Reviewerly/exHarmony huggingface dataset:
Machine Translation of Scheduling Joint Optimization Algorithm in Japanese Passive Statistics

Machine translation is different from written translation. How to improve the performance of machine translation has been a research hotspot in current research on machine translation. In this paper, based on the semantic analysis and research of Japanese passive, a joint optimization algorithm of scheduling has been proposed, and the machine translation of Japanese passive has been studied. At present, machine translation is more and more widely used. Machine translation has solved many vocabulary problems, and it can complete a large amount of translation work and save a lot of manual translation time. While improving the translation speed, in the process of Japanese passive translation, it is also found that direct machine translation shows many shortcomings, and the quality of passive translation is not particularly ideal, exposing the basic problems of machine translation, such as semantic errors, syntactic errors, unclear and rigid expressions, and messy structures. In response to the problems above, this paper has improved the machine translation model for scheduling joint optimization algorithms. The paper has proposed several optimization algorithms and used resource awareness and computing power scheduling algorithms to conduct experimental analysis of translation performance. Finally, it is found that, among the two scheduling optimization algorithms, the resource-aware scheduling algorithm has better performance. With the same data, the resource-aware scheduling algorithm has saved 15.5% of the time compared with the computing power scheduling algorithm, and the accuracy of Japanese passive translation was 6%, 5%, and 21% higher than the computing power scheduling algorithm under different data volumes. Not only has the time taken been shortened, but the translation accuracy has also been improved.
Semiotic Analysis of Charles Sanders Pierce in Nuriarta’s Cartoon Discourse

Nuriarta presents cartoons by utilizing two texts, namely visual text and verbal text. The visual text that is meant is pictures in the form of humans, plants and animals. Verbal text is a series of words that can be read and have meaning according to the message the cartoonist wants to convey. Cartoon discourse by Nuriarta has been uploaded on social media and published in Kompas, Radar Bali, and the Bali Post from 2016 to 2019. Cartoon discourse Nuriarta's work has been published in a book entitled Cartoons and News; Cartoons Bring News and Context by Nuriarta in 2019. The result of research that will be presented is a semiotic analysis of Pierce's nonverbal meaning in cartoon discourse by Nuriarta. The result of research that will be presented is a semiotic analysis of Pierce's nonverbal meaning in cartoon discourse by Nuriarta.
TEXT CLASSFICATION USING CNN AND CNN-LSTM

Text classification, language modelling, and machine translation are some of the applications.CNNs excel at retrieving local and position-invariant characteristics, but RNNs excel at classification based on a long-range semantic dependency instead of local key-value pairs.It shows out that CNNs perform admirably when used to NLP problems.The basic (Bow) model is a clear oversimplified based on faulty beliefs, but it has been the usual technique for years and has produced reasonable accuracy.The speed of CNNs is a major selling point.In this paper, Text classification is carried out by using a deep learning model that is CNN and a hybrid model using CNN-LSTM and compare the performance of two models.
Ontology-Based Traffic Accident Information Extraction on Twitter In Indonesia

Traffic accidents become one of the events that often occur in Indonesia. From the three-monthly report by the Indonesian National Police Traffic Police, there are about 25,000 traffic accidents. Many social media users, especially Twitter, share information about traffic accidents. Twitter has various information regarding traffic accidents. Therefore, this study aims to process and map information about traffic accidents contained on Twitter in Indonesia language. We use the domain ontology and Named-Entity Recognition for the data extraction process. Named-Entity Recognition is used for obtaining keywords from a tweet based on class categories such as actor, time, location, and information on the cause of the accident. This research generates a Named Entity Recognition (NER) model that can provide a reasonably accurate level of accuracy. Also, we create an ontology that can categorize the causes of traffic accidents based on the Directorate General of the Land Transportation Office, Indonesia. We found that the traffic accidents are generally caused by inadequate vehicle conditions with the main problem in the vehicle caused by brake failure, while environmental factors rarely cause traffic accidents. Moreover, the vehicle is the subclass that mostly appears in the tweets, where car is the most popular actor, followed by truck and motorcycle.
A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. The simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. Therefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for efficient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. The experimental results show that the proposed model can significantly improve the accuracy and operational efficiency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its effectiveness and advancement.
Engaging Vocational Students’ Self-efficacy in Reading Comprehension through Literature Circle

Many students still struggle to comprehend the text in the context of reading comprehension. Students lack motivation because they lack confidence in their reading comprehension abilities. Students are not engaged when they are not ambitious to read. Learning does not occur in this state. Although many previous studies have investigated the correlation between self-efficacy and reading comprehension, the application of reading strategies, in general, to assess students' self-efficacy is deemed less effective because students in Indonesia are unfamiliar with reading English, particularly reading comprehension. To fill the research gap previously identified, this study will examine the self-efficacy of vocational students in reading comprehension using Literature Circle as a reading strategy. The classroom action design was used in this study with fifteen vocational students. Data from a self-efficacy questionnaire and an interview were collected and analyzed using descriptive statistics. According to the findings of this study, the literature circle in this study helps students improve their self-efficacy and achieve aspects of reading comprehension assessment.
An Unmanned Underwater Vehicle Torpedoes Attack Behavior Autonomous Decision-Making Method Based on Model Fusion

The autonomous technology of unmanned platforms is the most dynamic frontier among fields of technology and, inevitably, is trending towards future development. Aiming at the dual requirements of reliable and real-time autonomous decision-making of unmanned underwater vehicles in complex and unfamiliar environments, this article proposes an intelligent decision-making method of attack behavior based on model fusion. The experimental dataset is generated through simulation modeling, and an appropriate amount of noise is added to simulate the observation error in a real situation. The threshold of weapon-hit probability is set according to the requirements of combat missions, and the decision-making of attack behavior is transformed into the problem of imbalanced sample classification with noisy data. Through theoretical analysis and experimental testing, the classification effects of algorithms such as Logistic Regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), and ensemble learning are compared. On this basis, the intelligent decision model is constructed by using synthetic minority oversampling technique resampling and three model fusion methods of voting, stacking, and blending. The experimental results show that compared with traditional simulation decision-making and common classification algorithms, the proposed method has higher accuracy, recall rate, area-under-the-curve value, and model generalization ability. It can not only effectively identify the impact of noise data on attack-behavior decision-making, but also ensures the decision-making speed through offline training, and provides references for the research in the field of equipment development and intelligent decision-making in the future.
Secure and Efficient Exchange of Threat Information Using Blockchain Technology

In recent years, sharing threat information has been one of the most suggested solutions for combating the ever-increasing number of cyberattacks, which stem from the system-wide adoption of Information and Communication Technology (ICT) and consequently endangers the digital and physical assets of organizations. Several solutions, however, were proposed to facilitate data exchange between different systems, but none were able to address the main challenges of threat sharing such as trust, privacy, interoperability, and automation in a single solution. To address these issues, this paper presents a secure and efficient threat information sharing system that leverages Trusted Automated Exchange of Intelligence Information (TAXIITM) standard and private blockchain technology to automate the threat sharing procedure while offering privacy, data integrity, and interoperability. The extensive evaluation of the solution implementation indicates its capability to offer secure communication between participants without sacrificing data privacy and overall performance as opposed to existing solutions.
Model Soups for Various Training and Validation Data

Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fine-tuning accuracy using the inference time and memory cost of a single model. We extended the model soups to create subsets of k training and validation data using a method similar to k-fold cross-validation and trained models on these subsets. First, we showed the correlation between the validation and test data when the models are synthesized, such that their training data contain validation data. Thereafter, we showed that synthesizing k of these models, after synthesizing models based on subsets of the same training and validation data, provides a single model with high test accuracy. This study provides a method for learning models with both high accuracy and reliability for small datasets such as medical images.
The implementation of mobile apps for visually impaired students’ mobility in undergraduate programme, Faculty of Education

This study aims to test the results of mobile applications, namely social mobility and communication orientation models that can be used on android mobiles. This application is applied for problem-solving learning in understanding the concept of a campus environment for undergraduate visually impaired students majoring in special education, Universitas Negeri Surabaya. This research was conducted to measure the feasibility of a mobile application implemented through a questionnaire; then it was reviewed by a validation expert. Furthermore, performance test was conducted in order to be tested by the users. The application was deliberately created so that it can be operated via a cell phone. Based on the process of validity testing and practicality evaluation of the mobile application by the validator (electrical engineer) of all assessment items, the score obtained was 3.3 (good results). Meanwhile, the assessment to test application users as practitioners obtained a score of 3.2 (good results). Thus, it can be concluded that the application of the social mobility orientation model and mobile application-based communication to solve the problem of understanding the campus environment concept in the Special Education Department of undergraduate students for visually impaired students proved its worthiness.&#x0D; &#x0D; Keywords: Social mobility and communication, mobile apps.
2. Deictic reference in space

In this chapter, we present an approach to spatial deixis as co-participants’ embodied and situationally embedded practices of co-orientation and joint attention to entities in their sensory reach. These practices combine gaze, pointing (by different means) and other bodily practices with verbal resources provided by the respective language systems. Such an approach to deictic reference also provides the foundations for the analysis of the “lived space” and how it is constructed in interaction. We claim that an appropriate starting point for the investigation of deictic reference is Bühler’s theory of the deictic field (Zeigfeld), which is a strictly ego-centric theory. We link Bühler’s approach to phenomenological work on deixis that foregrounds the primordial role of the body (Leib) as the origo of all spatial indices. Against this background, we further discuss the structuration of space through spatial demonstratives of proximity and distance and show that a “sociocentric” approach to spatial deixis is not adequate, even though the establishment of joint attention via deixis is a deeply interactional process. Finally, we show how Bühler’s ego-centric theory accounts for more complex forms of deixis in the imagination. We discuss examples for Bühler’s first and second case of deixis in the imagination and conclude with a case of hybrid referential practices in electronic media, drawing on an example from a virtual reality game
An exploratory study on the predominant programming paradigms in Python code

Python is a multi-paradigm programming language that fully supports object-oriented (OO) programming. The language allows writing code in a non-procedural imperative manner, using procedures, using classes, or in a functional style. To date, no one has studied what paradigm(s), if any, are predominant in Python code and projects. In this work, we first define a technique to classify Python files into predominant paradigm(s). We then automate our approach and evaluate it against human judgements, showing over 80% agreement. We then analyze over 100k open-source Python projects, automatically classifying each source file and investigating the paradigm distributions. The results indicate Python developers tend to heavily favor OO features. We also observed a positive correlation between OO and procedural paradigms and the size of the project. And despite few files or projects being predominantly functional, we still found many functional feature uses.
IMPROVING STUDENTS’ SPEAKING SKILL THROUGH TWO STAY TWO STRAY STRATEGY

This study is aimed at improving students’ speaking skill through Two Stay Two Stray Strategy. This study was a classroom action research. The subject of the research was the students of class X MIA-1 at SMA Teladan Sei Rampah consisting of 32 students. The research was conducted in two cycles consisting of three meetings in each. The instruments for collecting data were speaking test, observation sheet, diary notes and questionnaire. Based on the speaking test results, students’ scores kept improving in every test. On the orientation test, the mean score was 39.5. There were no students gaining score ≥ 75. In the first meeting of Cycle I, the mean score was 43. There were no students gaining score ≥ 75. In the second meeting, the mean score was 47.3. There were no students gaining score ≥ 75. In the third meeting, the mean score was 49.36. There were no students gaining score ≥ 75. In the first meeting of Cycle II, the mean score was 57.5. The number of students gaining score ≥ 75 was 5 (15.6%). In the second meeting, the mean score was 72.78. The number of students gaining score ≥ 75 was 20 (62.5%). In the third meeting, the mean score was 82.30. The number of students obtaining score ≥ 75 was 30 (93.75%). Based on the observation sheet, diary notes, and questionnaire, it was revealed the learning process ran well. The students were active and enthusiastic in learning. Thus, Two Stay Two Stray Strategy can improve students’ speaking skill
An improved ORB algorithm for the Unmanned Aerial Vehicle (UAV) image stitching task

Based on the image chunking processing theory, this paper proposes an improved ORB-based UAV image stitching algorithm that takes the pyramidal optical flow characteristics into consideration to overcome the weaknesses of the traditional algorithm (proneness to feature point aggregation, uneven distribution, and low feature matching accuracy). Firstly, the ORB algorithm is used to detect the feature points using the image chunking method of constructing moving masks. Subsequently, the non-maximum suppression method is used to reject the feature points aggregated in each mask, and the Hamming distance is used for feature matching after traversal. Following the step, the pyramid optical flow method is used to correct the motion displacement vector of the feature points and reject the mis-matched pairs after matching. Finally, the RANSAC algorithm is used to filter the redundant pairs to further improve the accuracy, and then the image is stitched together using the optimal stitching seam strategy and the fade-in-and-fade-out fusion algorithm. Overall, the aforementioned procedure increased the alignment rate to approximately 97% in the alignment stage, achieving a better alignment accuracy and a more accurate stitching effect.
Complex-valued Reconfigurable Diffractive Optical Neural Networks using Cost-effective Spatial Light Modulators

We demonstrate a reconfigurable diffractive optical neural network based on cost-effective spatial light modulators. We explored various diffraction calculation methods to match trainable models with experimental results. This lays the foundation for fast system deployment.
Upaya Peningkatan Pengetahuan Siswa Tentang Pentingnya Mitigasi Bencana di Desa Hutamonu, Kecamatan Botumoito, Kabupaten Boalemo

Abstract Indonesia is a country that has a high level of disaster risk. Community preparedness efforts, especially for children, are still lacking, marked by the number of victims of natural disasters which is still very high, with this effort increasing knowledge about disaster mitigation is still very important for the community. This service aims to increase public knowledge, especially students, about disaster mitigation efforts, through outreach activities in several schools. The results of the service show that the level of student knowledge about disaster mitigation is still very lacking and needs to be improved. It is hoped that further actions such as maintenance and supervision as control with the aim of these activities have optimal results to increase students' knowledge about disaster mitigation and the realization of disaster resilient schools.Keywords: Disaster Mitigation, Socialization , Disaster Resilient.
How to Solve a Classification Problem Using a Cooperative Tiling Multi-agent System?

Adaptive Multi-Agent Systems (AMAS) transform dynamic problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we propose a framework to transform a classification problem into a cooperative tiling of the input variable space. We show that it is possible to use linear classifiers for online non-linear classification on three benchmark toy problems chosen for their different levels of linear separability, if they are integrated in a cooperative Multi-Agent structure. The results obtained show a significant improvement of the performance of linear classifiers in non-linear contexts in terms of classification accuracy and decision boundaries, thanks to the cooperative approach.
Administration of SAP HANA Cloud

The SAP Business Technology Platform (SAP BTP) offers a wide range of services. Every SAP BTP service offers one or more service plans. A service plan is a subset of a service. The SAP HANA Cloud database service, for example, is available in a variety of configurations and sizes, as shown in Figure 2-1.
Representation Selective Self-distillation and wav2vec 2.0 Feature Exploration for Spoof-aware Speaker Verification

Text-to-speech and voice conversion studies are constantly improving to the extent where they can produce synthetic speech almost indistinguishable from bona fide human speech. In this regard, the importance of countermeasures (CM) against synthetic voice attacks of the automatic speaker verification (ASV) systems emerges. Nonetheless, most end-to-end spoofing detection networks are black-box systems, and the answer to what is an effective representation for finding artifacts remains veiled. In this paper, we examine which feature space can effectively represent synthetic artifacts using wav2vec 2.0, and study which architecture can effectively utilize the space. Our study allows us to analyze which attribute of speech signals is advantageous for the CM systems. The proposed CM system achieved 0.31% equal error rate (EER) on ASVspoof 2019 LA evaluation set for the spoof detection task. We further propose a simple yet effective spoofing aware speaker verification (SASV) method, which takes advantage of the disentangled representations from our countermeasure system. Evaluation performed with the SASV Challenge 2022 database show 1.08% of SASV EER. Quantitative analysis shows that using the explored feature space of wav2vec 2.0 advantages both spoofing CM and SASV.