Reviewerly/exHarmony huggingface dataset:
On Parity-Check Matrices of Twisted Generalized Reed-Solomon Codes

Twisted Generalized Reed-Solomon (TGRS) Codes are a class of linear codes which contain MDS codes but are not equivalent to GRS code. In recent years, many scholars have studied the construction of MDS self-dual codes through TGRS code. Among these works, the number of twists is actually quite small, and an important step in showing a TGRS code is self-dual is to find its parity-check matrix. In this paper, we focus on the computation of parity-check matrices of TGRS codes with almost no restrictions on twist and hook vectors. By application of elementary row (column) operations in linear algebra, we present a unified but elementary approach to obtain the parity-check matrices of TGRS codes. This paper creates a foundation for further study of MDS self-dual TGRS codes on a much broader scope.
A Design of Automatic Reference Paper Collection System Using Selenium and Bert Model

Nowadays, Google Scholar is often used to find reference papers of the current research and download the PDF files. Basically, the researcher needs to input related keywords at the form, run the site, visit suggested paper sites in output pages to check their overviews, and download the PDF files if necessary. This manual process can be time-consuming and error-prone. In this paper, we present a design of the web-based automatic reference paper collection system to help researchers to enhance their research activities by automating the process. This design adopts Selenium to automate the input/output procedure to Google Scholar by web scraping, and Bert pre-trained transformer model to extract the references matched with keywords. Python Flask is used for the web application server and Angular is for the user interface framework. The complete implementation and the evaluation of the proposal will be in future works.
ChatGPT in IoT Systems: Arduino Case Studies

Since the beginning of this year, the novel large language model (LLM) based ChatGPT conversational agent has been in spotlight, due to its comprehensiveness across many fields – from novel writing to playing board games. In this paper, it is explored how it can be leveraged within IoT systems, taking into account both the novel scenarios enabled relying on ChatGPT’s power of question answering and software development as well. As example, two case studies related to Arduino platform are considered: 1) ChatGPT-based predictions on sensor data collected by Arduino 2) model-driven automated Arduino code generation.
A Literature Mining Method Based on Curve Graph Information in Literature

Literature mining can help researchers quickly grasp the current development of the relevant scientific fields. For the material literature in PDF format, a method is proposed to intercept a single curve graph and recognize the names of its coordinate axis entities. The method first uses Yolov5s and PDFminer to obtain the images and text contents of the single curve graph of the literature, and matches the graphs and their corresponding titles. Then it uses cosine similarity and Jaccard similarity as indicators to find the description statement of the corresponding graph in the text as expanded text. Finally, the title and the expanded text are stitched together as the new input text, and Sci-Bert is used to generate dynamic vector encoding, and CRF is used to predict the label sequence with maximum probability to obtain the entity name. It is demonstrated that the proposed method can effectively truncate the curve graph in the literature and identify the corresponding axis entity names. Moreover, the NER based on the expanded text also outperforms the original model by 4% on the customized dataset.
The level of digital transformation of the Banking Science and Trainning Review in the context of the digital transformation of the national media system

In June 2023, the Ministry of Information and Communications issued the Digital Media Transformation Maturity Index. One of the important meanings of the Index is to help each media agency determine which stage they are in the digital transformation process, and therefore have an appropriate roadmap, plan, and digital transformation solutions. Based on a comprehensive document review and analysis, the article evaluates the digital transformation maturity of the Banking Science and Trainning Review according to the 5 pillars set by the criteria of the Ministry. The initial results indicate that the Journal has performed well in pillars (1) Strategy, (2) Digital Infrastructure, Platform, and Information Security, and (3) Organizational and Professional Consistency. However, the Journal should continue to work on enhancing in pillars (4) Readership, Audience, and Listenership, and (5) Level of Digital Technology Application. This results provide a foundation for the overall Banking Academy of Vietnam and the Banking Science and Trainning Review to develop further solutions to continue maturing in the digital transfromation journey.
Empowering machine learning with knowledge graphs for the semantic era

The widely used relational database model organizes data in the format of tables with columns and rows. By checking the tables, the relationship amongst the different data points can be identified. This has been doing well for business operations automation as the data volume is growing slowly. For complicated operations (which involve identifying relationships amongst data points kept in different tables), the relational database model is found inefficient and inadequate. There are other inadequacies in the hugely popular and widely relational database systems (RDBS). Therefore, there is a clarion call for pioneering database solutions for the impending knowledge era.On the other side, with the rising need for creating and managing intelligent devices and software products for business transformation, the role and responsibility of artificial intelligence (AI) methods go up significantly. Machine learning (ML) and deep learning (DL) algorithms play a very vital role in producing sophisticated AI systems and services. For AI algorithms to do their tasks, they need a lot of correct and cleaned data. Right data is to result in highly accurate predictions/inferences/conclusions.Data typically gets collected from different sources and cleansed. There are several dissimilar data formats and transmission protocols greatly complicating the goal of data integration. Therefore, there are insistences for competent data integration and virtualization technologies and tools, which play a very vital role in visualizing and realizing profoundly impactful AI systems. That is, there is a need for a fresh and flexible approach to fulfilling the complicated requirement of data integration. The need for data integration has given rise to many research works and thought processes into working out the best possible way to collect, store, manipulate, and maintain digital data. Knowledge graphs (KGs) are the graph databases emerging and getting established as the next-generation data management system for speeding up knowledge engineering and extraction towards digitally transformed businesses and societies. The popularity of KGs is increasing due to their potential and flexibility in dealing with complex and interrelated data.In a KG, the data is stored and information is depicted in a graphical format. This methodology in KGs can be applied to create a graphical representation of the relationships amongst all of its data points. Hence, even if the data points do not fit neatly together into a table, the associations between the data points can be evaluated fast and with much less computation power which is the explicit advantage over relational database.Formally, a KG is a directed labelled graph that intrinsically and illustratively represents relations between data points. A node of the KG represents a data point. The entity of this data point could be a person, a place, or a webpage, and an edge represents the relationship between a pair of data points.
Inclusion of XAI in artificial intelligence and deep learning technologies

As technology has advanced over the past few decades, the complexity of artificial intelligence (AI) systems has increased rapidly. While these systems can provide impressive results, they can also be difficult to understand, even for experts in the field. Explainable AI (XAI) is an emerging field of research focused on making AI systems more transparent and interpretable. In this article, we will explore what XAI is, why it matters, and how it works.XAI is an emerging field of research that aims to make AI systems more transparent, interpretable, and accountable. In recent years, AI has made significant advances in fields such as natural language processing, image recognition, and game playing. However, as AI systems become more complex and ubiquitous, it becomes increasingly important to ensure that they are used ethically and responsibly.One of the main challenges with AI is that it can be difficult for humans to understand how the system arrived at a particular decision. For example, a deep learning algorithm might be able to identify objects in an image with incredible accuracy, but it may not be clear how the system arrived at its conclusion. This can lead to a lack of trust in the system, particularly in high-stakes domains such as healthcare, finance, and criminal justice.Technically, XAI refers to a set of methods and techniques that enable AI systems to provide human-understandable explanations of their decisions, predictions, and actions. XAI utilizes various approaches such as rule-based systems, decision trees, and model-based techniques to produce explanations that can be interpreted and verified by humans. XAI aims to address the lack of transparency and accountability in traditional black-box AI systems referred in Figure 4.1, which can make it difficult for developers and users to understand and trust these systems. By providing interpretable explanations, XAI can increase the effectiveness, reliability, and trustworthiness of AI systems in a variety of applications.
Transformasi Digital InsurCo dengan Merancang Pengelolaan Risiko Teknologi Informasi Menggunakan Framework COBIT 2019 IT Risk Management Focus Area

Rapid advancements in Information Technology (IT) play a vital role in fostering innovation within a company. Previous research has successfully identified the significance of IT Governance and Management (ITGM) in the process of Digital Transformation (DT). However, a more comprehensive analysis is needed to understand the role of IT risk management in supporting such changes, particularly through a case study on the insurance company InsurCo. This study employs the Design Science Research (DSR) method, utilizing interview data and the COBIT 2019 IT Risk Management Focus Area framework to design an IT risk management approach that supports InsurCo's DT. Capability assessments, gap analysis, potential improvements, improvement designs, roadmap development, and impact estimation of implementation have been executed on seven ITGM components. InsurCo, being a State-Owned Enterprise, is bound by the regulation PER-2/MBU/03/2023 concerning the Guidelines for Governance and Significant Corporate Activities of State-Owned Enterprises. Additionally, the Financial Services Sector Master Plan 2021-2025 highlights the acceleration of DT. The prioritized ITGM objectives required by InsurCo are derived from three design factor determinations: focus areas, design mechanisms, and ITGM mechanisms concerning DT, resulting in the following prioritized ITGM objectives: APO12 Managed Risk, EDM03 Ensure Risk Optimization, and DSS04 Managed Continuity. There is an estimated average capability increase of 3.57% across these three ITGM objectives. The research outcome presents a recommended design for the seven components of the governance system, aiming to enhance the IT risk management capability index within InsurCo. This study contributes to a deeper understanding of the concept of prioritizing IT risk management to support organizational digital transformation, specifically providing practical implications for InsurCo to bolster the success of their strategic programs, and benefit the insurance industry.
Распределенное научное познание внутри академии и за ее пределами

In the article, I consider the problem of distributed scientific knowledge in two aspects: (1) from the point of view of distributed cognition as one of the ways for scientists to obtain scientific knowledge; (2) from the point of view of recruiting scientific personnel to the academy. I believe that in both the cases the problem of epistemic injustice in relation to new participants in the cognitive process remains. The concept of distributed cognition, in my opinion, is not able by itself to solve the problem of epistemic injustice and unequal access to both the results of scientific research and the status of their participants, that is, to make a transition from epistemic asymmetry to epistemic balance or parity of the parties. The most productive implication of the concept of distributed cognition is that its application to scientific practice highlights situations of epistemic injustice, each time on an ever-increasing scale. Finally, I return to the concept of science as a public good and consider some of its implications from the perspective of the classical understanding of science and the possibilities for redressing epistemic injustice.
Text Segmentation via Hierarchical Document Attention Model

With the rapid development of natural language processing technology, text segmentation has become an important task in text processing. However, existing text segmentation methods often perform poorly when faced with long texts and complex structures, requiring a more efficient and accurate approach. In this paper, we propose a new text segmentation method based on the Hierarchical Document Attention (HDA), which automatically identifies and segments different paragraphs in the text by analyzing and weighting the hierarchical structure of the text sequence data. Compared with existing methods, the model has higher accuracy and efficiency, and better supports tasks such as text analysis and information extraction. The main contribution of this paper is the proposal of a text segmentation method based on the HDA, which effectively models text sequences through multi-level attention mechanisms. Experimental verification on public datasets shows that this model exhibits good performance in text segmentation tasks.
GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems

Federated Learning is designed for training models using data distributed across multiple Internet of Things(IoT) devices or servers, reducing data transfer overhead and ensuring data security. However, the decentralization and diversity of IoT devices introduce statistical and system heterogeneity, which can lead to unstable model training and even system crashes. Although many studies attribute performance issues to client-drift caused by this heterogeneity, there’s a lack of insight into how different forms of heterogeneity impact local model gradient variations and model convergence. In this paper, we investigate model gradient distribution characteristics in heterogeneous training. We find that the challenge isn’t solely due to client-drift but is also closely linked to a high degree of model overfitting, which negatively affects local model training and equilibrium convergence. To address this challenge, we introduce an efficient framework called GOFL. First, GOFL incorporates the Federated Gradient Normalization (FGN) technique to maintain gradient distribution consistency while mitigating client-drift stemming from heterogeneity. We also highlight the benefits of FGN in reducing local model overfitting and improving convergence. Secondly, GOFL introduces the Federated Device Aggregation (FDA) strategy, a critical addition to FGN. It adaptively guides device selection and aggregation based on device contributions, ensuring a more balanced training approach in the face of system heterogeneity. The experimental results demonstrate that GOFL achieves state-of-the-art training accuracy while reducing the number of training rounds. In particular, it improves the accuracy of the classical FL framework FedAvg by 30.57% and reduces the number of convergence rounds by 5.17 times.
The Transient Nature of Emergent In-Context Learning in Transformers

Transformer neural networks can exhibit a surprising capacity for in-context learning (ICL) despite not being explicitly trained for it. Prior work has provided a deeper understanding of how ICL emerges in transformers, e.g. through the lens of mechanistic interpretability, Bayesian inference, or by examining the distributional properties of training data. However, in each of these cases, ICL is treated largely as a persistent phenomenon; namely, once ICL emerges, it is assumed to persist asymptotically. Here, we show that the emergence of ICL during transformer training is, in fact, often transient. We train transformers on synthetic data designed so that both ICL and in-weights learning (IWL) strategies can lead to correct predictions. We find that ICL first emerges, then disappears and gives way to IWL, all while the training loss decreases, indicating an asymptotic preference for IWL. The transient nature of ICL is observed in transformers across a range of model sizes and datasets, raising the question of how much to "overtrain" transformers when seeking compact, cheaper-to-run models. We find that L2 regularization may offer a path to more persistent ICL that removes the need for early stopping based on ICL-style validation tasks. Finally, we present initial evidence that ICL transience may be caused by competition between ICL and IWL circuits.
k-Parameter Approach for False In-Season Anomaly Suppression in Daily Time Series Anomaly Detection

Detecting anomalies in a daily time series with a weekly pattern is a common task with a wide range of applications. A typical way of performing the task is by using decomposition method. However, the method often generates false positive results where a data point falls within its weekly range but is just off from its weekday position. We refer to this type of anomalies as "in-season anomalies", and propose a k-parameter approach to address the issue. The approach provides configurable extra tolerance for in-season anomalies to suppress misleading alerts while preserving real positives. It yields favorable result.
Exploring Multi-Programming-Language Commits and Their Impacts on Software Quality: An Empirical Study on Apache Projects

Context: Modern software systems (e.g., Apache Spark) are usually written in multiple programming languages (PLs). There is little understanding on the phenomenon of multi-programming-language commits (MPLCs), which involve modified source files written in multiple PLs. Objective: This work aims to explore MPLCs and their impacts on development difficulty and software quality. Methods: We performed an empirical study on eighteen non-trivial Apache projects with 197,566 commits. Results: (1) the most commonly used PL combination consists of all the four PLs, i.e., C/C++, Java, JavaScript, and Python; (2) 9% of the commits from all the projects are MPLCs, and the proportion of MPLCs in 83% of the projects goes to a relatively stable level; (3) more than 90% of the MPLCs from all the projects involve source files in two PLs; (4) the change complexity of MPLCs is significantly higher than that of non-MPLCs; (5) issues fixed in MPLCs take significantly longer to be resolved than issues fixed in non-MPLCs in 89% of the projects; (6) MPLCs do not show significant effects on issue reopen; (7) source files undergoing MPLCs tend to be more bug-prone; and (8) MPLCs introduce more bugs than non-MPLCs. Conclusions: MPLCs are related to increased development difficulty and decreased software quality.
Automated Identification of Sexual Orientation and Gender Identity Discriminatory Texts from Issue Comments

In an industry dominated by straight men, many developers representing other gender identities and sexual orientations often encounter hateful or discriminatory messages. Such communications pose barriers to participation for women and LGBTQ+ persons. Due to sheer volume, manual inspection of all communications for discriminatory communication is infeasible for a large-scale Free Open-Source Software (FLOSS) community. To address this challenge, this study aims to develop an automated mechanism to identify Sexual orientation and Gender identity Discriminatory (SGID) texts from software developers' communications. On this goal, we trained and evaluated SGID4SE ( Sexual orientation and Gender Identity Discriminatory text identification for (4) Software Engineering texts) as a supervised learning-based SGID detection tool. SGID4SE incorporates six preprocessing steps and ten state-of-the-art algorithms. SGID4SE implements six different strategies to improve the performance of the minority class. We empirically evaluated each strategy and identified an optimum configuration for each algorithm. In our ten-fold cross-validation-based evaluations, a BERT-based model boosts the best performance with 85.9% precision, 80.0% recall, and 82.9% F1-Score for the SGID class. This model achieves 95.7% accuracy and 80.4% Matthews Correlation Coefficient. Our dataset and tool establish a foundation for further research in this direction.
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning

An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric memory, external structured knowledge, and external unstructured knowledge. Most existing prompting methods either rely on one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. In this work, we overcome these limitations by introducing a novel semi-structured prompting approach that seamlessly integrates the model's parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. Experimental results on open-domain multi-hop question answering datasets demonstrate that our prompting method significantly surpasses existing techniques, even exceeding those that require fine-tuning.
Adversarial Imitation Learning On Aggregated Data

Inverse Reinforcement Learning (IRL) learns an optimal policy, given some expert demonstrations, thus avoiding the need for the tedious process of specifying a suitable reward function. However, current methods are constrained by at least one of the following requirements. The first one is the need to fully solve a forward Reinforcement Learning (RL) problem in the inner loop of the algorithm, which might be prohibitively expensive in many complex environments. The second one is the need for full trajectories from the experts, which might not be easily available. The third one is the assumption that the expert data is homogeneous rather than a collection from various experts or possibly alternative solutions to the same task. Such constraints make IRL approaches either not scalable or not usable on certain existing systems. In this work we propose an approach which removes these requirements through a dynamic, adaptive method called Adversarial Imitation Learning on Aggregated Data (AILAD). It learns conjointly both a non linear reward function and the associated optimal policy using an adversarial framework. The reward learner only uses aggregated data. Moreover, it generates diverse behaviors producing a distribution over the aggregated data matching that of the experts.