Reviewerly/exHarmony huggingface dataset:
Efficient GNN Explanation via Learning Removal-based Attribution

As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultaneously achieving high fidelity and low computational costs in generating explanations has been a challenge for current methods. In this work, we propose a framework of GNN explanation named LeArn Removal-based Attribution (LARA) to address this problem. Specifically, we introduce removal-based attribution and demonstrate its substantiated link to interpretability fidelity theoretically and experimentally. The explainer in LARA learns to generate removal-based attribution which enables providing explanations with high fidelity. A strategy of subgraph sampling is designed in LARA to improve the scalability of the training process. In the deployment, LARA can efficiently generate the explanation through a feed-forward pass. We benchmark our approach with other state-of-the-art GNN explanation methods on six datasets. Results highlight the effectiveness of our framework regarding both efficiency and fidelity. In particular, LARA is 3.5 times faster and achieves higher fidelity than the state-of-the-art method on the large dataset ogbn-arxiv (more than 160K nodes and 1M edges), showing its great potential in real-world applications. Our source code is available at https://anonymous.4open.science/r/LARA-10D8/README.md.
Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning

The usability of Reinforcement Learning is restricted by the large computation times it requires. Curriculum Reinforcement Learning speeds up learning by defining a helpful order in which an agent encounters tasks, i.e. from simple to hard. Curricula based on Absolute Learning Progress (ALP) have proven successful in different environments, but waste computation on repeating already learned behaviour in new tasks. We solve this problem by introducing a new regularization method based on Self-Paced (Deep) Learning, called Self-Paced Absolute Learning Progress (SPALP). We evaluate our method in three different environments. Our method achieves performance comparable to original ALP in all cases, and reaches it quicker than ALP in two of them. We illustrate possibilities to further improve the efficiency and performance of SPALP.
Fair yet Asymptotically Equal Collaborative Learning

In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a "rich get richer" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/"rich" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality.
End-to-End Neural Network Compression via $\frac{\ell_1}{\ell_2}$ Regularized Latency Surrogates

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model's Floating Point Operations (FLOPs) or for on-device latency via a novel $\frac{\ell_1}{\ell_2}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization. Crucially, it is fast and runs in almost the same amount of time as single model training; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve $50\%$ reduction in FLOPs with only $1\%$ drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve $15\%$ reduction in FLOPs, and $11\%$ reduction in on-device latency without drop in accuracy, while still requiring $3\times$ less training compute than SOTA compression techniques. Finally, for transfer learning on smaller datasets, our technique identifies $1.2\times$-$1.4\times$ cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.
Solving Partial Differential Equations for Physical and Chemical Problems Using Physics-Informed Neural Networks

Numerous physical and chemical problems at a high school level can be described by ordinary differential equations (ODEs) and partial differential equations (PDEs). However, the underlying equations troubled high school students because they often lack advanced mathematical skills, such as discrete calculus. Our goal is not to elaborate on those skills, but to offer a shortcut to the solution. In this paper, we demonstrated the use of Physics-Informed Neural Networks (PINNs), a neural network which solves the PDEs by incorporating the PDEs into the loss functions. The heat transfer equation and second order chemical kinetics are the two chosen model problems for high school seniors. Using PINNs, we were able to solve these two problems without recurring to university math. Hence, we strongly recommend peers to employ this method for physical or chemical problems for high school students and beyond.
Reconstruction Old Students Image Using The Autoencoder Method

Image Processing is image processing with a digital computer to produce new images according to the user's wishes. One implementation is to reconstruct the image. Through the extraction stages are able to get the characteristics of an image. The algorithm used is Adam Optimization, which is an extension of the stochastic gradient reduction that has just seen wider adoption for deep learning applications in computer vision and natural language processing. In this study using the autoencoder technique, which is one variant of artificial neural networks that are generally used to "encode" data. Autoencoder is trained to be able to produce the same output as the input. This image reconstruction aims to process an image whose quality is not very clear to be clear. This if possible can be used to detect someone's face from a distance of photos. In reconstructing this image through the encode and decode process by defining Conv2D and Maxpool, it is processed into training with epoch 100 times while for the prediction process using Keras library. Then the last one gets an accuracy of 0,022. The final result is the output of the reconstructed image and calculation graph.
Design of Balanced Scorecard as a School’s Performance Measurement

For schools as educational institutions, it is essential to identify their conditions and determine the right strategy for their sustainability. The research aimed to design the Balanced Scorecard (BSC) as a performance measurement tool at Sekolah XYZ. The research applied a qualitative method with Simple Research Design (SRD) as an analytical model. For data analysis, the research applied the stages of preparation in BSC. The primary data collection method was conducted through interviews with five key informants. The measurement indicators consisted of five indicators from a financial perspective (weight: 25%), three indicators from a customer perspective (weight: 25%), nine indicators from an internal business process perspective (weight: 30%), and five measurement indicators from the learning and growth perspective (weight: 20%). In addition, the BSC design was equipped with a validation system and an evaluation system to support the BSC implementation at Sekolah XYZ. The results show that based on the vision and mission of the organization, there are 12 objective strategies to be achieved, with 22 measurement indicators that are appropriately implemented at Sekolah XYZs. The BSC at Sekolah XYZ is necessary to be designed so its management can easily trace and find the causes of the school’s success or failure. The results of the BSC design at Sekolah XYZ can be used as recommendations for other schools that want to implement BSC, with adjustments according to the conditions of each school.
Digital Forensics Techniques and Trends: A Review

The research work presented in this paper aims to review Digital Forensics (DF) techniques and trends. As computer technology advances day by day, the chances of data being misused and tampered with are also growing daily. The advancement in technology results in various cyber-attacks on computers and mobile devices. DF plays a vital role in the investigation and prevention of cyber-attacks. DF can be used to find the shreds of evidence and prevent attacks from happening in the future. Earlier presented reviews highlighted specific issues in DF only. This paper explores deeply DF issues by highlighting domain-specific issues and possible helpful areas for DF. This article highlights the investigation process framework and related approaches for the digital investigation process. The cognitive and human factors that affect the DF process are also presented to strengthen the investigation process. Nowadays, many DF tools are available in the industry that helps in DF investigation. A comparative analysis of the four DF tools is also presented. Finally DF performance is discussed. The submitted work may help the researchers go deeper into DF and apply the best tools and models according to their requirements
The implementation of modular, extensible, interoperable autonomy (MEIA) for an ArduCopter-equipped drone executing various missions

We take the first step to demonstrate the feasibility of using Modular, Extensible, Interoperable Autonomy (MEIA) to support the Internet of Military Things (IoMT) by implementing MEIA for an Aurelia drone. We set up a pipeline for autonomy development (PAD) which includes tools and processes that facilitate autonomy development. We implement both the middleware and internal data management to run our MEIA solution which includes the autonomy algorithms capable of executing various missions. We provide the results of our final demonstrations which mark the completion of this initial step to demonstrate MEIA as a viable cross-domain, autonomy architecture for IoMT.
Human Action Recognition Using Key-Frame Attention-Based LSTM Networks

Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference.
IMPLEMENTASI MODEL PEMBELAJARAN MURDER TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS VIII SMP/MTS

The study aims to see the average mathematics learning outcomes of Class VIII SMP/MTs students through the implementation of the MURDER learning model. The form of research is a quasi-experimental with a research design of nonequivalent control group design. Data collection techniques used in the study were test techniques (pretest and posttest) and observation sheets. From the data on the results of observing teacher activity, the percentage of teacher activity obtained was 87.5% and at the 2nd and 3rd meetings, the percentage obtained was 94.3% and 100% respectively and this was included in the very good criteria and based on inferential data analysis, posttest data obtained . This means that the average mathematics learning outcomes of experimental class students using the MURDER learning model are better than the control class using the conventional learning model.
A New Hybrid Model to Predict Human Age Estimation from Face Images Based on Supervised Machine Learning Algorithms

Abstract Age estimation from face images is one of the significant topics in the field of machine vision, which is of great interest to controlling age access and targeted marketing. In this article, there are two main stages for human age estimation; the first stage consists of extracting features from the face areas by using Pseudo Zernike Moments (PZM), Active Appearance Model (AAM), and Bio-Inspired Features (BIF). In the second step, Support Vector Machine (SVM) and Support Vector Regression (SVR) algorithms are used to predict the age range of face images. The proposed method has been assessed utilizing the renowned databases of IMDB-WIKI and WIT-DB. In general, from all results obtained in the experiments, we have concluded that the proposed method can be chosen as the best method for Age estimation from face images.
Implementation of the STAD Model on Mobile Learning Applications to Improve Student Learning Outcomes at Vocational Schools in Malang

Software Modeling is a discipline that studies procedures in developing software using various development models. This study aims to produce mobile learning media products using the STAD model to improve student learning outcomes in software modeling subjects. The development of this learning media uses the ADDIE development model. The test subjects in this study were students of class XI RPL SMKN 8 Malang who were divided into 3 trial subjects who were randomly selected, namely: (1) 2 individual trial subjects, (2) 10 small group trial subjects, and (3) large group trial subjects totaling 30 people. The results of media expert validation show that 95.2 percent of learning media is very valid and based on material experts 88 percent of the material in learning media is very feasible to use in the learning process. In the individual trial process, a percentage of 80.9 percent was obtained with very valid criteria, through small group trials, a percentage of 83.2 percent was obtained with very valid criteria, and large group trials showed that the media was very valid with a percentage of 85.9 percent. The results of increasing learning outcomes can be seen from the comparison before and after using the media by comparing the average grades and classical completeness scores of students. Before using the media, an average score of 67 was obtained with 63.3 percent classical completeness and after using the media, an average score of 88 was obtained with 96.6% classical completeness. Based on these results it is concluded that learning media is appropriate for use in the learning process
LcmUNet: A Lightweight Network Combining CNN and MLP for Real-Time Medical Image Segmentation

In recent years, UNet and its improved variants have become the main methods for medical image segmentation. Although these models have achieved excellent results in segmentation accuracy, their large number of network parameters and high computational complexity make it difficult to achieve medical image segmentation in real-time therapy and diagnosis rapidly. To address this problem, we introduce a lightweight medical image segmentation network (LcmUNet) based on CNN and MLP. We designed LcmUNet's structure in terms of model performance, parameters, and computational complexity. The first three layers are convolutional layers, and the last two layers are MLP layers. In the convolution part, we propose an LDA module that combines asymmetric convolution, depth-wise separable convolution, and an attention mechanism to reduce the number of network parameters while maintaining a strong feature-extraction capability. In the MLP part, we propose an LMLP module that helps enhance contextual information while focusing on local information and improves segmentation accuracy while maintaining high inference speed. This network also covers skip connections between the encoder and decoder at various levels. Our network achieves real-time segmentation results accurately in extensive experiments. With only 1.49 million model parameters and without pre-training, LcmUNet demonstrated impressive performance on different datasets. On the ISIC2018 dataset, it achieved an IoU of 85.19%, 92.07% recall, and 92.99% precision. On the BUSI dataset, it achieved an IoU of 63.99%, 79.96% recall, and 76.69% precision. Lastly, on the Kvasir-SEG dataset, LcmUNet achieved an IoU of 81.89%, 88.93% recall, and 91.79% precision.
Federated Learning You May Communicate Less Often!

We investigate the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, we study the evolution of the generalization error with the number of communication rounds between the clients and the parameter server, i.e., the effect on the generalization error of how often the local models as computed by the clients are aggregated at the parameter server. We establish PAC-Bayes and rate-distortion theoretic bounds on the generalization error that account explicitly for the effect of the number of rounds, say $ R \in \mathbb{N}$, in addition to the number of participating devices $K$ and individual datasets size $n$. The bounds, which apply in their generality for a large class of loss functions and learning algorithms, appear to be the first of their kind for the FL setting. Furthermore, we apply our bounds to FL-type Support Vector Machines (FSVM); and we derive (more) explicit bounds on the generalization error in this case. In particular, we show that the generalization error of FSVM increases with $R$, suggesting that more frequent communication with the parameter server diminishes the generalization power of such learning algorithms. Combined with that the empirical risk generally decreases for larger values of $R$, this indicates that $R$ might be a parameter to optimize in order to minimize the population risk of FL algorithms. Moreover, specialized to the case $R=1$ (sometimes referred to as "one-shot" FL or distributed learning) our bounds suggest that the generalization error of the FL setting decreases faster than that of centralized learning by a factor of $\mathcal{O}(\sqrt{\log(K)/K})$, thereby generalizing recent findings in this direction to arbitrary loss functions and algorithms. The results of this paper are also validated on some experiments.
Tuning parameter selectors for bridge penalty based on particle swarm optimization method

The bridge penalty is widely used as a penalty for selecting and shrinking predictors in regression models.Although its effectiveness is sensitive to the parameters you decide to use for shrinking and adjusting.The shrinkage and tuning parameters of the bridge penalty are chosen concurrently, and a continuous optimization process called particle swarm optimization is proposed as a means to do this.If implemented, the proposed method will greatly facilitate regression modeling with superior prediction performance.The results show that the proposed method is effective in comparison to other well-known methods, but this varies greatly depending on the simulation setup and the real data application.
BPTI: Bilingual Printed Text Images Dataset for Recognition Purposes

Datasets of text images are important for optical text recognition systems. Such datasets can be used to enhance performance and recognition rates. In this research work, we present a bilingual dataset consists of Arabic/English text images to address the lack of availability of bilingual text databases. The presented dataset consists of 97812 text images, which are categorized into two groups; Scanned page and digitized line images. Images of the two forms are written with 10 fonts and four sizes, and prepared/scanned with four dpi resolutions. The dataset preparation process includes text collection, text editing, image construction, and image processing. The dataset can be used in optical text recognition, optical font recognition, language identification, and segmentation. Different text recognition and language identification experiments have been conducted using images of the dataset and Hidden Markov Model (HMM) classifier. For the digitized images recognition experiments, the best-achieved recognition correctness is 99.01% and the best accuracy is 99.01%. The font that has the highest recognition rates was Tahoma. For the scanned images recognition experiments, Tahoma has also shown the highest performance with 97.86% for correctness and 97.73% for accuracy. For the language identification experiments, Tahoma has shown the performance with 99.98% for word-language identification rate.
Recommendation System Using the K-Nearest Neighbor Approach: A Case Study of Dual Camera Quality as a Smartphone Selection Criterion

Many smartphones today need to be more precise about choosing one that suits the user's needs. In fact, smartphone sellers sometimes need help recommending smartphones that suit buyers' needs. Generally, buyers search for smartphone specifications with keywords they desire, but the results appear different from what they expected. Users need the main specifications, such as Random Access Memory (RAM) and Read Only Memory (ROM) capacity, battery, and high camera quality. This research aims to implement the K-Nearest Neighbor (KNN) algorithm for recommendation smartphone selection based on the criteria mentioned. The data test results show that the combination of KNN with four criteria has good performance, as indicated by the accuracy, precision, recall, and f-measure values of 95%, 94%, 97%, and 95%, respectively.