Reviewerly/exHarmony huggingface dataset:
Incidence of the Sample Size Distribution on One-Shot Federated Learning

Federated Learning (FL) is a learning paradigm where multiple nodes collaboratively train a model by only exchanging updates or parameters.This enables to keep data locally, therefore enhancing privacy -statement requiring nuance, e.g.memorization of training data in language models.Depending on the application, the number of samples that each node contains can be very different, which can impact the training and the final performance.This work studies the impact of the per-node sample size distribution on the mean squared error (MSE) of the one-shot federated estimator.We focus on one-shot aggregation of statistical estimations made across disjoint, independent and identically distributed (i.i.d.) data sources, in the context of empirical risk minimization.In distributed learning, it is well-known that for a total number of m nodes, each node should contain at least m samples to equal the performance of centralized training.In a federated scenario, this result remains true, but now applies to the mean of the per-node sample size distribution.The demo enables to visualize this effect as well as to compare the behavior of the FESC (Federated Estimation with Statistical Correction) algorithm -a weighting scheme which depends on the local sample size -with respect to the classical federated estimator and the centralized one, for a large collection of distributions, number of nodes, and features space dimension.
Pengaruh Kelekatan Anak dengan Orang Tua Terhadap Kecenderungan Gelotophobia

Gelotophobia is the fear of someone being laughed at or looks ridiculous in the eyes of others which can cause the individual to withdraw from the surrounding environment and experience symptoms such as palpitations, shaking, shortness of breath, difficulty speaking, or even no expression at all which begins in childhood as a child as a result of the failed relationship between the baby and his caregiver, traumatic events that are repeated because of being laughed at and ridiculed, so that the bond between the child and his parents is not strong which affects the emergence of gelotophobia in children, especially if the parents do not show affection and warmth. This study aims to determine the effectt of parental attachment to the tendency of gelotophobia among children aged 16-18 years in Indonesia. This research uses a quantitative method with simple linear regression data analysis. The participants in this study were 443 people who were selected using purposive sampling technique. Data collection using the Inventory of Parent and Peer Attachment (IPPA) instrument, and the GELOPH-15 scale modified by the researcher and collected by questionnaires. The results showed that there was a significant effect of parental attachment to the tendency of gelotophobia.
Design and Implementation of a Track n trace 2D Multi Scanner Bulk Code Reader for Pharmaceutical Packaging

With various compliance initiatives surrounding the traceability of medical drugs coming into prominence and with federal regulations becoming more stringent, there is a need to efficiently manage the supply chain process involving drug packaging. To enable traceability of the drugs being packaged, many manufacturers are implementing barcoding of product labels which contains a minimum of the attributes namely GTIN, lot number, serial numbers, expiry date, manufacturing dates, etc., to enable traceability of the packaged item. Currently, manufacturers are looking for ways to keep their supply chain moving at an efficient and optimal process. One of the challenges was to build automated equipment providing a well-defined and properly structured framework for functioning, designing, and implementing of Track N Trace Counterfeit Risk Management (CRM) application to increase the speed of the aggregation with the child-to-parent aggregation and generation of online case labels after the required child's Quantity in Multi carton aggregation station. Track N Trace Counterfeit Risk Management (CRM) allows the manufacturers to establish the parent-child relationship during packaging their drugs. Also, various guidelines US Drug Supply Chain Security Act (DSCSA), EU regulation (Falsified Medicines Directive-FMD), Brazilian Health Regulatory Agency (ANVISA), Indonesia's National Agency of Drug and Food Control (NADFC), India’s Directorate General of Foreign Trade - DGFT, and Australia TGA will need to be integrated into one single source of equipment that can meet with their barcoding regulation for tracking of drugs packed. Machine Vision systems are finding themselves in use in the pharma machinery and the "multi-carton" equipment that has been built for implementation for pharmaceutical packaging uses the vision systems architecture. We aimed to build a counterfeit risk management software solution that ensures compliance with various regulatory agencies by building a common framework that can be extensible for any further changes to the guidelines and/or additional regulatory guidelines from other countries. Lots of design architecture work has been refined through continuous process improvement practices and we developed a software tool that improves efficiency and requires minimal changes to the current manufacturing/packaging supply chain lines.
USING VIDEO REVIEW STRATEGY IN DEVELOPING STUDENTS’ WRITING SKILL

AbstractIt is evident that watching video activity cannot be separated from people’s life nowadays as technology becomes easier to reach. Watching video is becoming one teaching strategies for its enjoyment end enthusiasm experienced by the students. To make this activity even more advantageous in teaching and learning process, reviewing video after watching it becomes more suggested. The purpose of this study was to monitor the growth of SMA Negeri 12 Makassar's writing skills. This study's goal is to gather empirical evidence regarding the differences in writing test scores between students who received video review strategy instruction and those who did not. There are 60 students in the study's population. Class XI IPS served as the experimental group in the study, which included 21 students, and XI MIPA served as the control group, which included 39 students. This study used a quasi-experimental, non-equivalent control group design as its research methodology. Pretest and posttest were used to gather the data. It sought to ascertain whether using a video review strategy when teaching writing can improve students' writing skills.
TEACHER REINFORCEMENT TO THE STUDENTS TO UNDERSTAND THE SIX ENGLISH TENSES

This thesis presents research about “Teacher Reinforcement to the Students to Understand the Six English Tenses at the Tenth Grade of Sma Islam Athirah Bone”, the students always find the difficulties in their tenses understanding. Reinforcement method is one of methods which can help the students to solve their difficulties in their tenses understanding. The problem statements of this thesis is: does the use of reinforcement effective to the student understand the six tenses at the tenth grade students of Sma Islam Athirah Bone. This research uses quantitative method with pre-experimental design. The researcher applies cluster sampling by choosing IPS class of the first year that consists of 27 students with one group pre-test and post-test design. This research employs a test based on the problem statement investigated, it is multiple choice test. Multiple choice test consists of pre-test was given to the students to know the students’ tenses understanding before applying reinforcement method and post-test is given to the students to know the students’ tenses understanding after applying reinforcement method, while the data obtains through tests are analyzed with mean score and the formula of t-test. After several meetings, this research finds out the use of reinforcement method is sigificantly effective in increasing the students tenses understanding. The result of this research shows : (1) the mean score obtained by the students through pre-test is 41,03 and post-test is 54,11. (2) the t-test value is higher than the t-table (10,052.048). It means that there is a significant difference between the result of the students’ pre-test and post-test. Therefore, null hypothesis (H0) is rejected and alternative hypothesis (H1) is accepted.
A Simplified Un-Supervised Learning Based Approach for Ink Mismatch Detection in Handwritten Hyper-Spectral Document Images

Hyper-spectral imaging has become the latest trend in the field of optical imaging systems. Among various other applications, hyper-spectral imaging has been widely used for analysis of printed and handwritten documents. This paper proposes an efficient technique for estimating the number of different but visibly similar inks present in a Hyper spectral Document Image. Our approach is based on un-supervised learning and does not require any prior knowledge of the dataset. The algorithm was tested on the iVision HHID dataset and has achieved comparable results with the state of the algorithms present in the literature. This work can prove to be effective when employed during the early stages of forgery detection in Hyper-spectral Document Images.
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering

The K-subspaces (KSS) method is a generalization of the K-means method for subspace clustering. In this work, we present local convergence analysis and a recovery guarantee for KSS, assuming data are generated by the semi-random union of subspaces model, where $N$ points are randomly sampled from $K \ge 2$ overlapping subspaces. We show that if the initial assignment of the KSS method lies within a neighborhood of a true clustering, it converges at a superlinear rate and finds the correct clustering within $\Theta(\log\log N)$ iterations with high probability. Moreover, we propose a thresholding inner-product based spectral method for initialization and prove that it produces a point in this neighborhood. We also present numerical results of the studied method to support our theoretical developments.
Ensemble pruning via an integer programming approach with diversity constraints

Ensemble learning combines multiple classifiers in the hope of obtaining better predictive performance. Empirical studies have shown that ensemble pruning, that is, choosing an appropriate subset of the available classifiers, can lead to comparable or better predictions than using all classifiers. In this paper, we consider a binary classification problem and propose an integer programming (IP) approach for selecting optimal classifier subsets. We propose a flexible objective function to adapt to desired criteria of different datasets. We also propose constraints to ensure minimum diversity levels in the ensemble. Despite the general case of IP being NP-Hard, state-of-the-art solvers are able to quickly obtain good solutions for datasets with up to 60000 data points. Our approach yields competitive results when compared to some of the best and most used pruning methods in literature.
Object Recognition in Different Lighting Conditions at Various Angles by Deep Learning Method

Existing computer vision and object detection methods strongly rely on neural networks and deep learning. This active research area is used for applications such as autonomous driving, aerial photography, protection, and monitoring. Futuristic object detection methods rely on rectangular, boundary boxes drawn over an object to accurately locate its location. The modern object recognition algorithms, however, are vulnerable to multiple factors, such as illumination, occlusion, viewing angle, or camera rotation as well as cost. Therefore, deep learning-based object recognition will significantly increase the recognition speed and compatible external interference. In this study, we use convolutional neural networks (CNN) to recognize items, the neural networks have the advantages of end-to-end, sparse relation, and sharing weights. This article aims to classify the name of the various object based on the position of an object's detected box. Instead, under different distances, we can get recognition results with different confidence. Through this study, we find that this model's accuracy through recognition is mainly influenced by the proportion of objects and the number of samples. When we have a small proportion of an object on camera, then we get higher recognition accuracy; if we have a much small number of samples, we can get greater accuracy in recognition. The epidemic has a great impact on the world economy where designing a cheaper object recognition system is the need of time.
Review of Persuasive User Interface as Strategy for Technology Addiction in Virtual Environments

In the era of virtuality, the increasingly ubiquitous technology bears the challenge of excessive user dependency, also known as user addiction. Augmented reality (AR) and virtual reality (VR) have become increasingly integrated into daily life. Although discussions about the drawbacks of these technologies are abundant, their exploration for solutions is still rare. Thus, using the PRISMA methodology, this paper reviewed the literature on technology addiction and persuasive technology. After describing the key research trends, the paper summed up nine persuasive elements of user interfaces (UIs) that AR and VR developers could add to their apps to make them less addictive. Furthermore, this review paper encourages more research into a persuasive strategy for controlling user dependency in virtual-physical blended cyberspace.
Constant-Round Linear-Broadcast Secure Computation with Penalties

It is known that Bitcoin enables achieving fairness in secure computation by imposing monetary penalties on adversarial parties. This functionality is called secure computation with penalties. Bentov and Kumaresan (Crypto 2014) introduced the claim-or-refund functionality that can be implemented via Bitcoin. They achieved secure computation with penalties with $O(n)$ rounds and $O(n)$ broadcasts for any function, where $n$ is the number of parties. After that, Kumaresan and Bentov (CCS 2014) showed a constant-round protocol. Unfortunately, this protocol requires $O(n^2)$ broadcasts. As far as we know, no protocol achieves $O(1)$ rounds and $O(n)$ broadcasts based on Bitcoin. This work accomplishes such efficiency in secure computation with penalties. We first show a protocol in a slightly relaxed setting called secure computation with non-equivalent penalties. This setting is the same as secure computation with penalties except that every honest party receives more than a predetermined amount of compensation, while the previous one requires that every honest party receives the same amount of compensation. Namely, our setting allows the compensations for honest parties to be non-equivalent. Moreover, we present a technique to remove the non-equivalence of our protocol without sacrificing efficiency. We then propose a new ideal functionality called claim-refund-or-give that can be implemented via Bitcoin.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation

Watch-time prediction remains to be a key factor in reinforcing user engagement via video recommendations. It has become increasingly important given the ever-growing popularity of online videos. However, prediction of watch time not only depends on the match between the user and the video but is often mislead by the duration of the video itself. With the goal of improving watch time, recommendation is always biased towards videos with long duration. Models trained on this imbalanced data face the risk of bias amplification, which misguides platforms to over-recommend videos with long duration but overlook the underlying user interests. This paper presents the first work to study duration bias in watch-time prediction for video recommendation. We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved. To remove the undesired bias but leverage the natural effect, we propose a Duration Deconfounded Quantile-based (D2Q) watch-time prediction framework, which allows for scalability to perform on industry production systems. Through extensive offline evaluation and live experiments, we showcase the effectiveness of this duration-deconfounding framework by significantly outperforming the state-of-the-art baselines. We have fully launched our approach on Kuaishou App, which has substantially improved real-time video consumption due to more accurate watch-time predictions.
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task

In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies source-only, reference-only, and source-reference-combined evaluation scenarios into one single model. Specifically, during the model pre-training phase, we first apply the pseudo-labeled data examples to continuously pre-train UNITE. Notably, to reduce the gap between pre-training and fine-tuning, we use data cropping and a ranking-based score normalization strategy. During the fine-tuning phase, we use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past years' WMT competitions. Specially, we collect the results from models with different pre-trained language model backbones, and use different ensembling strategies for involved translation directions.
Rethinking: Deep-learning-based Demodulation and Decoding

In this paper, we focus on the demodulation/decoding of the complex modulations/codes that approach the Shannon capacity. Theoretically, the maximum likelihood (ML) algorithm can achieve the optimal error performance whereas it has $\mathcal{O}(2^k)$ demodulation/decoding complexity with $k$ denoting the number of information bits. Recent progress in deep learning provides a new direction to tackle the demodulation and the decoding. The purpose of this paper is to analyze the feasibility of the neural network to demodulate/decode the complex modulations/codes close to the Shannon capacity and characterize the error performance and the complexity of the neural network. Regarding the neural network demodulator, we use the golden angle modulation (GAM), a promising modulation format that can offer the Shannon capacity approaching performance, to evaluate the demodulator. It is observed that the neural network demodulator can get a close performance to the ML-based method while it suffers from the lower complexity order in the low-order GAM. Regarding the neural network decoder, we use the Gaussian codebook, achieving the Shannon capacity, to evaluate the decoder. We also observe that the neural network decoder achieves the error performance close to the ML decoder with a much lower complexity order in the small Gaussian codebook. Limited by the current training resources, we cannot evaluate the performance of the high-order modulation and the long codeword. But, based on the results of the low-order GAM and the small Gaussian codebook, we boldly give our conjecture: the neural network demodulator/decoder is a strong candidate approach for demodulating/decoding the complex modulations/codes close to the Shannon capacity owing to the error performance of the near-ML algorithm and the lower complexity.
IGN : Implicit Generative Networks

In this work, we build recent advances in distributional reinforcement learning to give a state-of-art distributional variant of the model based on the IQN. We achieve this by using the GAN model's generator and discriminator function with the quantile regression to approximate the full quantile value for the state-action return distribution. We demonstrate improved performance on our baseline dataset - 57 Atari 2600 games in the ALE. Also, we use our algorithm to show the state-of-art training performance of risk-sensitive policies in Atari games with the policy optimization and evaluation.
Geometrically Guided Integrated Gradients

Interpretability methods for deep neural networks mainly focus on the sensitivity of the class score with respect to the original or perturbed input, usually measured using actual or modified gradients. Some methods also use a model-agnostic approach to understanding the rationale behind every prediction. In this paper, we argue and demonstrate that local geometry of the model parameter space relative to the input can also be beneficial for improved post-hoc explanations. To achieve this goal, we introduce an interpretability method called "geometrically-guided integrated gradients" that builds on top of the gradient calculation along a linear path as traditionally used in integrated gradient methods. However, instead of integrating gradient information, our method explores the model's dynamic behavior from multiple scaled versions of the input and captures the best possible attribution for each input. We demonstrate through extensive experiments that the proposed approach outperforms vanilla and integrated gradients in subjective and quantitative assessment. We also propose a "model perturbation" sanity check to complement the traditionally used "model randomization" test.
Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs

Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. We use a local distance preserving loss that is based on the continuous k-nearest neighbours graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. We generalise this approach to hierarchical variational auto-encoders thus learning generative models with geometrically consistent latent and data spaces. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party's compute environment or hardware is common. If the third party is not fully trusted, it is desirable to obfuscate the nature of the inputs and outputs, so that the third party can not easily determine what specific task is being performed. Provably secure protocols for leveraging an untrusted party exist but are too computational demanding to run in practice. We instead explore a different strategy of fast, heuristic security that we call Connectionist Symbolic Pseudo Secrets. By leveraging Holographic Reduced Representations (HRR), we create a neural network with a pseudo-encryption style defense that empirically shows robustness to attack, even under threat models that unrealistically favor the adversary.