Reviewerly/exHarmony huggingface dataset:
Enhancing Computer-Aided Cervical Cancer Detection using a Novel Fuzzy Rank-based Fusion

Cervical cancer is a severe and pervasive disease that poses a significant health threat to women globally. The Pap smear test is an efficient and effective method for detecting cervical cancer in its early stages. However, manual screening is labor-intensive and requires expert cytologists, leading to potential delays and inconsistencies in diagnosis. Deep Learning-based Computer-Aided Diagnosis (CAD) has shown significant results and can ease the problem of manual screening. However, one single model is sometimes insufficient to capture the complex data pattern for accurate disease prediction. In this work, we develop an end-to-end architecture utilizing three pre-trained models for the initial cervical cancer prediction. To aggregate the outcomes of these models, we propose a novel fuzzy rank-based ensemble considering two non-linear functions for the final level prediction. Unlike simple fusion techniques, the proposed architecture provides the final predictions on the test samples by considering the base classifier’s confidence in the predictions. To further enhance the classification capabilities of these models, we integrate advanced augmentation techniques such as CutOut, MixUp, and CutMix. The proposed model is evaluated on two benchmark datasets, SIPaKMeD, and Mendeley LBC, using a 5-fold cross-validation approach. On the SIPaKMeD dataset, the proposed ensemble architecture achieves a classification accuracy of 97.18% and an F1-score of 97.16%. On the Mendeley LBC dataset, the accuracy reaches 99.22% with an F1-score of 99.19%. Experimental results demonstrate the proposed architecture’s effectiveness and potential in cervical Pap smear image classification. This could aid medical professionals in making more informed treatment decisions, improving overall effectiveness in the testing process.
DeFiRanger: Detecting DeFi Price Manipulation Attacks

The rapid growth of Decentralized Finance (DeFi) boosts the blockchain ecosystem. At the same time, attacks on DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot directly detect DeFi attacks. That's because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pair <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</monospace> and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Y</monospace> in a Decentralized EXchange (DEX). In this work, we focus on the detection of two new types of price manipulation attacks. To this end, we propose a platform-independent method to identify high-level DeFi semantics. Specifically, we first construct the Cash Flow Tree (CFT) from a raw transaction and then lifting the low-level semantics to high-level ones, including five advanced DeFi actions. Finally, we use patterns expressed with the recovered DeFi semantics to detect price manipulation attacks. We implemented a prototype named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeFiRanger</small> that detected 14 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zero-day</i> security incidents. These findings were reported to affected parties or/and the community for the first time. Furthermore, the backtest experiment discovered 15 unknown historical security incidents. We further performed an attack analysis to shed light on the root causes of vulnerabilities incurring price manipulation attacks.
Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to their limited training samples and thus cannot beautify freely drawn sketches with both geometric and structural variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptual study.
TRANSMEDIAL (R)EVOLUTION: {SinOsc.ar(400, 800, 0, 0.1)multisensory.experience}.play;

The digital revolution and technology has led us towards a more intimate understanding of the acoustic instrument and its sound. What was the modern piano for Chopin was a tape recorder for Schaeffer. Today, there is little distinction between the sounds of acoustic, natural or electronic. In this paper I describe the shift of musical perception throughout recent technological developments. My starting point of exploration is the spectral attitude of 1979, which inspired further sonic evolution and changed our perception of the sound, performance and instrumental body or instruments, as well as introducing the computer as an essential instrument for composing. Further, I highlight the importance of software and discuss the ways in which software-generated musical ideas can incite human creativity and influence a post-digital vision of gesamtkunstwerk. The evolution of hybrid instruments and real-time audio-visual interactive software has led to changes in temporal freedom and created a multisensory experience. I explore how this transmission breaks down the barriers and limitations of the human creative mind and discuss how this can potentially lead to the new musical era. Finally, I reveal some art experiments within the concept of transmedial composition in recent times.
Motivasi Minat Budaya Membaca dan Bercerita pada Siswa Melalui Program Gerakan Literasi dalam Meningkatkan Intelektual di MI Guppi Serang

The aim of this research is to analyze the motivation for students' cultural interest in reading and telling stories through the literacy movement program in improving students' intellectual abilities at MI Guppi Serang. This research uses qualitative research. Participants in this research were 131 MI GUPPI Serang students, Karangreja District, Purbalingga Regency, Central Java in grades 4-6 MI. Data collection techniques use observation, interviews and documentation. The data analysis technique uses the Miles and Huberman model, namely data reduction, data presentation, and data verification. The results of the research show that students at MI GUPPI have good intellectual abilities after the existence of a culture of reading and telling stories through the literacy movement program. There needs to be collaborative efforts between schools, parents and the community in increasing students' motivation to read and tell stories, the most important obstacle in this effort. Increasing motivation for students' interest in reading is the lack of reference and reading sources
Exploring the intersection of Generative AI and Software Development

In the ever-evolving landscape of Artificial Intelligence (AI), the synergy between generative AI and Software Engineering emerges as a transformative frontier. This whitepaper delves into the unexplored realm, elucidating how generative AI techniques can revolutionize software development. Spanning from project management to support and updates, we meticulously map the demands of each development stage and unveil the potential of generative AI in addressing them. Techniques such as zero-shot prompting, self-consistency, and multimodal chain-of-thought are explored, showcasing their unique capabilities in enhancing generative AI models. The significance of vector embeddings, context, plugins, tools, and code assistants is underscored, emphasizing their role in capturing semantic information and amplifying generative AI capabilities. Looking ahead, this intersection promises to elevate productivity, improve code quality, and streamline the software development process. This whitepaper serves as a guide for stakeholders, urging discussions and experiments in the application of generative AI in Software Engineering, fostering innovation and collaboration for a qualitative leap in the efficiency and effectiveness of software development.
Maximum entropy GFlowNets with soft Q-learning

Generative Flow Networks (GFNs) have emerged as a powerful tool for sampling discrete objects from unnormalized distributions, offering a scalable alternative to Markov Chain Monte Carlo (MCMC) methods. While GFNs draw inspiration from maximum entropy reinforcement learning (RL), the connection between the two has largely been unclear and seemingly applicable only in specific cases. This paper addresses the connection by constructing an appropriate reward function, thereby establishing an exact relationship between GFNs and maximum entropy RL. This construction allows us to introduce maximum entropy GFNs, which, in contrast to GFNs with uniform backward policy, achieve the maximum entropy attainable by GFNs without constraints on the state space.
Efficacy of Machine-Generated Instructions

Large "instruction-tuned" language models (i.e., finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We conducted a quantitative study to figure out the efficacy of machine-generated annotations, where we compare the results of a fine-tuned BERT model with human v/s machine-generated annotations. Applying our methods to the vanilla GPT-3 model, we saw that machine generated annotations were 78.54% correct and the fine-tuned model achieved a 96.01% model performance compared to the performance with human-labelled annotations. This result shows that machine-generated annotations are a resource and cost effective way to fine-tune down-stream models.
Quantum multigraph states and multihypergraph states

We proposed two classes of multiparticle entangled states, the multigraph states and multihypergraph states, defined by unique operations on the edges and hyperedges. A key discovery is the one-to-one correspondence between the proposed multihypergraph states and the generalized real equally weighted states when d is prime. While for composite d, multihypergraph states form a subset of the generalized real equally weighted states. Meanwhile, we detailed a method for constructing real equally weighted states from hypergraph states and revealed the generalized real equally weighted states which cannot be generated from d-dimensional hypergraph states.
Proceedings of the Dialogue Robot Competition 2023

The Dialogic Robot Competition 2023 (DRC2023) is a competition for humanoid robots (android robots that closely resemble humans) to compete in interactive capabilities. This is the third year of the competition. The top four teams from the preliminary competition held in November 2023 will compete in the final competition on Saturday, December 23. The task for the interactive robots is to recommend a tourism plan for a specific region. The robots can employ multimodal behaviors, such as language and gestures, to engage the user in the sightseeing plan they recommend. In the preliminary round, the interactive robots were stationed in a travel agency office, where visitors conversed with them and rated their performance via a questionnaire. In the final round, dialogue researchers and tourism industry professionals interacted with the robots and evaluated their performance. This event allows visitors to gain insights into the types of dialogue services that future dialogue robots should offer. The proceedings include papers on dialogue systems developed by the 12 teams participating in DRC2023, as well as an overview of the papers provided by all the teams.
On the expressive power of inquisitive epistemic logic

Inquisitive modal logic, InqML, in its epistemic incarnation, extends standard epistemic logic to capture not just the information that agents have, but also the questions that they are interested in. We use the natural notion of bisimulation equivalence in the setting of InqML, as introduced in [Ciardelli/Otto: JSL 2021], to characterise the expressiveness of InqML as the bisimulation invariant fragment of first-order logic over natural classes of two-sorted first-order structures that arise as relational encodings of inquisitive epistemic (S5-like) models. The non-elementary nature of these classes crucially requires non-classical model-theoretic methods for the analysis of first-order expressiveness, irrespective of whether we aim for characterisations in the sense of classical or of finite model theory.
Best Paper Selection

MixEHR-Guided: A guided multi-modal topic modeling approach for large-scale automatic phenotyping using the electronic health record
Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model.In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.
Who is “R-n”: about one of the addressees of A.A. Blok's reviews of poets (one hundred years after their first publication by P.N. Medvedev)

The article examines the circumstances associated with the use of “cryptonyms” in the publication of A.A. Blok's resolutions on poets, carried out by P.N. Medvedev in 1923.The surname of the paleontologist and writer A.N. Ryabinin, one of the applicants for joining the Petrograd branch of the All–Russian Union of Poets, is revealed. For the first time, the full handwritten text of the collective document stored in the RO IRLI with the resolutions of the members of the Admissions Committee (A.A. Blok, M.L. Lozinsky, N.S. Gumilev, M.A. Kuzmin) about his book of poems “After the Thunderstorm” (1918) is introduced into scientific circulation. The emphasis is placed on the aesthetic principles of Blok's evaluation of the reviewed poems. The characteristics of scientific, literary and social activities of A.N. Ryabinin are compared with Block's ideas about the creative potential of the Russian scientific and technical intelligentsia and its role in the transformation of Russia. The positive reaction of A.M. Remizov to the work by A.N. Ryabinin is analyzed, aspects of their possible creative contacts are outlined.
About the Publication of the Final Volume of the “Letters” of V.S. Solovyov (Vol. 4. Pb.: Publishing House “Vremya”, 1923): Based on the Materials of G.P. Blok’s Letters to E.L. Radlov (1920‒1923). To the 100th anniversary of the publication. Appendix. Letters of G.P. Blok to E.L. Radlov (October 30, 1920–7 May 1923)

The article describes the history of the publication of the final 4 volum of the collection of “Letters” by V.S. Solovyov, published in 1923, in Soviet time, 15 years after the publication began (vol. 1) and 12 years after the release of the previous one (vol. 3), which, as is known, planned to complete the publication of the available at that moment of the philosopher’s letters. The acquisition of new letters prompted the historian of philosophy Ernest Lvovich Radlov (1854–1928), a friend and like-minded Solovyov, the permanent compiler and editor of the published volumes of his letters, to begin in 1919‒1920 in other cultural and historical conditions to prepare the 4th volume. The difficult history of the publication of the final volume has not yet received sufficient coverage in the literature about the philosopher. The publication of the surviving letters to Radlov the Editor-in-chief of the cooperative association “Vremya” G.P. Blok who worked on this volume during 1920–1923, to some extent illuminate the publishing process and the difficulties that arose during the preparation. Despite the fact that Radlov's response letters to the Block have not yet been revealed, the details reported in the letters allow us to restore the overall picture of both scientific and technical publishing work on the volume.
STDBNet: Shared Trunk and Dual-Branch Network for Real-Time Semantic Segmentation

Real-time semantic segmentation has broad prospects in computer vision. Existing state-of-the-art approaches generally employ bilateral networks to encode spatial and contextual information. Nevertheless, the real-time methods exhibit unsatisfactory performance. To address this problem, in this work we propose a novel shared trunk and dual-branch network named STDBNet for real-time semantic segmentation. In particular, we devise a Shared Trunk Module, which efficiently diminishes superfluous channels and parameters. Subsequently, we present a Split Dual-Branch Module, consisting of Detail Branch and Semantic Branch, with the former capturing detailed information and the latter capturing contextual information correspondingly. Furthermore, at the Semantic Branch, an Efficient Pyramid Pooling Module is designed towards the end to further expand the receptive fields and harvest multi-scale contextual information. Finally, to efficiently merge the features from the two branches and then get the final results, we introduce an Attention-optimized Feature Fusion Module, which utilizes redesigned spatial attention mechanism for feature augmentation. Extensive experiments conducted on the Cityscapes dataset indicate that the devised STDBNet obtains 77.6% mIoU with 91.5 FPS on one GeForce 2080Ti GPU, which outperforms BiSeNet with a favorable trade-off between accuracy and efficiency.
Linguistic Steganography: Hiding Information in Syntax Space

To enhance the embedding capacity, the existing linguistic steganography methods predominantly focus on the word or phrase level, with limited emphasis on the sentence level. Nevertheless, these approaches exhibit a deficiency in achieving an optimal balance between embedding capacity and semantic coherence. Moreover, compromised semantic coherence can potentially increase security risks. In this paper, we propose a novel sentence-level <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Stega</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> nography framework to <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> ide <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>I</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> nformation in <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>S</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> yntax <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>S</b></i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> pace (HISS-Stega) that enables larger embedding capacity while preserving better semantic coherence. Specifically, HISS-Stega builds a syntax-controlled paraphrase generation model to automatically modify the expression forms of the covertext, thereby augmenting the diversity of transformations. This enhancement contributes to the overall improvement in embedding capacity. Subsequently, a syntactic bins coding strategy is employed for successfully embedding secret information in the generated syntax space. Furthermore, HISS-Stega incorporates a semantic distortion function aimed at identifying the optimal syntactic structure for concealing secret information, thereby ensuring enhanced semantic coherence and mitigating potential security risks. The experimental results demonstrate that, in comparison to existing methods, HISS-Stega not only enhances the embedding capacity of each sentence but also maintains a high level of semantic coherence and anti-steganalysis capability.
ALGSL89: An Algerian Sign Language Dataset

Automatic Sign Language Recognition (ASLR) is an area of active current research that aims to facilitate communication between deaf and hearing people. Recognizing sign language, particularly in the context of Algerian Sign Language (ALGSL), presents unique challenges that have yet to be comprehensively explored. So far, to the best of our knowledge, no study has considered the ALGSL Recognition. This is mainly due to the lack of available datasets. To overcome this challenge, we propose the ALGSL89 dataset, a pioneering effort in ALGSL research. The ALGSL89 dataset encompasses 4885 videos, capturing 89 distinct ALGSL signs, recorded by 10 subjects. This dataset serves as a foundational resource for advancing ASLR research specific to the Algerian signing community. In addition, we provide a comprehensive analysis of its characteristics, including statistical insights and detailed information on handshapes, positions, trajectories, and the dynamic aspects of sign movements. These details are crucial for researchers to gain a nuanced understanding of the dataset, ensuring its effective utilization in ASLR studies. In order to test the validity of our dataset, we provide the results obtained by applying a set of deep learning models. Finally, we present SignAtlas, an innovative ALGSL recognition system based on Autoencoder model.
Lyapunov-Based Physics-Informed Long Short-Term Memory (LSTM) Neural Network-Based Adaptive Control

Deep neural networks (DNNs) and long short-term memory networks (LSTMs) have grown in recent popularity due to their function approximation performance when compared to traditional NN architectures. However, the predictions that may result from these networks often do not align with physical principles. This paper introduces the first physics-informed LSTM (PI-LSTM) controller composed of DNNs and LSTMs, where the weight adaptation laws are designed from a Lyapunov-based analysis. The developed PI-LSTM combines DNNs and LSTMs for the purpose of function approximation and memory while respecting the underlying system physics. Simulations were performed to demonstrate feasibility and resulted in a root mean square tracking error of 0.0185 rad and a 33.76% improvement over the baseline method.