Reviewerly/exHarmony huggingface dataset:
Combining Adversaries with Anti-adversaries in Training

Adversarial training is an effective learning technique to improve the robustness of deep neural networks. In this study, the influence of adversarial training on deep learning models in terms of fairness, robustness, and generalization is theoretically investigated under more general perturbation scope that different samples can have different perturbation directions (the adversarial and anti-adversarial directions) and varied perturbation bounds. Our theoretical explorations suggest that the combination of adversaries and anti-adversaries (samples with anti-adversarial perturbations) in training can be more effective in achieving better fairness between classes and a better tradeoff between robustness and generalization in some typical learning scenarios (e.g., noisy label learning and imbalance learning) compared with standard adversarial training. On the basis of our theoretical findings, a more general learning objective that combines adversaries and anti-adversaries with varied bounds on each training sample is presented. Meta learning is utilized to optimize the combination weights. Experiments on benchmark datasets under different learning scenarios verify our theoretical findings and the effectiveness of the proposed methodology.
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks

Generative adversarial networks (GANs) are known for their strong abilities on capturing the underlying distribution of training instances. Since the seminal work of GAN, many variants of GAN have been proposed. However, existing GANs are almost established on the assumption that the training dataset is clean. But in many real-world applications, this may not hold, that is, the training dataset may be contaminated by a proportion of undesired instances. When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution). To learn the target distribution from contaminated datasets, two purified generative adversarial networks (PuriGAN) are developed, in which the discriminators are augmented with the capability to distinguish between target and contaminated instances by leveraging an extra dataset solely composed of contamination instances. We prove that under some mild conditions, the proposed PuriGANs are guaranteed to converge to the distribution of desired instances. Experimental results on several datasets demonstrate that the proposed PuriGANs are able to generate much better images from the desired distribution than comparable baselines when trained on contaminated datasets. In addition, we also demonstrate the usefulness of PuriGAN on downstream applications by applying it to the tasks of semi-supervised anomaly detection on contaminated datasets and PU-learning. Experimental results show that PuriGAN is able to deliver the best performance over comparable baselines on both tasks.
Policy-Adaptive Estimator Selection for Off-Policy Evaluation

Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of actions, and noise level. Thus, the data-driven estimator selection problem is becoming increasingly important and can have a significant impact on the accuracy of OPE. However, identifying the most accurate estimator using only the logged data is quite challenging because the ground-truth estimation accuracy of estimators is generally unavailable. This paper thus studies this challenging problem of estimator selection for OPE for the first time. In particular, we enable an estimator selection that is adaptive to a given OPE task, by appropriately subsampling available logged data and constructing pseudo policies useful for the underlying estimator selection task. Comprehensive experiments on both synthetic and real-world company data demonstrate that the proposed procedure substantially improves the estimator selection compared to a non-adaptive heuristic. Note that complete version with technical appendix is available on arXiv: http://arxiv.org/abs/2211.13904.
The Moderating Role of Social Capital in the Relationship Between Green Supply Chain Management and Sustainable Business Performance: Evidence from Jordanian SMEs

Today's businesses are very worried about environmental issues because many corporate activities could harm the environment.Green Supply Chain Management (GSCM) approaches have increased in popularity in this field.However, small and medium-sized businesses (SMEs) are often less inclined to adopt environmental practices compared to large companies.Although few earlier researchers investigated the direct impact of GSCM on Sustainable Business Performance (SBP), this study makes a unique contribution by offering a comprehensive view of assessing the moderating role of social capital (SC) in the influence of implementing two main GSCM practices (which are Internal-GSCM and External-GSCM) on sustainability performance.A survey method was used to collect data from 420 Jordanian SMEs, and the partial least squares (PLS) method is applied to test hypothesized relationships.The findings reveal that the practices of GSCM, significantly enhance SBP.Surprisingly, SC does not moderate the relationships between the GSCM practices and SBP.This study emphasizes the value of GSCM practices in enhancing SBP among Jordanian SMEs.It is implied that adopting GSCM practices help SMEs enhance the efficient consumption of resources and reduce production cost, improving SBP.Most importantly, the sustainable or GSCM plays a significant role in reducing waste and harmful emissions, thus, improving environmental quality.Hence, this study provides some insights into the type of GSCM practices the SMEs require to apply to improve the desired sustainability performance.This study adds new insights to the extant literature while providing significant new avenues for future research.
Errors in Using English Verbs by EFL Students: Tenses and Aspects Analyses

Many scholars have conducted research on error analysis in English writing. However, scientific research on errors in using English verbs in terms of tense and aspect is still in its early stages. As a result, the purpose of this study was to identify the errors or mistakes in sentence translation from Indonesian to English that were related to tense and aspects. 25 English department students sat in the English syntax class and translated 12 Indonesian sentences into English. The findings reveal a total of 126 errors in sentence translation in terms of tense and aspect. The past perfect continuous tense, past perfect tense, present perfect continuous tense, future perfect continuous tense, future perfect tense, and future continuous tense are the most common errors. These findings indicate that the students were unfamiliar with the formulas and functions of these tenses, as well as aspect interpretation. As a result, English lecturers/teachers should prioritize English tenses and aspects for EFL students from the start.&lt;p&gt; &lt;/p&gt;
A Domain Question Answering Algorithm Based on the Contrastive Language-Image Pretraining Mechanism

Research on specific domain question-answering technology has become important with the increasing demand for intelligent question-answering systems.This paper proposes a domain question-answering algorithm based on the CLIP mechanism to improve the accuracy and efficiency of interaction.First, this paper reviewed relevant technologies involved in the question-answering field.Then, the question-answering model based on the CLIP mechanism was produced, including its design, implementation, and optimization.It also described the construction process of the specific domain knowledge graph, including graph design, data collection and processing, and graph construction methods.The paper compared the performance of the proposed algorithm with classic question-answering algorithms BiDAF, R-Net, and XLNet models, using a military domain dataset.The experimental results show that the proposed algorithm has advanced performance, with an F1 score of 84.6% on the constructed military knowledge graph test set, which is at least 1.5% higher than other models.We conduct a detailed analysis of the experimental results, which illustrates the algorithm's advantages in accuracy and efficiency, as well as its potential for further improvement.These findings demonstrate the practical application potential of the proposed algorithm in the military domain.
Snack Vending Machine Using QR Code as Payment Method

Vending machines can be used as a solution to overcome losses for selling and make the payment system easier. This study aims to design a snack vending machine with payment system using an Android-based QR Code scan application in which there is a balance as electronic money. In the payment application there is also an admin account to top up balances and view sales history, making it easier for sellers to monitor sales results. This snack vending machine uses ESP32 to store data as well as transmit data from every activity that occurs. There are 4 types of snacks in this vending machine. Selection of the type of snack using the Keypad and to regulate the release of the snack using a servo motor connected to a spiral wire. 16x2 LCD to display data and an Infrared Avoid Obstacle sensor to stop the servo from rotating when it detects food falling in front of it. Testing on a snack vending machine using a QR Code was carried out by trying to purchase 4 types of snacks, with each trial 10 times. The whole test is successful, the tool can issue snacks and transactions on the payment application run properly.
Enhancing Reading Comprehension by Using the Strategies of Scanning and Skimming for Institute of Technology Indonesia CIE's Fourth Level Students

&lt;p&gt;The purpose of this research is to determine the students' of CIE fourth level achievement in reading skill by scanning and skimming strategies, as well as the effectiveness of scanning and skimming strategies at Institute of Technology Indonesia. The pre-experimental research technique was employed by using a one-group pretest-posttest design. The population of this research was a fourth level CIE class of 60 students, and the sample was a class of IV-A pupils of 20 students. The instrument was a 25-question of multiple-choice survey. The findings prior to utilizing the scanning and skimming method, pretest; the maximum score was 80, the lowest score was 56, the mean score was 70.6, the median score was 72; and the mode score was 72, the standard deviation was 8.13, and the variance was 66.15. The posttest findings of the scanning and skimming approach were as follows: the highest score was 100, the lowest score was 72, the mean score was 92, the median score was 96, the mode score was 96, the variance was 80.84, and the standard deviation was 8,99. Based on the findings of the research, the tcount was 4.352 with a significant threshold of 0.05 and the ttable was 2,086, and the scores were compared. As a result, tcount exceeds ttable (4.352 &amp;gt; 2,086). It signifies that the research hypothesis (H1) has been accepted. In conclusion, utilizing scanning and skimming strategies has a considerable influence on reading comprehension at the fourth level students of Institute of Technology Indonesia.&lt;/p&gt;
The Influence of Clean and Healthy Behavior Counseling on Students' Knowledge of SDN 211/IV, Jambi City

Unhealthy behavior can lead to more serious problems such as the spread of infectious diseases. PHBS in schools includes actions taken by students, teachers, and the community around the school based on awareness of learning outcomes. This study aimed to determine the difference in knowledge before and after being given counseling about PHBS for students at SDN 211/IV Jambi City. This quantitative study uses a true experimental pre-test and post-test control group design approach. The sample in this study was 60 respondents. The data in this study were analyzed using statistical tests, namely the Paired t-test and the Independent t-test. The results showed that the value of students' knowledge about PHBS before being given counseling was an average of 11.94 and the value of students' knowledge about PHBS after being given counseling was an average of 14.58. The results of the t-test showed that there was a difference in the average score between the pre-test and post-test of 2.64 and the value of p = 0.000 (p-value &lt;0.05), meaning that there was a difference in students' knowledge before and after giving health counseling about PHBS in SDN 211/IV Jambi City. This study suggests that schools are encouraged to pay attention to students in terms of Clean and Healthy Behavior (PHBS) by including PHBS points in the material given to students.
Goal-Conditioned Q-learning as Knowledge Distillation

Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments, such as automatic curriculum generation and goal relabeling. In this work, we explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. In particular: the current Q-value function and the target Q-value estimate are both functions of the goal, and we would like to train the Q-value function to match its target for all goals. We therefore apply Gradient-Based Attention Transfer (Zagoruyko and Komodakis 2017), a knowledge distillation technique, to the Q-function update. We empirically show that this can improve the performance of goal-conditioned off-policy reinforcement learning when the space of goals is high-dimensional. We also show that this technique can be adapted to allow for efficient learning in the case of multiple simultaneous sparse goals, where the agent can attain a reward by achieving any one of a large set of objectives, all specified at test time. Finally, to provide theoretical support, we give examples of classes of environments where (under some assumptions) standard off-policy algorithms such as DDPG require at least O(d^2) replay buffer transitions to learn an optimal policy, while our proposed technique requires only O(d) transitions, where d is the dimensionality of the goal and state space. Code and appendix are available at https://github.com/alevine0/ReenGAGE.
Locally Optimal Solutions in the Shortest Unclosed Path Search Problem

The paper proposes methods for improving solutions of the shortest unclosed path (SUP) search problem by applying a brute force algorithm to parts of the path obtained by the greedy algorithms proposed in the previous work. This idea is basis for locally optimal solution applying. The auxiliary method is also described for the expanding the feasible domain of these algorithms. The work also proposes the selection of the several paths by the dissimilarity function for the further local optimization. This dissimilarity function defines a distance between two paths. The aim of the paths selecting is to search the paths which a more promising to improve. The paper compares the solutions obtained by several described algorithms for locally optimal solution applying. These algorithms allow to raise the results estimated in the previous work. This work also demonstrates multidimensional data visualization based on the shortest unclosed path by column chart of object distribution along the path and projection onto the path.
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control

Uncertainty quantification has been extensively used as a means to achieve efficient directed exploration in Reinforcement Learning (RL). However, state-of-the-art methods for continuous actions still suffer from high sample complexity requirements. Indeed, they either completely lack strategies for propagating the epistemic uncertainty throughout the updates, or they mix it with aleatoric uncertainty while learning the full return distribution (e.g., distributional RL). In this paper, we propose Wasserstein Actor-Critic (WAC), an actor-critic architecture inspired by the recent Wasserstein Q-Learning (WQL), that employs approximate Q-posteriors to represent the epistemic uncertainty and Wasserstein barycenters for uncertainty propagation across the state-action space. WAC enforces exploration in a principled way by guiding the policy learning process with the optimization of an upper bound of the Q-value estimates. Furthermore, we study some peculiar issues that arise when using function approximation, coupled with the uncertainty estimation, and propose a regularized loss for the uncertainty estimation. Finally, we evaluate our algorithm on standard MujoCo tasks as well as suite of continuous-actions domains, where exploration is crucial, in comparison with state-of-the-art baselines. Additional details and results can be found in the supplementary material with our Arxiv preprint.
LoNe Sampler: Graph Node Embeddings by Coordinated Local Neighborhood Sampling

Local graph neighborhood sampling is a fundamental computational problem that is at the heart of algorithms for node representation learning. Several works have presented algorithms for learning discrete node embeddings where graph nodes are represented by discrete features such as attributes of neighborhood nodes. Discrete embeddings offer several advantages compared to continuous word2vec-like node embeddings: ease of computation, scalability, and interpretability. We present LoNe Sampler, a suite of algorithms for generating discrete node embeddings by Local Neighborhood Sampling, and address two shortcomings of previous work. First, our algorithms have rigorously understood theoretical properties. Second, we show how to generate approximate explicit vector maps that avoid the expensive computation of a Gram matrix for the training of a kernel model. Experiments on benchmark datasets confirm the theoretical findings and demonstrate the advantages of the proposed methods.
A Gift from Label Smoothing: Robust Training with Adaptive Label Smoothing via Auxiliary Classifier under Label Noise

As deep neural networks can easily overfit noisy labels, robust training in the presence of noisy labels is becoming an important challenge in modern deep learning. While existing methods address this problem in various directions, they still produce unpredictable sub-optimal results since they rely on the posterior information estimated by the feature extractor corrupted by noisy labels. Lipschitz regularization successfully alleviates this problem by training a robust feature extractor, but it requires longer training time and expensive computations. Motivated by this, we propose a simple yet effective method, called ALASCA, which efficiently provides a robust feature extractor under label noise. ALASCA integrates two key ingredients: (1) adaptive label smoothing based on our theoretical analysis that label smoothing implicitly induces Lipschitz regularization, and (2) auxiliary classifiers that enable practical application of intermediate Lipschitz regularization with negligible computations. We conduct wide-ranging experiments for ALASCA and combine our proposed method with previous noise-robust methods on several synthetic and real-world datasets. Experimental results show that our framework consistently improves the robustness of feature extractors and the performance of existing baselines with efficiency.
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness

Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks. Yet the observed degradation in policy performance caused by imperceptible worst-case policy dependent translations along high sensitivity directions (i.e. adversarial perturbations) raises concerns on the robustness of deep reinforcement learning policies. In our paper, we show that these high sensitivity directions do not lie only along particular worst-case directions, but rather are more abundant in the deep neural policy landscape and can be found via more natural means in a black-box setting. Furthermore, we show that vanilla training techniques intriguingly result in learning more robust policies compared to the policies learnt via the state-of-the-art adversarial training techniques. We believe our work lays out intriguing properties of the deep reinforcement learning policy manifold and our results can help to build robust and generalizable deep reinforcement learning policies.
Speed Analysis of Binary Search and Interpolation Search for Searching Identification Numbers on National Identity Cards

Aims: The purpose of the work is to make comparisons and find the best search algorithm between binary search and interpolation search algorithms for searching identity number on Indonesian identity cards.&#x0D; Study Design: This study was designed by implementing binary and interpolation algorithms into C language, with a dataset of 5000.&#x0D; Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between November 2022 until March 2023.&#x0D; Methodology: Binary algorithms and interpolation search will be tested by searching for identity number on identity card data, totaling 5000 data. To measure the level of speed and effectiveness of the two algorithms, this will be done by trying to search for data in various amounts, namely 1000, 2000 and 5000 data, with the data sought between the two algorithms having the same value. Testing is carried out by implementing the two algorithms into C language to get the number of loops and execution time, and testing of the algorithms on each sample dataset is carried out five times to get the average value of the test results.&#x0D; Results: The results of the tests that have been carried out between the binary search and interpolation search algorithms from the existing dataset, found that the number of loops in the interpolation loop is 36.57% better than the binary search algorithm, while for execution time, binary search has a faster execution speed of 12.43% than interpolation search.&#x0D; Conclusion: It can be concluded that if the dataset is large enough, such as identity card data on Indonesian citizens, and if the computer specifications are adequate, binary search, which has a faster execution time, should be used; however, if the computer specifications are inadequate, interpolation search, which has a smaller number of loop processes, should be used.
A Stateless Key Updation Framework for Enhancing Data Security

Side-channel analysis (SCA) exploits the data leaked through outputs to reveal the secret key of cytological modules. The important threat of SCA lies within the ability to mount attacks over tiny components of the key and to mixture data over completely different encryptions. The threat of SCA are often dynamic the secret key at each run. Indeed, several contributions within the domain of outflow resilient cryptography tried to attain this goal. However, the projected solutions were computationally intensive and were designed to resolve the matter of the present cytologicalschemes.The generic framework of light-weight key change which will shield the present cytological standards and valuate the minimum necessities for heuristic SCA-security. Propose a whole resolution to shield the implementation of any standard mode of advanced cryptography customary. The answer maintains identical level of SCA-security (and typically better) because the state of the art, at a negligible space overhead whereas doubling the turnout.