Reviewerly/exHarmony huggingface dataset:
Kontribusi Penggunaan Literasi Digital Terhadap Peningkatan Hasil Belajar Pendidikan Agama Islam Siswa Di SMK Pawiyatan Surabaya

This study examines the use of digital literacy to increase the learning outcomes of PAI students at SMK Pawiyatan Surabaya. This study uses quantitative research with a correlational descriptive approach. Data collection using a questionnaire was carried out in a self-administered questionnaire, namely, the respondents answered the questionnaire themselves that had been made by the researcher. The sample used was 177 students as respondents. Hypothesis testing was carried out using the Partial Least Square (PLS) based Structural Equation Model (SEM) approach using the Smart PLS application version 3.0. The results showed that the ability to access digital tools (XI), the ability to search for information with digital devices (X2), and the ability to evaluate information from the Internet (X3) both partially and simultaneously had a significant positive effect on PAI learning outcomes (Y). Digital literacy skills are very important in learning, especially in finding PAI information with digital tools. The more professional students are in search for this information, their insights and knowledge references will increase so that student learning outcomes for PAI will also increase.
FaaSCtrl: A Comprehensive-Latency Controller for Serverless Platforms

&lt;p&gt;Serverless computing systems have become very popular because of their natural advantages with respect to auto-scaling, load balancing and fast distributed processing. Almost all serverless systems as of today define two QoS classes where one set of applications is deemed to be latency sensitive (LS) and the other set of applications needs to be handled on a best-effort basis (BE). LS jobs are often very short, and thus, they require low latencies and deterministic processing times (akin to soft real-time jobs). Hence, several performance metrics have been considered in the literature, such as the mean, tail latency, median, etc., for the different QoS classes. We use this standard setting and club all performance metrics into a performance vector that we shall refer to as the comprehensive latency (CL).&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;We design a system FaaSCtrl that is superior to prior work for every component of the CL for LS applications by a wide margin (e.g., 44.2% and 28% better than the nearest competing work for the variance and mean components, resp.). We achieve this by first understanding the reasons for a poor CL by performing detailed characterization experiments. Once, we identify the causative factors, we establish that the knobs to control these factors, such as process priorities and CPU affinities, have a complex relationship with the CL. This is a fit case for using a reinforcement learning (RL) method that has been used successfully in the past to solve problems where the benefit of a certain intervention is probabilistic in nature. Our detailed characterization experiments naturally segue into our RL formulation. The last section establishes that our RL-based system is robust and generalizes well to hitherto unseen situations.&lt;/p&gt;
FP²: Fully in-Place Functional Programming

As functional programmers we always face a dilemma: should we write purely functional code, or sacrifice purity for efficiency and resort to in-place updates? This paper identifies precisely when we can have the best of both worlds: a wide class of purely functional programs can be executed safely using in-place updates without requiring allocation, provided their arguments are not shared elsewhere. We describe a linear _fully in-place_ (FIP) calculus where we prove that we can always execute such functions in a way that requires no (de)allocation and uses constant stack space. Of course, such a calculus is only relevant if we can express interesting algorithms; we provide numerous examples of in-place functions on datastructures such as splay trees or finger trees, together with in-place versions of merge sort and quick sort. We also show how we can generically derive a map function over _any_ polynomial data type that is fully in-place. Finally, we have implemented the rules of the FIP calculus in the Koka language. Using the Perceus reference counting garbage collection, this implementation dynamically executes FIP functions in-place whenever possible.
Energy-efficient quantum non-demolition measurement with a spin-photon interface

Spin-photon interfaces (SPIs) are key devices of quantum technologies, aimed at coherently transferring quantum information between spin qubits and propagating pulses of polarized light. We study the potential of a SPI for quantum non demolition (QND) measurements of a spin state. After being initialized and scattered by the SPI, the state of a light pulse depends on the spin state. It thus plays the role of a pointer state, information being encoded in the light&amp;apos;s temporal and polarization degrees of freedom. Building on the fully Hamiltonian resolution of the spin-light dynamics, we show that quantum superpositions of zero and single photon states outperform coherent pulses of light, producing pointer states which are more distinguishable with the same photon budget. The energetic advantage provided by quantum pulses over coherent ones is maintained when information on the spin state is extracted at the classical level by performing projective measurements on the light pulses. The proposed schemes are robust against imperfections in state of the art semi-conducting devices.
Enhancing the accuracies by performing pooling decisions adjacent to the output layer

Abstract Learning classification tasks of $$({2}^{n}\times {2}^{n})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>×</mml:mo> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> inputs typically consist of $$\le n(2\times 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>≤</mml:mo> <mml:mi>n</mml:mi> <mml:mo>(</mml:mo> <mml:mn>2</mml:mn> <mml:mo>×</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> ) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using the CIFAR-10 database, that pooling decisions adjacent to the last convolutional layer significantly enhance accuracies. In particular, average accuracies of the advanced-VGG with $$m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>m</mml:mi> </mml:math> layers (A-VGGm) architectures are 0.936, 0.940, 0.954, 0.955, and 0.955 for m = 6, 8, 14, 13, and 16, respectively. The results indicate A-VGG8’s accuracy is superior to VGG16’s, and that the accuracies of A-VGG13 and A-VGG16 are equal, and comparable to that of Wide-ResNet16. In addition, replacing the three fully connected (FC) layers with one FC layer, A-VGG6 and A-VGG14, or with several linear activation FC layers, yielded similar accuracies. These significantly enhanced accuracies stem from training the most influential input–output routes, in comparison to the inferior routes selected following multiple MP decisions along the deep architecture. In addition, accuracies are sensitive to the order of the non-commutative MP and average pooling operators adjacent to the output layer, varying the number and location of training routes. The results call for the reexamination of previously proposed deep architectures and their accuracies by utilizing the proposed pooling strategy adjacent to the output layer.
Identification of clickbait news articles using SBERT and correlation matrix

Abstract Clickbait refers to the practice of using attention-grabbing or misleading headlines to attract readers to click on particular headlines or pieces of content. This technique often involves exaggerating claims or adding false information to attract traffic. In general, clickbait headlines are less similar to detailed articles associated with that headline compared to the standard news post. In our proposed system, we have exploited that to create features from headlines and its paragraph by using SBERT. Since the size of the paragraph is quite large compared to the headlines, we have selected a few sentences from the paragraph using a dissimilarity matrix. The extracted features from the headlines, target title and selected sentences of the paragraph are concatenated and classified using machine learning (ML) classifiers. The proposed model was tested extensively on two real-world datasets, and the results showed that it performed better than the current state-of-the-art models. The experimental results, the support vector machine (SVM) classifier with the concatenated embedding of six dissimilar sentences from paragraph exhibited the best performance with an accuracy of 0.84, weighted precision of 0.83, weighted recall of 0.84, and weighted F1-score of 0.82 surpassing the state-of-the-art model by 0.088 in terms of F1-score.
A Case Study of SW · AI Education for Multicultural Students in Jeju, Korea: Changes in Perception of SW · AI

The ratio of students with a multicultural background relative to the total number of students in South Korea is consistently going up due to the increasing number of multicultural students and decreasing school age population. Yet, the low level of digitalization in multicultural households requires an effort to address digital divide. This paper, accordingly, held an SW (software) and AI (artificial intelligence) camp on four occasions to 314 multicultural students living in Jeju and observed how the perception of participating students changed on SW and AI. The education camp was organized after analyzing the limitations of existing multicultural education and computer education as well as their issues. To validate effects of the education, a paired sample t-test before and after education and an independent sample t-test were carried out to make an analysis by education period and analyze education effect by background variables. Furthermore, text network analysis on short answers was made for an in-depth analysis of research results. It shows the research participants’ awareness of SW · AI changing for the positive post-camp in most sub-elements. However, self-efficacy of jobs related to SW · AI, which was one of the sub-elements, was lower post-camp than in pre-camp in a few cases. Since the average score of this particular element is noticeably lower than other average scores and research participants were not evenly distributed by grade, further improvement is warranted in follow-up research.
Distributing Quantum Circuits Using Teleportations

Scalability is currently one of the most sought-after objectives in the field of quantum computing. Distributing a quantum circuit across a quantum network is one way to facilitate large computations using current quantum computers. In this paper, we consider the problem of distributing a quantum circuit across a network of heterogeneous quantum computers, while minimizing the number of teleportations (the communication cost) needed to implement gates spanning multiple computers. We design two algorithms for this problem. The first, called Local-Best, initially distributes the qubits across the network, then tries to teleport qubits only when necessary, with teleportations being influenced by gates in the near future. The second, called Zero-Stitching, divides the given circuit into sub-circuits such that each sub-circuit can be executed using zero teleportations and the teleportation cost incurred at the borders of the sub-circuits is minimal. We evaluate our algorithms over a wide range of randomly-generated circuits as well as known benchmarks, and compare their performance to prior work. We observe that our techniques outperform the prior approach by a significant margin (up to 50%).
Generation and characterization of ultrabroadband polarization-frequency hyperentangled photons

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530--1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks.
Implementation of Dashboard Power Bi for Data Visualization of Graduates During Covid-19 Pandemic in The Faculty of Tarbiyah and Teaching Sciences IAIN Palopo

This study presents information regarding the distribution of graduates and the number of students who graduated during the pandemic of Covid-19 at the Faculty of Tarbiyah and Teaching Sciences IAIN Palopo through visualization of data depicted during the Covid19 pandemic, namely in a vulnerable time in 2020 to early 2022. This visualization is done by distributing graduate data into the Microsoft Power BI application and depicted in the form of a diagram, after that the data is visualized in the form of diagrams and numbers based on the categories that have been collected in the form of an Excel file which is then saved in a CSV file type so that the files obtained lighter than before so that the distribution of data in the form of information for graduates of the Faculty of Tarbiyah and Teaching Sciences at IAIN Palopo can be easily observed by policymakers in making decisions.
Improving Learning Achievement through Balanced Nutrition among Junior High School Students in Jayapura City, Indonesia

Background and Objectives: Optimal learning achievement is often obtained through the consumption of nutritious and balanced food, which prevents poor nutritional statuses, such as severely underweight, underweight, overweight, and obese. This is particularly relevant to Junior High School students aged 11-16 years, who are more prone to malnutrition. Therefore, this study aimed to improve learning achievement through balanced nutrition among Junior High School students. Materials and Methods: The study procedures were carried out using a quantitative approach with a cross-sectional design. The sample population consisted of 350 Junior High School students in Jayapura Municipality, Papua Province, Indonesia. Primary and secondary data were obtained from relevant sources and analyzed using frequency distribution analysis and statistical path analysis tests. Results: The results showed the indirect effect of exogenous variables on the endogenous, where nutritional knowledge and a balanced diet influenced nutritional status (p-value = 0.001), but physical activity had no impact (p-value = 0.167). Meanwhile, the direct effect of exogenous variables on the endogenous showed that nutritional knowledge, interest in learning, and nutritional status influenced students’ achievement (p-value = 0.001). Based on these results, nutritional status played a crucial role in improving the learning achievement of junior high school students.
Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. One such method uses computer aided detection to classify as benign or malignant from the histopathological images. Standard histopathological images were used from a Lung and Colon Cancer Histopathological Image Dataset (LC25000) which contains two classes of benign and malignant of 5000 each. Images were preprocessed and features extracted using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Feature selection methods used are KL Divergence and Invasive Weed Optimization (IWO). Seven different classifiers like SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without Feature selection and Hyperparameter Tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%
Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm

We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We develop an algorithm – tailored to overcome current constraints – that dynamically changes the utilization of bridge capacities and hence the amounts to be transferred across networks. We illustrate several scenarios using numerical simulations.
FaST-GShare: Enabling Efficient Spatio-Temporal GPU Sharing in Serverless Computing for Deep Learning Inference

Serverless computing (FaaS) has been extensively utilized for deep learning (DL) inference due to the ease of deployment and payper-use benefits.However, existing FaaS platforms utilize GPUs in a coarse manner for DL inferences, without taking into account spatio-temporal resource multiplexing and isolation, which results in severe GPU under-utilization, high usage expenses, and SLO (Service Level Objectives) violation.There is an imperative need to enable an efficient and SLO-aware GPU-sharing mechanism in serverless computing to facilitate cost-effective DL inferences.In this paper, we propose FaST-GShare, an efficient FaaS-oriented Spatio-Temporal GPU Sharing architecture for deep learning inferences.In the architecture, we introduce the FaST-Manager to limit and isolate spatio-temporal resources for GPU multiplexing.In order to realize function performance, the automatic and flexible FaST-Profiler is proposed to profile function throughput under various resource allocations.Based on the profiling data and the isolation mechanism, we introduce the FaST-Scheduler with heuristic auto-scaling and efficient resource allocation to guarantee function SLOs.Meanwhile, FaST-Scheduler schedules function with efficient GPU node selection to maximize GPU usage.Furthermore, model sharing is exploited to mitigate memory contention.Our prototype implementation on the OpenFaaS platform and experiments on MLPerf-based benchmark prove that FaST-GShare can ensure resource isolation and function SLOs.Compared to the time sharing mechanism, FaST-GShare can improve throughput by 3.15x, GPU utilization by 1.34x, and SM (Streaming Multiprocessor) occupancy by 3.13x on average.
Virtual Reality for Supporting Knowledge Sharing: An Exercise of Technology Assessment

Knowledge sharing is an important process in knowledge management to foster collaboration. For itseffective management, it is often suggested to use the support of adequate technologies. Virtual reality (VR) is a promising technology whose application in business increasingly attracts attention in activities, such as business meetings or training, where participants need to share knowledge in a complex context with communicational, social, and management implications. Due to the immersive capabilities, VR may provide new ways of sharing knowledge. However, due to the novelty of this technology, research is needed to evaluate its potential and drawbacks. This study aims to contribute to further understanding of whether and how the introduction of VR in organizations can favour knowledge sharing and collaboration between employees and to evaluate its potential, challenges, and prospects. It is based on a qualitative exercise of technology assessment based on two approaches: technology monitoring and collection of expert opinions using interviews with experts in organisations specialised in VR. Our analysis shows that VR has the potential to break barriers of time and space that may hinder effective human interactions. It can simplify the sharing of notions, data, and, more generally, knowledge, and allow people to connect and communicate as active protagonists. However, adopting VR may require organisational changes and has some limitations. In addition,appropriate knowledge-sharing models for VR applications still need to be developed. Consequently, our paper argues that although much is possible today with VR, further developments are still needed for this technology to reach complete maturity.
Unsupervised-learning-based calibration method in microscopic fringe projection profilometry

Microscopic fringe projection profilometry (MFPP) technology is widely used in 3D measurement. The measurement precision performed by the MFPP system is closely related to the calibration accuracy. However, owing to the shallow depth of field, calibration in MFPP is frequently influenced by low-quality target images, which would generate inaccurate features and calibration parameter estimates. To alleviate the problem, this paper proposes an unsupervised-learning-based calibration robust to defocus and noise, which could effectively enhance the image quality and increase calibration accuracy. In this method, first, an unsupervised image deblurring network (UIDNet) is developed to recover a sharp target image from the deteriorated one. Free from capturing strictly paired images by a specific vision system or generating the dataset by simulation, the unsupervised deep learning framework can learn more accurate features from the multi-quality target dataset of convenient image acquisition. Second, multi-perceptual loss and Fourier frequency loss are introduced into the UIDNet to improve the training performance. Third, a robust calibration compensation strategy based on 2D discrete Fourier transform is also developed to evaluate the image quality and improve the detection accuracy of the reference feature centers for fine calibration. The relevant experiments demonstrate that the proposed calibration method can achieve superior performance in terms of calibration accuracy and measurement precision.
Image Covert Communication With Block Regulation

Image covert communication camouflages or conceals sensitive information in regular digital images during transmission to avoid detection or interception. It is widely used in military, intelligence, and law enforcement agencies. Data hiding is a feasible solution for covert communication, which embeds confidential information in cover media while preserving its visual appearance. In this letter, we propose an image reversible data hiding (RDH) scheme based on block regulation, which embeds secret data using pixel permutation. Traditional permutation-based data embedding methods usually suffer from error restoration of the cover image. To solve this problem, we propose a regulation operation to pre-process the image blocks, and thus most of the image blocks are converted into regular blocks. The regulated image blocks can be exploited to embed secret data without restoration error. In addition, we propose an adaptive strategy to generate permutation table dynamically, which further enhances the security level. Experimental results indicate that the proposed scheme provides a good embedding capacity and preserves a high level of image visual quality.