Reviewerly/exHarmony huggingface dataset:
SmartBench

In recent years, a significant number of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs (KG) have been introduced. However, finding a benchmark that accurately evaluates the quality of a question answering system is a difficult task because of (1) the high degree of variations with respect to the fine-grained properties among the available benchmarks, (2) the static nature of the available benchmarks versus the evolving nature of KGs, and (3) the limited number of KGs targeted by existing benchmarks, which hinders the usability of QA systems in real deployment over KGs that are different from those which the QA system was evaluated using. In this demonstration, we introduce SmartBench, an automatic benchmark generating system for QA over any KG. The benchmark generated by SmartBench is guaranteed to cover all the properties of the natural language questions and queries that were encountered in the literature as long as the targeted KG includes these properties.
Organization IDs in Germany—Results of an Assessment of the Status Quo in 2020

Persistent identifiers (PIDs) for scientific organizations such as research institutions and research funding agencies are a further decisive piece of the puzzle to promote standardization in the scholarly publication process—especially in light of the already established digital object identifiers (DOIs) for research outputs and ORCID iDs for researchers. The application of these PIDs enables automated data flows and guarantees the persistent linking of information objects. Moreover, PIDs are fundamental components for the implementation of open science. For example, the application of PIDs for scientific organizations is of crucial importance when analyzing publications and the costs of the transition to open access at an institution. To find out more about the status quo of the use and adoption of organization IDs in Germany, a ‘Survey on the Need for and Use of Organization IDs at Higher Education Institutions and Non-University Research Institutions in Germany’ was conducted among 548 scientific institutions in Germany in the period from July 13 to December 4, 2020, as part of the DFG-funded project ORCID DE. One hundred and eighty-three institutions participated in what was the largest survey to date on organization IDs in Germany. The survey included questions on the knowledge, adoption, and use of organization IDs at scientific institutions. Moreover, respondent institutions were asked about their needs with regard to organization IDs and their metadata (e.g., in terms of relationships and granularity). The present paper provides a comprehensive overview of the results of the survey conducted as part of the aforementioned project and contributes to the promotion and increased awareness of organization IDs.
Impact of Mobile Phone Use on Students’ Moral and Learning Behaviour at Higher Secondary School Level

The study was sought to determine the impact of mobile phone use on students’ moral and learning behaviour. The study was descriptive in nature. Nineteen principals, 113 subject specialist and 114 secondary school teachers serving in 19 Government Higher Secondary School constituted the population of the study. Out of the total population 19 principals, 100 subject specialist and 76 secondary school teachers were randomly selected as a sample of the study. A closed ended questionnaire was developed containing 30 items with based on Likert scale related to the objectives of the study. The data were organized, analyzed, and interpreted by using frequency, percentage, mean and standard deviation. To test the hypotheses, chi-square technique was used. Findings of the study revealed that mobile phone use did not promote morality among students. Students using mobile phone did not give respect to teachers. Mobile phone use did not inculcate optimism in students’ thoughts. Students using mobile phone are not punctual. students having mobile phone are not obedient. Students using mobile phone are impatient. students using mobile phone did not follow classroom rules. students use mobile phone for whole day and night. students using mobile phone are not careful. using mobile phone did not give time to his brothers and sisters. mobile phone use badly affects students’ dignity. students using mobile phone develop the habit of cheating in exam. students using mobile phone are not taking interest in their classes. Students’ using mobile phone never complete their assignment on time. students using mobile phone did not take active part in classroom activities. students’ having mobile phone did not take good grades in internal exams. students’ having mobile phone often failed in external exams. It was recommended that measures may be taken to prohibit bringing mobile phones in the premises of school. A request may also be made to the parents to have a strong check on it at home. mobile phone may completely be banned on students
Envisioning New Futuring Models: Past, Plurality, and Positionality

This workshop will draw from critical scholarship to critique the popular futures cone model and invite participants to remake tools and icons for futuring. Creating visions of better futures can help align people toward positive societal change. Such far-future perspectives help people understand the outcomes of the difficult work towards making the significant transition happen—whether shifting to new sources of energy, ending police violence, or increasing access to democracy. However, the most prominent icon of design futuring, the Voros Cone, is inadequate for social equity contexts. Such a model ignores the importance of the past and portrays a single, narrow experience of the present. Our hope is to inspire participants to reflect on their own connection to time and to reimagine visual models to guide their personal design practice with futuring. We will share examples from our current research project as well as highlights from critical futures scholars who have explored visual representations of alternative futures. Interactive modules will begin with presentations by the facilitators, move into breakout rooms to give participants a chance to talk with others about time scales and positionality, then into quiet individual time to sketch, and finally sharing sessions to learn from each other’s unique and creative perspectives. Anyone from the community is welcome to participate. No specific artistic skills are required. There will be moments where we are asked to talk about our perspectives, listen attentively to others, and make visual representations of ideas (which can be done as simply or elaborately, digitally, or physically as you like). No previous knowledge about futures studies or social justice is required. Participants must be willing to acknowledge that structural oppression is a current and ongoing harm experienced by people around the world and be able to listen and talk about possibilities for more equity.
SPAMID-PAIR: A Novel Indonesian Post–Comment Pairs Dataset Containing Emoji

The detection of spam content is an important task especially in social media.It has become a topic to be continuely studied in Natural Language Processing (NLP) area in the last few years.However, limited data sets are available for this research topic because most researchers collect the data by themselves and make it private.Moreover, most available data sets only provide the post content without considering the comment content.This data becomes a limitation because the post-comment pair is needed when determining the context of a comment from a particular post.The context may contribute to the decision of whether a comment is a spam or not.The scarcity of non-English data sets, including Indonesian, is also another issue.To solve these problems, the authors introduce SPAMID-PAIR, a novel post-comment pair data set collected from Instagram (IG) in Indonesian.It is collected from selected 13 Indonesian actress/actor accounts, each of which has more than 15 million followers.It contains 72874 pairs of data.This data set has been annotated with spam/non-spam labels in Unicode (UTF-8) text format.The data also includes a lot of emojis/emoticons from IG.To test the baseline performance, the data is tested with some machine learning methods using several scenarios and achieves good performance.This dataset aims to be used for the replicable experiment in spam content detection on social media and other tasks in the NLP area.
Leveraging Experience in Lifelong Multi-Agent Pathfinding

In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a stream of tasks consisting of multiple locations to be visited by the agents on a shared graph while avoiding collisions with one another. L-MAPF is typically tackled by partitioning it into multiple consecutive, and hence similar, "one-shot" MAPF queries, as in the Rolling-Horizon Collision Resolution (RHCR) algorithm. Therefore, a solution to one query informs the next query, which leads to similarity with respect to the agents' start and goal positions, and how collisions need to be resolved from one query to the next. Thus, experience from solving one MAPF query can potentially be used to speedup solving the next one. Despite this intuition, current L-MAPF planners solve consecutive MAPF queries from scratch. In this paper, we introduce a new RHCR-inspired approach called exRHCR, which exploits experience in its constituent MAPF queries. In particular, exRHCR employs an extension of Priority-Based Search (PBS), a state-of-the-art MAPF solver. The extension, which we call exPBS, allows to warm-start the search with the priorities between agents used by PBS in the previous MAPF instances. We demonstrate empirically that exRHCR solves L-MAPF instances up to 39% faster than RHCR, and has the potential to increase system throughput for given task streams by increasing the number of agents a planner can cope with for a given time budget.
The Effect of Interactive Management Style on Academic Adjustment, Math Anxiety and Academic Engagement of Students

The Effect of Interactive Management Style on Academic Adjustment, Math Anxiety and Academic Engagement of Students
Grammar in Spoken Discourse: A Need Analysis of English Education Study Program Students

This research investigated the need analysis of Grammar in Spoken Discourse for the English Education Study Program. The objectives of this research were to find out: the current learning condition in teaching grammar at the English Education Study Program of University of Bengkulu and the needs for Grammar in Spoken Discourse course. This research used a mixed-method design. One hundred and fourteen students on fourth semester engaged as the correspondent of the questionnaire while four grammar lecturers were selected as the participant of interview. To gather the data, the questionnaire and interview were deployed and were further analyzed by using simple statistical of data analysis. The findings found that in the current conditions in learning grammar at English Education Study Program of University of Bengkulu already meet with the students’ needs in area of method, media and evaluation of learning. Then, Quantifier, Comparison, Form of Other, Stating Preference, How to Connect Ideas, Gerund, Infinitives, Modal Auxiliaries, Expressing Ability; Advice, Necessity, Requests, and Suggestions were materials needed for Grammar in Spoken Discourse course. There is difference between current learning situation and need analysis result for Grammar in Spoken Discourse in the context of material. The material prepares more contextual. Text which is related to the topic material of learning was prepared in order to enrich students’ knowledge.&#x0D; Keywords: Grammar in Spoken Siscourse, Need Analysis, Teaching Grammar
Human elements impacting risky habits in cybersecurity

Human elements in security or cybersecurity, in particular, have been an area, which has been less explored and underrated. Data leaks, Cyber-attacks, and malware attacks, which consequences of failures triggered by humans, are constantly raising. In fact, ninety-five percent of the cyber events are human-triggered or enabled. Several elements related to humans, such as (not limited to) psychological, situational, time pressure, characteristics, biases, influence cybersecurity habits. Through theme extraction using WordStar 8 and manual scanning of the chosen research papers, we fixated on such six significant human-related components or variables, which impact cybersecurity practices through a careful Systematic Literature Review. The paper shows how these components affect cybersecurity conduct and shows how these components are related and can often (all things are considered) lead to dangerous security practices. A significant research question investigated is how these elements affect cybersecurity conduct. We have drawn data for this study principally dependent on secondary research to create a theoretical framework that depicts the consequences of various human elements on one another, which influences cybersecurity propensities. We likewise clarify why a comprehension of human-related components or variables identified with digital security is significant and can add to fruitful innovations and programming. A thing to note here is that the study area, despite everything, remains not a completely examined field, and subsequently, specialized papers for this literature review were difficult to find. This examination is utilized as a base for more work to decrease the human variables driving cyber-attacks that have seen a sensational increment in the world of innovation, technology, and the web.
FANCY: A Diagnostic Data-Set for NLI Models

We present here FANCY (FActivity, Negation, Common-sense, hYpernimy), a new dataset with 4000 sentence pairs concerning complex linguistic phenomena such as factivity, negation, common-sense knowledge, hypernymy and hyponymy. The analysis is developed on two levels: coarse-grained for the labels of the Natural Language Inference (NLI), that is to say the task of determining whether a hypothesis is true (entailment), false (contradiction), or undetermined (neutral) and fine-grained for the linguistic features of each phenomenon. For our experiments, we analyzed the quality of the sentence embeddings generated from two transformer-based neural models, BERT (Devlin et al. 2018) and RoBERTa (Y. Liu et al. 2019), that were fine-tuned on MNLI and were tested on our dataset, using CBOW as a baseline. The results obtained are lower than the performance of the same models on benchmarks like GLUE (Wang et al. 2018) and SuperGLUE (Wang et al. 2019) and allow us to understand which linguistic features are the most difficult to understand.
Tensorized Multi-view Clustering via Hyper-graph Regularization

Multi-view clustering intent to separate data into different groups regarding their multiple traits. Existing tensor multi-view clustering techniques can explore the high-order associations of multi-perspective characteristics. However, they suffer from the following issues: (1) data features and local geometric structures in nonlinear subspace are often ignored; (2) the prior knowledge of singular values in the tensor kernel norm is not well utilized. To settle these problems, we propose a novel Markov chain tensor-based approach named Tensorized Multi-view Clustering via Hyper-graph Regularization(TMC-HR). Firstly, the third-tensor based on Markov chain transition probability is constructed and rotated to reduce the model complexity. Secondly, hyper-graph regularization is employed to save the high-order local geometrical structure imbedded in the original space. Thirdly, the weighted strategy is applied to the tensor composed of latent representations to extract the high-order relationships and diverse information between different views. Finally, an effective iterative method is utilized to solve the proposed TMC-HR. We conducted extensive experiments on benchmark datasets corresponding to different types to indicate that TMC-HR performs superior over other multi-view clustering approaches.
Human Detection with A Feedforward Neural Network for Small Microcontrollers

Motion detection is widely used in smart home applications. Nevertheless, it is crucial that a non-human movement does not trigger a motion sensor for various applications. For this purpose, camera-based sensors and machine learning are used. However, modern machine learning, such as deep neural networks (DNN), requires powerful hardware, which is expensive, has high power consumption, and requires a significant amount of storage space. Therefore, DNN object detection methods cannot be used directly on resource-constrained devices as the models require too much storage space. We present an object detection method for a resource-limited system where a camera-based human detection is computed directly on a small microcontroller to overcome these limitations. The key innovation enables object detection on a microcontroller with a small storage space and low computational power. With a unique image pre-processing and a feedforward artificial neural network (ANN), we reached a parameter reduction of the ANN of over 99 % compared to DNN image classification and object detection methods, allowing us to use the algorithm on an ESP32 with a detection rate of 83 ms. Furthermore, we also propose an approach that automatically generates a dataset from videos for the training of the ANN.
Asymptotically Necessary and Sufficient Quadratic Stability Conditions of T-S Fuzzy Systems Using Staircase Membership Function and Basic Inequality

Asymptotically necessary and sufficient quadratic stability conditions of Takagi-Sugeno (T-S) fuzzy systems are obtained by utilizing staircase membership functions and a basic inequality.The information of the membership functions is incorporated in the stability analysis by approximating the original continuous membership functions with staircase membership functions.The stability of the T-S fuzzy systems was investigated based on a quadratic Lyapunov function.The asymptotically necessary and sufficient stability conditions in terms of linear matrix inequalities were derived using a basic inequality.A fuzzy controller was also designed based on the stability results.The derivation process of the stability results is straightforward and easy to understand.Case studies confirmed the validity of the obtained stability results.
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution

The License Plate Recognition (LPR) field has made impressive advances in the last decade due to novel deep learning approaches combined with the increased availability of training data. However, it still has some open issues, especially when the data come from low-resolution (LR) and low-quality images/ videos, as in surveillance systems. This work focuses on license plate (LP) reconstruction in LR and low-quality images. We present a Single-Image Super-Resolution (SISR) approach that extends the attention/transformer module concept by exploiting the PixelShuftle layers capabilities and that has an improved loss function based on LPR predictions. For training the proposed architecture, we use synthetic images generated by applying heavy Gaussian noise in terms of Structural Similarity Index Measure (SSIM) to the original high-resolution (HR) images. In our experiments, the proposed method outperformed the baselines both quantitatively and qualitatively. The datasets we created for this work are publicly available to the research community at https://github.com/valfride/lpr-rsr/.
D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restoration

Night imaging with modern smartphone cameras is troublesome due to low photon count and unavoidable noise in the imaging system. Directly adjusting exposure time and ISO ratings cannot obtain sharp and noise-free images at the same time in low-light conditions. Though many methods have been proposed to enhance noisy or blurry night images, their performances on real-world night photos are still unsatisfactory due to two main reasons: 1) Limited information in a single image and 2) Domain gap between synthetic training images and real-world photos (e.g., differences in blur area and resolution). To exploit the information from successive long- and short-exposure images, we propose a learning-based pipeline to fuse them. A D2HNet framework is developed to recover a high-quality image by deblurring and enhancing a long-exposure image under the guidance of a short-exposure image. To shrink the domain gap, we leverage a two-phase DeblurNet-EnhanceNet architecture, which performs accurate blur removal on a fixed low resolution so that it is able to handle large ranges of blur in different resolution inputs. In addition, we synthesize a D2-Dataset from HD videos and experiment on it. The results on the validation set and real photos demonstrate our methods achieve better visual quality and state-of-the-art quantitative scores. The D2HNet codes and D2-Dataset can be found at https://github.com/zhaoyuzhi/D2HNet .
KOMPARASI ALGORITMA DATA MINING UNTUK ANALISIS SENTIMEN APLIKASI PEDULILINDUNGI

The COVID-19 pandemic has caused many changes to occur in Indonesia. In the PeduliLindungi application to the community, the government makes the application to the community in the hope of being able to provide a warning if it enters the covid-19 zone and various other information from covid-19 [1]. The main purpose of this study is to analyze the sentiments of PeduliLindungi users who are currently used during the Covid-19 pandemic, where this application has begun to be used to travel anywhere and anytime to find out whether the user has vaccinated or not and various other things. such as the spread of the virus and the location of vaccination. The dataset for this study was taken from the Play Store. The algorithm used is Support Vector Machine and Naive Bayes to classify the data set. The data collection technique is Text Mining and compares the results of the two specified algorithms. The results of this research are Support Vector Machine with TF IDF Vectorizer with 89.05% accuracy followed by Support Vector Machine with Count Vectorizer, Naive Bayes with TF IDF Vectorizer and Naive Bayes with Count Vectorizer.
Efficient Malicious Traffic Classification Methods based on Semi-supervised Learning

The proliferation of mobile communication systems, arrival of high-speed broadband networks and more complex network topologies have exacerbated cyber-threats. Cyber-warfare has become an aspect of modern war-fare that can no longer be overlooked. In recent years, network intrusions launched using the Internet have seriously undermined the security systems of many nations. Classifying malicious network traffic is the first step in network intrusion detection. In this paper, we propose three models using semi-supervised learning-based malicious traffic classification (MTC) methods that effectively improve the classification of traffic using a small proportion of labeled traffic data. Employing three different deep neural networks as feature extraction networks respectively, the proposed models use transductive transfer learning and domain adaptive ideas, and ladder networks as classification layers. Experimental results are provided to validate the proposed methods.