Reviewerly/exHarmony huggingface dataset:
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition

Named Entity Recognition (NER) systems often demonstrate great performance on in-distribution data, but perform poorly on examples drawn from a shifted distribution.One way to evaluate the generalization ability of NER models is to use adversarial examples, on which the specific variations associated with named entities are rarely considered.To this end, we propose leveraging expert-guided heuristics to change the entity tokens and their surrounding contexts thereby altering their entity types as adversarial attacks.Using expert-guided heuristics, we augmented the CoNLL 2003 test set and manually annotated it to construct a high-quality challenging set.We found that state-of-the-art NER systems trained on CoNLL 2003 training data drop performance dramatically on our challenging set.By training on adversarial augmented training examples and using mixup for regularization, we were able to significantly improve the performance on the challenging set as well as improve out-of-domain generalization which we evaluated by using OntoNotes data.We have publicly released our dataset and code at https://github.com/GT-SALT/Guided-Adversarial-Augmentation.
Dual Channel with Involution for Long-Tailed Visual Recognition

With the rapid increase of large-scale problems, the distribution of real-world datasets tends to be long-tailed.Existing solutions typically involve re-balancing strategies (i.e., re-sampling and re-weighting).Although they can significantly promote the classifier learning of deep networks, they will unexpectedly impair the representative ability of the learned deep features to a certain extent.Therefore, this paper proposes a dual-channel learning algorithm with involution neural networks (DC-Invo) to take care of representation learning and classifier learning concurrently.In this work, the most important thing is to combine ResNet and involution to obtain higher classification accuracy because of involution's wider coverage in the spatial dimension.The paper conducted extensive experiments on several benchmark vision tasks including Cifar-LT, Imagenet-LT, and Places-LT, showing that DC-Invo is able to achieve significant performance gained on long-tailed datasets.
Transfer learning for no-reference image quality metrics using large temporary image sets

One of the main problems of neural network-based noreference metrics design for image visual quality assessment is small size of image databases with mean opinion scores (MOS).For large networks which can memorize key features of several thousands of images, usage of the databases for metrics training may lead to overlearning.Since data augmentation for image quality assessment is limited by a horizontal image flipping only, the main way to decrease overlearning is to use transfer learning which can significantly speed up training process.In theis paper, we propose a new technique of transfer learning between networks of different architectures using a large set of images without MOS.We implemented the technique for transfer learning between pretrained KonCept512 metric and a IMQNet metric proposed in this paper.An effectiveness of the transfer learning is estimated in a numerical analysis.It is shown that the trained IMQNet metric provides significantly better correlation with KonCept512 metric (0.89) than other modern metrics.It is also shown that IMQNet pretrained by the proposed transfer learning shows better correlation with MOS of KonIQ-10k database (0.86) than IMQNet pre-trained using directly the MOS of KonIQ10k (0.73).
The Effectiveness of Applying Different Strategies on Recognition and Recall Textual Password

Using English words as passwords have been a popular topic in the last few years. The following article discusses a study to compare self-selection of the system-generated words for recognition and selfgenerated words for recall for nouns and mixture words. The results revealed no significant difference between recognition and recall of password nouns. The average memorability rate of noun recognition was 75.72%, slightly higher than noun recall 74.23% in long-term memory. Also, there was no significant difference between recognition and recall mixture word passwords. The average memorability rate of mixture word recognition was 95.23%, and recall was 84.14% in long-term memory. The authors concluded that the recognition and recall of mixed word passwords had a higher memorability rate than nouns.
MELDIGANSI (Media Pembelajaran Digital Akuntansi) Berbasis Android Bagi Siswa SMA

An accounting lesson is a lesson that requires students to be able to understand the concept of theory and calculation simultaneously. In order for learning to be interesting, varied, and interactive, learning media is needed for students so that they will be interested in participating in the learning process. Android-based digital learning media can help students understand learning both online and offline. The types of research use Research and Development (R&amp;D). The test of the validity of the Android-based learning media is carried out by competent validators consisting of material expert validators, linguists, and media experts. The development of digital accounting learning media was developed using a 4-D model, define, designed, develop and disseminate. However, this research is limited to the dissemination stage. The results of this study indicate that the development of accounting digital learning media is declared "very feasible" after going through the validity test stage by material experts with a score of 92.8%, validity testing by linguists obtained a score of 87.5%, and validity testing by media experts with a score of 95.6% so this product is very feasible to be tested.
Sistem Informasi Penjualan Badan Usaha Milik Desa Tulap Berbasis Web Menggunakan Framework Laravel

Tulap Village is one of the villages in Minahasa that has a Village-Owned Enterprise (BUMDES). In marketing activities, the products sold are still only offered to families, local communities, and tourist attractions in Tulap Village. Marketing outside the village must be carried out in cities such as Manado and Bumdes is looking for a place to sell these products. The rental service for goods is still limited to the surrounding community because information about the business is not yet available. The system development carried out in this study uses the Waterfall model (Pressman, 2010). System modeling is documented using Data Flow Diagrams (DFD). The result of this research is a sales information system that can help customers see the products being sold or the goods rented by BUMDes Tulap. Testing this system using the blackbox and whitebox testing method.
The Effectiveness of the Implementaton of Blended Learning in Economics

This research aims to compare the effectiveness of blended learning and online learning in influencing students’ learning outcomes, as well as the effectiveness of blended learning application based on the students’ learning outcomes, learning activities, and student responses in central bank, payment systems, and payment instruments chapter. This is a quasi-experimental study with a non-equivalent control group design. The sample used in this research consisted of 68 students from class X IPS 1 and X IPS 3 of SMAN 1 Porong 2020-2021. The data used in this study were obtained using tests, questionnaires, and observations which were analyzed using non-parametric statistical analysis, N-gain calculation, and descriptive statistical analysis. The results showed that (1) learning models affect student learning outcomes; (2) there is a significant difference in the average students’ learning outcomes between blended learning and online learning, (3) blended learning is more effective to be implemented than online learning, based on the students’ learning outcome, and (4) blended learning is effective to be implemented based on learning outcomes, activities, and student responses.
Assessing the state of forensic support to criminal investigations in Ghana: A case study in the Greater Accra Region

The study assessed the state of forensic support to criminal investigations by measuring public awareness, knowledge and perception of forensic science in Ghana. A simple random technique was employed to elicit information from 200 consented respondents in selected neighbourhoods in Accra through face to face interviews, questionnaire responses, and random phone calls. The study identified forensic science education as a key challenge among others in the forensic sector. The state of forensics support to criminal investigations in Ghana was rated on the scale of good (10.5%), bad (81.5%) and very bad (8%) indicating that forensic science in Ghana has “gone dark”. The study’s findings call for respective stakeholders and policymakers of Ghana to prioritize forensic support to criminal investigations to promote safety, security, and sustainable economic development.
Software implementation of intuitionistic fuzzy sets and some operators

In this paper, we present a software implementation of the framework of Intuitionistic Fuzzy Sets (IFSs). The presented implementation allows the user to interactively shape an IFS, to compute, plot and visualize various of operators for IFS and allows for the modeling of real world problems.
Development of the Love for Writing and Publishing Journal (LWPJ) Module for Higher Education

This paper details the development of the Love for Writing and Publishing Journal (LWPJ) Module, which encourages writing in a scientific and simple way through the approach of the Self Determination Theory, Social Constructivism Theory, and Successful Intelligence Theory. The content of the module was constructed through information obtained from a literature review and interviews with six experts who have produced many high-impact journal articles. The developed module was used in a two-day online training course, with participant monitoring before, during, and after the course through WhatsApp. This study used a mixed methods approach of design and development research. Using this method, the research was carried out in three phases. The first phase involved needs analysis; the second phase included design and development; and the third phase involved evaluation of the module. The research led to the successful development of the LWPJ Module by fostering a love for writing and publication of scholarly writing among course participants. The module benefits postgraduate students and academics at the higher education level by ensuring that scholarly articles are successfully completed and submitted for publication. This study has implications on the development of a culture of excellence and a culture of love for scholarly writing and publication among postgraduate students and academics from various walks of life at school and university levels.
New artificial neural network design for Chua chaotic system prediction using FPGA hardware co-simulation

&lt;p&gt;This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.&lt;/p&gt;
Methodology for using the knowledge base of generating two-variable functions

Mathematical knowledge bases are the development of classical mathematical reference books and encyclopedias, which, in turn, makes them an important tool for conducting various research in mathematical sciences and related fields. At present, there are various knowledge bases of mathematical objects. In this paper, we consider the knowledge base of the generating functions of two variables, which allows us to operate with multivariate objects. The relevance and significance of the work lies in solving various problems related to the mathematical apparatus of generating functions. In the paper we consider the use of the knowledge base of generating functions of two variables for solving problems of operating generating functions and obtaining coefficients for composition, reciprocal and compositional inverse generating functions of two variables and their powers, as well as obtaining explicit expressions for the coefficients of logarithmic derivatives of the generating functions. In addition, an inverse problem is considered aimed at obtaining generating functions for explicit expressions describing their coefficients. The use of the knowledge base of generating two-variable functions contributes to the process of constructing combinatorial generation algorithms for combinatorial objects defined by generating functions of many variables. As an example, the construction of combinatorial generation algorithms for sets defined by the generalized Narayana numbers is shown. The Narayana numbers describe classes of subsets for combinatorial sets defined by the Catalan numbers. In this paper, one of the combinatorial interpretations for the Narayana numbers is chosen - the set of the Dyck paths of length n, which have m peaks.
Grocery Product Recognition to Aid Visually Impaired People

This paper proposes a new approach in object recognition to assist visually impaired people. This approach achieved accuracy rates higher than the approaches proposed by the authors of the selected datasets. We applied Data Augmentation with other techniques and adjustments to different Pre-trained CNNs (Convolutional Neural Networks). The ResNet-50 based approach achieved the best results in the most recent datasets. This work focused on products that are usually found on grocery store shelves, supermarkets, refrigerators or pantries.
Psychological impact of the COVID-19 pandemic on healthcare professionals in the Al Ahsa Region, Saudi Arabia

Background.Recently, researchers have shown increased interest in the psychological effect of the pandemic on healthcare workers, as well as on the general population.Objectives.We aimed to investigate the incidence of adverse psychological effects, such as anxiety, depression and post-traumatic stress disorder, experienced by medical staff and healthcare professionals during the COVID-19 pandemic.Material and methods.This is a cross-sectional survey, in which participants were invited to take part in the study by completing a questionnaire.The survey gathered information on demographic data, symptoms of the Depression, Anxiety, Stress Scales (DASS-21) and the Impact of Events Scale-Revised (IES-R) tool.Results.The total and subscale of DAS-21 showed that 54.8% of all participants showed signs of depression, (60.1%) anxiety and (59.4%) stress.The faculty member of medicine was substantially correlated with the DASS-21 subscale depression and anxiety.In addition, the stress subscale of the DASS-21 was substantially higher with a medical intern.Similarly, the findings obtained from the preliminary study of participants who encountered or were exposed to COVID-19 patients had significantly higher stress subscale ratings in the Depression, Anxiety, Stress Scales.In addition, the married participants were correlated with higher scores in the (IES-R) tool.Conclusions.More than half of the participants in this study encountered mild to extreme psychological effects during the pandemic.This investigation's findings indicate that working in the medical profession (medical staff members and medical interns) is associated with depression, anxiety and stress.
Semi-supervised Graph Learning with Few Labeled Nodes

Graph-based semi-supervised learning, utilizing both a few labeled nodes and massive unlabeled nodes, has aroused extensive attention in the research community. However, for the graph with few labeled nodes, the performance of Graph Convolutional Networks (GCNs) will suffer from a catastrophic decline due to its intrinsic shallow architecture limitation and insufficient supervision signals. To accommodate this issue, we propose a novel Self-Training model (ST-LPGCN) which reinforces the pseudo label generation on the GCNs with Label Propagation algorithm (LPA). By making full use of the advantages of GCNs in aggregating the local node features and LPA in propagating the global label information, our ST-LPGCN improves the generalization performance of GCNs with few labeled nodes. Specifically, we design a pseudo label generator to pick out the nodes assigned with the same pseudo labels by GCN and LPA, and add them to the labeled data for the next self-training process. To reduce the error propagation of labels, we optimize the transition probability between nodes in LPA under the supervision of the pseudo labels. The extensive experimental results on four real-world datasets validate the superiority of ST-LPGCN for the node classification task with few labeled nodes.
Blind Extraction of Equitable Partitions from Graph Signals

Finding equitable partitions is closely related to the extraction of graph symmetries and of interest in a variety of applications context such as node role detection, cluster synchronization, consensus dynamics, and network control problems. In this work we study a blind identification problem in which we aim to recover an equitable partition of a network without the knowledge of the network’s edges but based solely on the observations of the outputs of an unknown graph filter. Specifically, we consider two settings. First, we consider a scenario in which we can control the input to the graph filter and present a method to extract the partition inspired by the well known Weisfeiler-Lehman (color refinement) algorithm. Second, we generalize this idea to a setting where only observe the outputs to random, low-rank excitations of the graph filter, and present a simple spectral algorithm to extract the relevant equitable partitions. Finally, we establish theoretical bounds on the error that this spectral detection scheme incurs and perform numerical experiments that illustrate our theoretical results and compare both algorithms.
A Biomimetic Approach as a Sustainably Architectural Design in Designing Resort Hotels: A Study Case in the Tourism Beach of Tanjung Karang, Regency of Donggala, Central Sulawesi, Indonesia

The Province of Central Sulawesi has an interestingly attractive potency of tourism beaches for tourists, such as white-sand beach, sunset spots, tropical beach forest, and the beauty of an underwater landscape, mainly coral reefs located in Tanjung Karang, the Regency of Donggala.The typographic preservation of this tourism destination is mostly still well-maintained, but some abandoned areas are also found due to tourists' or inhabitants' exploitation.It can be seen from buildings' layout exceeding the beach border, garbage stacking in the coastal area up to the sea, careless clear-water consumption, and unmanaged and uncontrolled waste management system.Based on these circumstances, a strategy to maintain existing natural preservation is highly required by the utilization of environmentally friendly energy.The sustainable strategy integrates hotel resort areas with its surroundings, viewed from biomimetic architecture, and contains climate, biology, architecture, and technology, collectively.The research, then, discusses how to respond to the natural condition of Tanjung Karang beach, by considering its surroundings in terms of adaptation to nature and evolution [1], and also utilizes regional climate-supported energy, optimally, such as the solar energy and heat, wind, and surroundings-sourced water.Further, the adaptation of a coastal-traditional architecture was also performed by duplicating (mimetic), a transformation was relevantly conducted.In this research, a biomimetic approach was not comprehensively done, but it was limited to a simple simulation process, consisting of site management, site analysis via environment interaction, the concept of the hotel resorts design considering traditional principles such as building style, the mass form of buildings, and a schematic and simulated management of partly renewable energy.
Analysis of Teaching Effect of English Classroom Mind Map Based on a Logistic Regression Model

In order to improve the effect of English classroom teaching, this paper combines the pulse neural network to identify the teaching process of English classroom and conducts real-time supervision of classroom dynamics in combination with the actual situation. Moreover, this paper constructs an intelligent system, uses the recognition method based on the spiking neural network image to collect the real-time dynamics of classroom teaching, and combines the logistic regression model to evaluate the teaching effect. After constructing the system framework, the system process analysis is carried out. This paper combines experimental research to verify the model of this paper. The experimental research results show that the analysis model of teaching effect of mind map in English classroom based on the logistic regression model proposed in this paper has good results, which also verifies that the mind map teaching can play an important role in English classroom teaching.
Research on Dynamic Assessment System of Composite Fault Risk of Transmission Line Based on Blockchain Energy

In order to study the dynamic assessment system of composite fault risk of transmission line based on blockchain energy and in order to study the transmission line compound fault risk dynamic assessment system based on blockchain, firstly, according to the coupling relationship between power grid and natural disasters, the information resources such as data collected by power grid intelligent devices and natural meteorology are excavated, and the overall architecture of power grid disaster early warning and decision-making system supported by blockchain is built. Then, from the perspective of risk, combined with analytic hierarchy process, an index system for reasonable evaluation of distribution network fault benchmark risk is established. Quantitative assessment and risk classification shall be carried out for the failure probability, failure impact consequence, and comprehensive failure risk, so as to facilitate the adoption of risk response measures. Finally, taking several 220 kV lines in the northwest and central part of a city as examples, the icing prediction analysis verifies the feasibility and effectiveness of the proposed power grid disaster early warning decision system based on blockchain to predict the icing thickness. The experimental results show that taking the icing disaster as an example, the MPC method is used to modify the icing thickness prediction model, improve the accuracy of the icing prediction model, and verify the feasibility and effectiveness of the prediction and early warning system based on blockchain.
Solving Static Permutation Mastermind using $O(n \log n)$ Queries

Permutation Mastermind is a version of the classical mastermind game in which the number of positions $n$ is equal to the number of colors $k$, and repetition of colors is not allowed, neither in the codeword nor in the queries. In this paper we solve the main open question from Glazik, Jäger, Schiemann and Srivastav (2021), who asked whether their bound of $O(n^{1.525})$ for the static version can be improved to $O(n \log n)$, which would be best possible. By using a simple probabilistic argument we show that this is indeed the case.