Reviewerly/exHarmony huggingface dataset:
Towards Decision-Friendly AUC: Learning Multi-Classifier with AUCµ

Area Under the ROC Curve (AUC) is a widely used ranking metric in imbalanced learning due to its insensitivity to label distributions. As a well-known multiclass extension of AUC, Multiclass AUC (MAUC, a.k.a. M-metric) measures the average AUC of multiple binary classifiers. In this paper, we argue that simply optimizing MAUC is far from enough for imbalanced multi-classification. More precisely, MAUC only focuses on learning scoring functions via ranking optimization, while leaving the decision process unconsidered. Therefore, scoring functions being able to make good decisions might suffer from low performance in terms of MAUC. To overcome this issue, we turn to explore AUCµ, another multiclass variant of AUC, which further takes the decision process into consideration. Motivated by this fact, we propose a surrogate risk optimization framework to improve model performance from the perspective of AUCµ. Practically, we propose a two-stage training framework for multi-classification, where at the first stage a scoring function is learned maximizing AUCµ, and at the second stage we seek for a decision function to improve the F1-metric via our proposed soft F1. Theoretically, we first provide sufficient conditions that optimizing the surrogate losses could lead to the Bayes optimal scoring function. Afterward, we show that the proposed surrogate risk enjoys a generalization bound in order of O(1/√N). Experimental results on four benchmark datasets demonstrate the effectiveness of our proposed method in both AUCµ and F1-metric.
Multi-task System for Multiple Languages Translation Using Transformers

In this work, we explore the problem of the multi-task Neural Machine Translation (NMT) model which can simultaneously translate given sentences from multiple source languages to a single target language. Our solution is supported by a recently proposed transformer model, where we extended the neural machine translation to a multi-task system that shares the common target language translation and separates the modeling of different source language representations. The multi-task system could be helpful in scenarios where insufficient data or imbalanced data exists. As the multi task system shares the parameters of different language pairs, the language pair with inadequate data could utilize the resources from data-abundant language pairs and can improve the translation quality. Our approach is also considerably faster learning when compared to separately trained NMTs.
Optimized Algorithms for Quantum Machine Learning Circuits

Machine learning models for quantum computers are more powerful in providing faster computation, and better generalization with fewer data achieving potential quantum advantage. This paper focuses on the most important quantum machine learning algorithms for the quantum environments. Quantum machine learning is applied to the data generated from quantum experiments and is capable of learning some properties from quantum states under certain conditions. Quantum machine learning algorithms map the input dataset to a higher dimensional feature space using kernel functions. This paper also highlights the learning algorithm on quantum state which are proven for their efficiency using multiple copies of quantum states. Further, the paper explains the techniques that measure the loss function to optimize the learning algorithm and minimize the loss function.
How does open science &amp;#8216;democratise&amp;#8217; and &amp;#8216;collectivise&amp;#8217; research?

A recent article in &lt;em&gt;The Scientist &lt;/em&gt;discusses the newly launched Higher Education Leadership Initiative for Open Scholarship (HELIOS). Composed of ‘leaders’ from over 75 US colleges and universities, HELIOS is committed to incentivising open science practices in order to make research more research more ‘inclusive, transparent, and efficient’. It is an approach designed to reorient assessment mechanisms towards open science practices,
RLC-Servo: a full-automatic hand-eye cooperative servo model based on reinforcement learning

Hand-eye cooperative servo refers that the robot can perform corresponding action adjustments by analyzing images taken from the camera to achieve the purposes like object tracking and grasping in a calibrated visual system. The process of analyzing images includes a series of algorithms such as feature extraction and feature matching. The traditional visual servo model provides a generally effective solution but cannot control the motion velocity of robotic arms efficiently, which brings two obvious drawbacks: The too-fast motion speed may cause the object falling out of the FOV of the camera, while the too-slow motion speed will cost a long time to convergence. In this paper, we propose an upgraded servo algorithm by introducing Reinforcement Learning (RL) into the process of controlling. We define the servo error based on feature points and divide it into a certain number of sections to construct the state space. The agent is trained to adjust the robotic action according to the specific servo error, thereby accelerating the servo convergence process while maintaining the feature points visible. We first perform extensive experiments in the virtual simulated scene and then build a real-world visual system for evaluation. Results show that the RL-based servo model is capable of reducing 59.6% of iterations in the virtual scene and 16.9% of iterations in the real scene by contrast with the traditional methods, which illustrates the robustness and efficiency of our proposed model.
Buildings with Photovoltaic Glazing System Using IoT

Buildings with photovoltaic glazing systems using IoT (BIPV) is an innovative technology that has the potential to not only generate electricity but also reduce energy consumption. BIPV systems are integrated into the building envelope, which means they serve the dual purpose of generating power and acting as a structural element of the building. This article is explaining the working and IoT-based control mechanism of a prototype model of Buildings with photovoltaic glazing systems, also called BIPV (Building-integrated photovoltaics). BIPV is an innovative technology that can reduce energy consumption apart from electricity production. As the population is increasing there is also an increase in the usage of electricity. To accommodate easy access to electricity, we designed a prototype model of photovoltaic glazing system using IoT. The integration of photovoltaic (PV) systems into buildings has gained significant attention due to the increasing demand for renewable energy sources and the need for sustainable building practices. IoT-enabled PV systems utilize smart sensors, data analytics, and automation to monitor and optimize solar energy’s generation, consumption, and storage, resulting in improved energy management and cost savings. The future of PV glazing systems looks promising with potential advancements in efficiency, integration, aesthetics, energy storage, smart connectivity, cost reduction, and sustainable building certifications.
AN ANALYSIS OF STUDENTS ABILITY IN READING COMPREHENSION DURING THE PANDEMIC OF COVID 19 AT X CLASS IN SMA NEGERI I BADAR ACADEMIC YEAR 2021/2022

This study deals with the students’ ability in reading comprehension during tha pandemic of covid 19 at X class in SMA Negeri 1 Badar Academic Year 2021/2022. Its objective is to know student ability in reading comprehension during pandemic Covid 19. The research is done by collecting the data and scoring the result of the answer and quantitative method to find out the result. This research is enough to know the aim of this research is an analyzing of students’ in reading ability during the Pandemic of Covid 19 at X Class in SMA Negeri 1 Badar by focusing on Recount Text ability.The data was analyzed and researcher found The students who were taught The Pandemic Of Covid 19 got better achievement that those who were taught by using conventional technique that was Teacher. the highest score of the pre-test was 65 and the lowest was 45. While the highest score of post-test was 90 and the lowest was 70. The result of means of Pre-Test was 54.67 while the result of deviation standard was 6.18 And the result of means of Post-Test was 80.33. While the result of deviation standard was 5.91. From this research, it was found that the can cause a positive, which is proven from the result of the test, t_test&gt;t_tableor ( 16.55&gt; 2.04 ) there is significance Students Ability in Reading Chomprehension During Skill The Pandemic Of Covid 19
YouTube Spam Comment Detection

YouTube is an open platform where anyone can create content and share it with the world via their own YouTube Channel. YouTube offers a wide range of content for its audience of all ages, including comedy shows, vlogging, cooking recipes, hacks, unboxing videos, and other content are available. If you’re a frequent YouTube viewer and have spent time reading YouTube comments, you might have noticed a repetitive pattern, along with abuse or childish insults and dubious viewpoints, which is commonly referred to as spam. The presence of such spam comments has a negative impact on a channel's reputation as well as the experience of normal users. YouTube has addressed the problem with very limited methods, such as YouTube Studio, which offers a “Report” or “Report as spam or abuse” option for comments, allowing the community to control the number of spam comments left on videos. While reviewing comments in YouTube Studio, creators can also report spam. However, these methods have attested to be not fruitful, as spam creators discovered methods to circumvent such heuristics. As a result, in this paper, we use six traditional machine learning algorithms to detect spam comments: Gaussian Naive Bayes, Support Vector Machine, Linear Classifier, AdaBoost, Random Forest, and Decision Tree Classifier. The data for the model was obtained from the Kaggle Repository, and the experiments were carried out using Google Collaboratory. Thus, we can achieve an accuracy of 91.39% with the AdaBoost Classifier, beating the current course of action by around 20%, and have shown to be extremely successful at detecting and removing spam comments.
PENGEMBANGAN SISTEM E-LEARNING BERBASIS MOODLE DI SMK N 3 PARIAMAN

The purpose of this study is the development of an E-Learning system as a learning medium at SMK N 3 Pariaman. The research on developing a moodle-based e-learning system at SMK N 3 Pariaman is based on the moodle method of R&amp;D (Research and Development). This research uses research and development (R&amp;D) methods. Research and development Research and development (R&amp;D) is a research method used to produce certain products, and test the effectiveness of these products. The research and development procedure basically consists of two main objectives, namely: developing the product, and testing the effectiveness of the product in achieving the goal. The first objective is referred to as the bearer function while the second objective is referred to as validation. The results of this research development of a Moodle-based Learning Management System (LMS) which has been conducted a Moodle-based Learning Management System (LMS) Validity Test at SMK N 3 Pariaman, states that a Moodle-based Learning Management System (LMS) with a material validation test value of 0.709% and a value of 0.709%. media validation test 0.780% declared valid.
Lattice Cryptography-Based Geo-Encrypted Contact Tracing for Infection Detection

The world has already witnessed many epidemic diseases in the past years, like H1N1, SARS, Ebola, etc. Now, Covid-19 has also been added to list, which is declared as pandemic by World Health Organization. One of the most commonly used methods to tackle the spread of such diseases is using mobile applications to perform contact tracing of the infected person. However, contact tracing applications involve transmitting sensitive location-based data of the infected person to the government servers. Therefore, recently this has raised a lot of concerns regarding the privacy of the infected persons. This work proposes a lightweight and secure encryption scheme, based on location-based encryption which can be used to transfer the location data to the server without compromising its security. The main aim of the work is to design an algorithm in such a way that the encrypted transferred data can only be decrypted at the server and in-between data leakage can be prevented. This work proposes to use location-based encryption combined with Learning with Errors problems in Lattices, which can provide a solution to privacy concerns in contact tracing, which will even be applicable in the post-quantum period.
Self-Paced Learning Based Graph Convolutional Neural Network for Mixed Integer Programming (Student Abstract)

Graph convolutional neural network (GCN) based methods have achieved noticeable performance in solving mixed integer programming problems (MIPs). However, the generalization of existing work is limited due to the problem structure. This paper proposes a self-paced learning (SPL) based GCN network (SPGCN) with curriculum learning (CL) to make the utmost of samples. SPGCN employs a GCN model to imitate the branching variable selection during the branch and bound process, while the training process is conducted in a self-paced fashion. Specifically, SPGCN contains a loss-based automatic difficulty measurer, where the training loss of the sample represents the difficulty level. In each iteration, a dynamic training dataset is constructed according to the difficulty level for GCN model training. Experiments on four NP-hard datasets verify that CL can lead to generalization improvement and convergence speedup in solving MIPs, where SPL performs better than predefined CL methods.
Latent Constraints on Unsupervised Text-Graph Alignment with Information Asymmetry

Unsupervised text-graph alignment (UTGA) is a fundamental task that bidirectionally generates texts and graphs without parallel data. Most available models of UTGA suffer from information asymmetry, a common phenomenon that texts and graphs include additional information invisible to each other. On the one hand, these models fail to supplement asymmetric information effectively due to the lack of ground truths. On the other hand, it is challenging to indicate asymmetric information with explicit indicators because it cannot be decoupled from the data directly. To address the challenge posed by information asymmetry, we propose the assumption that asymmetric information is encoded in unobservable latent variables and only affects the one-way generation processes. These latent variables corresponding to asymmetric information should obey prior distributions recovered approximately from original data. Therefore, we first propose a taxonomy of the latent variable that classifies the latent variable into transferrable (TV) and non-transferable (NTV) variables and further distinguish NTV as the dependent variable (DV) and the independent variable (IV). Next, we propose three latent VAE-based regularizations on TV, DV, and IV to constrain their distributions to well-designed prior distributions to introduce asymmetric information into models and enhance the preservation of shared contents. Finally, we impose the three proposed constraints on a cycle-consistent learning framework, back-translation (BT), named ConstrainedBT. Experimental results on three UTGA tasks demonstrate the effectiveness of ConstrainedBT on the information-asymmetric challenge.
Adaptive Mixing of Auxiliary Losses in Supervised Learning

In many supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective. For instance, knowledge distillation aims to mimic outputs of a powerful teacher model; similarly, in rule-based approaches, weak labeling information is provided by labeling functions which may be noisy rule-based approximations to true labels. We tackle the problem of learning to combine these losses in a principled manner. Our proposal, AMAL, uses a bi-level optimization criterion on validation data to learn optimal mixing weights, at an instance-level, over the training data. We describe a meta-learning approach towards solving this bi-level objective, and show how it can be applied to different scenarios in supervised learning. Experiments in a number of knowledge distillation and rule denoising domains show that AMAL provides noticeable gains over competitive baselines in those domains. We empirically analyze our method and share insights into the mechanisms through which it provides performance gains. The code for AMAL is at: https://github.com/durgas16/AMAL.git.
Prototyping Logic-Based AI Services with LogicUS

Currently, there is renewed interest in logic-related solutions for AI and Computer Science. The availability of software tools to support the realization of such studies (both as powerful and versatile prototyping tools and as teaching tools) has become a necessity. Intending to contribute to this field, we present a tool that allows the unification of different logic tasks, focused on Computer Logic but adaptable to the treatment in several subfields, contexts, and abstraction levels (LogicUS-LIB, LogicUS-NB, LogicUS-GUI). The tool provides a sound framework for two activity fields. On the one hand, in the topic of logic-based systems research, prototyping is facilitated in a relatively fast, simple, and highly adaptable way. On the other hand, in Education, by allowing the student to abstract from low-level execution of algorithms whilst preserving the conceptual structures and procedural methodologies underlying the logical foundations.
Robust Average-Reward Markov Decision Processes

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor goes to 1, and moreover when it is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.
AlphaSnake: Policy Iteration on a Nondeterministic NP-Hard Markov Decision Process (Student Abstract)

Reinforcement learning has been used to approach well-known NP-hard combinatorial problems in graph theory. Among these, Hamiltonian cycle problems are exceptionally difficult to analyze, even when restricted to individual instances of structurally complex graphs. In this paper, we use Monte Carlo Tree Search (MCTS), the search algorithm behind many state-of-the-art reinforcement learning algorithms such as AlphaZero, to create autonomous agents that learn to play the game of Snake, a game centered on properties of Hamiltonian cycles on grid graphs. The game of Snake can be formulated as a single-player discounted Markov Decision Process (MDP), where the agent must behave optimally in a stochastic environment. Determining the optimal policy for Snake, defined as the policy that maximizes the probability of winning -- or win rate -- with higher priority and minimizes the expected number of time steps to win with lower priority, is conjectured to be NP-hard. Performance-wise, compared to prior work in the Snake game, our algorithm is the first to achieve a win rate over 0.5 (a uniform random policy achieves a win rate &lt; 2.57 x 10^{-15}), demonstrating the versatility of AlphaZero in tackling NP-hard problems.
Zero-Shot Cross-Lingual Event Argument Extraction with Language-Oriented Prefix-Tuning

Event argument extraction (EAE) aims to identify the arguments of a given event, and classify the roles that those arguments play. Due to high data demands of training EAE models, zero-shot cross-lingual EAE has attracted increasing attention, as it greatly reduces human annotation effort. Some prior works indicate that generation-based methods have achieved promising performance for monolingual EAE. However, when applying existing generation-based methods to zero-shot cross-lingual EAE, we find two critical challenges, including Language Discrepancy and Template Construction. In this paper, we propose a novel method termed as Language-oriented Prefix-tuning Network (LAPIN) to address the above challenges. Specifically, we devise a Language-oriented Prefix Generator module to handle the discrepancies between source and target languages. Moreover, we leverage a Language-agnostic Template Constructor module to design templates that can be adapted to any language. Extensive experiments demonstrate that our proposed method achieves the best performance, outperforming the previous state-of-the-art model by 4.8% and 2.3% of the average F1-score on two multilingual EAE datasets.
Competencia mediática para identificar noticias falsas. Estudio de caso con estudiantes de cuarto de secundaria de un colegio en Lima

In a context where digital media increases the proliferation of information, this research addresses media literacy as a possible solution to fake news. This article analyzes the relationship that fourth-year high school students from a school in Lima have with the media they interact with, in order to evaluate the degree of development of their media literacy. To do this, the CRAAP test was applied, which allows us to know the criteria they take into account when distinguishing a fake news story from a true one based on levels of currency, relevance, authorship, accuracy, and purpose of the text. As a result, the students evaluated false and true information very similarly, leading to the conclusion that they do not possess an adequate level of media literacy. Additionally, some clues are given to interpret the problem.
Spatiotemporal Carbon-aware Scheduling in the Cloud: Limits and Benefits

As the demand for computing continues to grow exponentially and datacenters are already highly optimized, many have suggested leveraging computing workload's spatiotemporal flexibility. However, different workloads may have different degrees of flexibility, including execution deadlines, data protection laws, or latency requirements. These constraints, along with many others, limit the potential benefits of carbon-aware spatiotemporal workload shifting; the achievable benefits of these approaches are unclear-an aspect not addressed by prior research. Accurately quantifying the achievable benefits of carbon-aware spatiotemporal workload scheduling is critically important, as many in research and industry are already devoting significant time and resources to realize these benefits. To address the problem, we conduct a large-scale longitudinal analysis of carbon-aware spatiotemporal workload shifting to answer the following research question: What are the maximum carbon emission reductions that can be achieved due to temporal and spatial workload shifting for different types of cloud workloads and in different parts of the world?