Reviewerly/exHarmony huggingface dataset:
Organization of skills upgrading using means of distance learning at the tertiary level

The article notes that the problem of organization of professional development of teachers of higher educational institutions is related to the orientation of the higher education in Ukraine towards the European educational standards and is associated with the need for continuous professional development of the teaching staff at the tertiary level. Another factor is the necessity in actual professional reaction to the challenges brought about by the world pandemic and, as a result, digitalization of the basic social spheres and institutions, educational, in particular. The article provides the analysis and implementation of the world experience in organization of the process of educating and further professional development of teachers using contemporary educational online platforms. Based on modern research on the problem and taking into account the socio-economic requirements of the time, the main competences that determine the required level of the professional level of a teacher of the higher educational institution are determined. The article presents the experience of implementing upgrading skills programs for teachers of higher educational institutions using online platforms: trainings, webinars, master classes, participation in academic mobility programs, Master degree training programs which have been piloted at the universities of Kharkiv to develop certain competences.
Multimodal Named Entity Recognition for Semiconductor Failure Analysis

Abstract During the activity in the Failure Analysis (FA) laboratory, all corresponding findings and conclusions are included in a series of documents known as the FA reports. They shall, in the first place, inform the requestor about the analysis results. But additionally, they shall provide information to solve similar cases. Therefore, these documents play a key role in preserving the knowledge acquired by the engineers as they become available for consultation during future works. The different information systems in FA consist of databases, file shares, wikis, or other human-readable forms. However, the heterogeneity of these databases and the large number of independent documents make it inefficient for manual consultation. In this context, this paper proposes an application of Natural Language Processing (NLP) known as Named Entity Recognition (NER), consisting of an AI-based detection of key concepts in textual data in the form of annotations. These annotations can then be used to boost search systems or other AI models.
Subspace Rotation Algorithm for Implementing Restricted Hopfield Network as an Auto-Associative Memory

&lt;p&gt;This is the original idea for subspace rotation algorithm.&lt;/p&gt;
HOW EFFECTIVE IS THE USE OF THINK PAIR SHARE STRATEGY IN IMPROVING SECONDARY HIGH SCHOOL STUDENTS SPEAKING PERFORMANCE?

The objective of this study is to determine whether the Think-Pair-Share (TPS) technique can significantly improve students' speaking abilities. The researcher employed a quasi-experimental study design with a pre- and post-testing control group. This study included participation from forty students. Twenty students were assigned to the experimental group, while the remaining 20 were assigned to the control group. The result of the pair sample t-test indicates that there was a statistically significant improvement in the speaking performance of students before and after the adoption of the Think-Pair-Share approach (M = 11.98, T = -8.839), P 0.05. The overall findings of the study indicate that the Think-Pair-Share technique can improve the performance of the students. In addition, Think- Pair-Share provided the students with extra opportunities to communicate English. The students‟ English speaking confidence increased. They also contributed actively to the teaching and learning process.
A Survey of the Various Methodologies Towards making Artificial Intelligence More Explainable

Machines are being increasingly used in decision-making processes, resulting in the realization that decisions need explanations. Unfortunately, an increasing number of these deployed models are of a 'black-box' nature where the reasoning behind the decisions is unknown. Hence, there is a need for clarity behind the reasoning of these decisions. As humans, we would want these decisions to be presented to us in an explainable manner. However, explanations alone are insufficient. They do not necessarily tell us how to achieve an outcome but merely tell us what achieves the given outcome. For this reason, my research focuses on explainability/interpretability and how it extends to counterfactual thinking.
Synthesis of Quantum Circuits with an Island Genetic Algorithm

While advances in quantum hardware occur in modest steps, simulators running on classical computers provide a valuable test bed for the construction of quantum algorithms. Given a unitary matrix that performs certain operation, obtaining the equivalent quantum circuit, even if as an approximation of the input unitary, is a non-trivial task and can be modeled as a search problem. This work presents an evolutionary search algorithm based on the island model concept, for the decomposition of unitary matrices in their equivalent circuit. Three problems are explored: the coin for the quantum walker, the Toffoli gate and the Fredkin gate. The algorithm proposed proved to be efficient in decomposition of quantum circuits, and as a generic approach, it is limited only by the available computational power.
Non-uniform Mixing of Quantum Walks on the Symmetric Group

It is well-known that classical random walks on regular graphs converge to the uniform distribution. Quantum walks, in their various forms, are quantizations of their corresponding classical random walk processes. Gerhardt and Watrous (2003) demonstrated that continuous-time quantum walks do not converge to the uniform distribution on certain Cayley graphs of the Symmetric group, which by definition are all regular. In this paper, we demonstrate that discrete-time quantum walks, in the sense of quantized Markov chains as introduced by Szegedy (2004), also do not converge to the uniform distribution. We analyze the spectra of the Szegedy walk operators using the representation theory of the symmetric group. In the discrete setting, the analysis is complicated by the fact that we work within a Hilbert space of a higher dimension than the continuous case, spanned by pairs of vertices. Our techniques are general, and we believe they can be applied to derive similar analytical results for other non-commutative groups using the characters of their irreducible representation.
Secured Fiscal Credit Model: Multi-Agent Systems And Decentralized Autonomous Organisations For Tax Credit's Tracking

Tax incentives and fiscal bonuses have had a significant impact on the Italian economy over the past decade. In particular, the "Superbonus 110" tax relief in 2020, offering a generous 110% deduction for expenses related to energy efficiency improvements and seismic risk reduction in buildings, has played a pivotal role. However, the surge in construction activities has also brought about an unfortunate increase in fraudulent activities. To address this challenge, our research introduces a practical system for monitoring and managing the entire process of the Superbonus 110 tax credit, from its initiation to redemption. This system leverages artificial intelligence and blockchain technology to streamline tax credit management and incorporates controllers based on a Decentralised Autonomous Organisation architecture, bolstered by a Multi-agent System. The outcome of our work is a system capable of establishing a tokenomics framework that caters to the needs and functionalities of both investors and operators. Moreover, it features a robust control system to prevent inadvertent errors like double spending, overspending, and deceitful practices such as false claims of completed work. The collaborative approach between the Decentralised Autonomous Organisation and the Multi-agent System enhances trust and security levels among participants in a competitive environment where potential fraudsters might attempt to exploit the system. It also enables comprehensive tracking and monitoring of the entire Superbonus process. In the realm of engineering, our project represents an innovative fusion of blockchain technology and Multi-agent Systems, advancing the application of artificial intelligence. This integration guarantees the validation, recording, and execution of transactions with a remarkable level of trust and transparency.
State-Wise Safe Reinforcement Learning With Pixel Observations

In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations. Furthermore, incorporating state-wise safety constraints in the exploration and learning process, where the agent must avoid unsafe regions without prior knowledge, adds another layer of complexity. In this paper, we propose a novel pixel-observation safe RL algorithm that efficiently encodes state-wise safety constraints with unknown hazard regions through a newly introduced latent barrier-like function learning mechanism. As a joint learning framework, our approach begins by constructing a latent dynamics model with low-dimensional latent spaces derived from pixel observations. We then build and learn a latent barrier-like function on top of the latent dynamics and conduct policy optimization simultaneously, thereby improving both safety and the total expected return. Experimental evaluations on the safety-gym benchmark suite demonstrate that our proposed method significantly reduces safety violations throughout the training process, and demonstrates faster safety convergence compared to existing methods while achieving competitive results in reward return.
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative Decoding

Contemporary translation engines built upon the encoder-decoder framework have reached a high level of development, while the emergence of Large Language Models (LLMs) has disrupted their position by offering the potential for achieving superior translation quality. Therefore, it is crucial to understand in which scenarios LLMs outperform traditional NMT systems and how to leverage their strengths. In this paper, we first conduct a comprehensive analysis to assess the strengths and limitations of various commercial NMT systems and MT-oriented LLMs. Our findings indicate that neither NMT nor MT-oriented LLMs alone can effectively address all the translation issues, but MT-oriented LLMs can serve as a promising complement to the NMT systems. Building upon these insights, we explore hybrid methods and propose Cooperative Decoding (CoDec), which treats NMT systems as a pretranslation model and MT-oriented LLMs as a supplemental solution to handle complex scenarios beyond the capability of NMT alone. The results on the WMT22 test sets and a newly collected test set WebCrawl demonstrate the effectiveness and efficiency of CoDec, highlighting its potential as a robust solution for combining NMT systems with MT-oriented LLMs in machine translation.
Transduce and Speak: Neural Transducer for Text-to-Speech with Semantic Token Prediction

We introduce a text-to-speech(TTS) framework based on a neural transducer. We use discretized semantic tokens acquired from wav2vec2.0 embeddings, which makes it easy to adopt a neural transducer for the TTS framework enjoying its monotonic alignment constraints. The proposed model first generates aligned semantic tokens using the neural transducer, then synthesizes a speech sample from the semantic tokens using a non-autoregressive(NAR) speech generator. This decoupled framework alleviates the training complexity of TTS and allows each stage to focus on 1) linguistic and alignment modeling and 2) fine-grained acoustic modeling, respectively. Experimental results on the zero-shot adaptive TTS show that the proposed model exceeds the baselines in speech quality and speaker similarity via objective and subjective measures. We also investigate the inference speed and prosody controllability of our proposed model, showing the potential of the neural transducer for TTS frameworks.
A New Fine-grained Alignment Method for Image-text Matching

Image-text retrieval is a widely studied topic in the field of computer vision due to the exponential growth of multimedia data, whose core concept is to measure the similarity between images and text. However, most existing retrieval methods heavily rely on cross-attention mechanisms for cross-modal fine-grained alignment, which takes into account excessive irrelevant regions and treats prominent and non-significant words equally, thereby limiting retrieval accuracy. This paper aims to investigate an alignment approach that reduces the involvement of non-significant fragments in images and text while enhancing the alignment of prominent segments. For this purpose, we introduce the Cross-Modal Prominent Fragments Enhancement Aligning Network(CPFEAN), which achieves improved retrieval accuracy by diminishing the participation of irrelevant regions during alignment and relatively increasing the alignment similarity of prominent words. Additionally, we incorporate prior textual information into image regions to reduce misalignment occurrences. In practice, we first design a novel intra-modal fragments relationship reasoning method, and subsequently employ our proposed alignment mechanism to compute the similarity between images and text. Extensive quantitative comparative experiments on MS-COCO and Flickr30K datasets demonstrate that our approach outperforms state-of-the-art methods by about 5% to 10% in the rSum metric.
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis

Motivation: Disease diagnosis oriented dialogue system models the interactive consultation procedure as Markov Decision Process and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in the simple scenario when the action space is small, however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The high-level policy consists of amastermodel that is responsible for triggering a low-levelmodel, the lowlevel policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms. Results: Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches. Availability: The code and data is available from https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis Contact: 21210980124@m.fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.
OtterHD: A High-Resolution Multi-modality Model

In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precision. Unlike conventional models that are constrained by fixed-size vision encoders, OtterHD-8B boasts the ability to handle flexible input dimensions, ensuring its versatility across various inference requirements. Alongside this model, we introduce MagnifierBench, an evaluation framework designed to scrutinize models' ability to discern minute details and spatial relationships of small objects. Our comparative analysis reveals that while current leading models falter on this benchmark, OtterHD-8B, particularly when directly processing high-resolution inputs, outperforms its counterparts by a substantial margin. The findings illuminate the structural variances in visual information processing among different models and the influence that the vision encoders' pre-training resolution disparities have on model effectiveness within such benchmarks. Our study highlights the critical role of flexibility and high-resolution input capabilities in large multimodal models and also exemplifies the potential inherent in the Fuyu architecture's simplicity for handling complex visual data.
The influence of rainbow scrapbook learning media on student learning motivation

The low motivation of students in learning is partly due to the use of learning media which is still limited. The purpose of this study was to determine the effect of Rainbow Scrapbook media on the learning motivation of class IX students in the Integrated Social Sciences subject at SMP Negeri 50 Batam. This type of research is quantitative research with a quasi-experimental approach. The sampling technique used purposive sampling. The selected experimental class IX F consisted of 39 students and the control class IX D consisted of 40 students. The experimental class was given the treatment using Rainbow Scrapbook media, while the control class was given the treatment using textbook media. The research instrument is in the form of a learning motivation questionnaire with a Likert scale of 37 question items. The results of the research class based on the tabulation of the frequency distribution of 39 students obtained an average (mean) of 68.82, and the control class from the tabulation of the frequency distribution of 40 students obtained an average (mean) of 67.87. The t-test analysis obtained a t count of 1.664 and a t table of 1.660 with a significance of 0.05. Because the t count is greater than the t table, then Ha can be accepted and Ho is rejected, meaning that there is a significant effect of using Rainbow Scrapbook media on student learning motivation.
Enhancing twelve grader’s proficiency in performing procedure text using flashcards

Performing procedure text in English has its own complexities especially for twelve graders since they need to clearly present a series of action in logical order. When students performed their presentations online, they felt it was not as difficult as having an onsite presentation. Yet, the transition of learning mode in the current post-pandemic brings a greater challenge to them. To overcome this issue, this classroom action research offered the use of flashcards containing images and keywords that became the main attraction for students. The participants of this study were 28 students of a senior high school in Kediri. The results indicate that flashcards were significantly helpful in enhancing the twelve graders proficiency in performing procedure text with the increase from pre-test score of 76.79 into the post-test score 83.79 with a significance level of 000. It means that flashcards are able to improve student learning outcomes, especially in performing speaking for procedure text. Therefore, English speaking teachers are recommended to use flashcard to enrich student’s vocabulary and support the systematic presentation of procedure text.
A Declaration of Software Independence

A voting system should not merely report the outcome: it should also provide sufficient evidence to convince reasonable observers that the reported outcome is correct. Many deployed systems, notably paperless DRE machines still in use in US elections, fail certainly the second, and quite possibly the first of these requirements. Rivest and Wack proposed the principle of software independence (SI) as a guiding principle and requirement for voting systems. In essence, a voting system is SI if its reliance on software is ``tamper-evident'', that is, if there is a way to detect that material changes were made to the software without inspecting that software. This important notion has so far been formulated only informally. Here, we provide more formal mathematical definitions of SI. This exposes some subtleties and gaps in the original definition, among them: what elements of a system must be trusted for an election or system to be SI, how to formalize ``detection'' of a change to an election outcome, the fact that SI is with respect to a set of detection mechanisms (which must be legal and practical), the need to limit false alarms, and how SI applies when the social choice function is not deterministic.
Generative AI for Software Metadata: Overview of the Information Retrieval in Software Engineering Track at FIRE 2023

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects and an additional dataset generated individually by teams using large language models. Overall 56 experiments have been submitted by 17 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The labels generated from large language models increase the bias in the prediction model but lead to less over-fitted results.
A Simple and Efficient Baseline for Data Attribution on Images

Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs during deployment. However, current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. These approaches therefore come at a high computational cost, are memory intensive, and are hard to scale to large models or datasets. In this work, we focus on a minimalist baseline, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. Our method is model-agnostic and scales easily to large datasets. We show results on CIFAR-10 and ImageNet, achieving strong performance that rivals or outperforms state-of-the-art approaches at a fraction of the compute or memory cost. Contrary to prior work, our results reinforce the intuition that a model's prediction on one image is most impacted by visually similar training samples. Our approach serves as a simple and efficient baseline for data attribution on images.