Reviewerly/exHarmony huggingface dataset:
IoT Based Covid Chatbot using Telegram

The days of needing keyboards to avail a service are over. Users today use chatbots or voice assistants to interact with systems. Chatbots provide intelligent and adapted answers. The goal of a chatbot is to establish a productive conversation between humans and machines. For each user query that is considered input, the chatbot responds with an answer. From helping doctors manage their schedules to answering simple questions from the public, chatbots have proven to be very useful in healthcare. The global pandemic caused by the coronavirus has changed everyone's life and has affected it differently. People must be kept informed on various aspects. Purpose of this article is to discuss the implementation and model of a telegram-based chatbot system, which helps people understand the facts, precautions, preventive measures, and various other aspects related to the coronavirus. The bot also focuses on a person's mood and provides mental health support.
Exploring the use of machine learning outlier detection algorithms for DDoS identification

Servers and users rely on safe defenses against multiple attacks. Usual practices, however, normally are unable to deal with huge distributed attacks, such as DDoS. This is a malicious practice that aims to interrupt the flow of a network causing data congestion. Moreover, DDoS is a stealthy practice, as its traffic might present similar attributes to usual ones. With this in mind, in this paper, we use unsupervised, semi-supervised, and supervised machine learning algorithms to automatically analyze a selected network, detecting possible DDoS flows using PyOD library. We evaluate each of those types of algorithms and also explore the effects of previous feature selection on them.
SMARTER Teamwork: System for Management, Assessment, Research, Training, Education, and Remediation for Teamwork

His areas of scholarship include student team formation and peer evaluation; persistence, migration, and retention in engineering education; and data analysis and visualization for investigating and presenting quantitative data.Elements of his teaching practice include formal and informal cooperative learning and informal inquiry-based, hands-on experiences in labs, mini-labs, and student workshops.For a decade, he has been the key developer of reform of the experimental-based curriculum in his department.Most of his courses and laboratories involve instructor-
Improving the Predictive Power of Historical Consistent Neural Networks

The Historical Consistent Neural Networks (HCNN) are an extension of the standard Recurrent Neural Networks (RNN): they allow the modeling of highly-interacting dynamical systems across multiple time scales. HCNN do not draw any distinction between inputs and outputs, but model observables embedded in the dynamics of a large state space. In this paper, we propose to improve the predictive power of the (Vanilla) HCNN using three methods: (1) HCNN with Partial Teacher Forcing, (2) HCNN with Sparse State Transition Matrix, and (3) a Long Short Term Memory Formulation of HCNN. We investigated the effect of those long memory improvement methods on three chaotic time-series mathematically generated from the Rabinovich&ndash;Fabrikant, the Rossler System and the Lorenz system. To complement our study, we compared the accuracy of the different HCNN variants with well-known recurrent neural networks methods such as Vanilla RNN and LSTM for the same prediction tasks. Overall, our results show that the Vanilla HCNN is superior to RNN and LSTM. This is even more the case if you include the above long memory extensions (1), (2) and (3). We demonstrate that (1) and (3) are superior for the modeling of our chaotic dynamical systems. We show that for these deterministic systems, the ensembles are narrowed.
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity

Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data. However, in terms of complementarity, most existing approaches only can find representations with single complementarity rather than complementary information with diversity. In this paper, to utilize both complementarity and consistency simultaneously, give free rein to the potential of deep learning in grasping diversity-promoting complementarity for multi-view representation learning, we propose a novel supervised multi-view representation learning algorithm, called Self-Attention Multi-View network with Diversity-Promoting Complementarity (SAMVDPC), which exploits the consistency by a group of encoders, uses self-attention to find complementary information entailing diversity. Extensive experiments conducted on eight real-world datasets have demonstrated the effectiveness of our proposed method, and show its superiority over several baseline methods, which only consider single complementary information.
Measuring Membership Privacy on Aggregate Location Time-Series

While location data is extremely valuable for various applications, disclosing it prompts serious threats to individuals' privacy. To limit such concerns, organizations often provide analysts with aggregate time-series that indicate, e.g., how many people are in a location at a time interval, rather than raw individual traces. In this paper, we perform a measurement study to understand Membership Inference Attacks (MIAs) on aggregate location time-series, where an adversary tries to infer whether a specific user contributed to the aggregates. We find that the volume of contributed data, as well as the regularity and particularity of users' mobility patterns, play a crucial role in the attack's success. We experiment with a wide range of defenses based on generalization, hiding, and perturbation, and evaluate their ability to thwart the attack vis-à-vis the utility loss they introduce for various mobility analytics tasks. Our results show that some defenses fail across the board, while others work for specific tasks on aggregate location time-series. For instance, suppressing small counts can be used for ranking hotspots, data generalization for forecasting traffic, hotspot discovery, and map inference, while sampling is effective for location labeling and anomaly detection when the dataset is sparse. Differentially private techniques provide reasonable accuracy only in very specific settings, e.g., discovering hotspots and forecasting their traffic, and more so when using weaker privacy notions like crowd-blending privacy. Overall, our measurements show that there does not exist a unique generic defense that can preserve the utility of the analytics for arbitrary applications, and provide useful insights regarding the disclosure of sanitized aggregate location time-series.
The The Paratactic and Hypotactic Constructions in Research Article Abstracts at 2018 ISET Proceedings

The present study was a part of larger research aimed at analysing the research article abstracts taken from International Conference on Science and Education and Technology 2018 (ISET 2018). This study was descriptive study under SFL analysis in research article abstracts at 2018 ISET realized from 170 clause complexes. Under a SFL theory, the data were collected through retrieving twenty relevant article abstracts on the International Conference on Science and Education and Technology 2018 (ISET 2018). The article abstracts are analysed using the framewok of systemic functional linguistic as suggested by Halliday &amp; Matthiessen (2014). However, in this study the writer employs the taxis sytems which covers elaboration, extension, and enhancement. Besides, logico-semantic relation is also analysed which covers projection and expansion. The findings reveal that there are 81 clause complexes of parataxis which are realised 47%, afterwards, there are 35 clause complexes of hypotaxis which are realised 20.5%. Hence, the researchers is more using parataxis then hypotaxis. Bassed on the findings, it is recommended that English teacher can apply clause constructions to encourage the English learners to think critically. After being able to implement their critical thinking, the English learners are hoped to be able to read critically. Through having critical reading, they are suggested to be able to evaluate both their works and the other works as the main purpose in learning systemic functional linguistics.
ANALISIS MANAJEMEN RISIKO PADA PROYEK PEMBANGUNAN GEDUNG LABORATORIUM TERPADU DAN PERPUSTAKAAN MAN 1 MALUKU TENGAH

This study aims to determine the risks that occur in the Construction Project of the Integrated Laboratory Building and Man 1 Library, Central Maluku. The method used is a qualitative analysis method. The subjects in this study were workers in the Integrated Laboratory Building Development Project and the Man 1 Library in Central Maluku totaling 30 samples. The primary data collection techniques are through questionnaires and secondary data collection is through work items while the data analysis methods used are Validity Test, Reality Test, and data analysis with a risk matrix.
GeoRef

The GeoRef database, offered by EBSCO, was originally established by the American Geosciences Institute in 1966. This reference tool provides comprehensive access to more than 50 full-text international geoscience journals with mapping capabilities. Additional resources include environmental studies providing access to research and other literature, including maps, books, theses, journal articles, reports, and conference papers. Additionally, GeoRef is keyword searchable across millions of links to Web-based geoscience resources. Both the GeoRef database and the GeoRef Thesaurus are components of a larger database, GeoScienceWorld. The content of the GeoRef database grows at a rate of approximately 4.2% per year.
EFFECTIVITY OF DIGITIZED HEALTH INFORMATION CARD

With the emergence of new Coronavirus Disease (COVID-19) variants, the COVID-19 pandemic remained a global concern. As a result, the constant need to improve efficiency and credibility while preserving safety measures is of utmost priority. Going paperless is a great way to control the pandemic, and Quick Response (QR) code is an effective way to promote paperless transactions. The researchers incorporated the QR Code and Google Form into the Digitized Health Information Card. The researchers then assessed the QR Code’s effectiveness based on its accessibility, convenience, and the difference between the satisfaction of using digitized health information cards and manual writing. The researchers used SoGoSurvey forms called Product Evaluation and Overall Satisfaction Survey to measure the satisfaction and effectiveness rate of the Digitized Health Information Card. The researchers also used weighted mean to identify its effectiveness using 50 students as respondents who filled out the Google Forms posted on the Facebook page. Based on the data, positive feedback on the accessibility and convenience of using Quick Response code had been interpreted as accessible and very convenient. The findings show that the respondents were very satisfied with filling out digitized health information forms while somewhat unsatisfied with writing manually. As a result of the significant difference, the Quick Response Code is more effective in student welfare.
Pelatihan computational thinking guru MI se-Kecamatan Pabelan, Kabupaten Semarang

Learning in society 5.0 and the covid-19 pandemic requires several skills to be mastered, both by students and teachers. These skills are creativity, critical thinking, communication, collaboration, compassion, and computational thinking (6C). This paper describes the training carried out on teachers to improve computational thinking skills. A teacher must master computational thinking to prepare the next generation of potential, superior, and ready to face the era of society 5.0. The training target is MI teachers in Pabelan District, totaling 26 teachers. The training was divided into several stages, including a pretest, material presentation, practice working on logarithmic problems, creating a Bebras account, tutorial on making games using scratch software, and posttest. At the end of the activity, participants are asked to fill out a presence link as a condition to get a certificate. The training results showed that most of the participants could understand and implement computational thinking. It can be seen from the results of the posttest given. Participants were very enthusiastic and active in participating in the training. They were also happy because follow-up activities after the training were completed.
A Personalized Code Formatter: Detection and Fixing

The wide adoption of component-based software development and the (re)use of software residing in code hosting platforms have led to an increased interest shown towards source code readability and comprehensibility. One factor that can undeniably improve readability is the consistent code styling and formatting used across a project. To that end, many code formatting approaches usually define a set of rules, in order to model a commonly accepted formatting. However, this approach is mostly based on the experts’ expertise, is time-consuming and ignores the specific styling and formatting a team selects to use. Thus, it becomes too intrusive and may be not adopted. In this work, we present an automated mechanism that can be trained to identify deviations from the selected formatting style of a given project, given a set of source code files, and provide recommendations towards maintaining a common styling across all files of the project. At first, source code is transformed into small meaningful pieces, called tokens, which are used to train the models of our mechanism, in order to predict the probability of a token being wrongly positioned. Then, a number of possible fixes are examined as replacements of the wrongly positioned token and, based on a scoring function, the most suitable fixes are given as recommendations to the developer. Preliminary evaluation on various axes indicates that our approach can effectively detect formatting deviations from the project’s code styling and provide actionable recommendations to the developer.
Nanophotonic Reservoir Computing for COVID-19 Pandemic Forecasting

The coronavirus disease 2019 (COVID-19) has spread worldwide in unprecedented speed, and diverse negative impacts have seriously endangered human society. Accurately forecasting the number of COVID-19 cases can help governments and public health organizations develop the right prevention strategies in advance to contain outbreaks. In this work, a long-term six-month COVID-19 pandemic forecast in second half of 2021 and a short-term 30-day daily ahead COVID-19 forecast in December 2021 are successfully implemented via a novel nanophotonic reservoir computing (RC) based on silicon optomechanical oscillators (OMO) with photonic crystal (PhC) cavities. The testing-dataset forecast results of new cases, new deaths, cumulative cases, and cumulative deaths for six countries demonstrate that the forecasted blue curves are awfully close to the real red curves with exceedingly small forecast errors. Moreover, the forecast results commendably reflect the variations of the actual case data, revealing the different epidemic transmission laws in developed and developing countries. More importantly, the daily ahead forecast results during December 2021 of four kinds of cases for six countries illustrate that the daily forecasted values are highly coincident with the real values, while the relevant forecast errors are tiny enough to verify the good forecasting competence of COVID-19 pandemic dominanted by Omicron strain. Therefore, the implemented nanophotonic reservoir computing can provide some foreknowledge on prevention strategy and healthcare management for COVID-19 pandemic.
Siampra: An Effective Network for Uav Visual Tracking

The visual navigation system is an important module in intelligent unmanned aerial vehicle (UAV) systems as it helps to guide them autonomously by tracking visual targets. In recent years, Siamese network-based tracking algorithms have exhibited excellent performance but have been difficult to apply to UAV systems owing to their constrained resources. This paper proposes a simple and efficient tracking network called the Siamese Pruned ResNet Attention (SiamPRA) network and applies it to the visual navigation system of the UAV. SiamPRA is based on the SiamFC network, incorporates ResNet-24 as its backbone, and utilizes the spatial-channel attention mechanism, thereby achieving higher accuracy while reducing the number of computations. Further, sparse training and pruning are used to reduce the size of the model while maintaining high precision. Experimental results on the challenging benchmarks VOT2018 and UAV123 show that SiamPRA has a higher accuracy and lower complexity than other tracking networks.
Managing Conflicts of Stakeholders’ Emotional Goals In Requirements Engineering: A Case Study Approach

Context: Requirements engineering (RE) is integral to developing a socio-technical system (STS) focusing on end-user satisfaction and technology acceptance. However, designing a STS with minimal conflicts in a given problem domain is challenging because of its complexity and insatiable human needs. In particular, it can be challenging to address stakeholders' goals when there are divergent points of view, especially emotional ones.Objective: This paper addresses resolving conflicts in emotional requirements expressed as emotional goals for different STS stakeholders. The purpose was to understand human behavior, motivation, and strategies and harmonize their emotional differences.Method: The paper present a case study where conflicts in emotional goals by stakeholders are identified andresolved. The research method applied is a mixture of qualitative and quantitative methods by engagingstakeholders in an e-workshop. We worked out a strategy based on the existing methodologies to harmonize the emotional goals of different stakeholders.Results: The result provides a generic structure of the problem domain that shows how each emotional goal relates to the functional goals and roles of the problem domain and how the identified conflicts are resolved. Our findings provide both empirical, theoretical, and analytical implications.Conclusion: It is concluded that the human factor is an important consideration during the RE process of STSs. Specifically, it is essential to address the different stakeholders' goals amidst conflicting views, especially emotional ones. This will make it possible to harmonize stakeholders' emotional goals to develop emotionally acceptable software. It is also essential for the RE practitioners and researchers to have an all-inclusive approach that should have practical implications for various domains in managing conflicts usually expected in many ways.
Clustering Labels in Multi-Label Learning: A Study Using Randomness and Label Correlations

Multi-Label Classification is the task of simultaneously predicting a set of labels for an instance in Machine Learning. Typically, two approaches are used: global, which trains a single classifier to deal with all classes at once (what we refer to as a global partition); and local, which divides the problem into many binary problems (what we refer to as a local partition), solves them separately and then combines the results. Learning label correlations that occur in multi-label data can improve prediction performances in both approaches. We offer a way to find partitions of correlated labels that can be considered in-between the local and global traditional ones, in order to combine the benefits of both approaches. These are referred to as hybrid partitions. Our approach entails: partitioning the label space into sub-spaces based on label correlations; ii) generating and validating hybrid partitions; iii selecting the optimal partitions between them, and iv) evaluating their performances in a test set. Six different strategies for creating hybrid partitions are presented. To evaluate how good are the hybrid partitions found, we compare their results with the ones obtained by partitions generated by an oracle, an exhaustive, and a random strategy. Both the oracle and exhaustive strategies generate all possible label partitions for a dataset. While the oracle chooses the best one based on the test set alone, the exhaustive choices are based on validation. To create hybrid partitions, the random strategy selects labels at random. Experiments on eight well-known multi-label datasets revealed that both random and hybrid partitions might produce results that were superior or comparable to those achieved using the local and global techniques, the exhaustive and oracle search. This is an intriguing conclusion, indicating that existing global and local techniques may not accurately account for label correlations and still require significant improvements.
Multi-Stage Semantic Graph Embeddings for Compositional Zero-Shot Learning

We tackle the problem of compositional zero-shot learning (CZSL) where the task is to recognize novel composite semantic concepts (like red-tomato, wet-dog) consisting of states (red, wet) and objects (tomato, dog) characterizing the visual primitives. CZSL is a complex task given the fact that similar states may appear to be visually distinct, thus hampering the recognition performance. Recently, graph convolution networks (GCN) have been used to learn a compatibility function among the visual, state, and object embeddings. However, we postulate that these techniques do not fully explore the compositional nature of the semantic space where states, objects, and pairs follow different neighborhood topologies. To this end, we introduce the concept of multi-stage graph embeddings where separate GCNs are used to initially model the pairwise interactions for the states, objects, and the composition label embeddings, respectively. A composite GCN subsequently combines this information to learn a discriminative and neighborhood preserving latent semantic space ensuring the strong coupling of a composition label with its respective state and object. Further, we use a vision transformer for projecting the images onto the same latent space where the cross-modal information can be compared. An adaptive margin based cross-entropy loss is introduced to train the model end-to-end while ensuring enriched discrimination among the categories. Results on the MIT-States, UT-Zappos and C-GQA datasets confirm the superiority of the proposed approach.
Continual Learning with Score Pseudo Label for Instance Segmentation

Continual learning can retain old knowledge while learning new knowledge, which has been studied in image classification, object detection, and semantic segmentation, but has received little attention in instance segmentation. Continual learning in instance segmentation (CLIS) faces two major challenges: catastrophic forgetting and background shift. This paper proposes a method, called F eature- O utput D istillation and S core Pseudo Labels (FODS), which combines knowledge distillation and pseudo labels to solve two major challenges in CLIS. To alleviate catastrophic forgetting, the classification and bounding boxes of the selected proposals, as well as the intermediate features, are distilled using the knowledge distillation method. To address background shift, score pseudo labels that eliminate the cognitive bias problem in pseudo labels are presented. The extensive evaluation of COCO 2017 proves the effectiveness of our method, which significantly outperforms the most advanced method.