Reviewerly/exHarmony huggingface dataset:
The Use of Commas in Secondary-Education Students and Its Relationship with Reading Comprehension: The Case of Spanish

The correct use of punctuation marks in secondary-school students is essential for the comprehension of written texts and, therefore, for the students' academic success. However, the examination of this issue has often been overlooked in the literature. In the present study, we focused on the progression of comma usage (i.e., a punctuation mark that is often challenging to master) and examined its relation to reading comprehension. A sample of first- and fourth-year secondary-education students from Spain (N = 115) punctuated brief texts in which commas had been previously omitted. The original texts included various types of mandatory commas in Spanish. We also obtained a reading comprehension score from a standardized reading test. Results show that secondary-education students often did not correctly place the commas, with first-year secondary-education students only succeeding in the correct placement of commas 54.5% of the time in (Year 8 in the UK system). This figure increased to 80.9% in fourth-year secondary-education students (Year 11 in the UK system). As a control, this figure rose to 91.5% in first-year university students. Critically, better comprehenders were the ones with better use of punctuation marks (r = 0.33). These results are useful for developing teaching methods to improve writing/reading skills in secondary-school students.
The Use of Spinning Wheel Games to Improve Students’ Writing Procedural Texts

Most of the students' ability to write in English is still poor. Their text contains many errors in content, organization, and use of language. Students find it difficult to explain their ideas in written form, and students lack vocabulary, which can make it difficult to choose words to group into good writing. some teachers still find it difficult to apply media during learning this makes the class atmosphere less lively and makes them bored. the purpose of this research is to find out whether utilizing Spinning Wheel is effective or not in improving students’ writing Procedure Text. The method used in this research is descriptive research. I found a solution by describing the problem qualitatively. The end result is a game that imitates the Spinning Wheel Game while teaching English writing techniques that can increase students' interest, challenge their thinking and enable them to produce procedural texts while playing. The spinning wheel was used after the researcher explained the material about the procedure text. Students are asked to make groups and each group gets a different theme. themes are randomized with spinning wheel games by web wheelofnames.com. Therefore, the game is one of the tools that can be used in learning English, there are many learning media for learning writing skills. One game that can be used is Spinning Wheel. By using games students can learn to write procedure texts in a fun and interesting way. The spinning wheel game can be chosen as a tool for students to write procedure texts correctly and clearly. This game has been evaluated by several researchers and high school students. This game is very helpful in learning to write procedure text. These games can make students challenge their way of thinking, fun and enjoyable.
Sinjar Anticline Northwest of Iraq: A Tectonic Geomorphological Study

The Sinjar Anticline (Mountain) is an outstanding structural and geomorphic feature in the northwestern part of Iraq. The anticline is a double plunging with almost E – W trend dividing the gently rolling plain in which it is developed into two parts, Al-Jazira Plain in the south and Rabi’a Plain in the north. The Sinjar anticline is asymmetrical with steeper northern limb (45 – 80)° and gentler southern limb (15 – 25)°, its length is about 80 km, whereas the width ranges between (9.25 – 12. 5) km. The oldest exposed rocks belong to the Shiranish Formation, whereas the youngest rocks belong to the Fatha Formation. Different geomorphological and structural forms were observed through interpretation of satellite images, geological and topographical maps of different scales, beside field observations. Among those forms are: Abandoned alluvial fans, radial, inclined, and cross-shaped valleys, whale-back shape, wind gaps, and parasitic folds. All these forms are good indications about the lateral growth of the anticline. Neotectonic measurements were carried to estimate the rates of upward and downward movements. These estimations were performed by measuring the elevation of the contact between the Fatha (Middle Miocene) and Injana (Late Miocene) formations at different selected locations on both sides of the anticline. Keywords: Sinjar anticline, lateral growth, neotectonic movements, wind gap, abandoned alluvial fans
An efficient ontology model with query execution for accurate document content extraction

&lt;span lang="EN-US"&gt;The technique of extracting important documents from massive data collections is known as information retrieval (IR). The dataset provider coupled with the increasing demand for high-quality retrieval results, has resulted in traditional information retrieval approaches being increasingly insufficient to meet the challenge of providing high-quality search results. Research has concentrated on information retrieval and interactive query formation through ontologies in order to overcome these challenges, with a specific emphasis on enhancing the functionality between information and search queries in order to bring the outcome sets closer to the research requirements of users. In the context of document retrieval technologies, it is a process that assists researchers in extracting documents from data collections. It is discussed in this research how to use ontology-based information retrieval approaches and techniques, taking into account the issues of ontology modelling, processing, and the transformation of ontological knowledge into database search queries. In this research work, an efficient optimized ontology model with query execution for content extraction from documents (OOM-QE-CE) is proposed. The existing ontology-to-database transformation and mapping methodologies are also thoroughly examined in terms of data and semantic loss, structural mapping and domain knowledge applicability.&lt;/span&gt;
A parallel algorithm of multiple face detection on multi-core system

&lt;p&gt;&lt;span lang="EN-US"&gt;This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.&lt;/span&gt;&lt;/p&gt;
Mobile Robot Navigation in Dynamic Environments Taking into Account Obstacle Motion in Costmap Construction

An autonomous mobile robot must be capable of navigating through its environment safely in a collision-free manner. This is particularly challenging in dynamic environments, where the robot may find obstacles in unexpected locations and need to replan its path in accordance with the motion of the obstacles. This paper presents a new approach to costmap based navigation with a focus on the dynamic behaviour of the obstacles. The approach predicts future collisions using position and velocity estimates of the robot and the surrounding dynamic obstacles. The cost assignment process takes into account the regions of expected collisions, the uncertainty in the motion estimate of the obstacles, and their motion directions. This ensures collision free paths which do not pass in front of the obstacles, and hence the robot behavior is socially more acceptable.
Learning Tools for EFL Writing: What and How based on Upper Secondary School Students’ Perspectives

Writing in English is bit challenging since students should deal with not only organization and idea development but also the language. This study aims to explore the preferred learning tools used of upper secondary school students while writing and how the learning tools accommodate them in increasing the quality of their writing based upon their perspectives. This study belongs to descriptive qualitative research and took place at one of private upper secondary schools in Yogyakarta, Indonesia, with six students serving as research participants. A standardized open-ended interview and students’ writing works were used to acquire the data. The first finding revealed the learning tools opted by the students to support their writing are Youtube as a video sharing platform, Padlet as an online notice board, Grammarly, Google Translate, Grammar Checker as language support tools, and Google Docs and Microsoft Words for word processors. Meanwhile, from the two types of data, it is noticeable that students are helped by the tools. All of the aforementioned tools are able to linguistically and substantially improve their writing works by being language accommodation, idea development support, and writing organization assistance. Conclusion and recommendation are also discussed in this article after the findings and discussion are presented.
Think-Aloud Protocol Analysis: Revealing The Student's Problem in Reading Comprehension

Think-Aloud Protocol Analysis: Revealing The Student's Problem in Reading Comprehension
Identification of Content-Adaptive Image Steganography Using Convolutional Neural Network Guided by High-Pass Kernel

Digital images are very popular and commonly used for hiding crucial data. In a few instances, image steganography is misused for communicating with improper data. In this paper, a robust deep neural network is proposed for the identification of content-adaptive image steganography schemes. Multiple novel strategies are applied to improve detection performance. Two non-trainable convolutional layers is used to guide the proposed CNN with fixed kernels. Thirty-one kernels are used in both non-trainable layers, of which thirty are high-pass kernels and one is the neutral kernel. The layer-specific learning rate is applied for each layer. ReLU with customized thresholding is applied to achieve better performance. In the proposed method, image down-sampling is not performed; only the global average pooling layer is considered in the last part of the network. The experimental results are verified on BOWS2 and BOSSBase image sets. Content-adaptive steganography schemes, such as HILL, Mi-POD, S-UNIWARD, and WOW, are considered for generating the stego images with different payloads. In experimental analysis, the proposed scheme is compared with some of the latest schemes, where the proposed scheme outperforms other state-of-the-art techniques in the most cases.
Modularization Method to Reuse Medical Knowledge Graphs

During the creation and integration of a health care system based on medical knowledge graphs, it is necessary to review and select the vocabularies and definitions that best fit the information requirements of the system being developed. This implies the reuse of medical knowledge graphs; however, full importation of knowledge graphs is not a tractable solution in terms of memory requirements. In this paper, we present a modularization-based method for knowledge graph reuse. A case study of graph reuse is presented by transforming the original model into a lighter one.
Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS.
LEDBoard: Using Visual Feedback to Support Text Entry with Physical Keyboards

Physical keyboards persist as one of the most common input devices for personal computers. Low familiarity with the keyboard translates to frustration during text entry, as the user constantly shifts their attention between the keyboard and the screen, in order to locate the next key to press, and to inspect input for errors. To decrease the need for attention shifts, we present physical keyboard typists with visual feedback, within their field of view, using an RGB LED strip to indicate spelling errors in different colours. We conducted a user experiment with 36 participants. Users’ performance was evaluated a) without visual feedback, b) showing feedback with a LED strip on the keyboard, and c) showing feedback with a LED strip at the bottom of the screen. We find that our prototype improves corrective action behaviour for slow typists and reduces screen-glancing behaviour for fast typists.
Development of Augmented Reality as a Learning Medium for Recognition of Layers and Structures of the Earth

Augmented Reality is a technology that combines real-world measurement with the dimensions of the virtual world exposed in real-time. It doesn't completely replace what's in the real world, like virtual reality, but just adds to it. This is done by "painting" three-dimensional objects on markers, which are unique "patterns" that can be recognised by the application. Smartphones can develop Augmented Reality applications at low prices and can be used by many consumers. Augmented Reality can be сonsumed in various fields, one of which is the educational sphere as a learning media tool. It can be used to create a more interactive learning environment where students can interact directly with virtual world objects so that students can learn while playing. The application development process in this study is based on the waterfall model. The augmented reality app was developed for teachers and students as a medium in school. The app uses the Vuforia SDK software as a tool to create Augmented Reality. Java and C++ are based on Android. Real-world surface layer applications and earth structure recognition applications can read markers in Augmented Reality geography books. As a medium, they will display 3D models on the screen of android devices and display 3D on the layers and structures of the earth.
Motion Route Planning and Obstacle Avoidance Method for Mobile Robot Based on Deep Learning

In order to improve the motion route planning effect and obstacle avoidance effect of mobile robots, this paper combines the deep learning theory to analyze the motion route planning and obstacle avoidance process of mobile robots. According to the obstacle avoidance trajectory and constraints, this paper establishes a safe distance model for obstacle avoidance, then analyzes the braking process of the robot, and designs an improved safety model for obstacle avoidance. This model integrates two relatively mature safety models, complements their advantages and disadvantages, and comprehensively considers robot safety and the utilization of the motion path. According to the simulation test research, the robot based on deep learning proposed in this paper has a good motion route planning effect and obstacle avoidance effect and can effectively improve the autonomous motion effect of the robot.
Using model explanations to guide deep learning models towards consistent explanations for EHR data

It has been shown that identical deep learning (DL) architectures will produce distinct explanations when trained with different hyperparameters that are orthogonal to the task (e.g. random seed, training set order). In domains such as healthcare and finance, where transparency and explainability is paramount, this can be a significant barrier to DL adoption. In this study we present a further analysis of explanation (in)consistency on 6 tabular datasets/tasks, with a focus on Electronic Health Records data. We propose a novel deep learning ensemble architecture that trains its sub-models to produce consistent explanations, improving explanation consistency by as much as 315% (e.g. from 0.02433 to 0.1011 on MIMIC-IV), and on average by 124% (e.g. from 0.12282 to 0.4450 on the BCW dataset). We evaluate the effectiveness of our proposed technique and discuss the implications our results have for both industrial applications of DL and explainability as well as future methodological work.
Denoising research of petrographic thin section images with the global residual generative adversarial network

Petrographic thin section images have an important role in depositional environment inference, prediction of reservoir physical properties, and oil and gas analysis. To overcome the current challenges in thin section image denoising, we propose the global residual generative adversarial network (GR-GAN). Compared with the classical generative adversarial network (GAN), the residual network structure of the GR-GAN is reconstructed, and the loss function is redefined. The GR-GAN is then applied to denoise the thin section images in two different oilfields. The final denoising results confirmed that the GR-GAN achieves the best denoising effects on both visual evaluation metrics and objective evaluation metrics compared with colour block-matching 3D filtering (CBM3D), K-singular value decomposition (K-SVD), the GAN and a fast and flexible denoising network (FFDNet). Specifically, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) generated by the GR-GAN on the test set are 28.2410 and 0.9674, 28.1075 and 0.9443, and 27.9919 and 0.9399, respectively, when the Gaussian noise is 15 dB, 25 dB and 35 dB, respectively, in the thin section image of the small-pore and fine-throat-type structures of J Oilfield; however, the data become 27.2841 and 0.9228, 26.8177 and 0.9162, and 26.3043 and 0.9068 for CBM3D, respectively, and these data generated by other methods are between the aforementioned two sets of data. The normalized root mean squared error (NRMSE) generated by the GR-GAN and CBM3D with the test set are 0.0327 and 0.1382, 0.0584 and 0.1341, and 0.0786 and 0.1382, respectively, when the Gaussian noise is 15 dB, 25 dB and 35 dB, respectively, and the NRMSE generated by the other methods is also between the aforementioned two sets of data. For other types of thin section images, when the Gaussian noise is 15 dB, 25 dB and 35 dB, respectively, CBM3D, K-SVD, the GAN, FFDNet and the GR-GAN show similar denoising effects as previously described. Moreover, in a denoising experiment repeated more than 10 times with the above methods, the GR-GAN has the shortest mean running time of 1.0589 s, and the mean running times of CBM3D, K-SVD, the GAN and FFDNet are 6.4609 s, 155.3158 s, 1.9394 s and 1.0622 s, respectively.
Reducing Hallucinations in Neural Machine Translation with Feature Attribution

Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset. When trained on low-quality datasets, these models are prone to various error types, including hallucinations, i.e. outputs that are fluent, but unrelated to the source sentences. These errors are particularly dangerous, because on the surface the translation can be perceived as a correct output, especially if the reader does not understand the source language. We present a case study focusing on model understanding and regularisation to reduce hallucinations in NMT. We first use feature attribution methods to study the behaviour of an NMT model that produces hallucinations. We then leverage these methods to propose a novel loss function that substantially helps reduce hallucinations and does not require retraining the model from scratch.
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations

We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs). The core element of the method is a SNN, trained to use spike-encoded information about the solution at previous timesteps to predict spike-encoded information at the next timestep. After the network has been trained, it operates as an explicit numerical scheme that can be used to compute the solution at future timesteps, given a spike-encoded initial condition. A decoder is used to transform the evolved spiking-encoded solution back to function values. We present results from numerical experiments of using the proposed method for ODEs and PDEs of varying complexity.
THE USE OF PROBLEM BASED LEARNING ON STUDENTS’ WRITING SKILL

Abstract (10 Pt)This research aim was to measure the effect of Problem Based Learning towards students’ writing skill. The research was experimental research and the design used was one-group pretest-posttest design. The population of the study was twelve grade students of SMAN 1 Gunungsari. The class were selected as the samples was XIIA social class as experimental group consisted of 15 students from even serial number and XIIB Social class as control group consisted of 15 with odd serial numbers. The Experimental group was treated by Problem based Learning and control group was given treatment without Problem based Learning method. The instrument used in this research was writing test. In analyzing the data, the researchers used statistical analysis by SPSS software, in which it showed that the sig value of = 0.000 was lower than 0.05. Therefore, according to the results of the analysis, it could be concluded that there was a significant difference in students‟ of writing skill by using Problem based Learning method and those who taught without the Problem Based Learning method at SMAN 1 Gunungsari.