Reviewerly/exHarmony huggingface dataset:
Target Cultures Representation in English Course Books of Smart 10 and Bahasa Inggris X for Senior High School

This research is aimed to analyze the target cultural classification aspects of High School textbooks. The data for analysis were collected from two High School books entitled Smart 10 and Bahasa Inggris X. The method employed was the descriptive qualitative method, and it used the theory from Dweik &amp; Al-Sayyed (2015) about cultural classifications. This study explored and compared how the target culture was represented in two coursebooks that used purposive sampling. Three available units were selected in every book by employing systemic random sampling. Six aspects were analyzed; food &amp; beverages, person's name, places, social behavior, ecology, and economy. The results showed that target cultures represented by Smart 10 are more dominant than in Bahasa Inggris X's book.
ACE-M: Automated Control Flow Integrity Enforcement Based on MPUs at the Function Level

Control-flow integrity(CFI) ensures that the execution flow of a program follows the control-flow graph(CFG) determined at compile time. CFI is a security technique designed to prevent runtime attacks such as return-oriented programming (ROP). With the development of the Internet of Things (IoT), the number of embedded devices has increased, and security and protection techniques in embedded systems have become important. Since the hardware-based CFI technique requires separate hardware support, it is difficult to apply to an embedded device that is already arranged. In this paper, we propose a function-level CFI technique named ACE-M, which uses the memory protection unit (MPU) included in most embedded devices. MPU may provide attributes such as read-write-execute to the memory area. ACE-M has three steps: (1) initiate—inserts an MPU-related function into a specific position; (2) profiling—provides information for MPU configuration. After the initation step, several pieces of information can be determined; (3) set—modify the already-inserted function’s arguments. We propose a design that supports the MPU. In our model, the MPU becomes a control flow monitor that detects control flow errors(CFEs), and the inserted codes cause the MPU to act as a control flow checker. If the program deviates from the original control flow, the MPU raises an exception since its corresponding area will not be included in the executable area. This approach not only verifies the target address but also guarantees the running position. Our technique can detect any modification of the program counter (PC) to an arbitrary address.
The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District

Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.
ANALYSIS OF FOREIGN AND DOMESTIC EXPERIENCE METHODS OF DEVELOPING MEDIA SKILLS OF WORKING WITH INFORMATION TECHNOLOGIES FOR FUTURE SCIENCE TEACHERS

The views on the analysis of the experience of future teachers of Natural Sciences on the development of Information Technology mediacognition by a large number of foreign and local mediapedagog scientists and their discovery of the essence of mediacompetence in their scientific research work, their pedagogical potential and the conditions for the use of various information and communication technologies in the educational process.
Efficient multi-classifier wrapper feature-selection model: Application for dimension reduction in credit scoring

The task of identifying most relevant features for a credit scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task to improve the performance of the credit scoring model. The wrappers approach is usually used in credit scoring applications to identify the most relevant features. However, this approach suffers from the issue of subsets generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results which can be interpreted differently. Hence, we propose in this study an ensemble wrapper feature selection model which is based on a multi-classifiers combination. In a first stage, we address the problem of subsets generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two classifier arrangement approaches in order to select a set of mutually approved set of relevant features. The proposed method is evaluated on four credit datasets and has shown a good performance compared to individual classifiers results.
Visual word recognition: Attention, intention, context, and processing dynamics.

The notion that some mental processes are "automatic" while others are "controlled" is a distinction that appears in virtually all cognition textbooks, as well as in thousands of papers and book chapters. Indeed, so entrenched is the automatic side of this distinction that various leading computational accounts make no mention of it, but instead assume it implicitly. These models, and the field more generally, assume that processing is stimulus triggered and does not need any form of attention or an intention as a preliminary. Further, the fundamental processing dynamics underlying such automatic processing is widely seen as consisting of interactive activation and autonomous in that it unfolds in the same way across contexts. I review a number of findings from my lab that lead me to a different conclusion. Visual word recognition requires a consideration and integrated understanding of automaticity, attention, intention, context, and cognitive processing. I present various findings that challenge the preeminent role ascribed to interactive activation as implemented in the dominant computational models. I conclude that, going forward, the time is due for computational models of visual word recognition (and researchers in the field more generally) to acknowledge that the findings reported here constitute benchmarks that constrain theory and present opportunities for making meaningful advances in our understanding of visual word recognition (and perhaps of cognition more generally). A few proposals for how we might think about some of these processes are offered. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Optimalisasi Kapasitas Rooftop PV System Skala Rumah Tangga di Perumahan

One of the renewable energies in Indonesia is solar energy. The geographical condition of Indonesia, which is a tropical country, is one of the advantages it has for applying solar energy. The Indonesian government is gradually regulating the population system for Solar Power Plants (PLTS). The resident system is in demand because of the advantage that it can be connected to the PLN grid and used as an energy reserve. The installation of rooftop PLTS both in government buildings and on the roofs of houses has now become a common trend in the community as a form of participation in the use of New and Renewable Energy (EBT). Before installing PLTS Roofs, it is necessary to determine in advance how much capacity will be installed. This is also related to the community's question, what is the percentage of electricity costs if PLTS roofs are installed in bulk. These designs and calculations are generally carried out with the help of simulations. To discuss these problems, in this article, we will discuss a simulation of the potential for electrical energy generated from rooftop solar power plants in Cilacap Green Smart Living housing. The simulation was carried out with the help of the SolarGIS PV lunar device. Analytical calculations are carried out by software based on input of regional location and solar irradiation. SolarGis will calculate the daily output power generated by the rooftop PV mini-grid and the average monthly and yearly power. The simulation results show that one house with a daily energy requirement of 13 kWh by installing a rooftop PLTS with a capacity of 3 kWp, the demand for electrical energy imported from the PLN network to the house is only 7.8% in one year.
Extraction of image resampling using correlation aware convolution neural networks for image tampering detection

&lt;span&gt;Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.&lt;/span&gt;
Predicting native Chinese readers’ perception of sentence boundaries in written Chinese texts

Abstract The notion of sentencehood in Mandarin Chinese is much less well-defined than in many other languages, with a block of clauses often joined by commas without conjunctions and with the period often occurring at the end of a block of clauses to indicate meaning completeness rather than the completeness of a sentential structure. The potential factors that may affect native Chinese speakers’ judgment of meaning completeness and perception of sentence boundaries have not yet been systematically examined. In light of this research gap, this study investigates the factors that may play a role in native Chinese speakers’ sentence boundary perception. To this end, we conducted text re-punctuation experiments in two separate groups, a training group and a testing group, using different stimuli texts. The stimuli texts were annotated with multiple levels of linguistic information to identify potentially relevant variables that could affect the participants’ sentence boundary perception. Logistic regression and the Bayesian statistical methods were applied to test the potential effects of multiple variables on the participants’ responses. The logistic regression model trained on the data from the training group achieved a high level of accuracy in predicting the responses by the testing group. The model revealed a more important role of semantic information than syntactic information in the participants’ sentence boundary perception. The implications of our findings for understanding the perception of Chinese sentence boundaries are discussed.
Preserving similarity order for unsupervised clustering

Unsupervised clustering categorizes a sample set into several groups, where the samples in the same group share high-level concepts. As the clustering performances are heavily determined by the metric to assess the similarity between sample pairs, we propose to learn a deep similarity score function and use it to capture the correlations between sample pairs for improved clustering. We formulate the learning procedure in a ranking framework and introduce two new supervisory signals to train our model. Specifically, we train the similarity score function to guarantee 1) a sample should have a higher level of similarity with its nearest neighbors than others in order to achieve correct clustering, and 2) the ordering of the similarity between neighboring sample pairs should be preserved in order to achieve robust clustering. To this end, we not only study the relevance between neighboring sample pairs for local structure learning, but also study the relevance between each sample and the boundary samples for global structure learning. Extensive experiments on seven public available datasets validate the effectiveness of our proposed framework, including face image clustering, object image clustering, and real-world image clustering.
On the analysis of makespan and performance of the task‐based execution model for enterprise application integration platforms: An empirical study

Summary Increasingly enterprises rely on software applications to support their business processes. Since such processes are continually evolving to keep up with market dynamism, companies strive to increase their efficiency, for example, by optimising the integration of applications supporting these processes. Integration platforms are specialised software tools that allow creating integration processes so that applications can share data and functionality. However, this integration involves several challenges, especially when large volumes of heterogeneous data should be integrated and shared. The performance of an integration process, in terms of message processing, is directly related to the run‐time system of the integration platform. This article investigates the impact of the volume of messages and the number of threads used by a run‐time system on makespan and performance of an integration process. The greater is the number of messages per second received by the integration process, the high is the volume of messages. The study was based on a run‐time system with task‐based execution model and follows a strict protocol to conduct and report our empirical study. We observed an increment of makespan when increasing the volume of messages to integration processes and different behaviours when increasing the number of threads used in their executions. Makespan reduces as the number of threads increases, but only when the volume of inbound messages is not very high. We confirmed that there is a performance gain by increasing the number of threads to execute an integration process, but observed that the continuous increment of threads leads to degradation of the performance in this model.
Practical Security of High-Dimensional Quantum Key Distribution with Intensity Modulator Extinction

Quantum key distribution (QKD) has attracted much attention due to its unconditional security. High-dimensional quantum key distribution (HD-QKD) is a brand-new type of QKD protocol that has many excellent advantages. Nonetheless, practical imperfections in realistic devices that are not considered in the theoretical security proof may have an impact on the practical security of realistic HD-QKD systems. In this paper, we research the influence of a realistic intensity modulator on the practical security of HD-QKD systems with the decoy-state method and finite-key effects. We demonstrate that there is a certain impact in the secret key rate and the transmission distance when taking practical factors into security analysis.
RoiSeg: An Effective Moving Object Segmentation Approach Based on Region-of-Interest with Unsupervised Learning

Traditional video object segmentation often has low detection speed and inaccurate results due to the jitter caused by the pan-and-tilt or hand-held devices. Deep neural network (DNN) has been widely adopted to address these problems; however, it relies on a large number of annotated data and high-performance computing units. Therefore, DNN is not suitable for some special scenarios (e.g., no prior knowledge or powerful computing ability). In this paper, we propose RoiSeg, an effective moving object segmentation approach based on Region-of-Interest (ROI), which utilizes unsupervised learning method to achieve automatic segmentation of moving objects. Specifically, we first hypothesize that the central n × n pixels of images act as the ROI to represent the features of the segmented moving object. Second, we pool the ROI to a central point of the foreground to simplify the segmentation problem into a classification problem based on ROI. Third but not the least, we implement a trajectory-based classifier and an online updating mechanism to address the classification problem and the compensation of class imbalance, respectively. We conduct extensive experiments to evaluate the performance of RoiSeg and the experimental results demonstrate that RoiSeg is more accurate and faster compared with other segmentation algorithms. Moreover, RoiSeg not only effectively handles ambient lighting changes, fog, salt and pepper noise, but also has a good ability to deal with camera jitter and windy scenes.
MULTI VARIATE NEURO-STATISTICAL SPARSE TRANSFORM FOR GRAY SCALE IMAGES

Abstract-- The main objective of this paper is to examine the performance of Neuro- Statistical sparse transformation function for implementation in a still image vector coding based compression system. This paper discusses the important features of low bit-rate image coding which is based on recent developments in the theory of multivariate nonlinear piecewise polynomial approximation in still images. It combines Binary Space Partition (BSP) scheme with Geometric Wavelet (GW) tree approximation so as to efficiently capture curve singularities and provide a sparse representation of the image. The quality of the reconstructed image is measured objectively using Peak Signal to Noise Ratio. Experimental results show that the proposed image compression system yields higher compression with minimal loss.
Distractions and Coping Mechanisms in Blended Learning of Grade 10 Students in Philippine School Doha, S.Y. 2021-2022

Abstract: This study focuses on investigating the commonly faced distractions and coping mechanisms in blended learning used by the Grade 10 students in Philippine School Doha S.Y. 2021-2022. Specifically, it describes the (1) forms of distractions the respondents are accustomed to blended learning and (2) the types of coping mechanisms the respondents adopted in blended learning. Using the quantitative descriptive design, a self-made survey questionnaire was made and was validated. There are 149 respondents in the study selected through stratified random sampling. The internal and external types of distractions are given emphasis along with the perceived coping mechanisms such as professional-directed strategies and self-directed strategies. The results of the study showed that the commonly faced form of distractions of the students is internal distractions which is losing of concentration in blended learning due to fatigue, while the coping mechanism respondents used to is setting of achievable goals to meet or accomplish tasks. It is duly recommended that students may recognize the importance of coping mechanisms to face distractions in blended learning, likewise, teachers may use the results and findings of the study to empower students and parents in finding solutions to stay focused and motivated in blended learning.
The detection of oil spill onshore using the thermal band of landsat-8

Remote sensing is an advanced technique that can be used to detect hydrocarbons on land, and its advantages include rapid and low-cost detection. The detection of hydrocarbons on the surface is essential for environmental monitoring and the purpose of exploration. For oil and gas companies, the ability to locate hydrocarbon spill sites is important information for the success of future exploration wells. This study aimed to determine the quantities of hydrocarbons mixed with the soil as an indicator of its accumulation in the subsurface. In this paper, temperature data obtained from the landsat-8 satellite were used to determine the extent of the oil spill in the area using spatial interpolation and gradient techniques. Kriging interpolation is based on statistical models (i.e. including auto-correlation) geostatistical techniques can produce a forecast surface and also provide accurate forecasts. The results showed that there are 60 sites of thermal anomalies and temperature values are lowest and highest 23.2 o C to 91.11 o C, in the study area. Three different sites were chosen in terms of area and effect. They are located in the northern and southern Rumaila and Zubair fields. The location of the oil spills was determined with high accuracy after identifying the anomalies.
Image Reconstruction Based on ResV-Net for Electrical Impedance Tomography

Electrical impedance tomography (EIT) is a nonlinear and ill-posed inverse mathematical problem.Due to the above problem, the reconstruction image suffers from serious artifacts.To overcome shortcomings, we proposed a residual V-shaped deep convolutional neural network (ResV-Net).It consists of the feature extraction module and image reconstruction module which are optimized by ResBlock.The residual connection method can effectively increase the number of the forward information flow and reverse gradient flow in deep CNN and alleviate the problem of non-convergence caused by gradient vanishing.The simulation and experimental results show that the ResV-Net has a better visualization effect than the related imaging method.
An Integrated Analysis Framework of Convolutional Neural Network for Embedded Edge Devices

Recently, IoT applications using Deep Neural Network (DNN) to embedded edge devices are increasing. Generally, in the case of DNN applications in the IoT system, training is mainly performed in the server and inference operation is performed on the edge device. The embedded edge devices still take a lot of loads in inference operations due to low computing resources, so proper customization of DNN with architectural exploration is required. However, there are few integrated frameworks to facilitate exploration and customization of various DNN models and their operations in embedded edge devices. In this paper, we propose an integrated framework that can explore and customize DNN inference operations of DNN models on embedded edge devices. The framework consists of the GUI interface part, the inference engine part, and the hardware Deep Learning Accelerator (DLA) Virtual Platform (VP) part. Specifically it focuses on Convolutional Neural Network (CNN), and provides integrated interoperability for convolutional neural network models and neural network customization techniques such as quantization and cross-inference functions. In addition, performance estimation is possible by providing hardware DLA VP for embedded edge devices. Those features are provided as web-based GUI interfaces, so users can easily utilize them.