Reviewerly/exHarmony huggingface dataset:
SPRAG: Building and Benchmarking a Short Programming Related Answer Grading Dataset

Abstract Automated Short Answer Grading (ASAG) is one of the most studied applications of NLP in the education domain. Most of the work in this area focuses on natural language responses. The main contribution of this work is the building and benchmarking of the Short Programming Related Answer Grading dataset. The corpus contains questions and answers taken from python programming subjects involving symbols, keywords, and no grammar. We propose data collection, preprocessing, and annotating methods that enable the creation of the corpus. This paper also provides an initial analysis of the corpus. A range of pre-trained sentence-transformer models are evaluated on the dataset for classification. We find that fine-tuning the pre-trained models results in the best performance for binary classification. The dataset is freely available to the public.
PENGELOLAAN ARSIP DIGITAL PADA SURAT KEPUTUSAN DIREKSI (KD) DI PT POS INDONESIA (PERSERO)

Digital Archive Management of Director Decision Letters at PT Pos Indonesia (Persero). The Director Decision Letters of PT Pos Indonesia (Persero) is the company's vital archive. The purpose of this study is to describe how digital archive management of directors' decrees at PT Pos Indonesia (Persero) uses a qualitative descriptive approach. Methods of collecting data were through interviews, field notes, and documentation. Data analysis was carried out through data presentation and data conclusions. The results found include the process of creating archives being mediated using a scanning tool, the process of using archives being submitted to the archivist then retrieval of archives in the Director's Decree Search application, the maintenance process is carried out at the migration stage from the computer into the application, and the archive shrinkage process is not carried out.
MEDIA PEMBELAJARAN BAHASA BERBASIS SAMPAH ORGANIK SEBAGAI PENGENALAN ECOLITERACY SISWA SEKOLAH DASAR

The purpose of this study was to develop language learning media based on organic waste as an introduction to ecoliteracy for elementary school. This study uses a development research method that refers to the Borg and Gall development model. Data collection methods in this study were questionnaires and observations. Data analysis in this research is descriptive quantitative with categorization technique. Based on the results of the study, it showed that the final product of learning media development based on organic waste at SDN 3 Kabar after going through a series of development procedures, was feasible to be implemented. Based on the results of product trials that have been carried out, there are significant changes between the pretest and posttest on students' understanding of Ecoliteracy. The indicator “merasa dini bagian dari alam” when the pretest percentage of students who are able to understand is 29.6% and when the posttest is 63.0%. The percentage of indicator “mampu mendeskripsikan pengertian dan jenis sampah” from 14.8% become 63.0%. The last indicator is ”mampu mendeskripsikan dampak negative sampah” from a percentage of 0% increasing to 29.6%. This shows that the introduction of ecoliteracy through developing language learning media based on organic waste to students at SDN 3 Kabar was successful.Keyword: Language Learning Media Development, Organic Waste, Ecoliteracy
Influence of Motivation on Workers' Performance at the University of Abuja

This study investigated the relationship between motivation and workers' performance at the University of Abuja. It utilized a descriptive research design. Both primary and secondary data were utilized in the study. Descriptive statistics and multiple regression methodology were employed on a sample of 337 workers’ obtained from 2145 workers through the stratified sampling method. The findings revealed that salary increase, promotions, allowances, regular timely salary and involvement in decision-making had a positive and significant effect on workers' performance at the University of Abuja. However, bonuses and fringe benefits had a negative and significant influence on workers’ performance. Furthermore, workers of the University of Abuja were not motivated by annual increments, recognition and supervision. The study recommends that the management of the University of Abuja should sustain their policies on salary increases, promotions, allowances, regular timely salary and involvement in decision making since they were yielding the desired outcomes. In addition, the policies of the University regarding annual increment, recognition and supervision should be strengthened and made to be effective. Furthermore, the policy on bonuses and fringe benefits should be reviewed as a result of their counterproductive nature.
Untangling the Flows: How One Press's Metadata Gets Out Into The World (And How It Doesn’t)

When the University of Michigan Press was surprised to observe how poorly their humanities publications were represented in their institution's Research Information Management system, they started to look for explanations.This investigation expanded to document, for the first time, all of the paths our metadata takes to reach various content distributors, discovery services, and other links in the information supply chain, whether by automated or manual processes (or a combination of the two).The results revealed a tangle of criss-crossing, overlapping, and sometimes redundant metadata flows.This session will present a number of workflow diagrams to illustrate this status quo, with an emphasis on the prevalence or absence of persistent identifiers throughout the system, and identify a number of challenges that currently prevent our metadata from reaching its audience.
Certificateless Data Integrity Auditing in Cloud Storage with a Designated Verifier and User Privacy Preservation

With the rapid development of science and technology, enterprises will provide their customers with cloud data storage services. These massive amounts of data bring huge management costs to enterprises. Therefore, enterprises choose to store their data in professional cloud service providers and have third-party auditors check the integrity of cloud data to ensure security. Although the appearance of auditors reduces the enormous calculation pressure on enterprises, if the number of auditors is not limited, it will also bring an expensive management burden to enterprises. At the same time, in the process of performing data integrity auditing on behalf of the enterprise, auditors may be interested in some sensitive information of the enterprise’s customers (such as customer’s identity and specific content of customer data). Therefore, this paper proposes a remote data integrity auditing scheme based on designated verifiers. An essential feature of the scheme is that the auditor cannot obtain any customer’s identity information and data in the process of auditing; data integrity, the anonymity of the user’s identity, and data privacy are maintained in the process of auditing. Both theoretical analysis and experimental results show that our scheme is efficient and feasible.
Book Metadata: Basics, Best Practices and What’s Next

Book metadata is a frequent topic of discussion in our communities but it can still be difficult to understand why it's so important and what effects it has on downstream tools and services.This overview will cover many of the fundamentals along with examples of how metadata, content and analyses are all related.Best practices, emerging developments and the roles various stakeholders play in supporting metadata and discoverability will be included for context and discussion. 2.
Bitcoin mimicking portfolio

Bitcoin fluctuates based on sentiment and speculation.Its price has dropped by 50% from its highest in April 2021 to its lowest in July 2021.It then reached a new high in November 2021 and depreciated again by more than 50% in January 2022.The high fluctuation is one of the main reasons of its fairly low institutional investor participation.This study analyzed stock allocation in portfolio that mimics Bitcoin to provide an alternative investment with the same yield as Bitcoin and a lower risk.Previous studies have created mimicking portfolios for bonds, REIT, and foreign asset but there has not been one to study cryptocurrency mimicking portfolio, especially Bitcoin.The portfolios created in this study consists of 19 selected US stocks and 45 stocks from Indonesia LQ45.The weight optimization is achieved with Markowitz Theory.Mixed result was found in portfolio returns and lower standard deviation compared to Bitcoin.
Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value. Keywords: grayscale image, inertia weight, image segmentation, particle swarm optimization.
A Model Combining BDI Logic and Temporal Logics for Decision-Making in Emergency

Abstract Nowadays, we are dealing with panic and unpleasant situations in which, we are constrained to make crucial decisions in a limited delay, due to the mixed emotions that may affect our decision, especially FEAR, this kind of emotion occurs when unwanted or uncontrollable events are present in the environment. These recent years, fear modelling has been well researched and since this emotion is usually associated with the fact that one or more fundamental desires are at stake Unluckily, most of these models miss that FEAR does not always occur similarly in all agents. This paper proposes a new conceptual architecture with a new component by extending BDI logic with the emotion of FEAR, so that the new Emotional-BDI agents may better cope with extremely dynamic unpleasant situations in their surroundings. We also address how we verify the emotional properties by employing a model checker NuSMV. The proposed architecture confirms that NuSMV can be applied to verify the emotional specifications we can program agents that are capable of reasoning over emotions, our experimental results indicate the viability and efficiency of our model. Keywords: Emotional-BDI, Model checking, NuSMV, CUDD, Unpleasant situations.
NEURAL NETWORKS TRAINING BASED ON RANDOM SEARCH

The paper deals with a state-of-art problem, associated with neural networks training. Training algorithm (with special parallelization procedure) implementing the annealing method is proposed. The training efficiency is demonstrated by the example of a neural network architecture focused on parallel data processing. For the color image compression problem, it is shown that the proposed algorithm significantly outperforms gradient&#x0D; methods in terms of efficiency. The results obtained make it possible to improve the neural networks training quality in general, and can be used to solve a wide class of applied problems.
The PsychoEducation Tensor Model of Cognitive Architecture

The PsychoEducation Tensor Model of Cognitive Architecture describes all major cognitive domains or elemenary modules (EM) in a unified symmetry isometric with Einstein's Metric Tensor. This model of cognition is likewise positioned as one of seven primary tensors that, along with the Metric Tensor, describe all observable phenomena. This model of cognition was developed soley by Joseph Zalman Sheppard in his research work at the University of Victoria and Camosun College, British Columbia, Canada.
The Development of Mobile Geography Virtual Laboratory for Rock and Soil Practicum Studies

The concept of concrete lithosphere material has limitations in being verbally stimulated by the teacher, so students have difficulty understanding the material. During the learning process, lithosphere material is rarely carried out in practicum activities due to the lack of media that can involve students directly. This study aims to produce, measure feasibility, and determine the effectiveness of mobile geography virtual laboratory learning media. This research was included in R&amp;D research by adopting a 4-D development model. The results obtained, namely the Mobile Geography Virtual Laboratory media, are feasible and effectively used in rock and soil practicum activities. The results of this research can be an innovative solution for geography learning, especially in adequately supporting the implementation of practicum activities so that students can become more active and independent.
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention

Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by the global self-attention, various methods constrain the range of attention within a local region to improve its efficiency. Consequently, their receptive fields in a single attention layer are not large enough, resulting in insufficient context modeling. To address this issue, we propose a Pale-Shaped self-Attention (PS-Attention), which performs self-attention within a pale-shaped region. Compared to the global self-attention, PS-Attention can reduce the computation and memory costs significantly. Meanwhile, it can capture richer contextual information under the similar computation complexity with previous local self-attention mechanisms. Based on the PS-Attention, we develop a general Vision Transformer backbone with a hierarchical architecture, named Pale Transformer, which achieves 83.4%, 84.3%, and 84.9% Top-1 accuracy with the model size of 22M, 48M, and 85M respectively for 224 ImageNet-1K classification, outperforming the previous Vision Transformer backbones. For downstream tasks, our Pale Transformer backbone performs better than the recent state-of-the-art CSWin Transformer by a large margin on ADE20K semantic segmentation and COCO object detection & instance segmentation. The code will be released on https://github.com/BR-IDL/PaddleViT.
Fostering the Robustness of White-Box Deep Neural Network Watermarks by Neuron Alignment

The wide application of deep learning techniques is boosting the regulation of deep learning models, especially deep neural networks (DNN), as commercial products. A necessary prerequisite for such regulations is identifying the owner of deep neural networks, which is usually done through the watermark. Current DNN watermarking schemes, particularly white-box ones, are uniformly fragile against a family of functionality equivalence attacks, especially the neuron permutation. This operation can effortlessly invalidate the ownership proof and escape copyright regulations. To enhance the robustness of white-box DNN watermarking schemes, this paper presents a procedure that aligns neurons into the same order as when the watermark is embedded, so the watermark can be correctly recognized. This neuron alignment process significantly facilitates the functionality of established deep neural network watermarking schemes.
Strengthening Passwords Against Peeping Attacks

Despite a multitude of vulnerabilities of textual passwords, they are more likely to remain widespread since no scheme has become able to come close to providing all desired benefits. Among those vulnerabilities, peeping attacks are recognized as a real threat but, yet remain much unexplored. Most often, applications and systems use textual passwords for authentication, without considering the threat of peeping attacks. Our study provides the first numerical evidence of strength reduction to represent the impact of the attack. We introduce a novel authentication scheme that is conceptually different but purely text-based, as an endeavour towards strengthening textual passwords against the impact of peeping attacks. An experimental approach was used to collect data, simulating a peeping attack. Researchers intended were to provide an idea to the community, at the level of which the strength of a password can be reduced. This vulnerability is something crucial, yet haven't focused enough. Having such an understanding is desirable, as it can provide an image on the impact that these attacks can have on strength of textual passwords.
Advanced Perspective on Human Detection system with Hybrid Feature Set

Detecting and discriminating humans in video frames for surveillance applications is a demanding task. Identifying and highlighting humans by eliminating shadows from the video frames is vital for prudence motive. In this paper, a three-step procedure is proposed, which includes motion detection by background subtraction in live video frames, morphological gradient-based shadow removal, and human detection by Hybrid Feature Set (HFS), which comprises Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) with adaptive Neuro-Fuzzy inference system. The first step incorporates static background subtraction and dynamic background subtraction. The second step is to remove shadows by using a morphological gradient with the horizontal directional mask. The third step includes near-field, mid-field, and far-field human detection by using an adaptive Neuro-Fuzzy inference system. The results obtained from the various performed experimental analysis demonstrates diverse parametrical measures, which outperforms comparatively when benchmark databases and real-time surveillance video frames were used.
Development of Digital Comic Media for Learning Qira'ah for Fifth Grade Students of Madrasah Ibtidaiyah

This study aimed to develop, test the feasibility, determine the attractiveness, and determine the effectiveness of digital comic media for learning Arabic reading skills for the fifth-grade elementary school students. This was a research and development study using the ADDIE model which consisted of five stages, namely analysis, design, development, implementation, and evaluation. The data collection techniques deployed interviews, observations, documentation, and tests. The instruments were the questionnaires for validation of material experts, media experts, and linguists, and the questionnaire for students' responses to the attractiveness of the products developed. The digital comic media was classified as very feasible to be used in learning Arabic. The foregoing was evidenced by the score of feasibility test results from 2 material experts of 95% or in the very feasible category. The result of the feasibility test by 2 media experts demonstrated a score of 96% or in the very feasible category. The result of the feasibility test by linguists with a score of 76% fell into the feasible category. This digital comic media was interesting to use in learning Arabic. It was evidenced by the results of students' responses with a score of 85.57% or very interesting in the medium group trial and 85.16% or very interesting in the large group trial. This digital comic media had been proven to be able to increase students’ scores. Students’ scores increased by 32.30 with an average score of 55.65 before using the media and 88.00 after using digital comic media. The foregoing showed that the Arabic digital comic media developed by the researchers was an interesting and effective media to be used as learning media.
Tashmim al-Kitab al-Raqmy Muassasan ‘ala Tathbiq Flippdf ‎‎Professional fi Maharah al Istima’ li al-Fashl al-Tahdhiri

This research was conducted to design an e-book based on Flippdf Professional in Arabic listening comprehension for Matriculation Programme. The Matriculation Programme is an Arabic preparation class that is ‎prepared ‎for ‎prospective new students. This program aims to equalize ‎Arabic ‎competencies so that ‎the students can ‎communicate actively with ‎Arabic in academic ‎activities. However, this program ‎still ‎faces ‎difficulties in attaining teaching materials, ‎especially ‎listening comprehension materials which ‎make it ‎difficult for ‎educators to choose themes that are in accordance ‎with student ‎competencies, thus ‎making the learning process ineffective. The ‎development of an ‎e-book based on the ‎Flippdf Professional ‎ was chosen because of its advantages ‎over printed books ‎that can ‎fulfil the needs and learning objectives of listening comprehension. ‎The research design used in this ‎research is Research ‎and Development (R&amp;D) comprised of five ‎stages of the ADDIE ‎model ‎‎(Analyze, Design, Develop, Implementation, and Evaluation). The percentage value of validity from the material ‎aspect was 86%, while the media aspect was 95%, and 91% from ‎the language aspect. Calculation results of trials conducted ‎to determine the effectiveness of the product show that the ttest = -16.42 and ttable = 2,045 ‎with the percentage of error tolerance of α=0,05% it can be concluded that ttable&gt; tcount, and ‎the product developed is effective to increase the student grade.‎ The calculation result of the trials to determine the product's effectiveness for both the Experiment classes was considered effective with sig. (2-tailed) &lt;sig.0.05 = 0.000.
Visual Enhancement Capsule Network for Aspect-based Multimodal Sentiment Analysis

Multimodal sentiment analysis, which aims to recognize the emotions expressed in multimodal data, has attracted extensive attention in both academia and industry. However, most of the current studies on user-generated reviews classify the overall sentiments of reviews and hardly consider the aspects of user expression. In addition, user-generated reviews on social media are usually dominated by short texts expressing opinions, sometimes attached with images to complement or enhance the emotion. Based on this observation, we propose a visual enhancement capsule network (VECapsNet) based on multimodal fusion for the task of aspect-based sentiment analysis. Firstly, an adaptive mask memory capsule network is designed to extract the local clustering information from opinion text. Then, an aspect-guided visual attention mechanism is constructed to obtain the image information related to the aspect phrases. Finally, a multimodal fusion module based on interactive learning is presented for multimodal sentiment classification, which takes the aspect phrases as the query vectors to continuously capture the multimodal features correlated to the affective entities in multi-round iterative learning. Otherwise, due to the limited number of multimodal aspect-based sentiment review datasets at present, we build a large-scale multimodal aspect-based sentiment dataset of Chinese restaurant reviews, called MTCom. The extensive experiments both on the single-modal and multimodal datasets demonstrate that our model can better capture the local aspect-based sentiment features and is more applicable for general multimodal user reviews than existing methods. The experimental results verify the effectiveness of our proposed VECapsNet.