Reviewerly/exHarmony huggingface dataset:
Multi-objective Optimization of Autonomous Obstacle Surmounting Action for Tracked Robot Based on NSGA-II Algorithm

The obstacle surmounting action of the tracked robot is often given by the operator according to their own experience, meanwhile, the obstacle surmounting process has high power consumption and high motor performance requirements at times. In order to solve this problem, this paper firstly analyzes the mechanism of step obstacle surmounting and designs the autonomous obstacle surmounting actions. By analyzing and calculating the force, the dynamic model of obstacle surmounting is established. Afterward, based on NSGA-II algorithm, a multi-objective optimization is designed to get the pareto front of step obstacles in different heights from the perspectives of energy consumption and motor performance requirements. Finally, TOPSIS method is used to evaluate the optimal solution set so as to obtain the optimal obstacle surmounting parameters.
CDAIL-BIAS MEASURER: A Model Ensemble Approach for Dialogue Social Bias Measurement

Dialogue systems based on neural networks trained on large-scale corpora have a variety of practical applications today. However, using uncensored training corpora may have risks, such as potential social bias issues. Meanwhile, manually reviewing these training corpora for social bias content is costly. So, it is necessary to design a recognition model that automatically detects social bias in dialogue systems. NLPCC 2022 Shared Task 7 - Fine-Grain Dialogue Social Bias Measurement, aims to measure social bias in dialogue systems and provides a well-annotated Chinese social bias dialogue dataset - CDAIL-BIAS DATASET. Based on CDAIL-BIAS DATASET, this paper proposes a powerful classifier, CDAIL-BIAS MEASURER. Specifically, we adopt a model ensemble approach, which combines five different pre-trained language models, and uses adversarial training and regularization strategy to enhance the robustness of the model. Finally, labels are obtained by using a novel method - a label-based weighted voting method. The result shows that the classifier has a macro F1 score of 0.580 for social bias measurement in dialogue systems. And our result ranks the third, demonstrating the effectiveness and superiority of our model.
When expectation-maximization-based theories work or do not work: An eye-tracking study of the discrepancy between everyone and every one

中国科学院行为科学重点实验室, 中国科学院心理研究所, 北京 100101) ( 2 中国科学院大学, 北京 100049) ( 3 南开大学周恩来政府管理学院社会心理学系, 天津 300350) ( 4 浙江大学心理与行为科学系, 杭州 310028)
A Framework for Benchmarking Real-Time Embedded Object Detection

Object detection is one of the key tasks in many applications of computer vision. Deep Neural Networks (DNNs) are undoubtedly a well-suited approach for object detection. However, such DNNs need highly adapted hardware together with hardware-specific optimization to guarantee high efficiency during inference. This is especially the case when aiming for efficient object detection in video streaming applications on limited hardware such as edge devices. Comparing vendor-specific hardware and related optimization software pipelines in a fair experimental setup is a challenge. In this paper, we propose a framework that uses a host computer with a host software application together with a light-weight interface based on the Message Queuing Telemetry Transport (MQTT) protocol. Various different target devices with target apps can be connected via MQTT with this host computer. With well-defined and standardized MQTT messages, object detection results can be reported to the host computer, where the results are evaluated without harming or influencing the processing on the device. With this quite generic framework, we can measure the object detection performance, the runtime, and the energy efficiency at the same time. The effectiveness of this framework is demonstrated in multiple experiments that offer deep insights into the optimization of DNNs.
Towards a Distributed Inference Detection System in a Multi-Database Context

The omnipresence of services offered by diverse applications leads customers to share more and more personal data, among which some are sensitive. Dishonest entities perform inference attacks by querying non-sensitive data in order to deduce the stored sensitive data. Detecting those attacks is still an open problem in a setting where a dishonest entity has access to distinct data controllers' databases containing data collected from the same customer. This problem has been addressed considering a centralized detection system. However, this approach is limited because of this centralized nature where the system protects the customers' privacy at the expense of the data controllers' privacy. Hence, we propose in this article the description of a distributed architecture to detect inference attacks in a multi-database context, while preserving the privacy of both the applications and the customers.
Green AI from Kirchhoff to Shannon

From the earliest beginnings, communications (theorized by Shannon) and computations (abstracted by Turing and von Neumann) have relied on schemes able to augment reliability. One was proposed by von Neumann and the other one was introduced by Moore and Shannon. For computations the gate-level method of von Neumann was embraced, while another one at the device-level was crafted by Moore and Shannon laying the foundation of network reliability. This device-level scheme was also intended for enhancing the reliability of computations, but was kind of sidetracked and ended up being employed for communications. Since then, steady advances towards single digit nanometer transistors have been constant. Lately, advanced technologies are in high demand for supporting the growing computational needs on which AI is blooming (as well as IoT and G5). As advance CMOS are sensitive to variations, it seems normal to analyze networks for identifying optimal ones, and the Moore and Shannon concept is a perfect fit for transistors/devices built and organized as arrays: FinFET, MBCFET, GAAFET, etc. Still, defining optimality in this context is a non-trivial task, as the conflicting power consumption and reliability requirements need to be analyzed and optimized simultaneously. For a better understanding we decided to start performing very detailed simulations of $$3\,\times\,3$$ resistor/transistor networks including variations (relying on the free ASAP7 PDK). We are reporting and discussing a few of those simulations here. Their statistical processing will reveal subtle and unexpected links between classical Kirchhoff theorems (pertinent for power consumption) and Moore and Shannon reliability concepts (theoretical). Plenty of additional investigations will be needed. Still, the main conclusion (based on the preliminary simulations we shall present) is that identifying optimal networks for low-power reliable CMOS designs (for computations) is a complex but doable task.
Digital Forensic Analysis on Mobile-based Facebook Messenger services using National institute of Standard Technology Method

This study uses research objects obtained from the website decision3.mahkamahagung.go.id with a chronology of events that occurred in the Facebook Messenger application, it was found that there were messages in the form of text and images containing the crime of extortion.This study conducted a data search process to obtain digital data from the mobile-based Facebook Messenger application.The process of collecting data from Facebook Messenger uses the National Institute of Standards Technology (NIST) method.The data collection process (Collection) begins with collecting data from the source used for extortion, namely from the perpetrator's smartphone.second, examination of evidence, third, after digital evidence is obtained, analysis is carried out, and finally the process of making reports based on analysis (reporting).This study aims to digital data on the Facebook messenger application using qualitative data.The results of the evidence were found in the form of text messages and pictures, as well as information on when to access Facebook messenger on a smartphone.The evidence produced uses several tools, namely MOBILedit Forensic Expressand Systools SQLite Viewer.
Redundant IMU Fault Detection

The fault detection method of redundant strapdown IMU is closely related to its redundant configuration. This section introduces the starting point of scheme of redundant IMU configuration and typical redundant configurations. On this basis, the fault detection methods of redundant IMUs are analyzed.
Identifying named entity from researcher curricula

NER (Named Entity Recognition) is an essential task in recognizing real-world entities scattered in a document. The task has been beneficial for detecting people, institutions, and places. In a researcher's curriculum repository, a NER process can be beneficial for understanding the associated context of a given document. For example, it could be possible to identify which persons/institutions are present in a given researcher's curriculum. This process is fundamental to identifying experts to work on a project or collaboration among researchers. In this paper, we evaluate entity extraction methods' effectiveness for identifying entities from scientific publications, including vocabulary-based and model-based methods. We describe an analysis of existing NER tools while proposing a procedure to apply NER identification over curricula from the Brazilian Lattes Curricula platform.
Digital Learning: Differentiated Teaching Models using e-Twinning - I Communicate With My Neighbor Through Culture And Tradition: e - Twinning Project

One of the main goals of Special Education and E-learning for people with disabilities is the development and improvement of social communication, the interaction with those around them, and the socio-emotional empowerment of students for their inclusion as active and equal members in a privileged society as people - students with disabilities present deficits in the field of social skills. The area of social skills is considered one of the important areas of the individual's development and for its development and improvement, the basic condition is the existence of motivation. Motivations are the driving forces for social contacts and relationships centered on mutual understanding, active participation, and interaction of the individual. The above in Special Education and training is achieved through differentiated teaching models using ICTs and collaboration networks such as e-Twinning.
Modality in Satua Crucuk Kuning: Systemic Functional Linguistics Perspective

Text of I Crucuk Kuning is a Balinese narrative text which contains very important moral value for our lives. The text involves two main figures, namely; I Bawang and I Kasuna. I Bawang serves antagonist while I Kasuna serves protagonist. The text presents dialog as social interaction medium in which employ modalities expressed within speech functions. This paper analyzes the application of modality express by the participants. The theory used to analyze the text is the theory of modality as part of interpersonal meaning from systemic functional linguistics proposed by Halliday. The data were obtained from Satua Bali I Crucuk Kuning written by Tinggen (2011). The data were analyzed from both syntactic and semantic aspects and then described descriptively. The result of the research suggests that I Kasuna is has good character and never complain to do any given job. Meanwhile, I Bawang has bad character by slandering and underestimating I Kasuna. The text persuade us to do good behavior by filling promise which is made to others.
Singe Image Dehazing With Unsharp Masking and Color Gamut Expansion

Image dehazing is a fundamental problem in computer vision and has hitherto engendered prodigious amounts of studies.Recently, with the well-recognized success of deep learning techniques, this field has been dominated by deep dehazing models.However, deep learning is not always a panacea, especially for the practicalities of image dehazing, because high computational complexity, expensive maintenance costs, and high carbon emission are three noticeable problems.Computational efficiency is, therefore, a decisive factor in real-world circumstances.To cope with this growing demand, we propose a linear time algorithm tailored to three primitive parts: unsharp masking (pre-processing), dehazing, and color gamut expansion (post-processing).The first enhances the sharpness according to the local variance of image intensities.The second removes haze based on the improved color attenuation prior, and the third addresses a residual effect of color gamut reduction.Extensive experimental results demonstrated that the proposed method performed comparatively with popular benchmarks, notably deep dehazing models.With such a comparative performance, the proposed method is still fast and efficient, favoring real-world computer vision systems.
Evaluation of Classification Algorithms for Software Defect Prediction

For Software Engineering industry is essential to build software with high quality, but this has become difficult these days with the fact that size and complexity of developed software is high. For this reason, it is important to predict the quality of software in early phases, by this way, it helps to reduce testing resources. Some classification algorithms have been used for software defect prediction. In this paper five algorithms were evaluated and compared according to accuracy metric using three PROMISE datasets. For the evaluation, two classifiers were used which are focus on decision trees and three Bayesian classifiers. Results shows that even when percentages of accuracy are high on each classifier, decision trees got the highest percentage compared to Bayesian classifiers.
Study on spatial morphology of cold area settlements based on microclimate parameterized model

In the steady development of social economy, the quality of life of urban residents is getting higher and higher, the attention to the spiritual life is getting stronger and stronger, a good urban living environment has gradually become an important factor to evaluate the superiority of the city. Especially for the residential environment in cold areas, the winter is long and cold, accompanied by strong cold wind and snow, in-depth study of the spatial form of residential areas in cold areas, clear the corresponding microclimate conditions, can put forward targeted optimization measures, improve the comfort of outdoor environment, and ensure the quality of life safety of urban residents. Therefore, on the basis of understanding the research status of residential building planning and layout in recent years, this paper takes the spatial form of a region as an example to construct a microclimate parameterized model. The final research results prove that the climate environment of urban residential areas is targeted, different and personalized under the influence of the layout of residential areas. The effective layout means can show its regulating effect and effectively improve the environmental conditions of urban residents.
Quality Evaluation System of English Innovative Teaching Based on Hybrid Optimization Algorithm

Promoting the overall level of Chinese national English can promote the development of my country's social economy, and the key to improving English level lies in students. To improve English level, it is necessary to innovate English teaching methods. This paper designs an English innovative teaching quality evaluation system, and its research will help to deeply understand the problems existing in English innovative teaching, prompting teaching decision-makers to adjust English teaching strategies, thereby improving the efficiency of English teaching and creating a good environment for English teaching evaluation, improve the quality and efficiency of teaching evaluation, and then improve the quality of English teaching as a whole. This paper implements the system's user login function and evaluation result statistics function after testing the system, and compares the scoring results of the ant colony algorithm, genetic algorithm, and their hybrid optimization algorithms on the four dimensions of teaching quality, and obtains the hybrid optimization. The algorithm's score calculation accuracy is higher.
Auxiliary Teaching Software for English Reading Based on Machine Learning

With the development of modern computer-aided design theory, English-assisted teaching software has entered a new era. Auxiliary teaching software is playing an increasingly important role in every corner of modern education, and is even irreplaceable in some aspects. This paper designs an auxiliary teaching software for English reading based on machine learning . The hardware part includes a wireless dedicated fingerprint handheld terminal and an interactive handwriting board, which are the basis of the entire teaching-aiding software. After a survey on the satisfaction of application the software, it has been found that teachers and students are very satisfied with the auxiliary teaching software, and the application of the auxiliary teaching software also cultivates students' interest in English reading, and improves the students’ vocabulary and reading level.
Preliminary Attempt to Implement Quality Education in Mathematics Teaching

This paper discusses several ways to implement quality education in mathematics teaching from the aspects of paying attention to the teaching process of knowledge occurrence, strengthening the guidance of learning method, cultivating students' spirit of conscious exploration, and improving students' mathematical quality with transforming ideas.
Implementation of Traditional Vs. Transformer Machine Learning Models

Classical models have been mostly used in NLP tasks.There are several classical models like Naïve Bayes, SVM, CNN etc.In classical model we need to build the model from scratch.The model is trained, developed and then lastly deployed.With the advent of transformer models the process have been simplified.Transformer models are pretrained on larger datasets and just needs labelled dataset.It saves time, energy and resources.This report discusses the mechanism involved in workflow of classical and transformer ml models.Implementation of models have been also done for comparison.
Teacher’s Challenges and Solutions in Teaching Speaking in Kampung Inggris

Teaching speaking English in not an easy thing to do. There are several challenges faced by teachers, especially in teaching speaking because in speaking there are several aspects that must be mastered, including pronunciation, vocabulary, fluency and grammar. The purposes of this study is to explain the findings of the challenges faced by teachers in teaching speaking in Kampung Inggris, the problems faced by students in learning speaking in Kampung Inggris, and the potential solutions used by the teacher in teaching speaking in Kampung Inggris. In the data collection method, the researcher used semi-structured interviews by taking four participant namely two teachers and two students in Kampung Inggris Kediri, East Java, Indonesia. The data is processed using thematic analysis. The results of the study found that there were four challenges face by teachers in teaching speaking English, namely: 1). Different student backgrounds, 2). Various student abilities, 3). Pressure from the institutions, 4). Students’ anxiety about their abilities. The researcher also found four problems faced by students in learning speaking in Kampung Inggris, namely: 1). Students’ anxiety about their abilities, 2). The relationship between teachers and students is not close, 3). Class selection is not age propriate, 4). Negative influence from the environment. In addition, the researcher identified 4 potential solutions for teachers in teaching English speaking in Kampung Inggris, namely: 1). Build a good relationship between teachers and students, 3). The teacher determines which teaching matrials are mastered by the teacher, 4). The teacher provides motivation to students. From these finding, the researcher suggests improving the wuality of teachers by updating the classroom management system, improving teaching methods, making appropriate teaching materials and evaluating the teaching and learning process. This is expected to be able to make the teaching and learning process more effective and conducive for both teachers and students.&#x0D; Keywords: Teaching English Speaking, Student Problems in Learning Speaking, Challenges and Solutions in Teaching Speaking, Kampung Inggris
A Comparative Study on Reporting Verbs in ELT Research Article Introductions by Indonesian Authors

The study of reporting verbs (RVs) is rarely done by Indonesian scholars. Therefore, this research aimed to investigate how Indonesian writers used the RVs in research article introductions published in low-ranking and medium-ranking ELT journals and investigating the differences of the usage of RVs used by them in both groups of journals. This research used a quantitative approach with a comparative design that investigated 40 research article introductions published in low-ranking (SINTA 6: Journal of English Language Education, and SINTA 5: The Journal of English Literacy Education) medium-ranking (SINTA 4: Journal of English Education and Teaching, and SINTA 3: ELT Forum: Journal of English Language Teaching) ELT journals in 2021. The data were taken by using the purposive sampling technique and they were analyzed by using the RVs’ framework of Francis et al. (1996). The results revealed that Indonesian writers of low-ranking and medium-ranking ELT journals used all of the RVs in research article introductions, but the most frequent RVs used by them was “argue verb”. Moreover, Indonesian writers of low-ranking ELT journals used more “find and think verbs”. Meanwhile, “the argue and show verbs” were more often used by Indonesian writers of medium-ranking ELT journals. These findings indicate the importance of using the RVs in writing the research article introductions for Indonesian writers in the field of English language teaching. The writers are suggested to use more “Argue Verbs, followed by “Find and Shows Verbs” when writing research article introductions. Keywords: ELT journals, research article introduction, low-ranking journal, medium journal, reporting verb
Exploring Undergraduate Students’ Perspectives toward Computer-aided Translation Tools and Machine Translation: A Case Study of Students of the English Department

Computer-aided Translation (CAT) tools have attracted the attention of scholars who practice translation in various disciplines. The current study investigated undergraduate students’ Perspectives on CAT tools and Machine Translation (MT). More specifically, it investigates to what extent CAT tools improve productivity and translation quality. Finally, it also explores the other merits of using CAT tools. The present study used a qualitative analysis and semi-structured interviews to answer the research questions. The findings indicate that most of the students have a positive attitude toward using CAT tools. Furthermore, the findings show that lexical, syntactic, and special use of expressions as well as collocations and words that have cultural connotations were the most challenging that the students encountered when using MT. in addition. Improving productivity, enhancing quality, scalability, and connectivity are the four concepts mentioned by the subjects of the study as merits that made them use the CAT tool for translation other than using machine translation.