Reviewerly/exHarmony huggingface dataset:
Learning to Select the Relevant History Turns in Conversational Question Answering

The increasing demand for the web-based digital assistants has given a rapid rise in the interest of the Information Retrieval (IR) community towards the field of conversational question answering (ConvQA). However, one of the critical aspects of ConvQA is the effective selection of conversational history turns to answer the question at hand. The dependency between relevant history selection and correct answer prediction is an intriguing but under-explored area. The selected relevant context can better guide the system so as to where exactly in the passage to look for an answer. Irrelevant context, on the other hand, brings noise to the system, thereby resulting in a decline in the model's performance. In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand. We also propose an attention-based mechanism to re-rank the pruned terms based on their calculated weights of how useful they are in answering the question. In the end, we further aid the model by highlighting the terms in the re-ranked conversational history using a binary classification task and keeping the useful terms (predicted as 1) and ignoring the irrelevant terms (predicted as 0). We demonstrate the efficacy of our proposed framework with extensive experimental results on CANARD and QuAC -- the two popularly utilized datasets in ConvQA. We demonstrate that selecting relevant turns works better than rewriting the original question. We also investigate how adding the irrelevant history turns negatively impacts the model's performance and discuss the research challenges that demand more attention from the IR community.
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation

Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e.g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences. This is a computational challenge as presented by the practice of sequence generation problems, such as machine translation, where we often deal with a large action space (\textit{e.g.,} a vocabulary) and a long action sequence (\textit{e.g.,} a translation). In this work, we introduce two-stage sampling and dynamic sampling approaches to improve the sampling efficiency during training sequence generation models via RL. We experiment with our approaches on the traditional sequence generation tasks, including machine translation and abstractive summarization. Furthermore, we evaluate our approaches in RL from human feedback (RLHF) through training a large language model using the reward model. Experimental results show that the efficient sampling-based RL, referred to as ESRL, can outperform all baselines in terms of both training efficiency and memory consumption. Notably, ESRL yields consistent performance gains over the strong REINFORCE, minimum risk training, and proximal policy optimization methods.
A Deep Dive into the Google Cluster Workload Traces: Analyzing the Application Failure Characteristics and User Behaviors

Large-scale cloud data centers have gained popularity due to their high availability, rapid elasticity, scalability, and low cost. However, current data centers continue to have high failure rates due to the lack of proper resource utilization and early failure detection. To maximize resource efficiency and reduce failure rates in large-scale cloud data centers, it is crucial to understand the workload and failure characteristics. In this paper, we perform a deep analysis of the 2019 Google Cluster Trace Dataset, which contains 2.4TiB of workload traces from eight different clusters around the world. We explore the characteristics of failed and killed jobs in Google's production cloud and attempt to correlate them with key attributes such as resource usage, job priority, scheduling class, job duration, and the number of task resubmissions. Our analysis reveals several important characteristics of failed jobs that contribute to job failure and hence, could be used for developing an early failure prediction system. Also, we present a novel usage analysis to identify heterogeneity in jobs and tasks submitted by users. We are able to identify specific users who control more than half of all collection events on a single cluster. We contend that these characteristics could be useful in developing an early job failure prediction system that could be utilized for dynamic rescheduling of the job scheduler and thus improving resource utilization in large-scale cloud data centers while reducing failure rates.
Feedback in Cumulative Coursework: Action Research in a Blended Course

Action research calls for the solution of practical problems in the classroom as well as the expansion of theoretical knowledge. In this study, feedback in cumulative work is explored as a strategy for guiding and improving the teaching-learning process. By following a feedback loop: initial draft, feedback on first draft, final draft, and marking, 28 students in at the University Universidad Nacional Autónoma de México (UNAM) had the opportunity to receive guidance and demonstrate improvement in a blended learning, or b-learning course. The study lasted five weeks and engaged university learners as well as the educator in videoconferences focused on feed up, feedback, and feed forward.This pedagogical action research involved observation, research and planning, implementation, and reflection. Data was gathered on students’ access to technology and their perception upon effectiveness of remote learning based on their experience. Additionally, scores before and after treatment were registered and analyzed.The findings showed a general improvement after feedback sessions and learners were able to present enhanced final versions of tasks. The study’s main contributions are the confirmation of positive results on effective feedback as well as an opening to discussion, adaptation, and improvement of the practices presented.
A large language model-assisted education tool to provide feedback on open-ended responses

Open-ended questions are a favored tool among instructors for assessing student understanding and encouraging critical exploration of course material. Providing feedback for such responses is a time-consuming task that can lead to overwhelmed instructors and decreased feedback quality. Many instructors resort to simpler question formats, like multiple-choice questions, which provide immediate feedback but at the expense of personalized and insightful comments. Here, we present a tool that uses large language models (LLMs), guided by instructor-defined criteria, to automate responses to open-ended questions. Our tool delivers rapid personalized feedback, enabling students to quickly test their knowledge and identify areas for improvement. We provide open-source reference implementations both as a web application and as a Jupyter Notebook widget that can be used with instructional coding or math notebooks. With instructor guidance, LLMs hold promise to enhance student learning outcomes and elevate instructional methodologies.
SoK: Assessing the State of Applied Federated Machine Learning

Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.
On the Inherent Anonymity of Gossiping

Detecting the source of a gossip is a critical issue, related to identifying patient zero in an epidemic, or the origin of a rumor in a social network. Although it is widely acknowledged that random and local gossip communications make source identification difficult, there exists no general quantification of the level of anonymity provided to the source. This paper presents a principled method based on $\varepsilon$-differential privacy to analyze the inherent source anonymity of gossiping for a large class of graphs. First, we quantify the fundamental limit of source anonymity any gossip protocol can guarantee in an arbitrary communication graph. In particular, our result indicates that when the graph has poor connectivity, no gossip protocol can guarantee any meaningful level of differential privacy. This prompted us to further analyze graphs with controlled connectivity. We prove on these graphs that a large class of gossip protocols, namely cobra walks, offers tangible differential privacy guarantees to the source. In doing so, we introduce an original proof technique based on the reduction of a gossip protocol to what we call a random walk with probabilistic die out. This proof technique is of independent interest to the gossip community and readily extends to other protocols inherited from the security community, such as the Dandelion protocol. Interestingly, our tight analysis precisely captures the trade-off between dissemination time of a gossip protocol and its source anonymity.
AI Literature Review Suite

The process of conducting literature reviews is often time-consuming and labor-intensive. To streamline this process, I present an AI Literature Review Suite that integrates several functionalities to provide a comprehensive literature review. This tool leverages the power of open access science, large language models (LLMs) and natural language processing to enable the searching, downloading, and organizing of PDF files, as well as extracting content from articles. Semantic search queries are used for data retrieval, while text embeddings and summarization using LLMs present succinct literature reviews. Interaction with PDFs is enhanced through a user-friendly graphical user interface (GUI). The suite also features integrated programs for bibliographic organization, interaction and query, and literature review summaries. This tool presents a robust solution to automate and optimize the process of literature review in academic and industrial research.
Cyber Kill Chain Analysis Using Artificial Intelligence

Artificial Intelligence (AI) tools are promising multifaceted techniques for addressing the most mundane tasks for greater efficiency and high productivity. Cyber security space is one of the areas that AI is promising to revolutionize. This study will develop a conceptual and theoretical framework to support a research design that can simulate research in understanding how AI can be applied to Cyber Kill Chain phases. This study has reviewed 21 journal and conference articles mostly from IEEE Xplore database. An overview of the application of artificial intelligence (AI) in cybersecurity, particularly within the framework of the Cyber Kill Chain was provided in this study. It also emphasizes the limitations of traditional security approaches and the necessity for innovative and intelligent defense methodologies. The results of reviewing the relevant literatures discovered that the key components of cybersecurity, includes identity, asset management, automated configuration management, security control validation, governance, risk assessment, and vulnerability identification. A theoretical framework was developed which introduces the Cyber Kill Chain model with a Unified Kill Chain model to address its shortcomings. Application of AI in cybersecurity offers an optimistic solutions to address the evolving threat landscape. AI techniques, such as machine learning, anomaly detection, and behavioural analysis, have shown great potential in enhancing various aspects of cybersecurity. However, challenges related to data quality, adversarial attacks, and privacy concerns need to be addressed for successful implementation. Further research and development are crucial to fully harness the power of AI in cybersecurity and stay ahead of evolving cyber threats.
Pemanfaatan E-Learning pada Pembelajaran di Prodi PGMI Universitas Islam Negeri Raden Fatah Palembang

This study aims to find out how to use e-learning and the obstacles faced by lecturers and students when using e-learning in the learning process in the third semester of the Madrasah Ibtidaiyah Teacher Education (PGMI) Study Program. This study used a qualitative descriptive research method. The data collection tool was a questionnaire with interviews and observation. Data analysis techniques use Miles and Humberman's theory, namely, data reduction, data display, and drawing conclusions. The results showed the use of e-learning in the learning process in third semester of the PGMI study program was very effective. The data showed that almost all courses use e-learning, only two lecturers with the same subject, namely scouts, who do not use e-learning. The data also showed that 71.8% or 135 students said that e-learning was very effective in learning. The constraints faced by lecturers regarding the use of e-learning were that not all of the features in e-learning exist, for example, lecturer attendance was not yet available, so lecturers have to prepare manually outside of the e-learning application, and virtual face-to-face features of e-learning that were tough to accessed, while the students were more on the capacity limit of uploaded videos.
Strategi Pemimpin Madrasah dalam Meningkatkan Minat Baca dengan Membangun Budaya Literasi

The role of the head is very necessary in managing an institution to be more developed, the role of the head is also very helpful in the progress of the program carried out by the institution where the head can manage and motivate all components of the school with the right strategy. This study aims to analyze and understand the strategy of a leader in increasing reading interest in students by implementing literacy culture in a school, namely MI Ihyauddiniyah, Duren Village, Gading District, Probolinggo Regency. The research method uses a qualitative approach from the type of case study, to get an overview of a leader's strategy in increasing reading interest in students by applying literacy culture, observation of researchers by mingling directly in the field then conducting interviews with several informants, namely principals and teachers at school. The results showed that a leader's strategy in increasing reading interest in students by implementing literacy culture by; Compulsory reading culture, strengthening human resources, and procuring infrastructure. With the strategy carried out by the leadership in building a literacy culture, it can have implications for fostering children's interest in reading, writing skills, language proficiency and work, especially at MI Ihyauddiniyah.
Adaptation of young physical education teachers

The relevance of research on the adaptation of young physical education teachers is dictated by the modern challenges of Russian education, primarily the need to meet the targets of the national project «Education». Young teachers, in particular including physical education teachers, in the conditions of the new geopolitical reality, are that strategic resource, which should be the main thing in the formation of a new paradigm of education in Russia. The purpose of the study is to assess the adaptation process of young physical education teachers in Yekaterinburg, to identify the main reasons for the difficulty of entering the profession and moving to the next stage of professional development. The article is written on the basis of a study conducted by the authors in May 2023 of young teachers – teachers of physical culture, whose experience does not exceed five years. In total, 20 teachers of Yekaterinburg schools were interviewed. Socio-professional adaptation is understood as «the process of interaction between the individual and the professional environment, during which the development of goals, values, norms of professional activity is carried out, due to a combination of external and internal factors that ensure the optimal functioning and development of the individual in the profession».
MFD: Multi-Feature Detection of LLM-Generated Text

Abstract With the rapid development of large language models, their powerful capabilities have led to their rapid popularity in society. However, it not only brings great convenience to people’s life and work but also provides a favorable tool for criminals to carry out malicious behaviors. Therefore, to prevent the malicious use of large language models, there is a growing demand for a detector that can efffciently discriminate texts generated by large language models. In this paper, Multi-Feature Detection (MFD), a new zero-shot method, is introduced. MFD comprehensively considers log-likelihood, log-rank, entropy, and LLM-Deviation. LLM-Deviation is a new statistical feature proposed in this paper and has a clear distribution difference between texts generated by LLMs and those written by humans. Experiments show MFD is more effective than the existing zero-shot method. MFD improves the detection performance by 1.02 F1 score on average on the HC3-English dataset. In generalization ability, MFD is also very competitive compared with the existing zero-shot method.
Storage in the Deep Aquifer: Stochastic Process in Time and Frequency Domain Using Groundwater Level Response to Seismic Activation of the Tohoku Earthquake, Japan, 2011

Abstract This study delves the interplay of Rayleigh wave-groundwater-aquifer dynamics, utilizing seismic data from designated stations, particularly the magnitude 9 event on March 11, 2011 (02:46:23 PM epicenter), Tohoku Earthquake, Japan. Auto spectra uncover significant peaks, linking groundwater shifts with Rayleigh waves' vertical motion. High coherence between groundwater levels and seismogram vertical shifts confirms this connection. Coherence analysis validates it within a frequency range. Aquifer storage coefficient estimation aligns with confined aquifer norms, ranging from 1.0E-05 to1.0E-03. This consistency holds true across different methods and other earthquake event, highlighting seismic impact. The research emphasizes seismic relevance for groundwater insight. Expanding datasets is recommended for coefficient precision. These insights enhance understanding of aquifer behavior during seismic events, aiding groundwater management and earthquake readiness in susceptible regions. Further research should expand datasets for coefficient accuracy. Insights advance aquifer behavior comprehension during seismic events, guiding groundwater management and earthquake readiness in prone regions.
How to use the Covid-19 pandemic to teach Mathematics

Distance and hybrid learning has revealed many shortcomings in traditional teaching-learning approaches. Pedagogical staff of public schools found themselves in a particularly difficult situation, facing new challenges. The presented work deals with such key issues and problems of the science of pedagogy as: teaching methods, teaching technology and strategy. In particular, the paper presents how the creation of moving images can be used to solve mathematical problems, which develops mathematical literacy, logical and critical thinking, problem solving skills and observation in students.
Investigating the Ideology of Indonesian Universities through the Vision and Mission Statements: A Corpus-Assisted Discourse Study

Literatures have shown that discursive investigation on universities’ vision and mission statements can reveal the universities’ ideologies as well as the prevailing sociopolitical factors in the country. However, there is no such investigation within Indonesian higher education discourse yet. This study seeks to fill the gap by conducting a corpus-assisted analysis on the vision and mission statements of 100 Indonesian universities. By implementing CADS procedures, this study combines corpus linguistics techniques and ideological discourse analysis approach. The final results reveal the social responsibility of Indonesian universities to support public welfare and the emergence of internationalization movement within the educational system of the country, denoting collectivism and neoliberalism as ‘negotiating’ ideologies within the institutional discourse. It is related to none other than the view that universities are institutional bodies that cannot be stagnant as they must always be progressing towards the current development of society and even the world.
ANALYSIS OF THE DATA QUALITY OF SERVICE TRANSACTION ON INDONESIA’S BALANCE OF PAYMENTS

This study aims to analyze the root causes of data quality problems in Foreign Exchange Reporting (LLD) as the primary data source used in the preparation of the Indonesian Balance of Payments (NPI), especially in service transactions. The research method used is qualitative with a case study approach. Data collection was carried out through interviews, observation, and document analysis. The results of the study concluded that the data quality problem of LLD reporting was data accuracy. The root causes of the data quality problem of LLD reporting come from both the internal and external sides of Bank Indonesia. The root causes from the internal side of Bank Indonesia are complex transaction purpose codes (STT), acceleration of the development of service transaction, and manual data accuracy checks. While the root causes of the data quality problem from the external side of Bank Indonesia is the routine mutation of frontliner employees of the reporting bank and the lack of training conducted by the Head Office of the reporting bank. To address the root of the problem, several strategies are recommended to be implemented, namely adjustments to the STT list, training to LLD Account Officers, preparation of automated data accuracy check tools, preparation of e-learning and handbook about LLD reporting, as well as additional policies related to training obligations by Head Office of the reporting bank.