Reviewerly/exHarmony huggingface dataset:
Enhancing Human–Robot Collaboration through a Multi-Module Interaction Framework with Sensor Fusion: Object Recognition, Verbal Communication, User of Interest Detection, Gesture and Gaze Recognition

With the increasing presence of robots in our daily lives, it is crucial to design interaction interfaces that are natural, easy to use and meaningful for robotic tasks. This is important not only to enhance the user experience but also to increase the task reliability by providing supplementary information. Motivated by this, we propose a multi-modal framework consisting of multiple independent modules. These modules take advantage of multiple sensors (e.g., image, sound, depth) and can be used separately or in combination for effective human–robot collaborative interaction. We identified and implemented four key components of an effective human robot collaborative setting, which included determining object location and pose, extracting intricate information from verbal instructions, resolving user(s) of interest (UOI), and gesture recognition and gaze estimation to facilitate the natural and intuitive interactions. The system uses a feature–detector–descriptor approach for object recognition and a homography-based technique for planar pose estimation and a deep multi-task learning model to extract intricate task parameters from verbal communication. The user of interest (UOI) is detected by estimating the facing state and active speakers. The framework also includes gesture detection and gaze estimation modules, which are combined with a verbal instruction component to form structured commands for robotic entities. Experiments were conducted to assess the performance of these interaction interfaces, and the results demonstrated the effectiveness of the approach.
THE USE OF GRAMMAR TRANSLATION METHOD IN ENGLISH LEARNING TO THE SUB-DISTRICTS’ JUNIOR HIGH SCHOOLS IN TABANAN REGENCY

Grammar Translation Method is regarded as the old method that still used particularly at the school in sub-districts area. This research purposed to describe the use of Grammar Translation Method in English learning to the sub-districts junior high schools in Tabanan regency. The subjects of this research were three English teachers and 124 students. The data collection techniques used in this research were observation, interview, and documentation. Qualitative data analysis was applied to analyze the data. In accordance with data analysis, it was obtained that (1) Grammar Translation Method (GTM) was used in learning process includes the phases of observing, questioning, collecting data, associating, communicating, (2) Grammar Translation Method was used to increase students’ knowledge and skills in reading and writing. (3) The difficulties faced by the students were utilizing the appropriate vocabulary and structuring sentences. The conclusion of this research is the use of Grammar Translation Method is still required to increase students’ capability and skills, especially in reading and writing.
Exploring attention mechanism for graph similarity learning

Graph similarity estimation is a challenging task due to the complex graph structure. Though important and well-studied, three key aspects are yet to be fully handled in a unified framework: (i) how to exploit the node embedding by leveraging both local spatial neighborhood information and the global context, (ii) how to effectively learn richer cross graph interactions from a pairwise node perspective and (iii) how to map the similarity matrix into a similarity score by exploiting the inherent structure in the similarity matrix. To solve these issues, we explore multiple attention mechanisms for graph similarity learning in this work. More specifically, we propose a unified graph similarity learning framework involving (i) a hybrid of graph convolution and graph self-attention for node embedding learning, (ii) a cross graph co-attention (GCA) module for graph interaction modeling, (iii) similarity-wise self-attention (SSA) module for graph similarity matrix alignment and (iv) graph similarity matrix learning for predicting the similarity scores. Extensive experimental results on three challenging benchmarks including LINUX, AIDS, and IMDBMulti demonstrate that the proposed NA-GSL performs favorably against state-of-the-art graph similarity estimation methods. The code is available at https://github.com/AlbertTan404/NA-GSL.
THE EFFECTIVENESS OF USING SMARTPHONE IN LEARNING ENGLISH AT SMA NEGERI 3 BUKITTINGGI

This article aims to reveal the effectiveness of using Smartphones in learning English. The problem of the research was students werelessed motivated by the old learning media, students felt bored and lessed interested in learning through lecturedAnd lastly, studentswere sometimes embarrassed to asked directly to the teacher and prefer smartphones. Therefore, researcher was conducted a research at SMAN 3 Bukittinggi to found out whether the uses of smartphones was effective or not for learning English. This research was quantitative. The researcher used simple random sampling to determine the sample because it uses one way to pull out the lottery. The researcher was chose one of the five classes as the sample. The sample in this research were 36 students of class X E4. The data collection technique in this research was documentation in the form of student scores. The results showed that the use of smartphones was effective in learning English. The percentage of students was between 61 – 80% with the mean score calculated as 78,89with an effective assessment quality. This means that the data proves that the use of smartphones is effective in learning English in class X E4 SMAN 3 Bukittinggi.
AdaChain: A Learned Adaptive Blockchain

This paper presents AdaChain , a learning-based blockchain framework that adaptively chooses the best permissioned blockchain architecture to optimize effective throughput for dynamic transaction workloads. AdaChain addresses the challenge in Blockchain-as-a-Service (BaaS) environments, where a large variety of possible smart contracts are deployed with different workload characteristics. AdaChain supports automatically adapting to an underlying, dynamically changing workload through the use of reinforcement learning. When a promising architecture is identified, AdaChain switches from the current architecture to the promising one at runtime in a secure and correct manner. Experimentally, we show that AdaChain can converge quickly to optimal architectures under changing workloads and significantly outperform fixed architectures in terms of the number of successfully committed transactions, all while incurring low additional overhead.
Surveillance drone

This paper deals with the making of an autonomous nature of unmanned aerial vehicles to suit the purpose of rescue operation. It does so by easily approaching the areas where immediate help is required. With the added feature of live streaming, it takes video of such disastrous places. The video output is monitored on a real time basis and the exact location of critical condition is made known. The use of drones not only facilitates the accessibility of places faster but provides us with a wide view of the area increasing the range of the monitored area. Wave used image processing as well to be able to better detect the individual in need. Thus, the project can be summarized as an unmanned aerial vehicle that can be used in those areas where there is need of continuous monitoring and reaching humans is difficult and time consuming. This project is mainly focused on the use of drones as an automatic system that can reach a mapped destination without the intervention of human beings after a planned mission is loaded in its microcontroller.”
Optimizing Institutional Support in Improving Lecturer Performance in the Publication field in the Riau Islands

This study aims to determine the Effect of Optimizing Institutional Support in Improving Lecturer Performance in Publication at Private Universities in the Riau Archipelago.There are 12 private universities in the Riau Archipelago that have very low scientific publications, especially in terms of national and international publications.Over the past three years, data released by the government via the sinta.kemdikbud.go.id website show that lecturer publications have continued to decline.Institutional support related to information and
Goethe’s voice in the novel of Andrei Nikolev “Beyond Tula”

The paper examines the polygenetic nature of images and motives in the novel by Andrei Nikolev (Egunov), with particular attention on the “Faustian code”, which allows one to comprehend the European literary tradition. The writer is known to have been exceptionally passionate about Goethe’s works. He was close to the morphology of Goethe’s culture that drew the organizing world principle from the development of biological forms. Goethe’s ideas about the relationship between the cellular tissue of organisms and the expressive tissue of culture were a text-forming force in Egunov’s creativity. Goethe’s context covers a wide range of meanings: from the eschatological category of the transformation of phenomena and the world’s transformation to the farce, for example, in the story about the Hamelin piper. Nikolev’s novel has two realities overlapped without being autonomous and separate. Against the backdrop of the flickering plot of “Faust,” a plot twist appears: instead of a bet and a contract, one can see a convergence of the themes of Faust and Mephistopheles. Perception is interpreted as a respiratory problem. The narration is structured in such a way as to restrain the activity of themes associated with the image of the “fist” (Faust), transcending them with one meaning: creativity as the natural state of man. The search for the ultimate universal representation of the “poetic person” serves as the original meaning for the shape of events acquired in the novel. The mode of being that defines the poet possesses no categories of gender.
PENGARUH DUKUNGAN ORANG TUA DAN LINGKUNGAN SOSIAL TERHADAP PRESTASI BELAJAR PADA MATA PELAJARAN EKONOMI (LINTAS MINAT) DI SMA NEGERI 1 KALASAN

This study aimed to determine whether there were: 1) a positive influence of parental supports on students’ achievements in economics (cross-interest) subjects for high school students in Class XI MIPA and 2) a positive influence of the social environments on students’ achievement in economics subjects (cross interests) for high school students in Class XI MIPA. This research was astudy of ex post facto which was conducted in April 2021. The population of this study were students of Class XI MIPA at SMA Negeri 1 Kalasan, totaling 144 people. The research samples that were 108 students were taken by a random sampling technique. The data were collected using questionnaires and documentation. The hypothesis was tested using the Spearman Rank correlation tests. The results showed that: (1) there was no positive influence of parental support on learning achievements in economic subjects (cross interests) for high school students of Class XI MIPA, with the acquisition of sig. (1-tailed) = 0.067 0.05 and the value of r count = 0.145; and (2) there was no positive influence of the social environments on learning achievements in economic subjects (cross interests) for high school students of Class XI MIPA, with the acquisition of sig. (1-tailed) = 0.065 0.05 and the value of r count = 0.146.Keywords: Student Achievement, Parental Support, and Social Environment.
Digitalization and Gender Equality in Political Leadership in Sub-Saharan Africa

We examine the impact of digitalization on people's perceptions of women as political leaders in 34 Sub-Saharan African countries.We find that being a social media or internet user is linked to a higher likelihood of people supporting gender equality in political leadership.However, the intensive margin of usage does not appear to be significant.Furthermore, women's perceptions of gender equality in political leadership are more sensitive to internet and social media use than men's.The paper recommends policies for improving ICT infrastructure and investing in technological education.
Collaborative Innovation Ability Cultivation of College Students by Improving Achievement Transformation Efficiency

With the increasingly urgent demand for innovative talents, college students should have strong collaborative innovation ability because they are the backbone of future sci-tech innovation in China. Existing methods can be used to measure and evaluate to some extent the collaborative innovation and achievement transformation efficiency of college students. However, many of the methods focus on data of a certain period and neglect dynamic analysis. Dynamic comparisons of different periods need to be made because innovation activities and achievement transformation may be influenced by time factors in practical situations. In addition, some methods only focus on the quantity index of achievement transformation, and neglect quality evaluation. Therefore, this paper aimed to study the collaborative innovation ability cultivation of college students by improving the achievement transformation efficiency. Three-stage data envelopment analysis (DEA)-Windows model was used to calculate the collaborative innovation and achievement transformation efficiency of college students. This paper elaborated in detail the calculation process of these three stages, namely, the first-stage DEA-Windows analysis, the second-stage panel stochastic frontier analysis (SFA), and the third-stage adjusted DEA-Windows analysis. Based on the research needs of collaborative innovation ability cultivation of college students by improving the achievement transformation efficiency, this paper constructed an evaluation index system using Fare-Primont index model and Pastor method, and focused on the sci-tech innovation achievement transformation factors, such as output results and ability improvement. Finally, this paper showed the analysis results by combining with examples, which verified the proposed method was effective.
Community-Driven Programming

Mary Carrier serves as the Technology &amp; Growth Specialist for the four counties of the Mohawk Valley Library System in Schenectady, New York. Prior to this position, Mary dedicated over 15 years to teaching digital literacy and technology trends at the Clifton Park-Halfmoon Public Library, a suburban public library that has over 40,000 registered patrons and 1,500 visitors per day. The community has a strong presence in youth and family programs and is a popular place for teens and children to learn, play, and create. In 2015, she began offering coding and STEM classes to children and teens at the library and in the community as outreach programs. Mary will share her expertise in technology programming for children and teens and the importance of planning, preparing, and testing curriculum for coding and robotics classes.
Teachers’ voices of TPACK practices on ELT in the post-COVID-19 pandemic

This narrative study aimed to interpret evolving themes of teachers' TPACK practices through an entirely different lens. This study engaged two in-service EFL teachers, one who taught in a rural school and another who taught in an urban school. This study was conducted from May to October. In-depth interviews were utilised to shed light on their emotional experiences. Then, we analysed the data through Critical Thematic Analysis. Findings demonstrated how ongoing regulations generated conditions teachers encountered with shifting teachers’ paradigms. Participants truly believed that their TPACK has transitioned to the enhancement of the dominant technology-related knowledge domain (TCK, TPK &amp; TPACK). However, they experienced a deterioration of the quality of the non-technology-related knowledge domain (CK, PK &amp; PCK). There was a gap between teachers' perceived importance of technology-integrated activities and actual integration, which remained a persistent barrier to teachers' technology integration. The study suggests policymakers should pay heed to maintaining sustainable educational system stability rather than forcing teachers to implement an unsteady policy.
Tale of Two Tools

Consistent delivery of full-text content has been a challenge for libraries since the development of online databases. Library systems have attempted to meet this challenge, but link resolvers and early direct linking tools often fell short of patron expectations. In the last several years, a new generation of direct linking tools has appeared, two of which will be discussed in this article: Third Iron’s LibKey Discovery and Quicklinks by Ex Libris, a Clarivate company. Figure 1 shows the “Download PDF” link added by LibKey. Figure 2 shows the “Get PDF” link provided by Quicklinks. The way we configured our discovery interface, a resource cannot receive both the LibKey and Quicklinks PDF links. These two direct linking tools were chosen because they were both relatively new to the market in April 2021 when this analysis took place and they can both be integrated into Primo VE, the library discovery system of choice at the authors’ home institutions of SUNY College of Optometry and Ferris State University. Through analysis of the frequency of direct links, link success rate, and number of clicks, this study may help determine which product is most likely to meet your patrons’ needs.
Evaluating Public Website Performance: Content Analyses on Malang City Government Website

This study evaluates the performance of public websites in Malang City, a heavily digitizing government. Using content analysis, we determine the extent to which the availability of public information has been met and what records still need to be corrected by the Malang city government. This study examines five criteria: information quality, usability, privacy and security, interaction, and accessibility. As a result, the Malangkota. go. id website met the evaluation criteria for general public information availability. However, the government still needs improvement in terms of security and citizen-centered values. Thus, in the future, this research can be used as a basis for policy considerations by the Malang City government to improve the performance of its public services through the provision of good public information.
Predicting Stock Markets Using Binary Logistic Regression Based on Bry-Boschan Algorithm

In the stock market, there are bullish and bearish terms that are reflected in the movement of the stock price index. One of the stock price indexes listed on the Indonesia Stock Exchange (IDX) is the IDX Composite. Stock market conditions fluctuate along with changes in stock prices that move randomly, while investors expect market conditions to be active (bullish market). Several factors influence the movement of the IDX Composite, one of which is macroeconomic factors. The purpose of this research is to find out the condition of stock market as well as predict its condition using macroeconomics indicators. The method used to determine stock market conditions (bullish or bearish) is the Bry-Boschan algorithm, while the method used to predict the stock market using macroeconomic indicators is the binary logistic regression method. The Bry-Boschan algorithm is widely used to detect peaks and troughs in business cycle analysis. Binary logistic regression is used to model data with responses that have two categories or are in the form of binary numbers. Results show that the IDX Composite experienced 42 times (month) bearish periods and 191 times (month) experienced bullish periods. The obtained model has an accuracy value of 81.55%.
Summa: A Text Summarizer using LSTM based Encoder - Decoder Architecture with Attention Mechanism

Due to the exponential growth of online textual data, and rapid expansion of internet usage, tasks such as document management, text classification, and information retrieval have been significantly posing a challenge due to its huge size.A key tool for addressing this issue is Automatic Text Summarization (ATS).It is one of the most challenging tasks since there is no precise computational method for evaluating accuracy of a summary.[1] The main function of ATS is to automatically create a concise and apprehensive summary by extracting the main ideas from the source text.Models for abstractive summarization based on deep learning (DL) have recently been created to better balance and improve these two elements.The field of DL-based text summarization currently lacks a thorough literature review, nevertheless.This work offers researchers a thorough analysis of DL-based text summarization in order to close this gap.In this paper, many prevalent frameworks for text summarization are listed.We also propose Summa -an Abstractive text summarizer which uses Long Short Term Memory(LSTM) based Sequence to Sequence Model.Accuracy matrix or F1 score are not recommended, traditional evaluation measures for text summarisation are not that much relevant so, a novel measure, Bi-Lingual Evaluation Understudy(BLEU)[2] measure is used.The study reported the BLEU score of 0.71, with attention mechanism and 0.3, without Attention Mechanism, which is considered a good score.
K-Means – Resilient Backpropagation Neural Network in Predicting Poverty Levels

In solving economic problems, the government has implemented several development policies. However, this policy is considered to be too centered on big cities. So, through this research it is hoped that it can provide an overview related to regional groups that fall into the poorer category so that the government can also provide accelerated development policies that are oriented towards improving the economy of residents in the area. This study aims to determine the results of classifying district/city poverty levels in Indonesia as a basis for classification for predictions and to classify district/city poverty levels based on influencing factors. The method used in this study is K-Means Clustering using the poverty depth index and poverty severity index variables, then proceed with using the Backpropagation Neural Network (BNN) algorithm using the GRDP, per capita expenditure, human development index, and mean years of schooling. The results obtained using the K-Means algorithm are that there are 42 districts/cities that belong to cluster 1 where this region has a poverty index depth and severity index value that is higher than the 472 districts/cities in cluster 2. Furthermore, the cluster results are used as response variables for classification with BNN. The accuracy of the model obtained is very high, which is equal to 98.06, so the model is very feasible to be used as a poverty rate prediction model based on the variables used.