Reviewerly/exHarmony huggingface dataset:
Second Language Development (SLD): What Factors Might Affect Students’ English Learning During Covid19 Pandemic

Abstract. This study aimed to investigate factors affecting the students’ L2 language development. This issue was raised because second language acquisition is one of the most impressive and fascinating aspects of human communication skills. This study was also intended to help students effectively learn and understand the language. This was a descriptive quantitative study. The samples were 158 preservice students in the English Education Department, Universitas Borneo Tarakan. This data was collected using an online questionnaire through google form to indicate the students’ responses on how English as a second language is acquired by the learning experience and was analyzed using factorial analysis (principle componential analysis). This research found that second language development is affected by linguistic factors like vocabulary enrichment, the structure of the native language, and the ability to pronounce words. Besides, non-linguistic factors like cognitive styles, psychological aspects, personality traits, technology use, and age also influenced students’ learning processes and outcomes. It can be shown from personal characteristics and experiences of the learner, social and cultural environment both inside (virtual/ offline meeting) and outside (online assignment/ project/ task-based) of the classroom, the opportunities for communication, access to both oral and written corrective feedback and instruction let by teachers. Those factors were not fully controlled by the teachers but understanding those aspects will make teachers able to consider how students learn English in effective ways to mitigate their learning loss in rapid knowledge growth and technological advancement era. This study reflected teachers’ development on their professionalism and their contribution to the second language acquisition theories. Keywords: Language acquisition and learning, Teacher Professional Development, Linguistics, and Non-linguistics Factors, covid19 pandemic
ANALISIS CLUSTERING ZONASI RAWAN BENCANA PADA KANTOR BASARNAS PROV. JAMBI MENGGUNAKAN METODE K-MEANS

Indonesia is one of the countries that passes through the Pacific Ring of Fire or commonly called the Pacific Ring of Fire. The area is an area that often occurs natural disasters such as floods, droughts and hurricanes. The National SAR Agency (Basarnas), which is an Indonesian Non-Ministerial Government Institution that carries out tasks in the field of search and rescue (Search And Rescue / SAR). Natural disasters can occur suddenly or through a process that takes place slowly. In some types of disasters such as hurricanes, it is almost impossible to accurately predict when, where, and where they will occur. The shock occurred due to a lack of vigilance and preparedness in dealing with the threat of danger, resulting in many fatalities, injuries and material losses. For this reason, efforts are urgently needed to bring about the impact of disasters. In this study, the method used in grouping the data is K-Means clustering. The result of this research is the grouping of disaster-prone and non-disaster-prone areas which are represented in the Geographical Information System (GIS). Based on the grouping using K-Means Prov. Jambi is divided into 2 disaster groups with the characteristics of disaster-prone areas and non-disaster-prone areas. The 2 groups have validated their accuracy. Cluster 1 includes the city of Jambi, which has high rainfall and causes flooding, while cluster 2 includes Kerinci, Sarolagun, Batanghari, Muaro Jambi, East Tanjung Jabung, West Tanjung Jabung, Tebo, Bungo, Sungai Penuh City.
FAKTOR-FAKTOR YANG MEMPENGARUHI RENDAHNYA KEMAMPUAN MEMBACA PEMAHAMAN PADA SISWA KELAS V SD NEGERI SAKTEO KECAMATAN MOLLO TENGAH KABUPATEN TTS

The purpose of this study was to analyze the factors causing the low ability of students to read at SD Negeri Sakteo.This type of research is qualitative research.It is a research procedure that produces data and is descriptive in the form of written or spoken words and observable behavior. The research subjects were 5 students of class V (five), 1 class teacher, 1 school principal and 5 parents of class V students. Data collection is done by observation, interviews and documentation.The results of this study indicate that there are two factors that affect the low reading ability of fifth grade students at SD Negeri Sakteo, namely internal factors and external factors. Internal factors are caused by students' low interest in reading and students' lack of reading habits.External factors come from a less supportive school environment, limited books/reading materials, less optimal teacher role, and less supportive family environment.
Exploring Sistem Informasi Akuntansi Aplikasi GoCar Di Palembang

The purpose of this study is to determine the effect of business ethics; business process; technology to information; accounting information system. The background of this research is the importance of appropriate information technology as an important factor in improving the quality of accounting information systems. The population of this study are users of the Gocar application, namely online drivers to provide convenience to Gocar service users with advances in information technology enabling users to adopt more sophisticated and integrated information systems. Using the right technology, such as the latest accounting software or a secure database system and reliable accounting information systems will provide long-term benefits. This descriptive analysis examines the effect of constructs such as better decision making. In a business world that depends on technology, it is important for organizations to apply good business ethics, improve PR efficiency which is very important for business processes, and make optimal use of information technology to optimize the accounting information system in the GoCar application. &#x0D; Keywords: Business Ethics; Business process; Information Technology; Accounting information system; Technology Applications.
Synaptic Weighting of Spiking VCSEL-Neurons Using Integrated Photonic Microring Weight Banks

In this work, we present an optical spike processing architecture based on the combined operation of two types of photonic systems: silicon photonics (SiPh) microring resonator (MRR) weight banks [1] and all-optical spiking artificial neurons based on vertical cavity surface emitting lasers (VCSELs) [2]. These systems have been previously demonstrated as key-enabling building blocks in the rapidly growing field of neuromorphic photonics.
GAN‐based metal artifacts region inpainting in brain MRI imaging with reflective registration

Metallic magnetic resonance imaging (MRI) implants can introduce magnetic field distortions, resulting in image distortion, such as bulk shifts and signal-loss artifacts. Metal Artifacts Region Inpainting Network (MARINet), using the symmetry of brain MRI images, has been developed to generate normal MRI images in the image domain and improve image quality.T1-weighted MRI images containing or located near the teeth of 100 patients were collected. A total of 9000 slices were obtained after data augmentation. Then, MARINet based on U-Net with a dual-path encoder was employed to inpaint the artifacts in MRI images. The input of MARINet contains the original image and the flipped registered image, with partial convolution used concurrently. Subsequently, we compared PConv with partial convolution, and GConv with gated convolution, SDEdit using a diffusion model for inpainting the artifact region of MRI images. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. In addition, the artifact masks of clinical MRI images were inpainted by physicians.MARINet could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the test results of PConv, GConv, SDEdit, and MARINet, the masked MAEs were 0.1938, 0.1904, 0.1876, and 0.1834, respectively, and the masked PSNRs were 17.39, 17.40, 17.49, and 17.60 dB, respectively. The visualization results also suggest that the network can recover the tissue texture, alveolar shape, and tooth contour. Additionally, for clinical artifact MRI images, MARINet completed the artifact region inpainting task more effectively when compared with other models.By leveraging the quasi-symmetry of brain MRI images, MARINet can directly and effectively inpaint the metal artifacts in MRI images in the image domain, restoring the tooth contour and detail, thereby enhancing the image quality.
Fusion based Implementation of Underwater Image Enhancement Technique

Abstract- Light gets dispersed and absorbed when it passes through water, which causes underwater images to typically have low intensity and blurriness. Large, suspended particles, alongside foggy or water that is turbid and has an enormous diversity of particles, etc., are what produce scattering. Objects appear more bluish green over red because longer wavelengths are dispersed by water as depth increases. Red is absorbed by the water because it has a longer wavelength. Images are significantly damaged because of these phenomena, and as a result, they have poor contrast, undesired coloring, and poor visibility. Applications for underwater images can be discovered in a variety of sectors, including mineral identification, aquatic ecosystem analysis and evaluation, marine exploration, and much more. So, it is essential to improve underwater images to boost their quality so that they can be used for a wide range of projects while still retaining the important data they carry. In this paper, we have utilized the fusion-based mechanism to enhance the underwater images so that they can be used for different applications as required. The method we have used here is straight forward and simple to use. In our work, 5 sample images from the UIEB dataset have been taken, and the raw images then are subjected to color correction, image sharpening, and contrast enhancement. Fusion is performed in the last step to provide the improved underwater image. Experimental findings have been performed on raw and reference photos to compare their MSE and PSNR values in order to see the impact of the proposed methodology in underwater image enhancement.
Things to Consider in Blended Learning Context: Studentsâ€™ Vocabulary Learning Strategies

The move from teacher-centered to learner-centered pedagogy, which empowered students to take charge of their learning, has attracted the researcher to conduct this study. This paper explored the types of vocabulary learning strategies (VLSs) used by intermediate learners of English as a foreign language in Lampung universities and the relationship between VLSs and their speaking ability. It also attempted to find out the most-used and least-used strategies for learning the vocabulary of the English language. It adopted a quantitative correlational research design. One hundred seventy-nine participants as intermediate learners from nine different universities in Lampung participated in this study. An adopted 50-item questionnaire of vocabulary learning strategies was used for data collection. This questionnaire was distributed in two separate sessions: online via the Google Form link and offline through a printed questionnaire. The descriptive statistics analysis of the questionnaire's responses showed that the participants were medium strategy users (mean=3,11). Determination and memory strategies were the most frequently used, while social was the least used. Pearson correlation coefficient on SPSS revealed a fragile relationship between VLSs and the participants' speaking ability where the value is r&lt; 0,30 (r= 0,089). Finally, it is recommended that the teachers help their students become initiative and take responsibility for their success by providing them with a sense of vocabulary learning strategies to enhance their vocabulary knowledge. Moreover, teachers should present a wide range of (VLSs) to their students for both a classroom-related setting and self-directed vocabulary learning.
GHz-Rate Photonic Spiking Neural Network with a Single VCSEL

Photonic technologies provide a strong platform for the construction of neuromorphic processing systems, due to the potential for ultrafast, low-power operation on hardware-friendly implementations. One device, the Vertical Cavity Surface Emitting Laser (VCSEL) has demonstrated neural spiking behaviour ideal for such photonic neuromorphic processing [1].
Information and Data Processing Training (INFOLAHTA) with Microsoft Excel for Soldiers and Civil Servants KOREM 052/Wijayakrama Kodam Jaya

Within the KOREM 052/Wijayakrama Kodam Jaya environment, proficient information management and data processing have emerged as important determinants of success. In the contemporary military and civil service, mastery in extracting, analyzing, and interpreting information is indispensable for frontline service members and administrative civil servants. Therefore, this training activity is very relevant and essential in preparing them. Report and Data Processing (INFOLAHTA) addresses this need by equipping participants with the transformative potential of Microsoft Excel, positioned at the intersection of technological advancement and strategic skills. This training, led by Multimedia Nusantara University, facilitates training for soldiers and civil servants in Microsoft Excel to process data and information. The results obtained from the participants, combined with the trainers' expertise and support from KOREM 052/Wijayakrama Kodam Jaya, underscore the transformational impact of INFOLAHTA. Empowered Soldiers and Civil Servants emerge proficient at Excel, ready to perform their roles skillfully and confidently. This Community Outreach activity shows the importance of INFOLAHTA in improving professional competence and fostering a culture of continuous learning and innovation. The ongoing impact of this effort is expected to enrich the insights and capabilities of Soldiers and Civil Servants.
ENVIRONMENTAL GEOCHEMICAL INVESTIGATION OF THE GEOGENIC POLLUTION POTENTIAL OF BIDA FORMATION, NORTHERN BIDA BASIN NIGERIA

The environmental geochemistry of sandstones of Bida formation around Doko, northern Bida Basin Nigeria was studied to assess the potential for the release of toxic elements into the environment. Eight representative samples were collected and analysed to determine the total elemental concentration using X-ray fluorescence spectrometry (XRF). Four sub-samples were analysed for their near-total concentration of selected metals using atomic absorption spectrometry (AAS). The results show that many of the elements are depleted with mean enrichment ratios of less than 1. Ce, W, Nb and Pb are the exceptions with ER values of 116.4, 122.9, 25.26 and 2.22 respectively. A similar pattern was found with Igeo, where W, Ce and Nb fall within strongly to extremely polluted and extremely polluted classes. High to very high partition coefficients (KDi) for four selected PTEs show that metals are strongly held within the sandstone matrix rather than dissolving. Because the elements are not soluble, they will not be readily available for uptake by plant roots, or will not be directly toxic to soil biota. The undissolved metal pool also reflects the sandstone metal fraction that is unsusceptible to leaching and could therefore not contaminate water. We conclude that there is no significant geogenic pollution risk associated with PTE release and uptake from the formation. It is however recommended that further research should be carried out to investigate the phases hosting Ce, W, Nb and Zr in the sandstone in Doko and its environs.
Super-Resolution Method Based on Improved Generative Adversarial Networks

Generative adversarial networks (GANs) are widely utilized in the domain of image super-resolution (SR) due to their ability to produce images with enhanced human visual perception capabilities. The feature extraction process in GANs frequently results in a lack of high-frequency information, leading to false detail texture and image distortion in the reconstructed image. Moreover, the presence of complex structures can introduce training instability. To tackle these issues, we present a novel image super-resolution (SR) restoration methodology based on GAN. Firstly, we introduce a convolution block that combines ordinary convolution and local convolution to enhance local feature extraction. Additionally, we integrate the attention mechanism and the decoupling idea of depthwise separable convolution. This fusion aims to improve the performance of the residual dense block, allowing the network to effectively extract feature maps with rich information; Second, we incorporate a frequency loss function to optimize the loss of the generator. This loss function compensates for discrepancies at frequency interval between the authentic image and the generated image, thereby reducing image distortion; Finally, the discriminator combined spectral normalization and adaptive activation and used dual discriminator training to alleviate the training collapse problem caused by complex datasets and networks. The experimental results on the Set5, Set14, and BSD100 datasets demonstrate that the reconstructed image of the proposed method outperforms other classical algorithms in terms of objective evaluation indicators, demonstrating the efficacy of the proposed method.
Efficient and Enhanced Advanced Encryption Standard Algorithm with Chaos-Based Key Expansion

In order to address the increasing computational power and emerging attack methods, ensuring the security and confidentiality of data is of utmost importance. To overcome the limitations of the traditional AES encryption algorithm, an improved AES algorithm is proposed. The key expansion algorithm is enhanced by introducing a chaotic sequence, thereby improving the resistance of the round keys against attacks. Furthermore, the algorithm's computational complexity is significantly reduced by merging computational steps and utilizing lookup tables, resulting in a substantial improvement in execution efficiency. Experimental data comparisons demonstrate that the improved encryption algorithm possesses higher security and execution efficiency.
The Open Data Revolution

chapter Share on The Open Data Revolution Author: Nigel Shadbolt Search about this author Authors Info & Claims Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide WebSeptember 2023Pages 167–180https://doi.org/10.1145/3591366.3591381Published:05 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Learning from Noisy Labels with Some Adjustments of a Recent Method

In this paper we have used JoCoR, a fairly recent method for learning with label noise, that makes use of two neural networks with a joint loss function using an additional contrastive loss to increase the agreement between them. This method can be extended to more than two networks in a straightforward way. We have carried out experiments on the CIFAR-10 and CIFAR-100 datasets (contaminated by synthetic label noise) with this kind of extension using several contrastive losses. We have concluded that it makes a significant improvement if we use a third network, especially when we use Kullback-Leibler terms for all possible pairs of softmax outputs. Further extension also means some kind of improvement, but in the case of the CIFAR datasets, those were not so significant, maybe except the cases with lower ratio of label noise.
Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views

View planning for the acquisition of cardiac magnetic resonance (CMR) imaging remains a demanding task in clinical practice.Existing approaches to its automation relied either on an additional volumetric image not typically acquired in clinic routine, or on laborious manual annotations of cardiac structural landmarks. This work presents a clinic-compatible, annotation-free system for automatic CMR view planning.The system mines the spatial relationship-more specifically, locates the intersecting lines-between the target planes and source views, and trains U-Net-based deep networks to regress heatmaps defined by distances from the intersecting lines. On the one hand, the intersection lines are the prescription lines prescribed by the technologists at the time of image acquisition using cardiac landmarks, and retrospectively identified from the spatial relationship. On the other hand, as the spatial relationship is self-contained in properly stored data, for example, in the DICOM format, the need for additional manual annotation is eliminated. In addition, the interplay of the multiple target planes predicted in a source view is utilized in a stacked hourglass architecture consisting of repeated U-Net-style building blocks to gradually improve the regression. Then, a multiview planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers. For performance evaluation, the retrospectively identified planes prescribed by the technologists are used as the ground truth, and the plane angle differences and localization distances between the planes prescribed by our system and the ground truth are compared.The retrospective experiments include 181 clinical CMR exams, which are randomly split into training, validation, and test sets in the ratio of 64:16:20. Our system yields the mean angular difference and point-to-plane distance of 5.68 ∘$^\circ$ and 3.12 mm, respectively, on the held-out test set. It not only achieves superior accuracy to existing approaches including conventional atlas-based and newer deep-learning-based in prescribing the four standard CMR planes but also demonstrates prescription of the first cardiac-anatomy-oriented plane(s) from the body-oriented scout.The proposed system demonstrates accurate automatic CMR view plane prescription based on deep learning on properly archived data, without the need for further manual annotation. This work opens a new direction for automatic view planning of anatomy-oriented medical imaging beyond CMR.
Test-Time Detection of Backdoor Triggers

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.