Reviewerly/exHarmony huggingface dataset:
Dampak Kecanduan Game Online Terhadap Penilaian Hasil Belajar Siswa di SMP Al Mazaya Islamic Banjarmasin

Guidance and Counseling at this school, students who are addicted to online games have low learning outcomes. The low learning outcomes are also caused because the child pays less attention during the ongoing learning process in the classroom because of his laziness and the child does not do the assignments given by the subject teacher because friends invite him to play online games. This research is a qualitative research using data collection methods in the form of interviews and documentation. This research is a narrative in nature which produces descriptive data in the form of speech, writing, so as to systematically produce facts about the actual state of the object and pressure on the basic facts that occur at SMP Al Mazaya Islam Banjarmasin. Based on the results of data analysis that was carried out at SMP Al Mazaya Islamic Banjarmasin entitled "The Impact of Online Game Addiction on Assessment of Student Learning Outcomes at SMP Al Mazaya Islamic Banjarmasin, several things can be interpreted as follows: Factors of online game addiction in students at SMP Al Mazaya Islamic Banjarmasin is a feeling of laziness in doing schoolwork, inviting friends to play online games and lack of supervision from parents. The impact of online game addiction on the assessment of student learning outcomes at SMP Al Mazaya Islamic Banjarmasin is a decrease in report cards, disrupted learning concentration and lazy to follow learning.
TEACHERS’ PERCEPTION ON THE USE OF DISNEY MOVIES AS ENGLISH LANGUAGE TEACHING MEDIA FOR YOUNG LEARNERS

This research is a descriptive study of teachers’ perception using descriptive-qualitative. This research airmed to describe teachers’ perception on the use of Disney movies as English language teaching media for young learners. The researcher used interview to collect the data. There are three activities for the analysis of data, namely: data reduction, data display and conclusion drawing. The research results that the teachers mostly gave positive perception about the use of Frozen II as ELT media. They perceived Frozen II as suitable and recommended ELT media in teaching listening for young learners since it fulfills 4 functions of ELT media, namely; a) attentive, b) affective, c) cognitive, and d) compensatory functions. Therefore, there are some benefits of using Frozen II as ELT Media for young learners. First, students’ learning enthusiasm will increase. Second, Frozen II is appropriate ELT media that make the lesson clearer. Third, the use of Frozen II as ELT media avoid students’ boredom. Forth, by using Frozen II students can do different learning activities than usual.
ANALISIS SISTEM AKUNTANSI PENERIMAAN KAS DARI PIUTANG DALAM MENDUKUNG SISTEM PENGENDALIAN INTERNAL PADA PRIMKOP POLRESTABES SEMARANG

The viability of a business can be maintained if the management is able to maintain or develop performance improvements, one of which is by paying attention to the management of the company's accounting system. Currently Primkop Polrestabes Semarang has implemented an accounting system and internal control, but it still has some weaknesses. The purpose of this study was to determine the application of the accounting system for cash receipts from receivables and to determine the application of the internal control system to the accounting system for cash receipts from receivables at Primkop Polrestabes Semarang. The population in this study were the administrators and employees of Primkop Polrestabes Semarang as many as 19 people with a sample of 4 people at Primkop Polrestabes Semarang. The data collection method used is documentation. The data analysis method used is descriptive qualitative. The results showed that there were still some weaknesses in the accounting system for cash receipts from receivables at Primkop Polrestabes Semarang, namely cash was not fully deposited into the bank and deposits were not made immediately but cumulatively once a month. Accounting system for cash receipts from cash sales, there needs to be a warehouse function and a delivery function so that the amount of inventory can be controlled. It is necessary to deposit money to the bank in order to minimize the risk of loss and the risk of misuse. The internal control system for cash receipts from receivables is in accordance with the theoretical concept.
A Framework for Dealing With Cybersecurity Risks as Part of Information Security

In the current scenario, cybersecurity issues have emerged to be a major challenge for firms to deal with. The increased use of technologies has increased radically the volume and typology of information produced, exchanged, and managed by firms thus creating conditions for cybersecurity incidents or information breaches. In this situation, it becomes paramount for firms to recognize cybersecurity risks and be prepared to prevent them through the implementation of approaches and technologies able to ensure a high level of protection.In this chapter, we provide a framework for analyzing and managing cybersecurity risks. We employed a case study strategy to understand how the risk analysis process is carried out within an Information Security company. The study and observations obtained from this case study have permitted to define a framework useful for SME to deal with cybersecurity issues.
How a Hybrid Education Model Can Support the COVID-19 Sanitary Emergency Based on Information and Communication Technologies

This chapter presents a combined approach of social and open data to evaluate a hybrid education model with online and face-to-face classes. The study consists of a sample of 310 students from the UPIITA-IPN college. Thus, a grouping model was applied based on each student's profile and academic performance in various subjects to identify patterns and learning styles. In addition, a social sensor of emotions was implemented to measure reactions in online and face-to-face classes. It helped to identify which strategies and methods are most significant for student performance. Data were collected from forms and the Twitter social network, filtering data by general opinions about learning and experiences in class. Considering trends and patterns, we identified four types:Pattern (1) personalization of learning: This group stood out because online teaching allows you to work at your own pace and on your own schedule. In addition, a trend toward a more individualized learning approach or the versatility of personalizing learning was observed. Pattern (2) an excessive number of channels and information: This group of students was characterized by feeling overwhelmed by the amount of information they must process in an online environment, in addition, to using various communication channels (messaging, Classroom, Zoom, Teams, email, among others) this was associated with a feeling of isolation and a lack of commitment. Pattern (3) inequality and asynchronous learning: Students with difficult access to adequate resources at home (connection, own computer, etc.). They were characterized by not being able to have the same performance in the different learning activities and expressed that the content must be adapted to the individual needs of the students. Technical problems, such as Internet connection failures or problems with electronic devices, interrupted the learning process and generated frustration for students and teachers. Pattern (4) lack of social interaction: This affected the student's ability to develop social and emotional skills. Moreover, it generates difficulties for the students to collaborate, slowing the development of social and emotional skills. It concluded that a hybrid model is successful, having schemes combined with 65% face-to-face sessions and 35% online.
Fast full-field profilometry using a repetition-rate-scanned femtosecond laser

We present a coherence scanning interferometer based on a mode-locked femtosecond laser, enabling precise pulse alignment for cross-correlation interferogram acquisition. However, Nyquist's law necessitates step lengths below 1/8 of the central wavelength, while area-array detectors face limitations in pixel density and frame rate due to communication throughput. To overcome these challenges, we propose downsampling methods that preserve interferometric contrast. The simultaneous exposure method maintains a stationary state for exposure at each sampling point, mitigating contrast loss from phase shifts. The phase compensation technique reduces carrier frequency through acousto-optic modulators, preserving high-contrast interference signals without altering the scanning mechanism. Leveraging our optimized frequency domain analysis algorithm, both methods achieved nm-level measurement accuracy with μm-level scanning intervals, facilitating efficient profilometry.
Image denoising via multi-scale spatial frequency domain convolutional neural network Noise-Net

The existence of noise will seriously affect quality of image. Image denoising method is important for obtaining high-quality image. Most of traditional denoising methods need to estimate the noise level, which are unstable in the actual denoising scene. As a data-driven method, the development of deep learning shows great potential in the field of image denoising. In this paper, a method combining image denoising model and deep learning framework is proposed. Welldesigned multi-scale restoration network Noise-Net embedded this method optimizing neural network training to obtain ideal image recovery results. By down-sampling the original noisy image input at different scales, the noisy image features are extracted. These multi-scale features are summed and combined. The addition of the residual module improves the network training ability and effectively prevents the network from overfitting. The network is optimized by Convolutional Block Attention Module (CBAM). It can enable effective extraction of image features in the spatial and frequency domains. Network input is noisy image, clear image as label. The training phase is divided into two stages: noisy data generation and simulated images for pre-training. 2000 images of DOTA 1.0 dataset constitute as training set and 1000 images as test set. By adding different noises such as Gaussian noise and Poisson noise to the image, the data set is constructed with the label image. The loss function of the absolute minimum error is calculated and sent to the Adam optimizer for parameter optimization. Numerical simulation and experimental results show that Noise-Net has an effect on image denoising ability.
All dielectric metasurface-based diffractive neural networks all optical computing

Diffractive neural networks (<i>D</i><sup>2</sup><i>NNs</i>) have been transformative in many fields, motivating the development of various optical computing components. However, these optical computing components, achieving in diffractive optical elements (DOEs) fabricated using commercial 3D printers, are bulky, and the size of individual neurons is only comparable to the wavelength scale. They simultaneously suffer from challenges posed by high-order diffraction and low spatial utilization. Here, we present a design of <i>D</i><sup>2</sup><i>NNs</i> based on all-dielectric metasurfaces, which reduces the size of individual neurons of optical <i>D</i><sup>2</sup><i>NNs</i> to scales significantly smaller than the wavelength. In order to demonstrate that our metasurface-based <i>D</i><sup>2</sup><i>NNs</i> are smart and sensitive enough comparable to state-of-the-art DOE-based <i>D</i><sup>2</sup><i>NNs</i>, we numerically simulated the design of two <i>D</i><sup>2</sup><i>NNs</i> including an optical 1-bit half-adder and a full-adder. Our experiment on aforementioned half-adder proofs the advantages of our architecture. Metasurface-based optical computational elements can offer higher spatial neuron density, while completely eliminate high-order diffraction to demonstrate the feasibility. Our metasurface-based <i>D</i><sup>2</sup><i>NNs</i> can facilitate miniaturization and integration on all-optical information processing especially optical computing components, and will find applications in numerous space-constrained fields such as 6G communication, optical smart sensors, and robotics.
Hybrid diffractive neural networks for energy efficient image classification

In this work we investigate the integration of optical and electronic hardware to create ML and DL accelerators with reduced computational complexity and energy consumption. We compare the performance of optical or hybrid optical-electronic neural networks and verify that by using these computation architectures it is possible to perform classification tasks with performances comparable with standard electronic neural networks but saving computational resources up to a factor of 10.
Justifying Models

This chapter addresses the question of whether causal models can themselves be justified. We outline strategies for justifying models on the basis of prior data and so empirically grounding beliefs about the probative value of clues.
Going Wide, Going Deep

We turn to the problem of choosing between going “wide” and going “deep”: between seeking a little bit of information on a large number of cases versus studying a smaller number of cases intensively. We outline a simulation-based approach to identify the optimal mix of breadth and depth. Simulations suggest that going deep is especially valuable where confounding is a concern, for queries about causal pathways, and where models embed strong beliefs about causal effects. We also find that there are diminishing marginal returns to each strategy and that depth often provides the greatest gains when we have cross-case evidence on only a modest number of cases.
Evaluating Models

We describe strategies for figuring out whether a model is likely doing more harm than good and for comparing the performance of different models to one another.
Robots for Care: A Few Considerations from the Social Sciences

According to the scientific and technical literature, there is a continuous and significant increase of the importance of robots in the economy and society. Most of those robots are produced to cover industry needs, thinking of the robot as a substitute for human beings. They are supposed to improve the quality of work by taking over dangerous, tedious and dirty jobs that are not possible or safe for humans to perform. Thus, most of the envisaged scenarios do not foresee the need to take into consideration how to manage the social interaction between human and robot; once the robot is placed, the human being disappears from the scene. Nevertheless, the idea that robots can cover other human needs than those related to industry has also gained ground and new concerns and debates are taking place. Robots appear in environments that would have seemed unexpected not long ago, assuming duties traditionally assigned to humans. In this vein, we are witnessing an increasing expectation of advances in robotics and artificial intelligence. The possibility of involving robots in caring tasks seems closer than ever. There are pilot experiences with robots and older people, robots working in therapy for children with autism or robots in hospitals enrolled in a variety of tasks.
Seismic Analysis

Seismic Analysis is generally covered in graduate school. Most undergraduate programs do not include a course in Seismic Analysis and/or Seismic Design except as an elective. Because of the effects of climates change and fracking we believe that ultimately every State will require all new structures to satisfy a Code specified Seismic Design criteria.
The Application of Self-evaluation in English Reading Teaching

Self-evaluation is an important part of formative evaluation. Students' self-evaluation can not only enable students to find the problems in their learning and timely understand the weak links in their learning, but also enable teachers to clearly grasp the true ideas of students and implement targeted teaching according to the feedback given by students.
The Many Benefits of Implementing FAIR Assay Annotation

Genetic Engineering & Biotechnology NewsVol. 43, No. 10 Sponsored ContentThe Many Benefits of Implementing FAIR Assay AnnotationPublished Online:13 Oct 2023https://doi.org/10.1089/gen.43.10.18AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 43Issue 10Oct 2023 Information© 2023 by GEN PublishingTo cite this article:The Many Benefits of Implementing FAIR Assay Annotation.Genetic Engineering & Biotechnology News.Oct 2023.45-45.http://doi.org/10.1089/gen.43.10.18Published in Volume: 43 Issue 10: October 13, 2023PDF download
CALCULATING FUNCTION VALUES USING POWER SERIES IN PYTHON

Introduction. Power series are a powerful tool in mathematical analysis for approximating various functions. In this article, we explore the use of the Python programming language to calculate the values of functions by approximating them with power series. We will consider the theoretical foundations of building power series, their numerical calculation and provide an example for a better understanding.
5G-based Smart Health Care System Design Using IoT In Bangladesh

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
Enhancement of Backward Feature Elimination as a Pre-Processing Method for K Nearest Neighbor Algorithm Applied to Insurance Fraud Detection

This study presents novel enhancements to the Backward Feature Elimination (BFE) method for improved insurance fraud detection using the K-Nearest Neighbor (KNN) algorithm. The research addresses issues inherent in the baseline BFE process, such as the over-reliance on p-values, the potential for misleading results, and suboptimal feature selection leading to overfitting. To address these, the study integrates confidence intervals and feature importance into the BFE process, establishing a more robust and reliable criterion for feature selection. Moreover, feature engineering techniques are introduced during preprocessing to enhance model performance. The modified BFE method demonstrates superior performance over the baseline model regarding the recall, precision, and F1 score. Stratified K-Fold Cross-Validation, ROC-AUC Score, and Coefficient of Variation (CV) confirm the consistency and robustness of the enhanced model across varying data subsets. These innovations offer a comprehensive and reliable solution to feature selection in the BFE method, applied to the KNN model for effective insurance fraud detection. The study mitigates the issues related to p-value dependence and boosts model performance, paving the way for more accurate and robust fraud detection systems.
Developing Motion Detection Approach through Image Processing using Kalman Filtering and Gaussian Mixture Model

Motion tracking through image processing requires validating the subject's presence which can use for automated systems. However, detecting a moving subject in a real-time video is a complex approach in terms of visual scenes. The Gaussian Mixture Model (GMM) is a flexible tool for image processing modeling. The concept of Kalman Filtering can be applied for noise filtering of a real-time video and tracking the motion in the image. The algorithms used in this study are to develop a motion-tracking system that uses a bounding box to assign tracks to the detected motion. The algorithm developed in MATLAB software includes the parameters for the Kalman Filtering and GMM. The testing involved an IP camera, Arduino Uno as the indicator if motion is detected, and different values of the parameters for the training frame. The confusion matrix is used for the statistical analysis of the five trials to detect the True Positive and measure the system's accuracy. The study showed promising results as it obtained a true positive rate of 0.6000 from trial 4, having the applied parameters of 3 for the gaussian number, 110 for the training frame, and 0.4 for the minimum background ratio.
Privacy-Preserving Human Activity Recognition System for Assisted Living Environments

Automatic human activity recognition has numerous applications, especially in elderly support and healthcare. Several approaches for human activity recognition using a variety of sensors are available in the literature. While such frameworks are effective, each has limitations related to privacy, convenience, cost, and performance. In this paper, a robust framework for automatic human activity recognition is proposed that uses depth sensors that preserve privacy and are cost-effective. The depth sensors provide two data modalities, namely depth maps and skeleton sequences, used together for activity recognition. Two novel descriptors, Joint Position Descriptor (JPD) based on the position of joints; and Bone Angle Descriptor (BAD) based on bone inclination, are generated from the skeleton sequence data. The descriptors convey both spatial and temporal information and are scale and view-point invariant. Depth video clips are used along with the descriptors to deal with the issue of noisy and missing skeleton sequences. The data modalities and descriptors are fused using a two-level fusion strategy for a multi-channel Convolutional Neural Network (CNN) framework. The proposed system is validated and shown to be superior to the existing state of the art through comparisons over four widely used public datasets. A computational complexity analysis of the system confirms its efficacy in real time. A prototypical implementation of the proposed system further validates its practicability.
Automating Cloud Deployment for Real-Time Online Foundation Model Inference

Deep neural network (DNN) foundation models are currently exhibiting high prediction accuracy and strong adaptability to broad tasks with remarkably large model scales. They are increasingly becoming the backend support of DNN-driven real-time online services, e.g., Siri and Instagram. Such services require low-latency and cost-efficiency for quality-of-service and commercial competitiveness. When deployed in a cloud environment, these services call for an appropriate selection of cloud configurations (i.e., specific types of VM instances), as well as a considerate device placement plan that places the operations of the model to multiple GPUs via model parallelism for cost-efficiency. Currently, the deployment mainly relies on service providers’ manual efforts, which is not only onerous but also far from satisfactory oftentimes due to the huge joint search space of cloud configurations and device placement plans (for a same service, a poor deployment can incur significantly more costs by tens of times). In this paper, we attempt to efficiently automate the cloud deployment for real-time foundation model inference with minimum costs under the constraint of acceptably low latency. This attempt is enabled by 1) jointly leveraging the Bayesian Optimization and Deep Reinforcement Learning to adaptively unearth the (nearly) optimal cloud configuration and device placement with limited search time, and 2) enhancing the cost-efficiency of the deployment based on the probing-informed block multiplexing mechanism and Tensor Algebra SuperOptimizer. We implement a prototype system based on TensorFlow, conduct extensive experiments on top of Microsoft Azure, and demonstrate the generality and scalability of our solution. Results show that for lightweight DNN models and foundation models, our solution essentially saves inference costs by up to 15% and 47% with 57% and 38% lower search overheads respectively, compared with non-trivial baselines.