Reviewerly/exHarmony huggingface dataset:
Consensus embedding for multiple networks: Computation and applications

Abstract Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map the nodes into a low-dimensional space such that the nodes that are “similar” with respect to network topology are also close to each other in the embedding space. Real-world networks often have multiple versions or can be “multiplex” with multiple types of edges with different semantics. For such networks, computation of Consensus Embedding s based on the node embeddings of individual versions can be useful for various reasons, including privacy, efficiency, and effectiveness of analyses. Here, we systematically investigate the performance of three dimensionality reduction methods in computing consensus embeddings on networks with multiple versions: singular value decomposition, variational auto-encoders, and canonical correlation analysis (CCA). Our results show that (i) CCA outperforms other dimensionality reduction methods in computing concensus embeddings, (ii) in the context of link prediction, consensus embeddings can be used to make predictions with accuracy close to that provided by embeddings of integrated networks, and (iii) consensus embeddings can be used to improve the efficiency of combinatorial link prediction queries on multiple networks by multiple orders of magnitude.
Development of crowd detection warning system based on deep convolutional neural network using CCTV

The 2019 corona virus (Covid-19) pandemic is a global problem for now. One way to deal with the spread of the corona virus is to maintain a distance of at least one meter and stay away from crowds. Therefore, a crowd detection warning system based on a deep convolutional neural network (deep CNN) was developed using CCTV. The development of this system was carried out using the NVIDIA Jetson Nano microcontroller as the computing hardware. Crowd object detection uses the OpenCV library, the YOLOv3-Tiny algorithm, and the euclidean distance method to calculate the distance between 'person' objects. Based on the tests carried out on function and performance, the results obtained that this crowd detection warning system can detect 'person' objects with an accuracy rate of 92.79. In addition, this system has also been able to detect several types of colors from objects so that warning messages can be given more specifically on the color of the clothes of the 'person' in the detected crowd.
Implementasi Metode Certainty Factor Berbasis Android Pada Sistem Pakar Diagnosa Kecanduan Smartphone

Storing human knowledge in certain fields into the system to analyze a problem so that the system can make intelligent decisions as experts do and solve the problem. The tendency of excessive and inappropriate use of smartphones can lead to addiction, the impact of which can interfere with social interaction with the surrounding community and damage eye health, many people do not realize that there are changes that occur when they are addicted to smartphones, so a system that is able to analyze and provide solutions is needed addiction to smartphones. This study designs an expert system that will be used to analyze smartphone addiction. Users of this application seem to be dealing directly with experts in the field of psychology. The system design is carried out by creating a knowledge base and converting it into various rules as a description of knowledge. The system is designed by implementing the certainty factor method and using the Android Studio application to design the appearance as well as to build this expert system application. The results of this study reveal the level of addiction to smartphones and the solutions for each level. Application testing is also carried out to determine the accuracy of the system. The results of this test can be concluded that the expert system is good enough even though it can only analyze 3 levels of addiction, and the accuracy of this expert system application is 86.67%.
Emoticons production of Bing Dwen Dwen: Meme, online socialization and youth subculture

With new media technology growing leaps and bounds, the interaction between youth subculture and mainstream culture has taken on different characteristics than before. Since the opening of the 2022 Beijing Winter Olympics, youth groups have produced plentiful emoticons based on Bing Dwen Dwen through bricolage and homology. Our analysis classifies the current emoticons from two perspectives, content and function, and explore their meanings according to semiotics; our finding reveals that the youth uses them as social currency to achieve emotional catharsis, satisfy entertainment needs and gain a sense of community belonging; in addition, the symbolic transformation by youth also reflects the reconciliation between youth subculture and the sinking mainstream values
Evaluation of Lignocellulolytic Fungal Consortium for Composting Sugarcane Bagasse, Filter Cake and Manure

Three lignocellulolytic fungi isolates, Amblyosporium sp, Aspergillus sp1, and Aspergillus sp2, were tested for their ability to produce cellulase, hemicellulose, and ligninase enzymes as a consortium. They were also used in a mixture with sugarcane bagasse, filter cake and manure to make mixed compost. In the compost house of the State Agricultural Polytechnic of Pangkep, composting took place for 35 days. The composting process of the consortium of lignocellulolytic fungi was carried out at a temperature of 32°C-75°C, , with a water content of 12.58-30.32% and a pH of 6.80-8.20. The 35-day lignocellulolytic fungal consortium was able to speed up the composting process with a C-organic yield of 10.41-26.06%, a C/N ratio of 9.00-22.00%, phosphorus of 1.98%, magnesium of 6866 ppm, and sulfur of 0.25%. The fungal consortium was able to effectively speed up the composting process while also increasing nutrient content in various composting media compositions.&#x0D; Keywords: Fungi, lignocellulolytic, sugarcane, bagasse, manure
Responsible Digital Security Behaviour: Definition and Assessment Model

Digital landscape transforms remarkably and grows exponentially tackling important societal challenges and needs. In the modern age, futuristic digital concepts are ideated and developed. These digital developments create a diverse pallet of opportunities for organizations and their members like decision makers and financial personnel. Simultaneously, they also introduce different factors that influence users’ behaviour related to digital security. However, no method exists to determine whether users’ behaviour could be considered responsible or not, and in case this behaviour is irresponsible, how it could be managed effectively to avoid negative consequences. Thus far, no attempt was made to investigate this to the best of our knowledge. Then this research aims to: (i) introduce ‘responsible digital security behaviour’ notion, (ii) identify different factors influencing this behaviour, (iii) design a Bayesian Network model that classifies responsible/irresponsible digital security behaviour considering these factors, and (iv) draw recommendations for improving users’ responsible digital security behaviour. To address these, extensive literature review is conducted through technical, ethical, and social lenses in a Design Science Research approach for defining, building, and exemplifying the model. The results contribute to increasing digital security awareness and empowering in a responsible way users’ behaviours and decision-processes involved in developing and adopting new standards, methodologies, and tools in the modern digital era.
The Soft Skills of Software Learning Development: the Psychological Dimensions of Computing and Security Behaviours

When writing software code, developers typically prioritise functionality over security, either consciously or unconsciously through biases and heuristics. This is often attributed to tangible pressures such as client requirements, but little is understood about the psychological dimensions affecting security behaviours. There is an increasing demand for understanding how psychological skills affect secure software development and to understand how these skills themselves are developed during the learning process.
Analisis Bibliometrik Penelitian Tugas Akhir Mahasiswa di Fakultas Ekonomika dan Bisnis Universitas Gadjah Mada

The results of research in universities are expected to contribute to the development of science and technology and can provide benefits for the welfare of society. This study aims to map out topics from the 2020 student final project research to evaluate the achievement of the vision and mission of the Faculty of Economics and Business, Universitas Gadjah Mada. The research method used is a quantitative method with bibliometric analysis, namely mapping research topics based on co-word analysis. Data processing using VOSviewer. The results showed that the research theme for the final project of students from each department was in accordance with the flagship research theme of FEB UGM. The research theme for the final project of the Accounting Department students has conformity to clean organizational governance to create people with integrity, 71%, with dominant topics, Corporate Social Responsibility, internal control, performance, and Unified Theory of Acceptance and Use of Technology. The research theme for the final project of the students of the Department of Economics is that it is compatible with sustainable economic growth based on local wisdom, amounting to 63.9% with dominant topics, economic growth, unemployment, fixed effects, Autoregressive Distributed Lags, and the cost approach. Furthermore, the research theme for the final project of the Management Department students has conformity to the development of science-based innovation and information technology to improve quality education and research, by 61%, with dominant topics, abnormal returns, analysis of five forces porter, bank, business model canvas, leadership, and purchase intention.
Variable-rate hierarchical CPC leads to acoustic unit discovery in speech

The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones. In this paper we explore self-supervised learning of hierarchical representations of speech by applying multiple levels of Contrastive Predictive Coding (CPC). We observe that simply stacking two CPC models does not yield significant improvements over single-level architectures. Inspired by the fact that speech is often described as a sequence of discrete units unevenly distributed in time, we propose a model in which the output of a low-level CPC module is non-uniformly downsampled to directly minimize the loss of a high-level CPC module. The latter is designed to also enforce a prior of separability and discreteness in its representations by enforcing dissimilarity of successive high-level representations through focused negative sampling, and by quantization of the prediction targets. Accounting for the structure of the speech signal improves upon single-level CPC features and enhances the disentanglement of the learned representations, as measured by downstream speech recognition tasks, while resulting in a meaningful segmentation of the signal that closely resembles phone boundaries.
ProsocialDialog: A Prosocial Backbone for Conversational Agents

Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales. With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost. Empirical results show that Prost generates more socially acceptable dialogues compared to other state-of-the-art language and dialogue models in both in-domain and out-of-domain settings. Additionally, Canary effectively guides conversational agents and off-the-shelf language models to generate significantly more prosocial responses. Our work highlights the promise and importance of creating and steering conversational AI to be socially responsible.
The Application of Synchronous and Asynchronous Learning using E-learning on Elementary Linear Algebra

Learning activities were changed from face-to-face to full-online due to the COVID-19 pandemic at the end of 2019. Online learning through WhatsApp Group (WAG) was used by a mathematics teacher in higher education program in the early 2020. However, the learning result showed that 75% of preservice teacher were unable to accomplish their elementary linear algebra material. The combination of online learning through asynchronous method and synchronous learning was an alternative solution to solve the problem and enable preservice teachers having virtual face-to-face interactions with their lecturers. Therefore, this study was conducted to determine the improvement in preservice teachers' learning outcomes and responses to synchronous and asynchronous learning. The research design used was one group posttest-only with 23 subjects were selected by clustered random sampling. Data were analyzed by descriptive statistical analysis. The test results showed that learning outcomes after synchronous and asynchronous learning was effective. While the questionnaire results expressed most preservice teachers gave a positive response to both learning methods. Thus, the combination of the two methods could improve the effectiveness of online learning during the Covid-19 pandemic. It is recommended that higher education institutions provide innovative e-learning platforms for preservice teachers in developing further online learning methods.
Deep Learning-Based Bug Report Summarization Using Sentence Significance Factors

During the maintenance phase of software development, bug reports provide important information for software developers. Developers share information, discuss bugs, and fix associated bugs through bug reports; however, bug reports often include complex and long discussions, and developers have difficulty obtaining the desired information. To address this issue, researchers proposed methods for summarizing bug reports; however, to select relevant sentences, existing methods rely solely on word frequencies or other factors that are dependent on the characteristics of a bug report, failing to produce high-quality summaries or resulting in limited applicability. In this paper, we propose a deep-learning-based bug report summarization method using sentence significance factors. When conducting experiments over a public dataset using believability, sentence-to-sentence cohesion, and topic association as sentence significance factors, the results show that our method outperforms the state-of-the-art method BugSum with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum.
Identifying Language Learning Strategies Used by Polytechnic ESL Learners in Enhancing Speaking Skills

One of the primary reasons for global learners to study a language is to communicate.The Malaysian Education Blueprint aspires to guarantee that every student has an equal chance to progress and become competent in English in order to meet the requirements of a globalised world.As a result, the English Language Course syllabus is designed to mould students into competent language learners.However, in order to become effective language learners, one must use an appropriate language learning method to match the requirements of language tasks.Thus, the purpose of this research is to look at language learning strategies utilised by polytechnic students to improve their speaking abilities in English as a second language.A questionnaire was utilised to gather quantitative data in order to meet the goal of the study.This research included 37 ESL learners from a polytechnic in Sarawak.The results revealed that different learners use various learning strategies to enhance their speaking abilities in English as a second language.The results also expect to have a substantial influence on learners, particularly educators, in terms of incorporating the appropriate and successful language learning practises into their teaching and learning in order to establish a successful language learning environment.
Voice Based Summary Generation using LSTM

Abstract: Summary generation is utilized to have a fast outline of a total meeting without wasting much time. In this research paper, we are making a pipeline to create a summary of a voice recording. We will change the voice recording into text and afterward, we will utilize a deep learning model to produce an outline of that voice recording. We have 10 min voice recording as information and a rundown as result. Rundown age is essential of two sorts: Abstractive synopsis and extractive outline. Abstractive rundown creates new sentences with comparable significance as a synopsis of huge sentences and extractive outline produces sentences in the wake of pulling just significant expressions and catchphrases of enormous sentences. Keywords: Text Summary, Extractive, Abstractive, Natural Language processing, Machine Learning
A Trust-Based Recommender System for Personalized Restaurants Recommendation

Several online restaurant applications, such as TripAdvisor and Yelp, provide potential consumers with reviews and ratings based on previous customers’ experiences. These reviews and ratings are considered the most important factors that determine the customer’s choice of restaurants. However, the selection of a restaurant among many unknown choices is still an arduous and time- consuming task, particularly for tourists and travellers. Recommender systems utilize the ratings provided by users to assist them in selecting the best option from many options based on their preferences. In this paper, we propose a trust-based recommendation model for helping consumers select suitable restaurants in accordance with their preferences. The proposed model utilizes multi- criteria ratings of restaurants and implicit trust relationships among consumers to produce personalized restaurant recommendations. The experimental results based on a real-world restaurant dataset demonstrated the superiority of the proposed model, in terms of prediction accuracy and coverage, in overcoming the sparsity and new user problems when compared to other baseline CF-based recommendation algorithms.
Using Geogebra To Teach Parametric Problem Solving

Today, digital tools are an integral part of learning.The past two years showed that the use of information technologies in professional activities is a vital skill for teachers at all levels and in all fields of education.At present, a software product can be chosen for every lesson on any subject to help students in mastering it and help a teacher explain the related material.Parameter problems are considered to be the most difficult type of mathematics at school.In particular, it is complicated to understand the nature of the problems, and methods of solution.This article describes one of the instructional techniques of using GeoGebra while studying the topic The circle equation to further teach students to solve parameterized equation systems on the circle combination.The author's choice of GeoGebra is proved by its intuitive intelligibility and the Cyrillic interface, as well as by the fact that there is no need to install the program on a personal computer.The guidance software proposed in the article has been tested within the further curriculum for schoolchildren at MIP School: Mathematics, Informatics, and Physics set up at Sholom-Aleichem Priamursky State University.
An end-to-end framework for privacy risk assessment of AI models

We present a first-of-a-kind end-to-end framework for running privacy risk assessments of AI models that enables assessing models from multiple ML frameworks, using a variety of low-level privacy attacks and metrics. The tool automatically selects which attacks and metrics to run based on answers to questions, runs the attacks, summarizes and visualizes the results in an easy-to-consume manner.
Luminosity Control and Contrast Enhancement of Digital Mammograms Using Combined Application of Adaptive Gamma Correction and DWT-SVD

Enhancement of mammogram images against low-contrast and poor illumination is still a challenge for researchers. Focusing on such issues, this manuscript presents a two-stage enhancement technique for mammogram images. The first stage of the image enhancement deals with illumination control using the corrective-adaptive gamma correction (CAGC) approach. In the second stage, contrast enhancement operation on the luminosity-controlled image is incorporated. In order to enhance visual perception against low-contrast, the combined application of discrete wavelet transform (DWT) and singular value decomposition (SVD) is incorporated. The experimentation of the proposed technique was performed over a publicly available mini-MIAS dataset. The proposed technique is evaluated on various quantitative parameters such as Pearson correlation coefficient (PCC), universal image quality index (IQI), structural similarity index measurement (SSIM), contrast improvement index (CII), average mean brightness error (AMBE), and mean absolute error (MAE) and obtain the average values of 0.996, 0.912, 0.921, 1.098, 15.732, and 15.624 that are promising results as compared to the other traditional methods. This study also compares the proposed technique with state-of-art methods and achieves better performance, resulting in significant improvement in contrast enhancement and local information preservation of mammogram images.
Exploring the challenges of forensic technology in responding to credit card fraud in Sedibeng Region, South Africa

Today use of Credit Card even in developing countries has become a common scenario. People use it to shop, pay bills and for online transactions. This study explored the challenges of forensic technology in responding to credit card fraud as an approach used by the South African Police Service (SAPS) in Sedibeng region. This study was carried out utilising a qualitative approach and thirty nine interviews were carried out among officials deployed in the SAPS, community leaders, members of the community and bank managers. The key findings indicated that the challenges in responding to this scourge is that the perpetrators utilise advanced technologies resources (computer hacking software) as opposed to SAPS which does not have systems nor capacity to effectively respond to this crime. The limited resources to respond to this crime adequately. For recommendations; significant emphases should be directed on the promotion of public awareness through public education. The intensive training of SAPS officials and inter-governmental collaboration between the SAPS, community police forums, bank managers and South African Banking Risk Information Centre (SABRIC) in understanding this technology are highly advised.