Reviewerly/exHarmony huggingface dataset:
Underwater Image Stitching Method Based on AUV-guided Light Source Optimization

Background: Image stitching is the procedure of combining two or more pictures having the same feature point location information into a panoramic image, which is useful in target discovery, seabed research, and military applications. Most of existing underwater picture stitching technology makes use of well-lit photos, however natural light gradually diminishes during propagation of underwater autonomous submersibles plunge further into the sea. When the image focuses on the regions of lighting concentration and the larger dark, image details are lost and feature points are not matched, the perspective transformation matrix obtained does not reflect the mapping relationship of the entire image, resulting in a poor stitching effect and making it difficult to meet practical application requirements. Objective: This study aims to obtain underwater images with a good enhancement effect and improve feature point matching image. Methods: An adaptive image enhancement method based on adaptive light source optimization is proposed in this paper, underwater photos are preprocessed to enhance images for the feature registration. Results: The experimental consequence indicate that the improvement algorithm can improve picture standard with better detail performance and color recovery by preprocessing submerged photographs. Conclusion: It is accomplished that high exactness for images stitching by adding the feature points of the enhanced image for feature alignment.
A Survey on Code Representation

Recently, many machine learning models have been proposed to understand and analyze Programming Languages (PLs). While there are some similarities between PLs and Natural Language Processing (NLP), the former one has its own unique challenges. In this survey, we investigate current approaches tackling representation learning of codes and associated downstream tasks that can be solved with them. We present and compare the state-of-the-art models specifically designed for embedding PLs in low-dimensional space, and demonstrate how these embedding methods are related to representation learning approaches in NLP. We also compare benchmark experiments on multiple code-related tasks and evaluate the models for each specific application.
Ranking Trust Factors Affecting Risk Perception in Illicit Drug Purchase on the Darknet: A Large-Scale Survey Study in Hungary

The process of illicit drug trafficking on darknet markets is highly affected by various trust factors. Although the factors potentially affecting customers' risk perception can be identified based on previous research, cyber criminology has not produced empirical research ranking the importance of the specific factors. This study was designed to fill this gap by developing a tool that measures the importance of the various trust factors. To test out the measurement tool, a large-scale survey with projective situational questions was conducted among university students in Hungary. The sample (n = 5481) was compiled to include potential darknet market customers, respondents with above-average computer skills needed to access the darknet, and taking into account that university students are a group of society particularly exposed to drug consumption. The end product of this research is a trust matrix ranking the factors affecting illicit drug purchases on darknet markets. Among the factors, the survey's target group ranked reliable and undamaged delivery of goods and the reliability of vendors as the most important. The measurement tool developed in this research will facilitate further criminological research on vendor reputation. Its findings also point to the need for further research on delivery providers and predict that influencing the delivery-related risk perception of potential customers could effectively reduce demand.
New student admission information system design with payment gateway

Regarding the rapid phase of technological progress in all fields, as well as the pattern of people's lives that have been relatively advanced. The purpose of this research is to design a payment gateway-based student admission system at SMP Muhammadiyah 3 Waru using the waterfall method. Web-based information systems can be used as a means of improving information in the education department. In the existing research on the information system for new student admissions, payment processing is carried out manually by paying on the spot or transferring to the school accounts. It is required to confirm payments and utilize SMS gateways as an information forwarder to information. This system delivers complete information such as how to register, test, and payment gateway-based methods. The results obtained by all service menus on the system have been running well.
Joint upper &amp; expected value normalization for evaluation of retrieval systems: A case study with Learning-to-Rank methods

While original IR evaluation metrics are normalized in terms of their upper bounds based on an ideal ranked list, a corresponding expected value normalization for them has not yet been studied. We present a framework with both upper and expected value normalization, where the expected value is estimated from a randomized ranking of the corresponding documents present in the evaluation set. We next conducted two case studies by instantiating the new framework for two popular IR evaluation metrics (e.g., nDCG, MAP) and then comparing them against the traditional metrics. Experiments on two Learning-to-Rank (LETOR) benchmark data sets, MSLR-WEB30K (includes 30K queries and 3771K documents) and MQ2007 (includes 1700 queries and 60K documents), with eight LETOR methods (pairwise & listwise), demonstrate the following properties of the new expected value normalized metric: (1) Statistically significant differences (between two methods) in terms of original metric no longer remain statistically significant in terms of Upper Expected(UE) normalized version and vice-versa, especially for uninformative query-sets. (2) When compared against the original metric, our proposed UE normalized metrics demonstrate an average of 23% and 19% increase in terms of Discriminatory Power on MSLR-WEB30K and MQ2007 data sets, respectively. We found similar improvements in terms of consistency as well; for example, UE-normalized MAP decreases the swap rate by 28% while comparing across different data sets and 26% across different query sets within the same data set. These findings suggest that the IR community should consider UE normalization seriously when computing nDCG and MAP and more in-depth study of UE normalization for general IR evaluation is warranted.
Designing Optimization Problems with Diverse Solutions

We consider the problem of designing a linear program that has diverse solutions as the right-hand side varies. This problem arises in video game settings where designers aim to have players use different “weapons” or “tactics” as they progress. We model this design question as a choice over the constraint matrix A and cost vector c to maximize the number of possible supports of unique optimal solutions (what we call “loadouts”) of Linear Programs $$\max \{c^\top x \mid Ax \le b, x \ge 0\}$$ with nonnegative data considered over all resource vectors b. We provide an upper bound on the optimal number of loadouts and provide a family of constructions that have an asymptotically optimal number of loadouts. The upper bound is based on a connection between our problem and the study of triangulations of point sets arising from polyhedral combinatorics, and specifically the combinatorics of the cyclic polytope. Our asymptotically optimal construction also draws inspiration from the properties of the cyclic polytope.
A Fast Combinatorial Algorithm for the Bilevel Knapsack Problem with Interdiction Constraints

We consider the bilevel knapsack problem with interdiction constraints, a fundamental bilevel integer programming problem which generalizes the 0-1 knapsack problem. In this problem, there are two knapsacks and n items. The objective is to select some items to pack into the first knapsack such that the maximum profit attainable from packing some of the remaining items into the second knapsack is minimized. We present a combinatorial branch-and-bound algorithm which outperforms the current state-of-the-art solution method in computational experiments by 4.5 times on average for all instances reported in the literature. On many of the harder instances, our algorithm is hundreds of times faster, and we solved 53 of the 72 previously unsolved instances. Our result relies fundamentally on a new dynamic programming algorithm which computes very strong lower bounds. This dynamic program solves a relaxation of the problem from bilevel to 2n-level where the items are processed in an online fashion. The relaxation is easier to solve but approximates the original problem surprisingly well in practice. We believe that this same technique may be useful for other interdiction problems.
Adversarial Learning for Improved Patient Representations

In recent years, there has been an explosion in the amount of patient Electronic Health Records (EHR) made publicly available. This presents an opportunity to create predictive models that leverage the large amount of data to help guide healthcare worker’s decision-making capacity. However, Patient EHR data is often high-dimensional, sparse, temporal and multimodal. This presents an issue for predictive modeling with Machine Learning models, as many ML model archetypes are not built to handle these types of data. The temporality of EHR data also presents a complicating factor, as it contains multiple time series at different resolutions, which most ML models are not built to handle. This brings us to the topic of patient representation, which is the process of converting this raw EHR data into a dense, mathematical representation. Previous work in this field, however, has not leveraged the full potential of the data, since they opt to only deal with a single modality of data, or do not leverage the temporality of the data. In this paper, we attempt to create a network that creates a multimodal representation of EHR data by modeling it as a multiple sparse time series fusion task. We leverage transformers for sparse time series data, using a custom time-based positional encoding. We then fuse the data into a low-dimensional vector, which serves as our representation. We train the model on 2 separate tasks – Mortality prediction using a classification head, and we attempt to leverage a form of adversarial learning to improve the quality of the representation. We show that the patient representation extracted is meaningful and useful for downstream classification tasks. To our knowledge, this is the first attempt to leverage both adversarial learning and multimodality to create a patient representation. Source code can be found at: https://github.com/BharathSShankar/Patient-Rep-UROPS .
Development of a Conversational Agent for Tutoring Nursing Students to Interact with Patients

Conversational Intelligent Tutoring Systems can provide a more interactive and engaging learning experience compared to classical teaching methods. As such, conversational tutoring agents have their place as an additional learning resource for students, especially when a need arises for a student to work through a learning scenario which would be difficult or dangerous to reproduce in real life. Such is the case with the training of caretakers for nursing homes and hospitals, needing to train nurse-patient interaction. In this work we present our approach to the development of a conversational agent intended to simulate a patient. The conversational agent is flexible enough to handle large number of possible conversations with the help of automatic story generation methods. This approach enables us to deploy new conversational agents with no need for manual writing of every possible storyline, while remaining compatible with available commercial off-the-shelf chatbot frameworks. Our work was concluded with a user study validating positive usefulness of this type of a system.
An Approach to Generating Adaptive Feedback for Online Formative Assessment

In this paper, we propose a novel approach to generating adaptive feedback by identifying the chain of weakest learning objectives to a learner working in a domain. It combines the domain model based expert-driven model with question-answering based data-driven model. The domain model is an AND/OR graph of domain knowledge structure based on the revised Bloom’s taxonomy, defining the learning objectives of the domain and the corresponding pre-requisite relationships. The adaptive formative assessment process uses an improved Top-Two Thompson sampling algorithm for solving the best arm identification problem in the multi-armed bandit framework. The simulation results show the feasibility and performance of the proposed approach.
Language Proficiency Enhanced Knowledge Tracing

With an increasing interest in personalized learning, active research is being conducted on knowledge tracing to predict the learner’s knowledge state. Recently, studies have attempted to improve the performance of the knowledge tracing model by incorporating various types of side information. We propose a knowledge tracing method that utilizes the learner’s language proficiency as side information. Language proficiency is a key component of comprehending a question’s text and is known to be closely related to students’ academic performance. In this study, language proficiency was defined with Elo rating score and time window features, and was used in the knowledge tracing task. The dataset used in this study contains 54,470 students and 7,619,040 interactions, which were collected from a real-world online-learning platform. We conducted a correlation analysis to determine whether the language proficiency information of students was related to their ability to solve math word problems. In addition, we examined the effect of incorporating the language proficiency information on the knowledge tracing models using various baseline models. The analysis revealed a high correlation between the length of word problems and students’ language proficiency. Furthermore, in experiments with various baseline models, utilizing the language proficiency information improved the knowledge tracing model’s performance. Finally, when language proficiency information was incorporated, the cold start problem of the knowledge tracing model was mitigated. The findings of this study can be used as a supplement for educational instruction.
Towards Embodied Wearable Intelligent Tutoring Systems

Embodied cognition posits that human-environment interaction positively impacts thinking and learning, making it a valuable pedagogical tool. Technology in teaching and learning has seen tremendous maturation, such as the development of Intelligent Tutoring Systems (ITS). However, most ITS provide static learning experiences that do not incorporate embodiment, movement, and interaction with the space around the learner. This paper examines the results of using an embodied tutoring system across three case studies with different dimensions of embodiment. In all cases, we found trends highlighting how embodied tutoring systems can support learning. We also discuss different ways to incorporate embodiment into future research on ITS.
Automated Extraction of Domain Models from Textbook Indexes for Developing Intelligent Tutoring Systems

Domain modeling is an important task in designing, developing, and deploying intelligent tutoring systems and other adaptive instructional systems. We focus here on the more specific task of automatically extracting a domain model from textbooks. In particular, this paper explores using multiple textbook indexes to extract a domain model for computer programming. Our approach is based on the observation that different experts, i.e., authors of intro-to-programming textbooks in our case, break down a domain in slightly different ways, and identifying the commonalities and differences can be very revealing. To this end, we present automated approaches to extracting domain models from multiple textbooks and compare the resulting common domain model with a domain model created by experts. Specifically, we use approximate string-matching approaches to increase coverage of the resulting domain model and majority voting across different textbooks to discover common domain terms related to computer programming. Our results indicate that using approximate string matching gives more accurate domain models for computer programming with increased precision and recall. By automating our approach, we can significantly reduce the time and effort required to construct high-quality domain models, making it easy to develop and deploy tutoring systems. Furthermore, we obtain a common domain model that can serve as a benchmark or skeleton that can be used broadly and adapted to specific needs by others.
Automatic Execution of the Ontological Piloting Procedures

Intelligent educational systems currently use ontological knowledge modeling for their expert component. The associated semantics of the ontological languages allow for capturing highly complex domain knowledge. However, the automatic manipulation of knowledge to support the execution of a complex task by humans is a current research issue. This paper presents a solution for interpreting a complex ontological knowledge formalization about aircraft piloting procedures. This specific task is accomplished by human actors where multiple biases may compromise the safe execution. We are developing a solution to support pilots in their tasks’ execution. The domain theory interpretation must allow the reference model’s automatic execution in a complex environment. A reference model of the domain theory has been formalized with ontological language, a task ontology for the execution taxonomy, and a domain ontology containing the knowledge about the execution environment have also been previously developed. In this work, we propose an interpretor capable of manipulating the reference knowledge of the execution taxonomy within a simulation environment. Semantic rules are used to evaluate environmental constraints, task precondition, and execution to allow this automatic manipulation and execution. Once the solution is connected to a simulation environment, a reasoner manipulates the knowledge in order to evaluate the environment and autonomously execute the required tasks. This work is an intermediary step for providing a complete solution for supporting pilots’ execution of reference procedures in a simulation environment.
Joint Topic Model with Selected Side Information for Inter-University Syllabus Analysis Focusing on the Variety of Instructional Approaches

In recent university education, instructional approaches such as active learning have been varied, and inter-university opportunities to take classes have expanded due to the spread of MOOCs. Therefore, students have to refer to many syllabi, which significantly increases their burden when taking classes. In order to support syllabus browsing, a method of inter-university analysis of syllabi should be useful. However, the way universities describe the course syllabus and its related instructional approaches are not uniform among universities, it is not sufficient to simply search by apparent words in the syllabus texts, and it is necessary to capture latent relationships among words. In order to solve these problems, the authors propose Joint Topic Model with Selected Side Information (SS-JTM) to obtain relationships between the contents and instructional approaches of classes of several universities by selecting instructional approaches to be used as side information from syllabi. Functional extension of topic models has the possibility of performance degradation, but the results of evaluation experiments using Web syllabi from several universities have confirmed that SS-JTM performs as well as the baseline topic models such as LDA or JTM, and thus the functionality has been successfully extended.
Improving Collaboration via Automated Intelligent Nudges

Micromanagement is one of the major obstacles of effective collaboration and therefore, pair programming. This is the first research to investigate a computational method that provides intelligent real-time nudges to avoid micromanagement with the aim of improving collaboration. We conducted human experiments using the computational method and statistically evaluated the effect of the method. Our method (implemented as a video chat system) resulted in 24% less micromanagement during pair programming when compared to scenarios without our method.
An Approach to Automatic Flight Deviation Detection

Aircraft pilots are constantly undergoing situations where they must process significant amounts of data in very small periods of time, which may lead to mistakes. We have created a deviation detection system that is capable of auditing the cockpit in real time to detect actions that have been incorrectly added, omitted, or done out of sequence. This model assesses deviations based on hierarchical task data found in the Ontological Reference Model for Piloting Procedures, which contains knowledge-based reference procedures assembled by experts in the domain. Pilot actions are compared to ontology reference sequences using the Needleman-Wunsch algorithm for global alignment, as well as a Siamese LSTM network. An API that can be expanded to several Aerospace Simulators, as well as a Runner, was created to enable the Deviation Framework to connect to the XPlane simulator for real-time system monitoring. Synthetically created data containing sequence mutations were analyzed for testing. The results show that this framework is capable of detecting added, omitted, and out of sequence errors. Furthermore, the capabilities of Siamese networks are leveraged to understand the relation of certain sequence chain anomalies so that they can correctly be ignored (such as certain tasks that can be performed out of order from the reference sequence). These deviation assessments are capable of being run Real-Time (10 Hz) and have been clocked at 0.0179 s for one Takeoff sequence containing 23 actions. The evaluation results suggest that the proposed approach could be applied in aviation settings to help catch errors before harm is done.
Understanding the Usages and Effects of a Recommendation System in a Non-formal Learning Context

Learning support faces many challenges in a non-formal context: the pedagogical recommendations delivered aimed at learners with very different objectives and studying methods, and the analyses of learners’ behavior toward the recommendations are based almost exclusively on learning traces from the learning platform. In this context, we propose a recommendation system for educational resources in an extracurricular mobile application. Our system is based on a pedagogical score, on the learner history and ensures a renewal of the recommended chapters. By analyzing the usage traces of more than 10,000 learners over 4 months, we show that delivered recommendations are mainly used as a revision tool when learners face difficulties, and that they mostly remain in chapters of their current level grade. Usage traces also reveal that following recommendations has a positive impact on the learning experience.