorithm used here to compare the accuracy of Novel image classification.Results: The Convolution neural network (CNN) produces 82.14% accuracy in predicting facial expressions on the dataset,whereas ResNet produces 78.01%accuracy.Convolutional neural network (CNN) is better than ResNet.With (p<0.05),there is a statistically significant difference between the research groups.Conclusion : Convolutional Neural Network provides better outcomes in accuracy rate when compared to ResNet for predicting facial expressions.