Reviewerly/exHarmony huggingface dataset:
Container Instrumentation and Enforcement System for Runtime Security of Kubernetes Platform with eBPF

Containerization is a fundamental component of modern cloud-native infrastructure, and Kubernetes is a prominent platform of container orchestration systems. However, containerization raises significant security concerns due to the nature of sharing a kernel among multiple containers, which can lead to container breakout or privilege escalation. Kubernetes cannot avoid it as well. While various tools, such as container image scanning and configuration checking, can mitigate container workload vulnerabilities, these are not foolproof and cannot guarantee perfect isolation or prevent every active threat in runtime. As such, a policy enforcement solution is required to tackle the problem, and existing solutions based on LSM (Linux Security Module) frameworks may not be adequate for some situations. To address this, we propose an enforcement system based on BPF-LSM, which leverages eBPF (extended Berkeley Packet Filter) technology to provide fine-grained control and dynamic adoption of security policies. In this paper, we compare different LSM implementations to highlight the challenges of current enforcement solutions before detailing the design of our eBPF-based Kubernetes Runtime Instrumentation and Enforcement System (KRSIE). Finally, we evaluate the effectiveness of our system using a real-world scenario, as measuring the performance of a policy enforcement system is a complex task. Our results show that KRSIE can successfully control containers’ behaviors using LSM hooks at container runtime, offering improved container security for cloud-native infrastructure.
Guaranteed Expectation of the Flock Position with Random Distribution of Items

We consider estimation problems for a flock of linear systems with random matrices and additive uncertain disturbances. Because of uncertainty we may only assume that the state of each flock’s item belongs to some random information set that can be built according to measurement equations. Our goal is to define and investigate a mean value of random information sets. All the sets are described by its support functions. Some theorems on the approximation of the sets by be simpler objects are given. Particular cases, where matrices of the systems or probability spaces have special formes, are elaborated. In conclusion we review some examples.
“Why My Essay Received a 4?”: A Natural Language Processing Based Argumentative Essay Structure Analysis

Writing argumentative essays is a critical component of students' learning. Previous works on automatic assessments on essay writing often focused on providing a holistic score for the input essay, which only summarized the essay's overall quality. However, to provide more pedagogical value and equitable educational opportunities for all students, an automatized system needs to provide detailed feedback on students' essays. To address this issue, we developed an essay argumentative structure feedback system to support educators and students. We employed natural language processing (NLP) and data mining techniques to explore the association between argumentative structure and essay scores. First, we proposed a cross-prompt, sentence-level ensemble model to classify the argumentative elements and extract the argumentative structures from the essay. The model worked across multiple datasets and achieved high performance. Second, after applying the classification model on the ACT writing tests, we performed a sequential mining process to extract representative argumentative structures. Our findings highlight the role of organizational argumentative structure in essay scoring. Furthermore, we found a common argumentative structure used by the high-scored essays. Finally, with the knowledge of argumentative elements and structures used in the previous essays, we proposed a feedback tool design to complement the current AES systems and help students improve their argument writing skill.
Feedback and Open Learner Models in Popular Commercial VR Games: A Systematic Review

Virtual reality (VR) educational games are engaging as VR can enable high levels of immersion and presence. However, for effective learning, educational literature highlights the benefits if such games have effective feedback. We aimed to understand the nature of feedback provided in popular commercial VR educational games. To discover this, we systematically reviewed 260 commercially available educational games from VIVEPORT, Oculus and Steam, the key platforms for VR games. We assessed if they offered key forms of feedback we identified from the literature, score, levels, competition, self, self-comparison, till correct, accuracy, process, and Open Learner Models (OLMs). We found that just 67 games (26%) had any of these forms of feedback and just four had OLMs. Our key contributions are: (1) the first systematic review of feedback in commercial VR games; (2) literature-informed definition of key forms of feedback for VR games; (3) understanding about OLMs in commercial VR games.
Teacher Talk Moves in K12 Mathematics Lessons: Automatic Identification, Prediction Explanation, and Characteristic Exploration

Talk moves have been shown to facilitate enriched discussion and conversation in classrooms, leading to improved student learning outcomes. To support teachers in enhancing discursive techniques and providing timely feedback on their classroom discourse, this paper proposed two BERT-based deep learning models for automatically identifying teacher talk moves in K12 mathematics lessons. However, the proposed discourse models have complex structures and cannot offer clear explanations on the prediction of teacher utterance, potentially leading teachers to distrust the model. To address this issue, this paper employed three model-agnostic interpreting methods from explainable artificial intelligence and transparently explained to teachers how the model predictions were made by computing and displaying the word relevance. The analysis results confirmed the validity of these explanations. Further, the paper investigated the interpreting results to uncover key characteristics of each type of teacher talk moves. The findings indicated that simple words and phrases could serve as representative indicators of talk moves (e.g., phrases centering on "agree" and "disagree" in the case of talk move getting students to relate to another's idea), which shows the potential to assist teachers in mastering discursive techniques. We believe that the paper built a solid step towards building an automated classroom discourse analysis system and fully addressing the interpretability concerns of deep learning-based discourse models.
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues

In second language vocabulary learning, existing works have primarily focused on either the learning interface or scheduling personalized retrieval practices to maximize memory retention. However, the learning content, i.e., the information presented on flashcards, has mostly remained constant. Keyword mnemonic is a notable learning strategy that relates new vocabulary to existing knowledge by building an acoustic and imagery link using a keyword that sounds alike. Beyond that, producing verbal and visual cues associated with the keyword to facilitate building these links requires a manual process and is not scalable. In this paper, we explore an opportunity to use large language models to automatically generate verbal and visual cues for keyword mnemonics. Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues. We investigate the effectiveness of our approach via a human participant experiment by comparing it with manually generated cues.
Real-Time Hybrid Language Model for Virtual Patient Conversations

Advancements in deep learning have enabled the development of online learning tools for medical training, which is important for remote learning. However, face-to-face interaction is essential for practicing human-centric skills such as clinical skills. Presently, in medical training, such interactions can be mimicked using deep learning methodologies. However, the understanding of such models is often limited whereby lightweight models are unable to generalize beyond scope while large language models tend to produce unexpected responses. To overcome this, we propose a hybrid lightweight and large language model for creating virtual patients, which can be used for real-time autonomous training of trainee doctors in clinical settings using online platforms. This ensures high-quality and standardized learning for all individuals regardless of location and background.
Semi-supervised K-Means Clustering via DC Programming Approach

Though clustering is related to unsupervised machine learning and does not require any prior information on data items, in many real-life settings, there may be some expert knowledge on data labels or the properties of clusters known in advance. Obviously, such knowledge may be used to guide clustering process and improve the quality of found partitions. The clustering problems that involve some additional information on tags are called semi-supervised or constrained clustering problems. One distinguishes instance-level and cluster-level constraints usually formalized as the so-called must-link and cannot-link constraints or minimum/maximum cluster size. In this paper, we consider the constrained minimum sum-of-squares (k-means) clustering (MSSC) problem that incorporates both instance- and cluster-level constraints. As far as we know, such a semi-supervised MSSC problem has not been considered in the literature yet. We formulate this clustering problem and some of its particular cases as DC (difference of convex) optimization problems. Then, we develop a solution approach based on a special local search method. We carry out computational experiments on test problem instances demonstrating the efficiency of the proposed solution approach.
Investigating the Utility of Self-explanation Through Translation Activities with a Code-Tracing Tutor

Code tracing is a foundational programming skill that involves simulating a program's execution line by line, tracking how variables change at each step. To code trace, students need to understand what a given program line means, which can be accomplished by translating it into plain English. Translation can be characterized as a form of self-explanation, a general learning mechanism that involves making inferences beyond the instructional materials. Our work investigates if this form of self-explanation improves learning from a code-tracing tutor we created using the CTAT framework. We created two versions of the tutor. In the experimental version, students were asked to translate lines of code while solving code-tracing problems. In the control condition students were only asked to code trace without translating. The two tutor versions were compared using a between-subjects study (N = 44). The experimental group performed significantly better on translation and code-generation questions, but the control group performed significantly better on code-tracing questions. We discuss the implications of this finding for the design of tutors providing code-tracing support.
Unsupervised Concept Tagging of Mathematical Questions from Student Explanations

Assigning concept tags to questions enables Intelligent tutoring systems (ITS) to efficiently organize resources, help identify students' strengths and weaknesses, and recommend suitable learning materials accordingly. Manual tagging is time-consuming, and inefficient for large question banks, and could lead to consistency issues due to differences in the perspectives of individual taggers. Automatic tagging techniques can efficiently generate consistent tags at lower costs. Generating automatic tags for mathematical questions is challenging as the question text is usually short and concise, and the question as well as the answer text contains mathematical symbols and formulas. However, prior works have not studied this problem extensively. In this context, we conducted a study in a graduate-level linear algebra course to understand if student explanations to solving mathematical problems can be employed to generate concept tags associated with those questions. In this paper, we propose a method called Unsupervised Skill Tagging (UST) to extract concept tags associated with a given question from explanation text. Using UST on the explanations generated, we show that the explanations indeed contain the expert-specified concept tags.
What and How You Explain Matters: Inquisitive Teachable Agent Scaffolds Knowledge-Building for Tutor Learning

Students learn by teaching a teachable agent, a phenomenon called tutor learning. Literature suggests that tutor learning happens when students (who tutor the teachable agent) actively reflect on their knowledge when responding to the teachable agent's inquiries (aka knowledge-building). However, most students often lean towards delivering what they already know instead of reflecting on their knowledge (aka knowledge-telling). The knowledge-telling behavior weakens the effect of tutor learning. We hypothesize that the teachable agent can help students commit to knowledge-building by being inquisitive and asking follow-up inquiries when students engage in knowledge-telling. Despite the known benefits of knowledge-building, no prior work has operationalized the identification of knowledge-building and knowledge-telling features from students' responses to teachable agent's inquiries and governed them toward knowledge-building. We propose a Constructive Tutee Inquiry that aims to provide follow-up inquiries to guide students toward knowledge-building when they commit to a knowledge-telling response. Results from an evaluation study show that students who were treated by Constructive Tutee Inquiry not only outperformed those who were not treated but also learned to engage in knowledge-building without the aid of follow-up inquiries over time.
Automated Hand-Raising Detection in Classroom Videos: A View-Invariant and Occlusion-Robust Machine Learning Approach

Hand-raising signals students' willingness to participate actively in the classroom discourse. It has been linked to academic achievement and cognitive engagement of students and constitutes an observable indicator of behavioral engagement. However, due to the large amount of effort involved in manual hand-raising annotation by human observers, research on this phenomenon, enabling teachers to understand and foster active classroom participation, is still scarce. An automated detection approach of hand-raising events in classroom videos can offer a time- and cost-effective substitute for manual coding. From a technical perspective, the main challenges for automated detection in the classroom setting are diverse camera angles and student occlusions. In this work, we propose utilizing and further extending a novel view-invariant, occlusion-robust machine learning approach with long short-term memory networks for hand-raising detection in classroom videos based on body pose estimation. We employed a dataset stemming from 36 real-world classroom videos, capturing 127 students from grades 5 to 12 and 2442 manually annotated authentic hand-raising events. Our temporal model trained on body pose embeddings achieved an $$F_{1}$$ score of 0.76. When employing this approach for the automated annotation of hand-raising instances, a mean absolute error of 3.76 for the number of detected hand-raisings per student, per lesson was achieved. We demonstrate its application by investigating the relationship between hand-raising events and self-reported cognitive engagement, situational interest, and involvement using manually annotated and automatically detected hand-raising instances. Furthermore, we discuss the potential of our approach to enable future large-scale research on student participation, as well as privacy-preserving data collection in the classroom context.
Help Seekers vs. Help Accepters: Understanding Student Engagement with a Mentor Agent

Help from virtual pedagogical agents has the potential to improve student learning. Yet students often do not seek help when they need it, do not use help effectively, or ignore the agent's help altogether. This paper seeks to better understand students' patterns of accepting and seeking help in a computer-based science program called Betty's Brain. Focusing on student interactions with the mentor agent, Mr. Davis, we examine the factors associated with patterns of help acceptance and help seeking; the relationship between help acceptance and help seeking; and how each behavior is related to learning outcomes. First, we examine whether students accepted help from Mr. Davis, operationalized as whether they followed his suggestions to read specific textbook pages. We find a significant positive relationship between help acceptance and student post-test scores. Despite this, help accepters made fewer positive statements about Mr. Davis in the interviews. Second, we identify how many times students proactively sought help from Mr. Davis. Students who most frequently sought help demonstrated more confusion while learning (measured using an interaction-based ML-based detector); tended to have higher science anxiety; and made more negative statements about Mr. Davis, compared to those who made few or no requests. However, help seeking was not significantly related to post-test scores. Finally, we draw from the qualitative interviews to consider how students understand and articulate their experiences with help from Mr. Davis.
Automated Program Repair Using Generative Models for Code Infilling

In educational settings, automated program repair techniques serve as a feedback mechanism to guide students working on their programming assignments. Recent work has investigated using large language models (LLMs) for program repair. In this area, most of the attention has been focused on using proprietary systems accessible through APIs. However, the limited access and control over these systems remain a block to their adoption and usage in education. The present work studies the repairing capabilities of open large language models. In particular, we focus on a recent family of generative models, which, on top of standard left-to-right program synthesis, can also predict missing spans of code at any position in a program. We experiment with one of these models on four programming datasets and show that we can obtain good repair performance even without additional training.
Improving Adaptive Learning Models Using Prosodic Speech Features

Cognitive models of memory retrieval aim to describe human learning and forgetting over time. Such models have been successfully applied in digital systems that aid in memorizing information by adapting to the needs of individual learners. The memory models used in these systems typically measure the accuracy and latency of typed retrieval attempts. However, recent advances in speech technology have led to the development of learning systems that allow for spoken inputs. Here, we explore the possibility of improving a cognitive model of memory retrieval by using information present in speech signals during spoken retrieval attempts. We asked 44 participants to study vocabulary items by spoken rehearsal, and automatically extracted high-level prosodic speech features—patterns of stress and intonation—such as pitch dynamics, speaking speed and intensity from over 7,000 utterances. We demonstrate that some prosodic speech features are associated with accuracy and response latency for retrieval attempts, and that speech feature informed memory models make better predictions of future performance relative to models that only use accuracy and response latency. Our results have theoretical relevance, as they show how memory strength is reflected in a specific speech signature. They also have important practical implications as they contribute to the development of memory models for spoken retrieval that have numerous real-world applications.
A Deep Learning Driven Feature Based Steganalysis Approach

The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic algorithms can obtain diverse features of each algorithm. Then a multi-classifier implemented as lightgbm is used to predict the matching algorithm. Experimental results on four types of JPEG steganographic algorithms prove that the proposed method can improve the detection accuracy in the scenario of steganographic algorithm mismatch.
Confusion, Conflict, Consensus: Modeling Dialogue Processes During Collaborative Learning with Hidden Markov Models

There is growing recognition that AI technologies can, and should, support collaborative learning. To provide this support, we need models of collaborative talk that reflect the ways in which learners interact. Great progress has been made in modeling dialogue for high school and college-age learners, but the dialogue processes that characterize collaborative talk between elementary learner dyads are not currently well understood. This paper reports on a study with elementary school learners (4th and 5th grade, ages 9–11 years old) coded collaboratively in dyads. We recorded dialogue from 22 elementary school learner dyads, covering 7594 total utterances. We labeled this corpus manually with dialogue acts and then induced a hidden Markov model to identify the underlying dialogue states and the transitions between these states. The model identified six distinct hidden states which we interpret as Social Dialogue, Confusion, Frustrated Coordination, Exploratory Talk, Directive & Disagreement, and Disagreement & Self-Explanation. The HMM revealed that when students entered into a productive exploratory talk state, the primary way they transitioned out of this state is when they became confused or reached an impasse. When this occurred, the learners then moved into states of disputation and conflict before re-entering the Exploratory Talk state. These findings can inform the design of AI agents who support young learners' collaborative talk and help agents determine when students are conflicting rather than collaborating.
Robust Team Communication Analytics with Transformer-Based Dialogue Modeling

Adaptive training environments that can provide reliable insight into team communication offer great potential for team training and assessment. However, traditional techniques that enable meaningful analysis of team communication such as human transcription and speech classification are especially resource-intensive without machine assistance. Additionally, developing computational models that can perform robust team communication analytics based on small datasets poses significant challenges. We present a transformer-based team communication analysis framework that classifies each team member utterance according to dialogue act and the type of information flow exhibited. The framework utilizes domain-specific transfer learning of transformer-based language models pre-trained with large-scale external data and a prompt engineering method that represents both speaker utterances and speaker roles. Results from our evaluation of team communication data collected from live team training exercises suggest the transformer-based framework fine-tuned with team communication data significantly outperforms state-of-the-art models on both dialogue act recognition and information flow classification and additionally demonstrates improved domain-transfer capabilities.