Reviewerly/exHarmony huggingface dataset:
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; Both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities. Project Page: https://github.com/johnson111788/Drag-Drop
Twitter Sentiment Analysis in Tourism with Polynomial Naïve Bayes Classifier

Lombok has become a favorited tourist destination in the world. Therefore, tourism is a mainstay sector in regional development in West Nusa Tenggara. The contribution of the tourism sector shows an increasing trend. Tourist expenditures are distributed to various sectors. The tourism sector has a positive impact on the regional economy. The local government has prepared to improve the quality and quantity of tourism in Lombok. The results of local government efforts need to be analyzed so that future policies are on target. Analysis can be done on the satisfaction of tourists who travel to Lombok. It would be very difficult to get satisfaction data from all tourists through questionnaires. But on the other hand, tourist satisfaction is usually posted on their social networks. One of the social media that is widely used by tourists is Twitter. Their tweets contain not only expressions of natural beauty but also criticism, suggestions, and complaints during their visit. In addition, the tweet data on twitter is open access. This study tries to analyze the sentiment on Twitter which contains tweets of tourists who have visited Lombok. Sentiment analysis is performed using the Polynomial Naive Bayes Classifier. Sentiment results are classified into positive and negative sentiments. The results of this sentiment are expected to help related agencies or other tourism actors to improve the quality and quantity of regional tourism. The results showed that the positive sentiment on the security factor were 50.65%, the cost 75.32%, accommodation 62.33% and the cleanness factor 77.92%.
Effect of Connected Courses on Student Retention in the First year Curriculum

This paper describes results of an initiative to place freshmen residents of an engineering and technology residential community into a common course in their first semester.The motivation of this initiative is to enhance retention of first-year freshmen by facilitating study group formation.Freshman engineering students at the investigator's university are provided access to multiple retention-focused initiatives including residential hall-based mathematics tutoring, supplemental instruction in mathematics and physics sections, and industry mentoring.The school began an initiative to intentionally enroll freshmen in the engineering-focused residential hall in a common course in the first year of engineering curricula.Students in the focused interest community were pre-registered for selected courses, based upon their interest in engineering and their academic preparedness.During the registration period, students could opt out of the pre-registered course.The purpose of this effort is to enhance student success by fostering social connections of students outside of class, facilitating study group formation.This paper evaluates the performance of students in first year connected courses and lessons learned from the connected course cohorts.First-year retention of students in connected classes was higher in each cohort in comparison to students without connected classes.
Improving Access to Engineering Education: Unlocking Text and Table Data in Images and Videos

Accessibility of media, including visual media, is a significant concern in creating engineering education that is inclusive and accessible; without access, students who are blind or have low vision are unable to learn from today's engineering materials.This project presents a new accessibility tool, implemented as a user-friendly browser extension to extract and create accessible structured text -with a focus on tables of information -when embedded within web-based images and video media.The app can extract information to be used in a speech-to-text output of a screen reader; or text-to-braille device; pasted into a programming editing environment (e.g., Matlab, Jupyter notebook, Microsoft Code, or text editor); and further, used as the initial source input for manual or automated construction of audio descriptions to accompany the original media for future dissemination.In addition to improving access to engineering education, this project is a productivity tool for students seeking more access to textual data presented in image form.For example, it serves as a tool for all engineers and student engineers who seek to extract and re-use tabular information embedded inside an image or video that otherwise would require manual entry.The system uses the React.jsframework and Tesseract Optical Character Recognition (OCR) engine.The tool preserves privacy because it runs entirely inside the browser: no image data leaves the client.It can extract information from any web page on any website supported by the Chrome browser.Users can screenshot images and videos, extract text and numbers in scientific notation, determine the tabular structure, and meaningfully extract text from tables in tabular form (separated into rows and columns).We describe its design: the user interface features that allow its use by people with low-vision and access specialists.Upon initiation, the user selects a media HTML element to process; the user can choose to extract text from this frame or change the focus to another object on the page.The extraction area is adjustable by keyboard or pointer/touch interaction.Finally, the user chooses to either screen-capture the area and download the image, extract text from the area, or extract tabular information.To make the tool intuitive, interaction is structured as a wizard where users are guided along the stepwise process but can go back to previous steps.We provide examples of the best and worst output of our accessibility tool when applied to engineering education content and evaluate its accuracy and performance to extract tabular information from image samples from engineering disciplines.
System Design, Evaluation and Applications of Domain Term Extraction from Engineering Videos

Understanding the meanings of domain-specific terms is essential to academic success in college-level STEM courses.However, it can be challenging for students to obtain correct spellings and precise definitions of domain-specific terms from lecture videos, given the limited lecture time, rarity of the terms, and possibly confusing pronunciations.To provide accurate speech-to-text transcription, and enable students to search for domain-specific terms and obtain term definitions in real-time, we designed, implemented, and evaluated the PhraseHinter tool, a text analytics pipeline that efficiently extracts domain-specific terms from engineering educational videos.The tool is lightweight and adaptable to online instruction platforms.In our approach, a series of key scenes are initially extracted from a lecture video using a novel scene detection algorithm.The algorithm employs a support vector machine to classify image differences based on pixel, face, and text similarity information [2].A domain corpus is built by using the optical character recognition (OCR) technique to extract text from the scenes.A sequence of text-cleaning algorithms is applied to the domain corpus to filter out invalid characters, punctuation, and stop words.Frequent phrases are identified using standard text mining algorithms including PrefixSpan [15].Using the TF-IDF metric [16], we compare the cleaned corpus to the background corpus to determine domain-specific terms and phrases.The proposed PhraseHinter tool has been successfully integrated into ClassTranscribe [4,11,3,19,2,10], a web-based video lecture platform, for multiple purposes: 1) Improve the Microsoft Azure Speech-to-Text accuracy by preparing a list of domainspecific terms with high confidence of occurrence in the audio, 2) Provide the input for the glossary tool, another text analytics service in ClassTranscribe that automatically generates the explanation for the domain-specific terms, and, currently in progress, 3) Provide search capability in order to locate the moments in the video when a domainspecific term is visually presented.In this paper, we evaluate the performance and accuracy of the PhraseHinter system based on a representative corpus of videos from different engineering disciplines with domain-specific terms and phrases correctly pre-identified.We share the evaluation dataset to the education community for further research.In addition, we present the source code and provide guidance for instructors who would like to adopt the tool.
Criticality in FitzHugh-Nagumo oscillator ensembles: Design, robustness, and spatial invariance

Abstract Reservoir computing is an efficient and flexible framework for decision-making, control, and signal processing. It uses a network of interacting components varying from abstract nonlinear dynamical systems to physical substrates. Despite recent progress, the hardware implementation with inherent parameter variability and uncertainties, such as those mimicking the properties of living organisms’ nervous systems, remains an active research area. To address these challenges, we propose a constructive approach using a network of FitzHugh-Nagumo oscillators, exhibiting criticality across a broad range of resistive coupling strengths and robustness without specific parameter tuning. Additionally, the network’s activity demonstrates spatial invariance, offering freedom in choosing readout nodes. We introduce an alternative characterization of criticality by analyzing power dissipation, and demonstrate that criticality supports the robustness of the classification accuracy with respect to the readout shrinkage. Our results indicate criticality as a valuable property for classification problems, and provides design concepts for bio-inspired computational paradigms.
U.S. Copyright Office's Questions about Generative AI

Generating more questions than answers.
Game-theoretic private blockchain design in edge computing networks

Considering the privacy challenges of secure storage and controlled flow, there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace. A collaboration dilemma arises when the participants are self-interested and lack feedback of complete information. Traditional blockchains have similar faults, such as trustlessness, single-factor consensus, and heavily distributed ledger, preventing them from adapting to the heterogeneous and resource-constrained Internet of Things. In this paper, we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private blockchain. The design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable proofs. We formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and leaders. We theoretically prove that the Stackelberg equilibrium exists and the group evolution is stable. Then, we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata survival. To continuously validate a block, the metadata of the optimum rating, privacy, and proofs are extracted to store on a lightweight blockchain. Moreover, to increase resource utilization, surplus computing power is scheduled flexibly to enhance security by degrees. Finally, the evaluation results show the validity and efficiency of our model, thereby solving the collaboration dilemma in the private blockchain.
Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer – a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. In particular, the presented method identified contrast sets with higher support and precision than popular competitors like STUCCO, CSM-SD, or pysubgroup. The algorithm was implemented as a part of the RuleKit suite.
PhDnet: A novel physic-aware dehazing network for remote sensing images

Remote sensing haze removal is a popular computational imaging technique that directly obtains clear remote sensing data from hazy remote sensing images. Apart from prior-based methods, deep-learning-based methods have performed well in the past years for their powerful non-linear mapping from hazy to haze-free domains. Most dehazing networks are established on atmospheric scattering models. However, these models cannot be easily embedded into deep learning to guide feature extraction. In this study, we introduce a haze extraction model that combines residual learning with the atmospheric scattering model and build a novel physic-aware dehazing network based on this model to achieve effective haze removal for remote sensing images with physical interpretability. By combining multi-scale-gating convolution with a haze extraction unit, PhDnet meets the need for remote sensing image haze removal. We verify the advantage of PhDnet by applying this model on multiple synthetic hazy datasets and real hazy images. We also implement comprehensive studies about the effectiveness of its haze extraction unit and other components. The source code and pre-trained models are available at https://github.com/colacomo/PhDnet.
Complete genuine multipartite entanglement monotone

A complete characterization and quantification of entanglement, particularly the multipartite entanglement, remains an unfinished long-term goal in quantum information theory. As long as the multipartite system is concerned, the relation between the entanglement contained in different partitions or different subsystems need to take into account. The complete multipartite entanglement measure and the complete monogamy relation is a framework that just deals with such a issue. In this paper, we put forward conditions to justify whether the multipartite entanglement monotone (MEM) and genuine multipartite entanglement monotone (GMEM) are complete, completely monogamous, and tightly complete monogamous according to the feature of the reduced function. Especially, with the assumption that the maximal reduced function is nonincreasing on average under LOCC, we proposed a class of complete MEMs and a class of complete GMEMs via the maximal reduced function for the first time. By comparison, it is shown that, for the tripartite case, this class of GMEMs is better than the one defined from the minimal bipartite entanglement in literature under the framework of complete MEM and complete monogamy relation. In addition, the relation between monogamy, complete monogamy, and the tightly complete monogamy are revealed in light of different kinds of MEMs and GMEMs.
A Formulation of Structural Design Optimization Problems for Quantum Annealing

We present a novel formulation of structural design optimization problems specifically tailored to be solved by qa. Structural design optimization aims to find the best, i.e., material-efficient yet high-performance, configuration of a structure. To this end, computational optimization strategies can be employed, where a recently evolving strategy based on quantum mechanical effects is qa. This approach requires the optimization problem to be present, e.g., as a qubo model. Thus, we develop a novel formulation of the optimization problem. The latter typically involves an analysis model for the component. Here, we use energy minimization principles that govern the behavior of structures under applied loads. This allows us to state the optimization problem as one overall minimization problem. Next, we map this to a qubo problem that can be immediately solved by qa. We validate the proposed approach using a size optimization problem of a compound rod under self-weight loading. To this end, we develop strategies to account for the limitations of currently available hardware. Remarkably, for small-scale problems, our approach showcases functionality on today’s hardware such that this study can lay the groundwork for continued exploration of qa’s impact on engineering design optimization problems.
Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

This study focused on seismic event detection in a volcano using machine learning by leveraging the advantages of software/hardware co-design for a system on a chip (SoC) based on field-programmable gate array (FPGA) devices. A case study was conducted on the Copahue Volcano, an active stratovolcano located on the border between Argentina and Chile. Volcanic seismic event processing and detection were integrated into a PYNQ-based implementation by using a low-end SoC-FPGA device. We also provide insights into integrating an SoC-FPGA into the acquisition node, which can be valuable in scenarios where stations are deployed solely for data collection and holds the potential for the development of an early alert system.
STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning

Medaka (Oryzias latipes), as a crucial model organism in biomedical research, holds significant importance in fields such as cardiovascular diseases. Currently, the analysis of the medaka ventricle relies primarily on visual observation under a microscope, involving labor-intensive manual operations and visual assessments that are cumbersome and inefficient for biologists. Despite attempts by some scholars to employ machine learning methods, limited datasets and challenges posed by the blurred edges of the medaka ventricle have constrained research to relatively simple tasks such as ventricle localization and heart rate statistics, lacking precise segmentation of the medaka ventricle edges. To address these issues, we initially constructed a video object segmentation dataset comprising over 7000 microscopic images of medaka ventricles. Subsequently, we proposed a semi-supervised video object segmentation model named STAVOS, incorporating a spatial-temporal attention mechanism. Additionally, we developed an automated system capable of calculating various parameters and visualizing results for a medaka ventricle using the provided video. The experimental results demonstrate that STAVOS has successfully achieved precise segmentation of medaka ventricle contours. In comparison to the conventional U-Net model, where a mean accuracy improvement of 0.392 was achieved, our model demonstrates significant progress. Furthermore, when compared to the state-of-the-art Tackling Background Distraction (TBD) model, there is an additional enhancement of 0.038.
Positivity-preserving non-Markovian master equation for open quantum system dynamics: Stochastic Schrödinger equation approach

Positivity preservation is naturally guaranteed in exact non-Markovian master equations for open quantum system dynamics. However, in many approximated non-Markovian master equations, the positivity of the reduced density matrix is not guaranteed. In this paper we provide a general class of time-local, perturbative, and positivity-preserving non-Markovian master equations generated from stochastic Schr\"odinger equations, particularly quantum-state-diffusion equations. Our method has an expanded range of applicability for accommodating a variety of non-Markovian environments. We show the positivity-preserving master equation for a three-level system coupled to a dissipative bosonic environment as a particular example to exemplify our general approach. We illustrate the numerical simulations with an analysis explaining why the previous approximated non-Markovian master equations cannot guarantee positivity. Our work provides a consistent master equation for studying the non-Markovian dynamics in ultrafast quantum processes and strong-coupling systems.
Power of noisy quantum states and the advantage of resource dilution

Entanglement distillation allows to convert noisy quantum states into singlets, which can, in turn, be used for various quantum technological tasks, such as quantum teleportation and quantum key distribution. Entanglement dilution is the inverse process: singlets are converted into quantum states with less entanglement. While the usefulness of distillation is apparent, practical applications of entanglement dilution are less obvious. Here, we show that entanglement dilution can increase the resilience of shared quantum states to local noise. The increased resilience is observed even if diluting singlets into states with arbitrarily little entanglement. We extend our analysis to other quantum resource theories, such as quantum coherence, quantum thermodynamics, and purity. For these resource theories, we demonstrate that diluting pure quantum states into noisy ones can be advantageous for protecting the system from noise. Our results demonstrate the usefulness of quantum resource dilution, and provide a rare example for an advantage of noisy quantum states over pure states in quantum information processing.