Reviewerly/exHarmony huggingface dataset:
Mathematical diversity of parts for a continuous distribution

Authors:
raw_author_name: Brian Castellani
raw_affiliation_string: Durham University, Durham University, Durham, Durham, DH1 3LE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND

Abstract: Abstract The current paper is part of a series exploring how to link diversity measures (e.g., Gini-Simpson index, Shannon entropy, Hill numbers) to a distribution’s original shape and to compare parts of a distribution, in terms of diversity, with the whole. This linkage is crucial to understanding the exact relationship between the density of an original probability distribution, denoted by p ( x ), and the diversity D in non-uniform distributions, both within parts of a distribution and the whole. Empirically, our results are an important advance since we can compare various parts of a distribution, noting that systems found in contemporary data often have unequal distributions that possess multiple diversity types and have unknown and changing frequencies at different scales (e.g. income, economic complexity ratings, rankings, etc.). To date, we have proven our results for discrete distributions. Our focus here is continuous distributions. In both instances, we do so by linking case-based entropy, a diversity approach we developed, to a probability distribution’s shape for continuous distributions. This allows us to demonstrate that the original probability distribution g 1 , the case-based entropy curve g 2 , and the slope of diversity g 3 ( c ( a , x ) versus the <?CDATA ${c}_{(a,x)}* \mathrm{ln}{A}_{(a,x)}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>c</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> <mml:mo>*</mml:mo> <mml:mi>ln</mml:mi> <mml:msub> <mml:mrow> <mml:mi>A</mml:mi> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mi>a</mml:mi> <mml:mo>,</mml:mo> <mml:mi>x</mml:mi> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:msub> </mml:math> curve) are one-to-one (or injective). Put simply, a change in the probability distribution, g 1 , leads to variations in the curves for g 2 and g 3 . Consequently, any alteration in the permutation of the initial probability distribution, which results in a different form, will distinctly define the graphs g 2 and g 3 . By demonstrating the injective property of our method for continuous distributions, we introduce a unique technique to gauge the level of uniformity as indicated by D / c . Furthermore, we present a distinct method to calculate D / c for different forms of the original continuous distribution, enabling comparison of various distributions and their components.
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Authors:
raw_author_name: Yu-Cheng Chou
raw_affiliation_string: Department of Computer Science, Johns Hopkins University, Baltimore, 21218, USA
raw_author_name: Bowen Li
raw_affiliation_string: Department of Computer Science, Johns Hopkins University, Baltimore, 21218, USA
raw_author_name: Deng-Ping Fan
raw_affiliation_string: Computer Vision Lab, ETH Zürich, Zürich, 8001, Switzerland
raw_author_name: Alan Yuille
raw_affiliation_string: Department of Computer Science, Johns Hopkins University, Baltimore, 21218, USA
raw_author_name: Zongwei Zhou
raw_affiliation_string: Department of Computer Science, Johns Hopkins University, Baltimore, 21218, USA

Abstract: 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

Authors:
raw_author_name: Ahmad Ashril Rizal
raw_affiliation_string: Universitas Islam Negeri Mataram
raw_author_name: Gibran Satya Nugraha
raw_affiliation_string: Universitas Mataram
raw_author_name: Rian Asmara Putra
raw_affiliation_string: Universitas Bumigora
raw_author_name: Dara Puspita Anggraeni
raw_affiliation_string: Universitas Nahdlatul Wathan

Abstract: 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%.
Improving Access to Engineering Education: Unlocking Text and Table Data in Images and Videos

Authors:
raw_author_name: Lawrence Angrave
raw_affiliation_string: University of Illinois at Urbana Champaign

Abstract: 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

Authors:
raw_author_name: Lawrence Angrave
raw_affiliation_string: University of Illinois Urbana-Champaign University of Illinois Urbana-Champaign

Abstract: 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

Authors:
raw_author_name: Bakr Al Beattie
raw_affiliation_string: Chair of Digital Communication Systems, Ruhr-University Bochum, Universitätsstraße 150, 44801, Bochum, Germany
raw_author_name: Petro Feketa
raw_affiliation_string: School of Mathematics and Statistics, Victoria University of Wellington, PO Box 600, Wellington, 6140, New Zealand
raw_author_name: Karlheinz Ochs
raw_affiliation_string: Chair of Digital Communication Systems, Ruhr-University Bochum, Universitätsstraße 150, 44801, Bochum, Germany

Abstract: 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

Authors:
raw_author_name: Pamela Samuelson
raw_affiliation_string: University of California, Berkeley, CA, USA

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

Authors:
raw_author_name: Daoqi Han
raw_affiliation_string: Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China
raw_author_name: Yang Liu
raw_affiliation_string: Beijing Institute of Astronautical Systems Engineering, Beijing, China; Beijing Institute of Technology, Beijing, China
raw_author_name: Fangwei Zhang
raw_affiliation_string: Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China
raw_author_name: Yueming Lu
raw_affiliation_string: Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China

Abstract: 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

Authors:
raw_author_name: Adam Gudyś
raw_affiliation_string: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; Institute of Innovative Technologies, EMAG, Leopolda 31, 40-189 Katowice, Poland
raw_author_name: Marek Sikora
raw_affiliation_string: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; Institute of Innovative Technologies, EMAG, Leopolda 31, 40-189 Katowice, Poland
raw_author_name: Łukasz Wróbel
raw_affiliation_string: Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; Institute of Innovative Technologies, EMAG, Leopolda 31, 40-189 Katowice, Poland

Abstract: 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

Authors:
raw_author_name: Jiang He
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, Wuhan, China
raw_author_name: Qiangqiang Yuan
raw_affiliation_string: Collaborative Innovation Center of Geospatial Technology, Wuhan, China; School of Geodesy and Geomatics, Wuhan University, Wuhan, China
raw_author_name: Yi Xiao
raw_affiliation_string: School of Geodesy and Geomatics, Wuhan University, Wuhan, China
raw_author_name: Liangpei Zhang
raw_affiliation_string: Collaborative Innovation Center of Geospatial Technology, Wuhan, China; State Key Laboratory of Information Engineering, Survey Mapping and Remote Sensing, Wuhan University, Wuhan, China

Abstract: 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

Authors:
raw_author_name: Yu Guo
raw_affiliation_string: School of Mathematical Sciences, Inner Mongolia University, Hohhot, Inner Mongolia 010021, People’s Republic of China

Abstract: 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.