Reviewerly/exHarmony huggingface dataset:
Event Recommendation System using Hybrid Method Based on Mobile Device

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesnâ€™t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.
Autopoesis in on line learning

Conception of autopoesis has an important place among conceptions of self-organization. Biologists Humberto Maturana and Francisco Varela consider living beings as self-referring and self-constructing autonomous systems, namely, as autopoietic systems. They proclaimed: “All doing is knowing. All knowing is doing” in their famous book “The Tree of Knowledge”. It concerned all living beings, including human beings with the biological roots of their cognition. Author of the article choose this conception of self-organization as working model of on line teaching and learning just because the lack of such biologist roots in on-line communication. Unconsciousness but influenced features of live communication by humans with their “embodied mind” just loose in communication on line. Analyzing own experience of off line and on line teaching in frame of autopoetic approach, author tries to seek the means to overcome the restrictions of on line teaching and learning. The idea by Maturana and Varela about continually recurrent interactions between participants of doing and knowing which provide their creation of common world in common linguistic field became the main point of conclusion. Active communications between lector and students and between students in workshops can partly compensate an absence of live communication. It means that standard obvious relation between quantity of lectures and workshops have to be changed with free choice by lector among new special forms of on line learning. Original work of students for solving the tasks are preferable, especially if the tasks are connected with scientific or technologic researches. In any case supporting the discussions and debates, teachers can stimulate those recurrent interactions which guarantee the autopoesis as self-construction of common world in which self-organization of young persons is possible in their becoming the young specialists. The concept of autopoesis is able to maximize the success of communication between teachers and students and students to each other to provide feedback in the joint actions of cognition, creating the effect of sustainable self-organization in learning.
Multi-Agent Intention Progression with Black-Box Agents

We propose a new approach to intention progression in multi-agent settings where other agents are effectively black boxes. That is, while their goals are known, the precise programs used to achieve these goals are not known. In our approach, agents use an abstraction of their own program called a partially-ordered goal-plan tree (pGPT) to schedule their intentions and predict the actions of other agents. We show how a pGPT can be derived from the program of a BDI agent, and present an approach based on Monte Carlo Tree Search (MCTS) for scheduling an agent's intentions using pGPTs. We evaluate our pGPT-based approach in cooperative, selfish and adversarial multi-agent settings, and show that it out-performs MCTS-based scheduling where agents assume that other agents have the same program as themselves.
Likelihood-free Out-of-Distribution Detection with Invertible Generative Models

Likelihood of generative models has been used traditionally as a score to detect atypical (Out-of-Distribution, OOD) inputs. However, several recent studies have found this approach to be highly unreliable, even with invertible generative models, where computing the likelihood is feasible. In this paper, we present a different framework for generative model--based OOD detection that employs the model in constructing a new representation space, instead of using it directly in computing typicality scores, where it is emphasized that the score function should be interpretable as the similarity between the input and training data in the new space. In practice, with a focus on invertible models, we propose to extract low-dimensional features (statistics) based on the model encoder and complexity of input images, and then use a One-Class SVM to score the data. Contrary to recently proposed OOD detection methods for generative models, our method does not require computing likelihood values. Consequently, it is much faster when using invertible models with iteratively approximated likelihood (e.g. iResNet), while it still has a performance competitive with other related methods.
Learning Class-Transductive Intent Representations for Zero-shot Intent Detection

Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage. To address this problem, we propose a novel framework that utilizes unseen class labels to learn Class-Transductive Intent Representations (CTIR). Specifically, we allow the model to predict unseen intents during training, with the corresponding label names serving as input utterances. On this basis, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately. CTIR is easy to implement and can be integrated with existing ZSID and GZSID methods. Experiments on two real-world datasets show that CTIR brings considerable improvement to the baseline systems.
Parameter Analysis of Differential Evolution Based Oversampling Approach for Highly Imbalanced Datasets

Nowadays, almost all performed activities are saved into databases. Data mining methods such as classifiers utilize these datasets for discovering hidden patterns and rules. Proposed methods for classification problems are generally developed considering approximately balanced datasets. However, imbalanced datasets that have the unequal instance numbers in its classes emerges as a common problem in most real domains. Many approaches at the data level are proposed to enable better classification of imbalanced datasets. Differential Evolution Based Oversampling Approach For Highly Imbalanced Datasets (DEBOHID) is one of the proposed method in order to handle this issue on imbalanced datasets. DEBOHID approach utilizes the crossover and mutation processes of DE for generating new synthetic samples. The parameters used by the crossover and mutation processes affect the solution quality. Therefore, in this study, solution quality in highly imbalanced datasets for different crossover and mutation parameter values of DEBOHID approach is investigated. Experimental studies are carried out by using three classifiers and two evaluation metrics for different parameter values. The obtained results are compared with well-known approaches in the literature.
Partition-Aware Graph Pattern based Node Matching with Updates

<i>Graph Pattern based Node Matching</i> (GPNM) is to find all the matches of the nodes in a data graph <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math></inline-formula> based on a given pattern graph <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math></inline-formula> . GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math></inline-formula> are updated frequently. However, the existing GPNM methods either need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated <inline-formula><tex-math notation="LaTeX">$G_P$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$G_D$</tex-math></inline-formula> or incrementally perform the GPNM procedure for each of the updates, leading to low efficiency. Although the elimination relations between updates and partitions of data graphs are considered in the state-of-the-art method, it still suffers from low efficiency as only the labels of nodes are considered in the partitions. Therefore, there is a pressing need for a new method to efficiently deliver the node matching results on the updated graphs. In this paper, we propose a new Partition-aware GPNM algorithm, called P-GPNM, where we propose two new partition methods, i.e., <i>connection-based partition</i> and <i>density-based partition</i> . In these two methods, P-GPNM considers the dense connections between partitions and the inner connections inside a single partition, respectively. The experimental results on five real-world social graphs demonstrate that our proposed P-GPNM is much more efficient than the state-of-the-art GPNM methods.
A comparison between higher-order nonclassicalities of superposition engineered coherent and thermal states

We consider an experimentally obtainable superposition (SUP) operator, defined by using a generalized superposition of products of field annihilation (a) and creation (a † ) operators of the type A = saa † + ta † a with s 2 + t 2 = 1. We apply this SUP operator on coherent and thermal quantum states, and the states thus produced are referred to as SUP-operated coherent state (SOCS) and SUP-operated thermal state (SOTS), respectively. In the present work, we report a comparative study between the higher-order nonclassical properties of SOCS and SOTS. The comparison is performed by using a set of nonclassicality witnesses (e.g., higher-order anti-bunching, higher-order sub-Poissonian photon statistics, higher-order squeezing, Agarwal–Tara parameter, Klyshko’s condition). The existence of higher-order nonclassicalities in SOCS and SOTS have been investigated for the first time. In view of possible experimental verification of the proposed scheme, we present exact calculations to reveal the effect of non-unit quantum efficiency of quantum detector on higher-order nonclassicalities.
Boundary Knowledge Translation based Reference Semantic Segmentation

Given a reference object of an unknown type in an image, human observers can effortlessly find the objects of the same category in another image and precisely tell their visual boundaries. Such visual cognition capability of humans seems absent from the current research spectrum of computer vision. Existing segmentation networks, for example, rely on a humongous amount of labeled data, which is laborious and costly to collect and annotate; besides, the performance of segmentation networks tend to downgrade as the number of the category increases. In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation. Ref-Net contains a Reference Segmentation Module (RSM) and a Boundary Knowledge Translation Module (BKTM). Inspired by the human recognition mechanism, RSM is devised only to segment the same category objects based on the features of the reference objects. BKTM, on the other hand, introduces two boundary discriminator branches to conduct inner and outer boundary segmentation of the target object in an adversarial manner, and translate the annotated boundary knowledge of open-source datasets into the segmentation network. Exhaustive experiments demonstrate that, with tens of finely-grained annotated samples as guidance, Ref-Net achieves results on par with fully supervised methods on six datasets. Our code can be found in the supplementary material.
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies

We consider the problem to learn a concept or a query in the presence of an ontology formulated in the description logic ELr, in Angluin's framework of active learning that allows the learning algorithm to interactively query an oracle (such as a domain expert). We show that the following can be learned in polynomial time: (1) EL-concepts, (2) symmetry-free ELI-concepts, and (3) conjunctive queries (CQs) that are chordal, symmetry-free, and of bounded arity. In all cases, the learner can pose to the oracle membership queries based on ABoxes and equivalence queries that ask whether a given concept/query from the considered class is equivalent to the target. The restriction to bounded arity in (3) can be removed when we admit unrestricted CQs in equivalence queries. We also show that EL-concepts are not polynomial query learnable in the presence of ELI-ontologies.
Comparative Method of Weighted Product and TOPSIS to Determine The Beneficiary of Family Hope Program

The Family Hope Program (PKH) is a government program that provides cash assistance to impoverished households. The implementation of PKH in Cimrutu Village has not been implemented optimally, namely prioritizing the targets of PKH participants who are not yet on targets. This happened because the officers in registering the poor were still using manual methods. To simplify the work and avoid miscalculation of data with the old system, a decision support system was built that could help make decisions on PKH recipients quickly and more accurately. The calculation method used is the Weighted Product (WP) method. Data collection methods used in this study were interviews and documentation. System development in this study uses waterfall through black-box testing. System design tools in the form of DFD and ERD. The software used in making this application is Visual Studio 2012, Xampp, and Crystal Reports. The programming language used is Java with its supporting database using MySQL. This decision support system is expected to be able to help officers in Cimrutu Village in selecting and determining communities that are eligible for PKH.
Pelatihan penyusunan Ice Breaking untuk penguatan kompetensi calon guru

In every teaching and learning process includes English teaching, there is a phase called ice breaking. The activity that generally only lasts a few minutes is a paramount aspect. Every English teacher or even the candidate has to be skillful in preparing effective ice breaking. Although ice breaking is viewed as a crucial stage in a teaching process, many students miss this phase when they have to do teaching practice. Therefore, the community service team regarding a workshop about icebreaking is still needed by the students as the teacher candidates. This workshop was conducted online via Google Meet in two days, and the participants were the students of the English Language Teaching Department of Universitas Nahdlatul Ulama Purwokerto. In the workshop, the participants learned the theories related to ice breaking and the examples of ice breaking. They also did some practice on creating ice breaking. Overall, the two-day workshop ran well. The questioner's result also tends to be positive and based on the tasks submitted, the participant's ability to create ice breakers is good enough
What Drives and Sustains Self-Assignment in Agile Teams

Self-assignment, where software developers choose their own tasks, is a common practice in agile teams. However, it is not known why developers select certain tasks. It is important for managers to be aware of these reasons to ensure sustainable self-assignment practices. We investigated developers’ preferences while they are choosing tasks for themselves. We collected data from 42 participants working in 25 different software companies. We applied Grounded Theory procedures to study and analyse factors for self-assigning tasks, which we grouped into three categories: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-based</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">developer-based</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opinion-based</i> . We found that developers have individual preferences and not all factors are important to every developer. Managers share some common and varying perspectives around the identified factors. Most managers want developers to give higher priority to certain factors. Developers often need to balance between task priority and their own individual preferences, and managers facilitate this through a variety of strategies. More <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk-averse</i> managers encourage expertise-based self-assignment to ensure tasks are completed quickly. Managers who are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">risk-balancing</i> encourage developers to choose tasks that provide learning opportunities only when there is little risk of delays or reduced quality. Finally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">growth-seeking</i> managers regularly encourage team members to pick tasks outside their comfort zone to encourage growth opportunities. Our findings will help managers to understand what developers consider when self-assigning tasks and help them empower their teams to practice self-assignment in a sustainable manner.
Centralizing digital resources for data management, processing, and analysis for enterprise scale imaging research

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You Don’t Fool Me: On Scams, Scambaiting, Deception, and Epistemological Ambiguity at R/scambait on Reddit

With a focus on the online phenomena of scamming and scambaiting, this article explores users’ communicative activities on Reddit’s r/scambait subreddit. Drawing on a representative corpus viewed through grounded theory, we establish the basic categories of posts and then unpack those further to reveal the deceptive practices being undertaken by both scammers and scambaiters, as well as Redditors’ untruthfulness in their fabricated posts. The analysis reveals that the r/scambait subreddit exists as a site of humorous entertainment arising from various forms of deception. Scammers’ deceptive strategies are depicted as amusingly naïve and inefficient, while scambaiters’ deceptive messages targeted at scammers demonstrate great creativity and wittiness. In both cases, scammer-victims are disparaged for being immensely gullible or downright stupid; and Redditors earn online plaudits for submitting the most upvoted posts. Our significant finding is that posts such as those at r/scambait should never be taken at face value due to their inherent epistemological ambiguity, to which the users choose to remain oblivious or indifferent. Furthermore, on a general plane, this study indicates a potential shift in the emic understanding of the concept of “scambaiting” from a punitive measure and an educational instrument to a creative practice geared toward posters’ kudos and users’ joint humorous experience through “baitertainment” and “scamusement.”
A Novel Fault Diagnosis Strategy for Heterogeneous Wireless Sensor Networks

Fault diagnosis is a guarantee for the reliable operation of heterogeneous wireless sensor networks, and accurate fault prediction can effectively improve the reliability of wireless sensor networks. First, it summarizes the node fault classification and common fault diagnosis methods of heterogeneous wireless sensor networks. After that, taking advantage of the short learning time, fewer parameter settings, and good generalization ability of kernel extreme learning machine (KELM), the collected sample data of the sensor node hardware failure is introduced into the trained kernel extreme learning machine and realizes the fault identification of various hardware modules of the sensor node. Regarding the regularization coefficient <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>C</mi> </math> and the kernel parameter <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>s</mi> </math> in KELM as the model parameters, it will affect the accuracy of the fault diagnosis model of the kernel extreme learning machine. A method for the sensor nodes fault diagnosis of heterogeneous wireless sensor networks based on kernel extreme learning machine optimized by the improved artificial bee colony algorithm (IABC-KELM) is proposed. The proposed algorithm has stronger ability to solve regression fault diagnosis problems, better generalization performance, and faster calculation speed. The experimental results show that the proposed algorithm improves the accuracy of the hardware fault diagnosis of the sensor nodes and can be better applied to the node hardware fault diagnosis of heterogeneous wireless sensor networks.
Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.
A federated interpretable scorecard and its application in credit scoring

In this paper, we propose a vertical federated learning (VFL) structure for logistic regression with bounded constraint for the traditional scorecard, namely FL-LRBC. Under the premise of data privacy protection, FL-LRBC enables multiple agencies to jointly obtain an optimized scorecard model in a single training session. It leads to the formation of scorecard model with positive coefficients to guarantee its desirable characteristics (e.g., interpretability and robustness), while the time-consuming parameter-tuning process can be avoided. Moreover, model performance in terms of both AUC and the Kolmogorov–Smirnov (KS) statistics is significantly improved by FL-LRBC, due to the feature enrichment in our algorithm architecture. Currently, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.
Bipartite Network Analysis Utilizing Survey Data to Determine Student and Tool Interactions in a Makerspace

Her research area is design cognition including systematic methods and tools for innovative design with a particular focus