Reviewerly/exHarmony huggingface dataset:
PEMBERDAYAAN PAGUPON LITERASI SEBAGAI UPAYA MENUMBUHKAN MINAT BACA BAGI SISWA DI SD MUHAMMADIYAH BANTUL KOTA DI ERA REVOLUSI INDUSTRI 4.0

This study aims to determine: 1). Implementation of paguponliteracy at Muhammadiyah Bantul City Elementary School; 2) To what extent Pagupon literacy can increase reading interest in students; 3). The role of schools in empowering literacy pagupons as an effort to increase student interest in reading at SD Muhammadiyah Bantul Kota. This study uses a qualitative phenomenological approach. Sources of data are teachers, principals, librarians, data collection techniques by observation and interviews. The data analysis technique used source triangulation and technical triangulation. The research results are as follows: 1). Muhammadiyah Bantul City Elementary School has empowered literacy through literacy pagupon effectively and optimally. It is proven that many students like to visit the pagupon which is in the front yard of the school library, the students are diligent in reading the books placed on the pagupon, the students are enthusiastic and interested in continuing to take books from the pagupon to be studied in turn; learning is not only done in the classroom but also in front of the pagupon (Pagupon literacy camp); 2) The existence of the pagupon can attract students to read the books provided in the paguponliteracy. 3) The role of teachers, school principals and librarians is very optimal in providing exemplary, improving facilities and books in the pagupon, the teacher invites students to always analyze books read together, and motivates students to always visit pagupons to increase knowledge.
Evaluation and Improvement of Application Deployment in Hybrid Edge Cloud Environment : Using OpenStack, Kubernetes, and Spinnaker

Traditional mechanisms of deployment of deferent applications can be costly in terms of time and resources, especially when the application requires a specific environment to run upon and has a dif ...
A Unified Approach to Anomaly Detection

Anomalous actors are becoming increasingly sophisticated both in methodology and technical ability. Fortunately, the companies impacted by anomalous behaviour are also generating more data and insights on potential anomalous cases than ever before. In this paper, a unified approach to managing the complexity of constructing a useable anomaly detection system is presented. The unified approach is comprised of three algorithms, a Neural Architecture Search (NAS) implementation for autoencoders, an anomaly score threshold optimisation algorithm, and a Gaussian scaling function for anomaly scores. NAS is applied to a data set containing instances of credit card fraud. The NAS algorithm is used to simulate a population of 50 candidate deep learning architectures, with the best performing architecture being selected based on a balanced score, comprised of an average of the Area under the ROC curve, Average Precision and normalised Matthews Correlation Coefficient scores. The threshold optimisation algorithm is used to determine the appropriate threshold between the classes, for the purposes of producing the binary classification outcome of each architecture. The Gaussian scaling algorithm is applied to the raw anomaly scores of the optimal architecture into order to generate useable probability scores. Not only did the proposed unified approach simplify the process of selecting an optimal neural architecture whose output is interpretable by business practitioners and comparable with other probability score producing models, but it also contributes to anomaly detection in a transactional setting by eliminating subjective thresholds when classifying anomalous transactions.
Algorithm for Detecting Polarity of Opinions in University Students Comments on Their Teachers Performance

Sentiment analysis is a text classification task within the area of natural language processing whose objective is to detect the polarity (positive, negative or neutral) of an opinion given by a certain user. Knowing the opinion that a person has toward a product or service is of great help for decision making, since it allows, among other things, potential consumers to verify the quality of the product or service before using it. This paper presents the results obtained from the automatic identification of the polarity of comments emitted by university students in a survey corresponding to the performance of their professors. In order to carry out the identification of the polarity of comments, a technique based on automatic learning is used, which initially makes a manual labeling of the comments and then these results allow to feed different learning algorithms in order to create the classification models that will be used to automatically label new comments, and thus determine their polarity as positive or negative.
Health Insurance Data Processing using Linked Data

The data in the organization is distributed among multiple structured databases. The large database makes the process of risk analysis difficult, as data is distributed in the organization. Information gathering for Risk analysis is more subjective and therefore, processing incomplete information over distributed databases increase more fault in risk analysis. Linked Data representation helps to make structured, distributed data more related, combined and ready to be processed. Linked Data approach makes data interlinked and semantically rich, extracting meaning with the use of machines and eliminating the human subjectivity factor in assessing insurance risk. Using Linked data, information retrieval process can be easier as data or databases interlinked semantically. The proposed technique uses a linked data approach for risk analysis and related information retrieval methods over structured data. The work efficiency is also tested and found to be good.
Benchmarking Automated Review Response Generation for the Hospitality Domain

Online customer reviews are of growing importance for many businesses in the hospitality industry, particularly restaurants and hotels. Managerial responses to such reviews provide businesses with the opportunity to influence the public discourse and to attain improved ratings over time. However, responding to each and every review is a time-consuming endeavour. Therefore, we investigate automatic generation of review responses in the hospitality domain for two languages, English and German. We apply an existing system, originally proposed for review response generation for smartphone apps. This approach employs an extended neural network sequence-to-sequence architecture and performs well in the original domain. However, as shown through our experiments, when applied to a new domain, such as hospitality, performance drops considerably. Therefore, we analyse potential causes for the differences in performance and provide evidence to suggest that review response generation in the hospitality domain is a more challenging task and thus requires further study and additional domain adaptation techniques.
A Nonparametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles

Identifying anomalies in multidimensional data sets is an important yet challenging task in many real-world applications. A special case arises when anomalies are occluded in a small subset of attributes. We propose a new subspace analysis approach, called agglomerative attribute grouping (AAG), that searches for subspaces composed of highly correlative (in the general sense) attributes. Such correlations among attributes can better reflect the behavior of normal observations and hence, can be used to improve the identification of abnormal data samples. The proposed AAG algorithm relies on a generalized multiattribute measure (derived from information theory measures over attributes’ partitions) for evaluating the “information distance” among various subsets of attributes. To determine the set of subspaces, AAG applies a variation of the well-known agglomerative clustering algorithm with the proposed measure as the underlying distance function, whereas in contrast to existing methods, AAG does not require any tuning of parameters. Finally, the set of informative subspaces can be used to improve subspace-based analytical tasks, such as anomaly detection, novelty detection, forecasting, and clustering. Extensive evaluation over real-world data sets demonstrates that (i) in the vast majority of cases, AAG outperforms both classical and state-of-the-art subspace analysis methods when used in anomaly and novelty detection ensembles; (ii) it often generates fewer subspaces with fewer attributes each, thus resulting in faster training times for the anomaly and novelty detection ensemble; and (iii) the generated subspaces can also be useful in other analytical tasks, such as clustering and forecasting. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This research was partially supported by the Israeli Ministry of Economy (METRO 450 Consortium within the frame of MAGNET program) as well as by the Koret foundation grant for Smart Cities and Digital Living 2030. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/2526218/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0027 ).
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory

Bayesian deep learning is a promising approach towards improved uncertainty quantification and sample efficiency. Due to their complex parameter space, choosing informative priors for Bayesian Neural Networks (BNNs) is challenging. Thus, often a naive, zero-centered Gaussian is used, resulting both in bad generalization and poor uncertainty estimates when training data is scarce. In contrast, meta-learning aims to extract such prior knowledge from a set of related learning tasks. We propose a principled and scalable algorithm for meta-learning BNN priors based on PAC-Bayesian bounds. Whereas previous approaches require optimizing the prior and multiple variational posteriors in an interdependent manner, our method does not rely on difficult nested optimization problems and is agnostic to the variational inference method in use. Our experiments show that the proposed method is not only computationally more efficient but also yields better predictions and uncertainty estimates when compared to previous meta-learning methods and BNNs with standard priors.
Assisted Requirements Engineering - What Will Remain in the Hands of the Future Requirements Engineer? (Invited Keynote)

Requirements engineering (RE) is widely considered one of the most difficult and risky activities in software and systems engineering. Since RE requires communication, and despite other ideas and experiments, tasks around textual content remains at the center of the RE for most projects. With a daily evolving field of natural language processing (NLP), the question is: Which of these tasks will - independent from any technological and methodological advancements - stay in the hands of the requirements engineer and which tasks will be automated? This paper will take a look into the crystal ball. Based on analogies from programming and autonomous driving, and based on an analysis of the abilities of NLP and abilities of other modern technologies, I present a vision of the life of a future requirements engineer.
VARIASI PEMBELAJARAN BAHASA INDONESIA MAHASISWA UNIVERSITAS BINA DARMA DI MASA PANDEMI

This study aims to describe the variations in teaching lecturers, variations in assignments and inhibiting factors for learning Indonesian for Bina Darma University students during the pandemic using qualitative descriptive methods as well as through interviews and observations. Data analysis was carried out through data reduction, data presentation, and drawing conclusions. The results showed: (1) The teaching variations of the lecturers were very diverse, which was adjusted to the mastery and skills of the lecturers in using IT to interact with students, such as using E-Learning, E-Mail, Zoom, Google Classroom, WhatsApp, Youtube content; (2) Variations in assignment are also very diverse according to the ability of lecturers to use IT; (3) External and internal factors are inhibiting factors for the variation of Indonesian Language learning among Bina Darma University Students during the Pandemic Period.
Pemanfaatan Sumber Belajar Dalam Meningkatkan Hasil Belajar Siswa Sekolah Dasar

The use of various learning resources should be used by students to assist students in obtaining various information regarding learning. Learning resources play an important role in the efforts to solve problems encountered by students in the learning process. Learning resources that can be used for learning needs are of various types and forms. Learning resources can be in the form of people, materials, environment, learning tools and equipment, techniques and messages. With the procurement and use of various learning resources that are more varied by students, they can provide information in order to improve the quality of learning. If good learning quality is achieved, good learning outcomes will also be achieved. The widest possible use of learning resources is used by students for learning needs and efforts to get maximum learning outcomes, so these learning resources need to be developed and managed systematically, well and functionally. Thus learning resources provide a positive contribution in improving learning outcomes of elementary school students.
An Overview of the Cyber Resiliency Level® (CRL®) Framework for Weapon, Mission, and Training Systems

The deficiency in risk awareness, coupled with rapid technology changes within complex environments that are continuously under attack, make measuring cyber resiliency a hard problem. Lockheed Martin (LM) Fellows and cybersecurity subject matter experts from across the corporation developed and piloted the Cyber Resiliency Level® (CRL™) Framework as a standard way to measure the cyber resiliency maturity of weapon, mission, and/or training systems. The CRL™ Framework can be used to assist stakeholders in prioritizing risks and selecting courses of action for maximum effect against cyber-attacks; and, provides stakeholders with an understanding of cyber investments necessary for increased cyber resilience.
Methodology of Highway Engineering

Academician Pan Jiazheng pointed out in his article Philosophical Thinking in the Construction of Water Conservation (Journal of the China Academy of Water Conservation and Hydropower Science, Volume 1, Issue 1, June 2003): Water conservation engineers have many disciplines to master and cannot spend much energy on philosophical issues. But one’s thoughts and deeds are always dominated by one’s epistemology and world outlook. If there is any deviation in these aspects, although you have a good heart and master modern scientific and technological knowledge, it will often get half the result with twice the effort and even lead to unexpected consequences. In this way, it is helpful for hydraulic engineers to read some philosophical books. But engineers don’t have to read great classics, and sometimes a high-level philosophical essay is not as useful as a proverb. The article vividly summarizes the experience of water conservation projects into eight problems and explains the profound in simple terms, including: (1) the philosophy of looking in the mirror; (2) the philosophy of air travel; (3) the philosophy of taking traditional Chinese medicine; (4) the philosophy of holding eggs; (5) the philosophy of eating arsenic; (6) the philosophy of physical examination; (7) the philosophy of managing children; (8) the philosophy of eating crabs.
A New System for Fake Profile Recognition in Social Networks

The billions of users all over the world are spending online social network, such as Twitter, Facebook, Tumbler and LinkedIn. The flaws in this social media may indications to abuse user’s information and execute an attack of identity cloning. In this work mainly focuses, a new method for hiding data to hide precise details in profile pictures to learn botnets and fake profiles. In this paper, presents an ordering and investigation of recognition mechanisms of genetic copy attacks on online network of social, purely established on attribute likeness, friend network likeness, and profile inquiry for a time interval and record of Internet Protocol sequences. This work, proposals an algorithm for transform discrete wavelet for hiding the data. So, the system can prevent the replica attacks and offer the whole user data privacy protective. Similarly, when users upload the profile image or photos it would be first watermarked and then updated. Java static watermarking systems and algorithms is been used for watermarking procedure. Here tracking any fake users updating the same profile picture given easier and tracking their IP address also became easier. Also, our system raises certain features which can be investigated to the users through process of registration. Thus, providing secure authentication. Hence avoiding clone attacks in social media became easier.
Analysis of Alignment Phenomenon in Simple Teacher-student Networks with Finite Width

Recent theoretical analysis suggests that ultra-wide neural networks always converge to global minima near the initialization under first order methods. However, the convergence property of neural networks with finite width could be very different. The simplest experiment with two-layer teacher-student networks shows that the input weights of student neurons eventually align with one of the teacher neurons. This suggests a distinct convergence nature for ``not-too-wide'' neural networks that there might not be any local minima near the initialization. As the theoretical justification, we prove that under the most basic settings, all student neurons must align with the teacher neuron at any local minima. The methodology is extendable to more general cases, where the proof can be reduced to analyzing the properties of a special class of functions that we call {\em Angular Distance (AD) function}. Finally, we demonstrate that these properties can be easily verified numerically.
Results of a Single Blind Literary Taste Test with Short Anonymized Novel Fragments.

It is an open question to what extent perceptions of literary quality are derived from text-intrinsic versus social factors. While supervised models can predict literary quality ratings from textual factors quite successfully, as shown in the Riddle of Literary Quality project (Koolen et al., 2020), this does not prove that social factors are not important, nor can we assume that readers make judgments on literary quality in the same way and based on the same information as machine learning models. We report the results of a pilot study to gauge the effect of textual features on literary ratings of Dutch-language novels by participants in a controlled experiment with 48 participants. In an exploratory analysis, we compare the ratings to those from the large reader survey of the Riddle in which social factors were not excluded, and to machine learning predictions of those literary ratings. We find moderate to strong correlations of questionnaire ratings with the survey ratings, but the predictions are closer to the survey ratings. Code and data: https://github.com/andreasvc/litquest
Range partitioning within sublinear time: Algorithms and lower bounds

Range partitioning is a typical and mostly used data partitioning method and has became a core operation in most of big data computing platforms. Given an input L of N data items admitting a total order, the goal of range partitioning is to divide the whole input into k ranges containing the same number of data items. There is a trivial lower bound Ω(N) for the exact partitioning algorithms, since they need to at least make a full scan of the whole data. In the context of big data computing, even algorithms with O(N) time are not always thought to be efficient enough, the ultimate goal of designing algorithms on big data is usually to solve problems within sublinear time. Therefore, it is well motivated and important to study sublinear algorithms for the range partitioning problem. The paper aims to answer three questions. For the internal memory (RAM) model, since sophisticated sampling based (ϵ,δ)-approximation partitioning algorithm with O(klog⁡(N/δ)ϵ2) time cost has been proposed, the first question is what a lower bound we can obtain for sublinear partitioning algorithms. For the external memory (I/O) model, as far as we know, no previous works give external partitioning algorithms with performance guarantee within sublinear time, therefore the two questions are what the upper bound and the lower bound we can achieve for sublinear external partitioning algorithms. To answer the above questions, based on the RAM and I/O model, the paper studies the lower and upper bounds for the range partitioning problem. For the RAM model, a lower bound Ω(k(1−δ)ϵ2) for the cost of sampling based partitioning algorithms is proved. For the I/O model, two lower bounds of the sampling cost required by sublinear external range partitioning algorithms are proved, which indicate that at least a full scan of the whole input is needed in the worst case and a general sublinear external partitioning algorithm does not exist. Motivated by the hard instances utilized in the proof of lower bounds, a model for describing the input distributions of the range partitioning problem in practical applications is proposed. Finally, for the special cases described by the model, a sublinear external partitioning algorithm with O(klog⁡(N/δ)wBϵ2) I/O cost is designed.
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds and human faces, synthesising images with multiple objects is still very difficult. In this paper, we propose an effective way to combine Text-to-Image (T2I) synthesis with Visual Question Answering (VQA) to improve the image quality and image-text alignment of generated images by leveraging the VQA 2.0 dataset. We create additional training samples by concatenating question and answer (QA) pairs and employ a standard VQA model to provide the T2I model with an auxiliary learning signal. We encourage images generated from QA pairs to look realistic and additionally minimize an external VQA loss. Our method lowers the FID from 27.84 to 25.38 and increases the R-prec. from 83.82% to 84.79% when compared to the baseline, which indicates that T2I synthesis can successfully be improved using a standard VQA model.
GIS Information for Solar PV Energy Siting: A Case Study in the Borough of State College, PA, USA

This document presents a GIS-based model for the assessment of the potential for the installation of photovoltaic systems in urban environments, installed at the rooftop of buildings. The work represents an improvement of the methodology developed by Bayrakci-Boz (2015) as it adds a three-dimensional view of the site of study, and also (and more importantly) it is capable of extracting useful information from low-resolution LiDAR data. The methodology was used to map the solar photovoltaic potential within the Borough of State College, PA, USA.
ATBox results for OAEI 2020

ATBox matcher is a scalable system for instance (Abox) and schema (Tbox) matching.It uses two pipelines for generating candidates for the schema and instance matching,and utilizes the schema matches to further improve the instance correspondences.Using a string blocking method, ATBox is able to align large ontologies and can run on OAEItracks like largebio and knowledge graph. The results look promising, but further features for better finding correct instance matches can be developed.