Reviewerly/exHarmony huggingface dataset:
Real-time hybrid quantum-classical computations for trapped-ions with Python control-flow

In recent years, the number of hybrid algorithms that combine quantum and classical computations has been continuously increasing. These two approaches to computing can mutually enhance each others' performances thus bringing the promise of more advanced algorithms that can outmatch their pure counterparts. In order to accommodate this new class of codes, a proper environment has to be created, which enables the interplay between the quantum and classical hardware. For many of these hybrid processes the coherence time of the quantum computer arises as a natural time constraint, making it crucial to minimize the classical overhead. For ion-trap quantum computers however, this is a much less limiting factor than with superconducting technologies, since the relevant timescale is on the order of seconds instead of microseconds. In fact, this long coherence time enables us to develop a scheme for real-time control of quantum computations in an interpreted programming language like Python. In particular, compilation of all instructions in advance is not necessary, unlike with superconducting qubits. This keeps the implementation of hybrid algorithms simple and also lets users benefit from the rich environment of existing Python libraries. In order to show that this approach of interpreted quantum-classsical computations (IQCC) is feasible, we bring real-world examples and evaluate them in realistic benchmarks.
Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study

Large pre-trained language models help to achieve state of the art on a variety of natural language processing (NLP) tasks, nevertheless, they still suffer from forgetting when incrementally learning a sequence of tasks. To alleviate this problem, recent works enhance existing models by sparse experience replay and local adaption, which yield satisfactory performance. However, in this paper we find that pre-trained language models like BERT have a potential ability to learn sequentially, even without any sparse memory replay. To verify the ability of BERT to maintain old knowledge, we adopt and re-finetune single-layer probe networks with the parameters of BERT fixed. We investigate the models on two types of NLP tasks, text classification and extractive question answering. Our experiments reveal that BERT can actually generate high quality representations for previously learned tasks in a long term, under extremely sparse replay or even no replay. We further introduce a series of novel methods to interpret the mechanism of forgetting and how memory rehearsal plays a significant role in task incremental learning, which bridges the gap between our new discovery and previous studies about catastrophic forgetting.
BIG Hype: Best Intervention in Games via Distributed Hypergradient Descent

Hierarchical decision making problems, such as bilevel programs and Stackelberg games, are attracting increasing interest in both the engineering and machine learning communities. Yet, existing solution methods lack either convergence guarantees or computational efficiency, due to the absence of smoothness and convexity. In this work, we bridge this gap by designing a first-order hypergradient-based algorithm for Stackelberg games and mathematically establishing its convergence using tools from nonsmooth analysis. To evaluate the \textit{hypergradient}, namely, the gradient of the upper-level objective, we develop an online scheme that simultaneously computes the lower-level equilibrium and its Jacobian. Crucially, this scheme exploits and preserves the original hierarchical and distributed structure of the problem, which renders it scalable and privacy-preserving. We numerically verify the computational efficiency and scalability of our algorithm on a large-scale hierarchical demand-response model.
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations

Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shift scenarios, where the downstream and training distributions differ. However, the theoretical understanding of its transferability remains limited. In this paper, we develop a theoretical framework to analyze the transferability of self-supervised contrastive learning, by investigating the impact of data augmentation on it. Our results reveal that the downstream performance of contrastive learning depends largely on the choice of data augmentation. Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability. Based on these theoretical insights, we propose a novel method called Augmentation-robust Contrastive Learning (ArCL), which guarantees to learn domain-invariant features and can be easily integrated with existing contrastive learning algorithms. We conduct experiments on several datasets and show that ArCL significantly improves the transferability of contrastive learning.
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion

As a key component of automated speech recognition (ASR) and the front-end in text-to-speech (TTS), grapheme-to-phoneme (G2P) plays the role of converting letters to their corresponding pronunciations. Existing methods are either slow or poor in performance, and are limited in application scenarios, particularly in the process of on-device inference. In this paper, we integrate the advantages of both expert knowledge and connectionist temporal classification (CTC) based neural network and propose a novel method named LiteG2P which is fast, light and theoretically parallel. With the carefully leading design, LiteG2P can be applied both on cloud and on device. Experimental results on the CMU dataset show that the performance of the proposed method is superior to the state-of-the-art CTC based method with 10 times fewer parameters, and even comparable to the state-of-the-art Transformer-based sequence-to-sequence model with less parameters and 33 times less computation.
In all LikelihoodS: How to Reliably Select Pseudo-Labeled Data for Self-Training in Semi-Supervised Learning

Self-training is a simple yet effective method within semi-supervised learning. The idea is to iteratively enhance training data by adding pseudo-labeled data. Its generalization performance heavily depends on the selection of these pseudo-labeled data (PLS). In this paper, we aim at rendering PLS more robust towards the involved modeling assumptions. To this end, we propose to select pseudo-labeled data that maximize a multi-objective utility function. The latter is constructed to account for different sources of uncertainty, three of which we discuss in more detail: model selection, accumulation of errors and covariate shift. In the absence of second-order information on such uncertainties, we furthermore consider the generic approach of the generalized Bayesian alpha-cut updating rule for credal sets. As a practical proof of concept, we spotlight the application of three of our robust extensions on simulated and real-world data. Results suggest that in particular robustness w.r.t. model choice can lead to substantial accuracy gains.
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction

As machine learning models, specifically neural networks, are becoming increasingly popular, there are concerns regarding their trustworthiness, specially in safety-critical applications, e.g. actions of an autonomous vehicle must be safe. There are approaches that can train neural networks where such domain requirements are enforced as constraints, but they either cannot guarantee that the constraint will be satisfied by all possible predictions (even on unseen data) or they are limited in the type of constraints that can be enforced. In this paper, we present an approach to train neural networks which can enforce a wide variety of constraints and guarantee that the constraint is satisfied by all possible predictions. The approach builds on earlier work where learning linear models is formulated as a constraint satisfaction problem (CSP). To make this idea applicable to neural networks, two crucial new elements are added: constraint propagation over the network layers, and weight updates based on a mix of gradient descent and CSP solving. Evaluation on various machine learning tasks demonstrates that our approach is flexible enough to enforce a wide variety of domain constraints and is able to guarantee them in neural networks.
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning

Transfer learning in Reinforcement Learning (RL) has been widely studied to overcome training issues of Deep-RL, i.e., exploration cost, data availability and convergence time, by introducing a way to enhance training phase with external knowledge. Generally, knowledge is transferred from expert-agents to novices. While this fixes the issue for a novice agent, a good understanding of the task on expert agent is required for such transfer to be effective. As an alternative, in this paper we propose Expert-Free Online Transfer Learning (EF-OnTL), an algorithm that enables expert-free real-time dynamic transfer learning in multi-agent system. No dedicated expert exists, and transfer source agent and knowledge to be transferred are dynamically selected at each transfer step based on agents' performance and uncertainty. To improve uncertainty estimation, we also propose State Action Reward Next-State Random Network Distillation (sars-RND), an extension of RND that estimates uncertainty from RL agent-environment interaction. We demonstrate EF-OnTL effectiveness against a no-transfer scenario and advice-based baselines, with and without expert agents, in three benchmark tasks: Cart-Pole, a grid-based Multi-Team Predator-Prey (mt-pp) and Half Field Offense (HFO). Our results show that EF-OnTL achieve overall comparable performance when compared against advice-based baselines while not requiring any external input nor threshold tuning. EF-OnTL outperforms no-transfer with an improvement related to the complexity of the task addressed.
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model

In this paper, a self-supervised model that simultaneously predicts a sequence of future frames from video-input with a novel spatial-temporal attention (ST) network is proposed. The ST transformer network allows constraining both temporal consistency across future frames whilst constraining consistency across spatial objects in the image at different scales. This was not the case in prior works for depth prediction, which focused on predicting a single frame as output. The proposed model leverages prior scene knowledge such as object shape and texture similar to single-image depth inference methods, whilst also constraining the motion and geometry from a sequence of input images. Apart from the transformer architecture, one of the main contributions with respect to prior works lies in the objective function that enforces spatio-temporal consistency across a sequence of output frames rather than a single output frame. As will be shown, this results in more accurate and robust depth sequence forecasting. The model achieves highly accurate depth forecasting results that outperform existing baselines on the KITTI benchmark. Extensive ablation studies were performed to assess the effectiveness of the proposed techniques. One remarkable result of the proposed model is that it is implicitly capable of forecasting the motion of objects in the scene, rather than requiring complex models involving multi-object detection, segmentation and tracking.
UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction

This paper describes the submission of UZH_CLyp for the SemEval 2023 Task 9 "Multilingual Tweet Intimacy Analysis". We achieved second-best results in all 10 languages according to the official Pearson's correlation regression evaluation measure. Our cross-lingual transfer learning approach explores the benefits of using a Head-First Fine-Tuning method (HeFiT) that first updates only the regression head parameters and then also updates the pre-trained transformer encoder parameters at a reduced learning rate. Additionally, we study the impact of using a small set of automatically generated examples (in our case, from ChatGPT) for low-resource settings where no human-labeled data is available. Our study shows that HeFiT stabilizes training and consistently improves results for pre-trained models that lack domain adaptation to tweets. Our study also shows a noticeable performance increase in cross-lingual learning when synthetic data is used, confirming the usefulness of current text generation systems to improve zero-shot baseline results. Finally, we examine how possible inconsistencies in the annotated data contribute to cross-lingual interference issues.
Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection

Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization trajectory during training has not been explored clearly. In this paper, we first find the performance of OOD detection suffers from overfitting and instability during training: 1) the performance could decrease when the training error is near zero, and 2) the performance would vary sharply in the final stage of training. Based on our findings, we propose Average of Pruning (AoP), consisting of model averaging and pruning, to mitigate the unstable behaviors. Specifically, model averaging can help achieve a stable performance by smoothing the landscape, and pruning is certified to eliminate the overfitting by eliminating redundant features. Comprehensive experiments on various datasets and architectures are conducted to verify the effectiveness of our method.
THEORETICAL GROUNDS AND METHODOLOGICAL PRINCIPLES OF IMPLEMENTING THE PEDAGOGICAL TECHNOLGY CLIL

The article deals with theoretical approaches and principals that are on the ground of popular and well-known pedagogical technology CLIL.With the help of this studied technology students see the learning process as an interesting path.The founder of this approach was D. Marsh but this educational principal has gained enormous popularity among a lot of the scientists and its fame is still growing.In the article it has been analyzed, that the approach of CLIL is based on the theory, that the performing learning algorithm can easily be used on both languages provided that both languages are sufficiently advanced.The "Iceberg Theory" has been studied as a part of "Common Underlying Proficiency".That means that the students should possess while their learning process bilingual skills which are universal.For both languages.The theories of S. Toukomma and I. Cummins have been searched as well.Doe to these theories the relationship between cognitive ability and individual bilingualism can be represented with thresholds with a certain level of bilingual competence.The main problem in applying the Threshold Theory is to define precisely the level of linguistic competence which a child has to achieve in order, firstly, to avoid the negative consequences of bilingualism and, secondly, to gain the cognitive benefits of his/her bilingualism.We have also analyzed BICS and CALP theories.They helped to explain why the education system for minority language children sometimes fails.It was found that according to J. Cummins theory, students' cognitive/academic language competence is not developed enough for them to cope with the demands of the curriculum which is designed for monolingual majority language children.D. Coyle's Language Triptych was paid attention to in order to explain how language № 2( 16) 2023 147 is used in a CLIL context, which is subdivided into Language of learning, Language for learning, Language through learning.
VISUAL IMAGE CAPTION GENERATOR USING LSTM

In the current era, photo captioning has end up one of the maximum broadly required tools.Moreover, there are built in programs that generate and offer a caption for a sure photo, all this stuff are achieved with the assist of deep neural community fashions.The version is skilled to maximise the chance of the goal description sentence given the education photo.Image Caption Generation has usually been a examine of exquisite hobby to the researchers withinside the Artificial Intelligence department.In this paper, an superior photo captioning version-along with item detection, colour analysis, and photo captioning-is proposed to mechanically generate the textual descriptions of photographs.The integration of the photo caption and colour popularity is then achieved to offer higher descriptive information of photographs.Being capable of software a system to correctly describe a photo or an surroundings like a median human has foremost programs withinside the subject of robot vision, business, Skin vision and plenty of more.A US Company is predicting crop yield the use of photographs from satellite.we gift distinct photo caption producing fashions primarily based totally on deep neural networks, focusing at the diverse RNN, CNN, LSTM strategies and reading their impact at the sentence era which version offers higher accuracy and generates the preferred results.
The implementation of REAP strategy to improve students reading comprehension of analytical expositional text in digital era

and Ponder (REAP) strategy using technology to improve analytical exposition text comprehension of the private senior high school students. The research was in an experimental study with pre-test and post-test design. In experimental class consisted of 26 students and in the control class consisted of 26 students as the sample of this research. The data were collected by using reading comprehension test with multiplechoice form and analyzed by using t-test formula. The finding showed that there was a difference mean-score before and after using REAP Strategy. So, by implementing REAP Strategy to students’ analytical exposition text comprehension at grade XI of private senior high school students in Indonesia in digitalization era, the students’ reading comprehension improved. Moreover, it is suggested next researchers, to conduct a deeper exploration in teaching expositional texts using REAP strategy.&#x0D; Keywords: REAP strategy; digital era; senior high school
Development of Viva Video Digital Art as a Learning Media at SMA Negeri 8 Padangsidimpuan

Video is an electronic media that is able to combine audio and visual technology together so as to produce a dynamic and interesting impression. The purpose of the study was to find out the assessment of expert validators in viva video learning media feasibility test based on BSNP questionnaire. The benefits of this research provide interesting and enjoyable learning activities so that students become more active in learning, as well as enrich the teacher’s knowledge about alternative learning media. The average result of all aspects of the standardized due diligence based on the BSNP questionnaire before revision 3.12 with validity criteria is quite good {Quite valid and needs revision}. Meanwhile, after revision 3.76 with validity criteria both {Valid and do not need revision (feasible)}. The development of viva video media in electrolyte and non electrolyte solution material is expected to be applied to students in school.&#x0D; Keywords: Viva Video; digital art; learning media; BSNP
The improving speaking skills through role playing strategies in students at muhammadiyah university of palangkaraya

There are four basic skills in English, namely Listening, Speaking, Reading and Writing, speaking skills are still not maximally spared by students. [1]which states that speaking is an event of conveying one’s intentions (ideas, thoughts, heart contents) to others using spoken language so that the meaning is understood by others. Not a few students of Universitas Muhammadiyah Palangkaraya still cannot understand well and do not understand the sentences spoken by the interlocutors who use English. Role playing is one of the strategies recently seen as very useful and effective in learning speaking because role playing can arouse enthusiasm and motivate students and provide an injection to speak in English. This study aims to determine student activities during training and to find out whether the use of role playing strategies can improve students’ speaking skills. This Classroom Action Research was designed and implemented in the Study Program at the Faculty of Agriculture and Forestry, Farmacy and the Islamic Faculty of Universitas Muhammadiyah Palangkaraya. The research subjects were 30 students of the Universitas Muhammadiyah Palangkaraya S1 Program. who take English courses. The stages of this research consisted of planning, implementing the action, observing and reflecting. The results obtained from this activity based on the reflection stage were that the average score of students was 7.5 on the speaking ability test where in the initial test the average student score was below 70. Student activities in role playing activities also increased where they could speak using English with a predetermined theme although there are still some vocabulary and grammar that need to be improved.&#x0D; Keywords: Speaking skills; role playing; basic skills; English
Stylistic directions of accordion-bayan music in modern art

The article demonstrates that modern button accordion culture is an important part of the cultural paradigm of society, spiritual self-development and self-expression of peoples and nationalities. The ontological status of bayan and accordion performance is highlighted in the context of various styles, determined by the specifics of national cultural landscapes and historical realities.
EFL students’ beliefs towards usefulness of corrective feedback in cooperative learning environment

The current study aimed to find out English as a foreign language (EFL) students’ beliefs regarding the usefulness of corrective feedback (CF) in a cooperative learning (CL) environment. This study was a qualitative research design in which a narrative approach was used. The data were collected using interview questions as the research instrument. The researchers had a close observation, prepared notes, and made a record of interview responses from each student to explore and understand their beliefs towards CF provided by the instructors in the CL environment. The findings of this qualitative study revealed that EFL teachers get motivation and inspiration from EFL learners to give a suitable CL environment to second language learners which made the provided CF more effective. In the CL environment, the results indicate that EFL learners realised that they can find solutions to their problems in CL, and in their interviews, they stated that they would not be able to find solutions to their errors individually.&#x0D; Keywords: Cooperative learning, corrective feedback, English as a foreign language, perceptions
Data Analysis Training Using Python at JNE

The need for the Data Analyst profession is very high, triggered by the development of digital technology. Thus, Matana University through the statistics study program feels it is important to carry out training activities in the field of data analysis. The aim is to provide benefits to companies that are partners in the implementation of this Community Service (PKM) is JNE. Participants who take part in the training are limited to twenty people, so that the quality of learning is more optimal with the practicum learning method. The training instructor also involves at least two i-students as assistants in each meeting. This training was carried out in collaboration with the Learning Center team at JNE. The implication of the training is a change in the ability of data analysis for the participants, even ordinary people, and further training will soon be held.