Reviewerly/exHarmony huggingface dataset:
Self-efficacy in online learning during the Covid-19 pandemic: Modification and implementation instrument for biology education department students

This study aims to modify the online learning self-efficacy scale (OLSES) developed by Zimmerman and Kulikowich (2016) and apply it to see the self-efficacy profile of students of the Department of Biology Education, Yogyakarta State University in online learning during the Covid-19 pandemic. This research approach is quantitative with a survey method conducted by giving OLSES questionnaires to 84 students from the Department of Biology Education. The OLSES instrument consists of 19 valid questions (Pearson Product Moment value of all items > .215; p < .05) and reliable (Cronbach's Alpha value .902) to be used as a self-efficacy scale in online learning. The results showed that 1 item had a median value of 3, item about the use of online libraries, but the other median items were 4 and 5, that is indicating a high self-efficacy profile of students majoring in biology education. Inferential analysis shows that students' self-efficacy online learning during the Covid-19 pandemic is not influenced by gender (p-value is .989), grade level (p-value is .756), and student study programs (p-value is .725).This study aims to modify the online learning self-efficacy scale (OLSES) developed by Zimmerman and Kulikowich (2016) and apply it to see the self-efficacy profile of students of the Department of Biology Education, Yogyakarta State University in online learning during the Covid-19 pandemic. This research approach was quantitative with a survey method conducted by giving OLSES questionnaires to 84 students from the Department of Biology Education. The OLSES instrument consists of 19 questions that were valid (Pearson Product Moment value of all items > .215; p <. 05) and reliable (Cronbach's Alpha value .902) to be used as a self-efficacy scale in online learning. The results showed that 1 item had a median value of 3, this item was about the use of online libraries, but the other median items had a value of 4 and 5, that was indicating a high profile of self-efficacy in online learning. Inferential analysis showed that students' self-efficacy online learning during the Covid-19 pandemic is not influenced by gender (p-value is .989), grade level (p-value is .756), and student study programs (p-value is .725).
Cryptanalysis of Tiny Encryption Algorithm Based on SMT Solvers Using HPC

Tiny Encryption Algorithm (TEA) is a block cipher algorithm that uses symmetric key of 128-bits. It performs 32 rounds for encryption/decryption. TEA uses addition modulo $$2^{32}$$ , XoR, and shift operations in its algorithm. We convert TEA algorithm into nonlinear system of equations and solve the system using Satisfiability Modulo Theory solvers (SMT) on a desktop computer and also on high performance computing (HPC) facility. As solving system of equations is NP-complete problem, we tried solving the system for various number of rounds out of 32 rounds. The solver $$Z_3 (py)$$ , a Satisfiability Modulo Theories (SMT) solver, has been chosen to perform algebraic cryptanalysis. We could solve the system up to 5th round and found the actual secret key successfully among few solutions of high probable keys which we got from solver with in 15,576.34 min using HPC. Nonlinearity will increase as number of round increases, so solving high nonlinear system is very difficult. Our aim is to solve a specific kind of nonlinear system of equations. We tried to recover the partial keys for rounds greater than 5. Results of the key recovery are present in this paper.
Development of Android multiple representative media: Enhancing cognitive achievement and motivation of student in chemistry

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Witdya Ayu Setianingsih, Kristian Handoyo Sugiyarto; Development of Android multiple representative media: Enhancing cognitive achievement and motivation of student in chemistry. AIP Conf. Proc. 17 March 2023; 2556 (1): 040020. https://doi.org/10.1063/5.0110561 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
A Survey on Automatic Text Summarisation

Due to the massive volume of textual data which is produced by the Internet and in other archives of like news stories, academic papers, legal documents, etc., automatic text summarisation (ATS) is becoming increasingly crucial. The process of text summarisation is nothing but retrieving the significant data from the input document and then summarising it. While increasing the data’s magnitude, it is critical to summarise the data manually and time consuming, so numerous applications require ATS. Hybrid, extractive, and abstractive are the various method in ATS. In extractive method, the summary is formed by choosing the significant sentences from the source document. The source document is represented using the abstractive technique in an intermediate representation, which then provides summary using sentences different from the source phrases. The hybrid technique combines abstractive and extrapolative methods. Despite all of the suggested strategies, the produced summaries still differ significantly from the summaries made manually. Most research concentrates on the extractive strategy. Techniques that are abstract and hybrid must be given more attention. This paper provides a full evaluation for researchers by covering the several ATS components—approaches, techniques, building blocks, processes, and future research goals.
Cognitive features for sentence stress pattern description

The paper proposes cognitive features for describing sentence stress patterns produced by emphasized and non-emphasized intonational contours of utterances. Cognitive features consist in hierarchies of binary cognitive structures described in terms of the functional categories of the cognitive model proposed in the paper. The cognitive perspective of the model transforms weak-strong “metrical” structures used by Ladd (2008), into binary structures with one nuclear element and two structural levels: CU_argument-CU_predicate CU_emotional-CU_rational element. A set of rules are presented, for nucleus identification in both emphasized and non-emphasized contours. Intonational contours analyzed in the paper correspond to some sentences also discussed in Ladd (2008). Cognitive analyses aim to assign different cognitive descriptions to different utterances even in the case of the same sentence when they produce different sentence stress patterns. We conclude that the prosody conveys non-linguistic meaning which must be understood at the cognitive or pre-linguistic level.
3D-virtual lab to resolve laboratory work’s problem during COVID-19 pandemic

Laboratory work is tremendously notable in chemistry learning process in order to construct the theory that has been obtained in the regular class. However during the Covid-19 pandemic laboratory work has never been conducted since the pandemic started out as an outbreak in Wuhan because the government banned any offline learning process at school. Therefore in this case this study set out to develop a 3D-virtual lab to overcome this obstacle. The 3D-virtual lab is urgently required since it could be employed as a supplement or a substitute as long as the laboratory work in a conventional lab is still restricted. In this study, the research and development cycle model consists of five stages namely the ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The material developed was acid-base titration. The product was run properly with the help of oculus and controller devices. The product was validated by three experts based on the content, the learning instruction, and the technique aspects. The validation results were in the form of score and suggestions. The validation score of the product given by the experts was 0,83 and then the data suggestion was applied to develop the product itself. The next step was product legibility assessment that was conducted by eight educators and twenty four students. The three aspects (content, learning, and technique) were assessed by educators while only one aspect (technique) was assessed by students. Evidently it was found that the product legibility score given by educators was 94%. Whereas the legibility of the product given by students was 98%. The data analysis was proven valid in terms of the criteria and feasible to use after conducting evaluation. Therefore, evidently the 3D-virtual lab becomes one of learning media that enables the students to conduct online laboratory work during the pandemic. In addition, the 3D-virtual lab minimizes any risks during the conventional laboratory work or guarantees safety, reduces the operational cost, and makes laboratory work more accessible.
A High-Performance Dual-Context MQ Encoder Architecture Based on Extended Lookup Table

Multiple quantization (MQ) encoder is a basic entropy encoder widely used in multimedia encoding systems, especially in JPEG2000 and JBIG2, which play a key role and make outstanding contributions to digital imaging. However, due to its strict serial working characteristics, the MQ encoder has become a key bottleneck restricting the optimization of JPEG2000 encoding performance. Many single ejection encoder architectures use pipeline staging to improve the throughput, but they have all reached their limits. A high-performance MQ encoder based on a novel lookup table is proposed to overcome this bottleneck, which improves the throughput while utilizing reasonable hardware resources and with low-power consumption. Experimental results show that the proposed encoder architecture can encode two symbols per cycle with a throughput of 1204.82 MSymbol/s, and it has a power consumption of only 11.05 mW. Compared with the conventional multi-context coder, the hardware utilization is reduced by 26%, and the hardware utilization efficiency is improved by 16%. Compared with the conventional single-context coder, the throughput is improved by 14.3%, and the power consumption is reduced by 40.1%.
Marketing Communication by Businesses in the Cerebral Palsy Community

The cerebral palsy family community is a group of parents who have children with brain paralysis.In this community they have a variety of activities aimed at therapy so that it can stimulate their child's motor, sensory and cognitive abilities.Members of this community are very active in participating in every activity because not all parents are economically able to treat their children through medical services at the hospital.Most parents in this community also make efforts to increase their child's medical expenses.Even though they are small businesses, they are trying to do marketing to reach consumers.It is not easy to carry out marketing activities for the products or services they produce because they have to coincide with caring for children with cerebral palsy.Children with cerebral palsy need the attention and help of their parents in their daily activities.Therefore, this study aims to see how parents of children with cerebral palsy communicate the products and services they market to consumers.Then what is the role of the community in their marketing communication activities.Generally, marketing communication is always related to the 7P theory (product, place, promotion, price, process, people, physical environment) but in this case, of course it is unique because there are limitations and a community base that they use as a means to market their products and services.This research method is a case study with qualitative approach, and I collect data with interview and field observations, researchers interviewed five informants.The conclusion in this study is that the community becomes a means to market their products, community members work together to promote the efforts of their members because they feel the same fate.Meanwhile, the name of the community, namely "Cerebral Palsy Family" became a marketing communication brand to sell sympathetic stories that made consumers touched to make product purchases.
Spatiotemporal Learning via Mixture Importance Gaussian Filtering With Sparse Regularization

This letter proposes a novel spatiotemporal learning model via mixture importance Gaussian filtering (MIGF). In the MIGF, we explore the causal mapping between the target's true parameters and the latest measurement, and present an approach for elegantly combining the deterministic Gaussian-Hermite integral with the stochastic importance sampling method. This formulation allows for the use of soft-constrained and sparse regularization to reduce the truncation error and improve the weight adaptivity. We also provide an effective causal invariant updating rule to learn the parameters of this constrained dynamic model with a convergence guarantee. Experimental results show that MIGF can effectively eliminate the constraint uncertainty for the constrained dynamics.
Robust Object Detection Against Multi-Type Corruption Without Catastrophic Forgetting During Adversarial Training Under Harsh Autonomous-Driving Environments

It is important to build robust object detector (ROD) in real-world applications because snow, rain, fog, motion blur, and various kinds of corruption can occur in autonomous-driving environments. Adversarial training (AT) is one of the best solutions to build a robust deep neural network. However, applying AT has a risk of sacrificing clean performance, even though robustness is improved, which is called catastrophic forgetting (CF). In particular, CF in an autonomous-driving environment is more challenging for two reasons. The first is CF is worsened due to various types of corruption. The second is the degradation of clean performance can lead to a risk of overall performance degradation because more than 60% of the total data is clean (based on Bdd100k). Therefore, we propose an ROD framework to ensure not only robustness against corruption but also prevent degradation of clean performance, despite the two aforementioned difficulties. The ROD framework utilizes a training methodology with an adversarial defense module (ADM) based on the intermediate representative feature (IRF) concept. This framework can improve robustness without CF under multi-corruption environments. In this paper, we report on three main achievements. The first is that the mean performance under corruption (mPC) of RetinaNet was improved by 32.14% with an mAP degradation of only 0.2%, based on COCO 2017. The second is that our method achieved state-of-the art results with 86.8% relative performance under corruption (rPC) compared to Hybrid Task Cascades with 64.6% rPC on the ROD benchmark. The third is that our ROD methodology achieved 32.29% and 31.54% mPC at 15-type seen corruption and four-type unseen corruption, respectively. The ROD framework is also applied to an autonomous-driving domain showing that it operates well, even under harsh environments in the Bdd100k dataset.
Adaptive Anchor Matching Strategy for Face Detection

Face detection is a fundamental task for numerous face-related applications (e.g., face recognition and age estimation), which directly affects the performance of the subsequent processing. Recent anchor-based face detectors have demonstrated the great potential by matching anchors and target boxes during training, which is crucial for high performance and training efficiency. However, existing anchor matching strategies still suffer from: 1) ignoring the inherent relationship between the targets and the anchors, which may cause unsuitable matched pairs, 2) adopting a fixed matching threshold, which cannot meet the varying demands of matched pairs for quality and quantity in different feature levels and training processes, and 3) the heuristic anchor setting, whose matching range is too narrow to capture about 20% target faces in the training dataset. This paper proposes an Adaptive Anchor Matching Strategy (AAMS) to address these issues, which selectively assigns proper targets to anchors in different feature levels by using adaptive matching thresholds and a robust anchor setting determined by the statistical characteristic of the training samples. Extensive experiments on popular benchmarks reveal that the proposed approach has significant improvements on anchor-based models and outperforms the recent state-of-the-arts methods in terms of both accuracy and generalization.
CST-RL: Contrastive Spatio-Temporal Representations for Reinforcement Learning

Learning representations from high-dimensional observations is critical for training of pixel-based continuous control tasks with reinforcement learning (RL). Without proper representations, the training will be very inefficient, requiring long training time and huge training data to learn directly from low-level pixel observations. Yet, a lot of information in such observations may be redundant or irrelevant. A common approach to solving this problem is to train auxiliary objectives alongside the main RL objective. The additional objectives provide more signals to the model and reduce the training time, resulting in better sample efficiency. A representative work is Contrastive Unsupervised Representations for Reinforcement Learning (CURL), which leverages contrastive learning to assist RL to learn useful representations. Although CURL performs very well in extracting spatial information from pixel inputs, it is found to overlook potential temporal signals. In this paper, a contrastive spatio-temporal representation learning framework for RL, called CST-RL, is introduced, which leverages 3D Convolutional Neural Network (3D CNN) alongside contrastive learning for sample-efficient RL. It pays attention to both spatial and temporal signals in pixel observations. Experiments based on DMControl show that CST-RL outperforms CURL in all six environments after 500K environment steps and only needs half of the steps to achieve the standard score in the majority of cases.
Digital Filter Architecture Based on Modified Winograd Method F(2× 2, 5× 5) and Residue Number System

Improving the characteristics of digital signal processing devices is an important task in many practical problems. The paper proposes the architecture of a two-dimensional digital filter with a 5×5 mask, in which calculations are performed according to the Winograd method in the Residue Number System (RNS) with moduli of a special type. Theoretical analysis and hardware Field-Programmable Gate Array simulation are presented. The results show that the fragment throughput fr/s (number of fragments per second) of the device is 29.6% – 724.7% higher than state-of-the-art solutions. This is achieved by the combination of the Winograd method, which reduces the number of multiplications, with the RNS arithmetic, which performs addition and multiplication under smaller operands in parallel. However, our experiments showed that the proposed method requires up to 2.54% – 11.01% more Look-Up Tables and 3.58% – 19.83% higher power consumption compared to known analogues.
Dataset: Digital Forensic Literature with a Focus on Software: Trends, Institutions, and Authors (2012-2022)

This data article presents a thorough examination of digital forensic literature with a focus on software from 2012 to 2022, retrieved from lens.org, with the aim of identifying the most influential institutions, authors, and research trends. Using keywords "digital forensic AND software," the data collection process involved filtering by date range, document type, and subject matter, resulting in 427 journal articles. The analysis highlights the most productive institutions, years, fields of study, active authors, and publishers. This data is valuable for researchers and practitioners in digital forensics, particularly those interested in software applications, providing insights into the most significant contributions and aiding future research development.&#x0D; Highlights:&#x0D; &#x0D; Research in the field of digital forensics is growing rapidly, with the Université de Lausanne being the leading institution in the number of publications.&#x0D; Computer science is the most popular field of study in digital forensics, with a focus on analysing software and file systems.&#x0D; Elsevier B.V. is the leading publisher in digital forensics, with the journal Digital Investigation as the main source of publications.&#x0D; &#x0D;
Dataset: Cyber Law and Piracy: A Comprehensive Analysis of Scholarly Works from 2012-2022

This data article aims to compile and analyze scholarly works on cyber law and piracy within the legal domain using Lens.org's search and filtering tools. The methodology involves an initial keyword search followed by three filtering stages, narrowing the scope to journal articles published between 2012 and 2022. The analysis highlights the most active authors, institutions, publishers, and countries/regions. Results indicate that University of Louisville, George E Higgins, Springer Nature, and the United States are leading contributors in this field. The findings offer valuable insights for researchers seeking relevant literature on cyber law and piracy in the context of law.&#x0D; Highlights:&#x0D; &#x0D; The study provides a comprehensive overview of scholarly works related to cyber law and piracy in the legal domain, enabling efficient access to relevant literature.&#x0D; The analysis highlights key contributors in the field, including authors, institutions, publishers, and countries/regions, identifying leaders in the domain.&#x0D; The study demonstrates the effectiveness of Lens.org's search and filtering tools in identifying and analyzing relevant scholarly works within specific research areas.&#x0D; &#x0D; &#x0D;
Artificial Intelligence-Based Breast and Cervical Cancer Diagnosis and Management System

Breast cancer and cervical cancer are two of the most common and deadly malignancies in women. Early diagnosis and treatment can save lives and improve quality of life. However, there is a shortage of pathologists and physicians in most developing countries, including Ethiopia, preventing many breast and cervical cancer patients from early cancer screening. Many women, particularly in low resource settings, have limited access to early diagnosis of breast and cervical cancer and receive poor treatment which in turn increases the morbidity and mortality due to these cancers. In this paper, an integrated intelligent decision support system is proposed for the diagnosis and management of breast and cervical cancer using multimodal im-age data. The system includes breast cancer type, sub-type and grade classification, cervix type (transformation zone) detection and classification, pap smear image classification, and histopathology-based cervical cancer type classification. In addition, patient registration, data retrieval, and storage as well as cancer statistical analysis mechanisms are integrated into the proposed system. A ResNet152 deep learning model was used for classification tasks and satisfactory results were achieved when testing the model. The developed system was deployed to an offline web page which has added the advantage of storing the digital medical images and the labeled results for future use by the physicians or other researchers.