Reviewerly/exHarmony huggingface dataset:
Torrit: A GUI-Based Power System Simulation Platform

An adequate education on power system operations and controls requires a hands-on experience on a graphical user interface (GUI) based software. At present, most commercial software do not have free editions with high flexibility and most freeware do not have good interfaces. This paper introduces a GUI-based application called "Torrit" for executing operations of power systems, especially for transmission systems. It is written in Python for it's rapid development ability. Torrit's main window includes a single canvas with some standard graphical interactions like create, delete, copy, move, double click etc. The beta version of this application is the focus of this paper that allows executing, saving and re-opening a project in three different modes: per unit computations, power flow, and state estimation. However, it is still in a rudimentary stage and many extensions are planned for future to match the needs of both academia and industry.
Reasoning about Temporary Coalitions and LTL-definable Ordered Objectives in Infinite Concurrent Multiplayer Games

We propose enhancing the use of propositions for denoting decisions and strategies as established in temporal languages such as CTL*, if interpreted on concurrent game models. The enhancement enables specifying varying coalition structure. In quantified CTL* this technique also enables quantifying over coalition structure, and we use it to quantify over an extended form of strategy profiles which capture temporary coalitions. We also extend CTL* by a temporal form of a binary preference operator that can be traced back to the work of Von Wright. The resulting extension of quantified CTL* can be used to spell out conditions on the rationality of behaviour in concurrent multiplayer games such as what appear in solution concepts, with players having multiple individual objectives and preferences on them, and with the possibility to form temporary coalitions taken in account. We propose complete axiomatisations for the extension of CTL* by the temporal preference operator. The decidability of the logic is not affected by that extension.
Does My Representation Capture X? Probe-Ably

Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. Probing studies may have misleading results, but various recent works have suggested more reliable methodologies that compensate for the possible pitfalls of probing. However, these best practices are numerous and fast-evolving. To simplify the process of running a set of probing experiments in line with suggested methodologies, we introduce Probe-Ably: an extendable probing framework which supports and automates the application of probing methods to the user's inputs.
Adaptive Bayesian Reticulum

Neural Networks and Decision Trees: two popular techniques for supervised learning that are seemingly disconnected in their formulation and optimization method, have recently been combined in a single construct. The connection pivots on assembling an artificial Neural Network with nodes that allow for a gate-like function to mimic a tree split, optimized using the standard approach of recursively applying the chain rule to update its parameters. Yet two main challenges have impeded wide use of this hybrid approach: (a) the inability of global gradient ascent techniques to optimize hierarchical parameters (as introduced by the gate function); and (b) the construction of the tree structure, which has relied on standard decision tree algorithms to learn the network topology or incrementally (and heuristically) searching the space at random. Here we propose a probabilistic construct that exploits the idea of a node's unexplained potential (the total error channeled through the node) in order to decide where to expand further, mimicking the standard tree construction in a Neural Network setting, alongside a modified gradient ascent that first locally optimizes an expanded node before a global optimization. The probabilistic approach allows us to evaluate each new split as a ratio of likelihoods that balances the statistical improvement in explaining the evidence against the additional model complexity --- thus providing a natural stopping condition. The result is a novel classification and regression technique that leverages the strength of both: a tree-structure that grows naturally and is simple to interpret with the plasticity of Neural Networks that allow for soft margins and slanted boundaries.
Implementasi Satua Sebagai Penguatan Kearifan Lokal Pada Lingkungan Multikultural Di Kelas X SMA Taman Rama Jimbaran Kabupaten Badung

The purpose of this research is the implementation of the learning of Bali Language especially about satua Bali as one very important aspect in as a strengthened point for local wisdom of Balinese culture in multicultural environment. This is a qualitative research, where the primary data is used as the main data based on the result of online observation, interview and questioner results. While the supporting data is achieved from the literature, result of previous research, and all the relevant references about the title of the research. The decision about the interviewer is done using purposive sampling technique. The data collecting was done by using observation, questioner, interview techniques, literary studies and documentation study. After data is collected, it is analyzed by reducing, classifying and making the summary. The results of the research can be stated as follows: (1) The learning process of satua Bali as a strengthened point for local wisdom is done by preparing the syllabus and lesson plan before implementing the lessons. Online learning process consists of three activities those are initial activity, main activity and closing activity. (2) The factors that influence students, understanding of satua Bali as a strengthened point for local wisdom are from internal factors for learning of the students those are the eagerness of students to study, students’ language ability, students’ source of learning and the environment. The external factors are interactions, knowing and solving students’ difficulties in learning and lack of periods for the students. (3) The strategy used in satua Bali learning process as a strengthened point for local wisdom are asking the teacher, discus with the classmates, repetition in reading the text carefully, pay attention properly, finding and writing the meaning of each word, watching the video of masatua Bali, and preparing oneself better.
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning

We explore value decomposition solutions for multi-agent deep reinforcement learning in the popular paradigm of centralized training with decentralized execution(CTDE). As the recognized best solution to CTDE, Weighted QMIX is cutting-edge on StarCraft Multi-agent Challenge (SMAC), with a weighting scheme implemented on QMIX to place more emphasis on the optimal joint actions. However, the fixed weight requires manual tuning according to the application scenarios, which painfully prevents Weighted QMIX from being used in broader engineering applications. In this paper, we first demonstrate the flaw of Weighted QMIX using an ordinary One-Step Matrix Game (OMG), that no matter how the weight is chosen, Weighted QMIX struggles to deal with non-monotonic value decomposition problems with a large variance of reward distributions. Then we characterize the problem of value decomposition as an Underfitting One-edged Robust Regression problem and make the first attempt to give a solution to the value decomposition problem from the perspective of information-theoretical learning. We introduce the Maximum Correntropy Criterion (MCC) as a cost function to dynamically adapt the weight to eliminate the effects of minimum in reward distributions. We simplify the implementation and propose a new algorithm called MCVD. A preliminary experiment conducted on OMG shows that MCVD could deal with non-monotonic value decomposition problems with a large tolerance of kernel bandwidth selection. Further experiments are carried out on Cooperative-Navigation and multiple SMAC scenarios, where MCVD exhibits unprecedented ease of implementation, broad applicability, and stability.
Empirical Study of Overfitting in Deep FNN Prediction Models for Breast Cancer Metastasis

Overfitting is defined as the fact that the current model fits a specific data set perfectly, resulting in weakened generalization, and ultimately may affect the accuracy in predicting future data. In this research we used an EHR dataset concerning breast cancer metastasis to study overfitting of deep feedforward Neural Networks (FNNs) prediction models. We included 11 hyperparameters of the deep FNNs models and took an empirical approach to study how each of these hyperparameters was affecting both the prediction performance and overfitting when given a large range of values. We also studied how some of the interesting pairs of hyperparameters were interacting to influence the model performance and overfitting. The 11 hyperparameters we studied include activate function; weight initializer, number of hidden layers, learning rate, momentum, decay, dropout rate, batch size, epochs, L1, and L2. Our results show that most of the single hyperparameters are either negatively or positively corrected with model prediction performance and overfitting. In particular, we found that overfitting overall tends to negatively correlate with learning rate, decay, batch sides, and L2, but tends to positively correlate with momentum, epochs, and L1. According to our results, learning rate, decay, and batch size may have a more significant impact on both overfitting and prediction performance than most of the other hyperparameters, including L1, L2, and dropout rate, which were designed for minimizing overfitting. We also find some interesting interacting pairs of hyperparameters such as learning rate and momentum, learning rate and decay, and batch size and epochs. Keywords: Deep learning, overfitting, prediction, grid search, feedforward neural networks, breast cancer metastasis.
Deploying Artificial Intelligence in Service Delivery in Educational Institutions

The introduction of Artificial Intelligence technology enables the integration of Chatbot systems into various aspects of education. This technology is increasingly being used for educational purposes. Chatbot technology has the potential to provide quick and personalized services to everyone in the sector, including institutional employees and students. This paper presents a systematic review of previous studies on the use of Chatbots in education. Keywords: Deployment, Artificial Intelligence, Service Delivery, Educational Institutions
Neuro-Symbolic Verification of Deep Neural Networks

Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over the inputs and output of a network. While adversarial robustness and fairness fall under this category, many real-world properties (e.g., "an autonomous vehicle has to stop in front of a stop sign") remain outside the scope of existing verification technology. To mitigate this severe practical restriction, we introduce a novel framework for verifying neural networks, named neuro-symbolic verification. The key idea is to use neural networks as part of the otherwise logical specification, enabling the verification of a wide variety of complex, real-world properties, including the one above. Moreover, we demonstrate how neuro-symbolic verification can be implemented on top of existing verification infrastructure for neural networks, making our framework easily accessible to researchers and practitioners alike.
TEACHING PSYCHOLOGY STUDENTS PROFESSIONALLY ORIENTED READING

The article deals with the problem of teaching psychology students reading professionally oriented English texts. It highlights the importance of the development of reading skills in English speaking class and investigates the component skills that are critical to reading comprehension development when teaching English for specific purposes. In this research we try to analyze main curricular principles proposed by scientists to guide reading instructions which can be successfully used when teaching professionally oriented reading to psychology students. In this article we explore goals for reading instructions and try to translate them into practice in various ways in our ESP classroom. Among the component skills that are critical to reading ESP comprehension development we distinguish word-recognition efficiency, vocabulary building, main-idea comprehension practice, discourse-structure awareness, reading strategies for professional goals, reading fluency, extensive reading and students’ motivations. We pay special attention to reading fluency, extensive reading and psychology students’ reading motivation which are also considered very important component skills for reading comprehension development. We tried to understand how to prepare our psychology students for their reading demands that that they will almost certainly encounter in their professional life.
Models and procedures for classification and forecasting of nondeterministic processes according to chaotic dynamics parameters

The article investigates the processes of classification, modeling and short-term prediction of nondeterministic time sequences, which are represented by antipersistent time series (ATS). The subject of analysis - procedures for classification and forecasting the pa-rameters of such models. The object of research is the processes of modeling and analysis of parameters of nondeterministic time series of ATS with a uniform step. The aim of the work is to increase the efficiency and accuracy of methods and algorithms for classification, modeling and forecasting of ATS. Models and methods of fractal analysis are used to study the properties of ATS, on the basis of which the categories of processes of numerical series are established. With the help of aggregation of ATS levels correct mathematical models of classification of nondeterministic time sequences are developed, and also algorithmic and software means of their realization are formed. Examples of models of numerical series obtained using the aggregation procedure presented in the study are given. It is established that the most detailed and stable is the classification of ATS based on data aggregation schemes without level crossing. The comparative analysis of numerical efficiency of algorithms of classification of ATS is carried out and the task of formation of procedures of interpolation and short-term forecasting of ATS is realized. An instrumental software environment is presented, which provides a correct study of algorithms for modeling and classification of antipersistent time series. Recommendations on the procedures for modeling ATS classification algorithms are of practical importance.
Do People Prefer "Natural" code?

Natural code is known to be very repetitive (much more so than natural language corpora); furthermore, this repetitiveness persists, even after accounting for the simpler syntax of code. However, programming languages are very expressive, allowing a great many different ways (all clear and unambiguous) to express even very simple computations. So why is natural code repetitive? We hypothesize that the reasons for this lie in fact that code is bimodal: it is executed by machines, but also read by humans. This bimodality, we argue, leads developers to write code in certain preferred ways that would be familiar to code readers. To test this theory, we 1) model familiarity using a language model estimated over a large training corpus and 2) run an experiment applying several meaning preserving transformations to Java and Python expressions in a distinct test corpus to see if forms more familiar to readers (as predicted by the language models) are in fact the ones actually written. We find that these transformations generally produce program structures that are less common in practice, supporting the theory that the high repetitiveness in code is a matter of deliberate preference. Finally, 3) we use a human subject study to show alignment between language model score and human preference for the first time in code, providing support for using this measure to improve code.
Is Supervised Learning With Adversarial Features Provably Better Than Sole Supervision?

Generative Adversarial Networks (GAN) have shown promising results on a wide variety of complex tasks. Recent experiments show adversarial training provides useful gradients to the generator that helps attain better performance. In this paper, we intend to theoretically analyze whether supervised learning with adversarial features can outperform sole supervision, or not. First, we show that supervised learning without adversarial features suffer from vanishing gradient issue in near optimal region. Second, we analyze how adversarial learning augmented with supervised signal mitigates this vanishing gradient issue. Finally, we prove our main result that shows supervised learning with adversarial features can be better than sole supervision (under some mild assumptions). We support our main result on two fronts (i) expected empirical risk and (ii) rate of convergence.
Visually Connecting Historical Figures Through Event Knowledge Graphs

Knowledge graphs store information about historical figures and their relationships indirectly through shared events. We developed a visualization system, VisKonnect, for analyzing the intertwined lives of historical figures based on the events they participated in. A user's query is parsed for identifying named entities, and related data is retrieved from an event knowledge graph. While a short textual answer to the query is generated using the GPT-3 language model, various linked visualizations provide context, display additional information related to the query, and allow exploration.
Knowledge-Based Stable Roommates Problem: A Real-World Application

The Stable Roommates problem with Ties and Incomplete lists (SRTI) is a matching problem characterized by the preferences of agents over other agents as roommates, where the preferences may have ties or be incomplete. SRTI asks for a matching that is stable and, sometimes, optimizes a domain-independent fairness criterion (e.g., Egalitarian). However, in real-world applications (e.g., assigning students as roommates at a dormitory), we usually consider a variety of domain-specific criteria depending on preferences over the habits and desires of the agents. With this motivation, we introduce a knowledge-based method to SRTI considering domain-specific knowledge, and investigate its real-world application for assigning students as roommates at a university dormitory. This paper is under consideration for acceptance in Theory and Practice of Logic Programming (TPLP).
Toward Practical Quantum Embedding Simulation of Realistic Chemical Systems on Near-term Quantum Computers

Quantum computing has recently exhibited great potentials in predicting chemical properties for various applications in drug discovery, material design, and catalyst optimization. Progress has been made in simulating small molecules, such as LiH and hydrogen chains of up to 12 qubits, by using quantum algorithms such as variational quantum eigensolver (VQE). Yet, originating from limitations of the size and the fidelity of near-term quantum hardware, how to accurately simulate large realistic molecules remains a challenge. Here, integrating an adaptive energy sorting strategy and a classical computational method, the density matrix embedding theory, which effectively finds a shallower quantum circuit and reduces the problem size, respectively, we show a means to circumvent the limitations and demonstrate the potential toward solving real chemical problems. We numerically test the method for the hydrogenation reaction of C6H8 and the equilibrium geometry of the C18 molecule, with basis sets up to cc-pVDZ (at most 144 qubits). The simulation results show accuracies comparable to those of advanced quantum chemistry methods such as coupled-cluster or even full configuration interaction, while the number of qubits required is reduced by an order of magnitude (from 144 qubits to 16 qubits for the C18 molecule) compared to conventional VQE. Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.
StyleGAN-induced data-driven regularization for inverse problems

Recent advances in generative adversarial networks (GANs) have opened up the possibility of generating high-resolution photo-realistic images that were impossible to produce previously. The ability of GANs to sample from high-dimensional distributions has naturally motivated researchers to leverage their power for modeling the image prior in inverse problems. We extend this line of research by developing a Bayesian image reconstruction framework that utilizes the full potential of a pre-trained StyleGAN2 generator, which is the currently dominant GAN architecture, for constructing the prior distribution on the underlying image. Our proposed approach, which we refer to as learned Bayesian reconstruction with generative models (L-BRGM), entails joint optimization over the style-code and the input latent code, and enhances the expressive power of a pre-trained StyleGAN2 generator by allowing the style-codes to be different for different generator layers. Considering the inverse problems of image inpainting and super-resolution, we demonstrate that the proposed approach is competitive with, and sometimes superior to, state-of-the-art GAN-based image reconstruction methods.
The Students’ Experiences in Implementing Duolingo-Assisted Listening Learning

Technology has evolved and produced a wide range of devices to enhance education and teaching methods that can assist students in learning languages. This study aims to describe students’ experiences in implementing Duolingo-assisted Listening learning. The participants of this research were three Junior high school students in Karawang. The method and design in this research are to use descriptive qualitative research conducted through Observation, interviews, and Questionnaires. This research analyzes the data using thematic analysis. The findings showed that the participants initially had difficulty listening, and after Duolingo-assisted, students were happy, easier, excited, and motivated to learn listening skills using Duolingo. Due to the gamification of learning and listening exercises provided by Duolingo, students find it easier to learn listening. Because it offers students new experiences in learning English, Duolingo is an effective tool for use in the classroom. In conclusion, this study indicates that students have positive experiences implementing Duolingo-assisted Listening Learning.