Reviewerly/exHarmony huggingface dataset:
A Family of 1D Chaotic Maps without Equilibria

In this work, a family of piecewise chaotic maps is proposed. This family of maps is parameterized by the nonlinear functions used for each piece of the mapping, which can be either symmetric or non-symmetric. Applying a constraint on the shape of each piece, the generated maps have no equilibria and can showcase chaotic behavior. This family thus belongs to the category of systems with hidden attractors. Numerous examples of chaotic maps are provided, showcasing fractal-like, symmetrical patterns at the interchange between chaotic and non-chaotic behavior. Moreover, the application of the proposed maps to a pseudorandom bit generator is successfully performed.
Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach

Ship imaging position plays an important role in visual navigation, and thus significant focuses have been paid to accurately extract ship imaging positions in maritime videos. Previous studies are mainly conducted in the horizontal ship detection manner from maritime image sequences. This can lead to unsatisfied ship detection performance due to that some background pixels maybe wrongly identified as ship contours. To address the issue, we propose a novel rotational you only look once (YOLO) based model (RYM) to accurately yet fast detect ships from maritime images by considering ship rotation angle. The proposed RYM model are designed to detect tilted ships from images with the help of a rotation decoupled (RD) head, attentional mechanism and bidirectional feature network (BiFPN). The experimental results suggested that RYM can obtain satisfied ship detection performance considering that average accuracy reaches 96.7%. The precision and recall indicators are 93.2% and 94.7%, respectively. The proposed framework can be applied into real-time ship detection task due to that the processing speed is 45.6 frames per second (FPS).
Linear-Scaling Quantum Circuits for Computational Chemistry

We have recently constructed compact, CNOT-efficient, quantum circuits for Fermionic and qubit excitations of arbitrary many-body rank [Magoulas, I.; Evangelista, F. A. J. Chem. Theory Comput. 2023, 19, 822]. Here, we present approximations of these circuits that substantially reduce the CNOT counts even further. Our preliminary numerical data, using the selected projective quantum eigensolver approach, show up to a 4-fold reduction in CNOTs. At the same time, there is practically no loss of accuracy in the energies compared to the parent implementation, while the ensuing symmetry breaking is essentially negligible.
Dynamic Coati Optimization Algorithm for Biomedical Classification Tasks

Medical datasets are primarily made up of numerous pointless and redundant elements in a collection of patient records. None of these characteristics are necessary for a medical decision-making process. Conversely, a large amount of data leads to increased dimensionality and decreased classifier performance in terms of machine learning. Numerous approaches have recently been put out to address this issue, and the results indicate that feature selection can be a successful remedy. To meet the various needs of input patterns, medical diagnostic tasks typically involve learning a suitable categorization model. The k-Nearest Neighbors algorithm (kNN) classifier’s classification performance is typically decreased by the input variables’ abundance of irrelevant features. To simplify the kNN classifier, essential attributes of the input variables have been searched using the feature selection approach. This paper presents the Coati Optimization Algorithm (DCOA) in a dynamic form as a feature selection technique where each iteration of the optimization process involves the introduction of a different feature. We enhance the exploration and exploitation capability of DCOA by employing dynamic opposing candidate solutions. The most impressive feature of DCOA is that it does not require any preparatory parameter fine-tuning to the most popular metaheuristic algorithms. The CEC’22 test suite and nine medical datasets with various dimension sizes were used to evaluate the performance of the original COA and the proposed dynamic version. The statistical results were validated using the Bonferroni–Dunn test and Kendall’s W test and showed the superiority of DCOA over seven well-known metaheuristic algorithms with an overall accuracy of 89.7%, a feature selection of 24%, a sensitivity of 93.35% a specificity of 96.81%, and a precision of 93.90%.
Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.
Vulnerability Detection with Graph Simplification and Enhanced Graph Representation Learning

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in learning the graph representations of source code and are commonly adopted by existing DL-based vulnerability detection methods. However, the existing methods are still limited by the fact that GNNs are essentially difficult to handle the connections between long-distance nodes in a code structure graph. Besides, they do not well exploit the multiple types of edges in a code structure graph (such as edges representing data flow and control flow). Consequently, despite achieving state-of-the-art performance, the existing GNN-based methods tend to fail to capture global information (i.e., long-range dependencies among nodes) of code graphs. To mitigate these issues, in this paper, we propose a novel vulnerability detection framework with grAph siMplification and enhanced graph rePresentation LEarning, named AMPLE. AMPLE mainly contains two parts: 1) graph simplification, which aims at reducing the distances between nodes by shrinking the node sizes of code structure graphs; 2) enhanced graph representation learning, which involves one edge-aware graph convolutional network module for fusing heterogeneous edge information into node representations and one kernel-scaled representation module for well capturing the relations between distant graph nodes. Experiments on three public benchmark datasets show that AMPLE outperforms the state-of-the-art methods by 0.39%-35.32% and 7.64%-199.81% with respect to the accuracy and F1 score metrics, respectively. The results demonstrate the effectiveness of AMPLE in learning global information of code graphs for vulnerability detection.
Search to Pass Messages for Temporal Knowledge Graph Completion

Completing missing facts is a fundamental task for temporal knowledge graphs (TKGs).Recently, graph neural network (GNN) based methods, which can simultaneously explore topological and temporal information, have become the state-of-the-art (SOTA) to complete TKGs.However, these studies are based on hand-designed architectures and fail to explore the diverse topological and temporal properties of TKG.To address this issue, we propose to use neural architecture search (NAS) to design data-specific message passing architecture for TKG completion.In particular, we develop a generalized framework to explore topological and temporal information in TKGs.Based on this framework, we design an expressive search space to fully capture various properties of different TKGs.Meanwhile, we adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost.We further conduct extensive experiments on three benchmark datasets.The results show that the searched architectures by our method achieve the SOTA performances.Besides, the searched models can also implicitly reveal diverse properties in different TKGs.
Synthetic Sample Selection for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) has emerged as a pivotal research domain in computer vision, owing to its capability to recognize objects that have not been seen during training. Despite the significant progress achieved by generative techniques in converting traditional GZSL to fully supervised learning, they tend to generate a large number of synthetic features that are often redundant, thereby increasing training time and decreasing accuracy. To address this issue, this paper proposes a novel approach for synthetic feature selection using reinforcement learning. In particular, we propose a transformer-based selector that is trained through proximal policy optimization (PPO) to select synthetic features based on the validation classification accuracy of the seen classes, which serves as a reward. The proposed method is model-agnostic and data-agnostic, making it applicable to both images and videos and versatile for diverse applications. Our experimental results demonstrate the superiority of our approach over existing feature-generating methods, yielding improved overall performance on multiple benchmarks.
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models

Detecting fake images is becoming a major goal of computer vision. This need is becoming more and more pressing with the continuous improvement of synthesis methods based on Generative Adversarial Networks (GAN), and even more with the appearance of powerful methods based on Diffusion Models (DM). Towards this end, it is important to gain insight into which image features better discriminate fake images from real ones. In this paper we report on our systematic study of a large number of image generators of different families, aimed at discovering the most forensically relevant characteristics of real and generated images. Our experiments provide a number of interesting observations and shed light on some intriguing properties of synthetic images: (1) not only the GAN models but also the DM and VQ-GAN (Vector Quantized Generative Adversarial Networks) models give rise to visible artifacts in the Fourier domain and exhibit anomalous regular patterns in the autocorrelation; (2) when the dataset used to train the model lacks sufficient variety, its biases can be transferred to the generated images; (3) synthetic and real images exhibit significant differences in the mid-high frequency signal content, observable in their radial and angular spectral power distributions.
Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style

This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intracategory, and cross datasets) of ZS-SBIR with just one network (“everything”), and (ii) we would really like to understand how this sketch-photo matching operates (“explainable”). Our key innovation lies with the realization that such a cross-modal matching problem could be reduced to comparisons of groups of key local patches - akin to the seasoned “bag-of-words” paradigm. Just with this change, we are able to achieve both of the aforementioned goals, with the added benefit of no longer requiring external semantic knowledge. Technically, ours is a transformer-based cross-modal network, with three novel components (i) a self-attention module with a learnable tokenizer to produce visual tokens that correspond to the most informative local regions, (ii) a cross-attention module to compute local correspondences between the visual tokens across two modalities, and finally (iii) a kernel-based relation network to assemble local putative matches and produce an overall similarity metric for a sketch-photo pair. Experiments show ours indeed delivers superior performances across all ZS-SBIR settings. The all important explainable goal is elegantly achieved by visualizing cross-modal token correspondences, and for the first time, via sketch to photo synthesis by universal replacement of all matched photo patches. Code and model are available at https://github.com/buptLinfy/ZSE-SBIR.
Physical Reservoir Computing Based on Nanoscale Materials and Devices

Abstract Bioinspired computation systems can achieve artificial intelligence, bypassing fundamental bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential data processing such as recurrent neural networks (RNNs) suffer from problems of high complexity and low efficiency. Physical systems assembled with nanoscale materials and devices represent as an alternative route to serve as the core component for physically implanted reservoir computing. In this review, an overview of the development of the paradigm of physical reservoir computing (PRC) is provided and the typical physical reservoirs constructed with nanomaterials and nanodevices are described. The physical reservoirs based on multiple nanomaterials overcome the problems of RNN, show strong robustness, and effectively deal with tasks with improved reliability and availability. Finally, the challenges and perspectives of nanomaterial and nanodevice‐based PRC as a component of next‐generation machine learning systems are discussed.
Feature selection based on the best-path algorithm in high dimensional graphical models

This paper proposes a new algorithm for an automatic feature selection procedure in High Dimensional Graphical Models. The algorithm, called Best-Path Algorithm (BPA), rests on a filter method and performs feature selection based on mutual information. Over the last years, filter methods have been successfully employed to reduce the size of the input dataset and retain, at the same time, the relevant feature information for modeling and classification problems. However, the extant filter algorithms are mostly heuristic or require high computational effort. The BPA overcomes these drawbacks by taking advantage of the links between variables brought to the fore by the Edwards's algorithm. Once the High Dimensional Graphical Model, depicting the probabilistic structure of the variables, is determined, the BPA selects the best subset of features by analyzing its path-steps. The path-step that includes the variables with the most predictive power for the target one is then determined via the computation of the entropy correlation coefficient. This index, being based on the notion of (symmetric) Kullback-Leibler divergence, is closely connected to the mutual information that the path-step variables share with that of interest. The BPA application to simulated and real-word benchmark datasets highlights its potential and greater effectiveness compared to alternative extant methods.
Research Methods Using R

<italic>Research Methods Using R</italic> provides advanced data analysis for the biological sciences. It starts off with an introduction to the topic. The text then moves on to look at the <italic>R</italic> environment in more detail. The third chapter considers cleaning and preparing data for analysis, which is a very important part of the process. There follows a chapter on statistical tests as linear models. The text moves on to consider power analysis and meta-analysis. Models are considered next with a chapter on mixed-effect models. There is detail on stochastic methods and non-linear curve fitting. Towards the middle of the book, the text looks at Fourier analysis and multivariate t-tests. There follows a chapter on multidimensional scaling and k-means clustering. The text also describes correcting for multiple comparisons. Towards the end of the text, there are chapters on signal detection theory, Bayesian statistics, and plotting graphs and data visualisation. Finally, the last chapter covers reproducible data analysis.
A Privacy-Preserving Quantum Blockchain Technique for Electronic Medical Records

Nowadays, electronic medical records are increasing rapidly. Electronic medical records consist of sensitive and confidential information. These pieces of information need to be protected from attackers during the exchange with patients. Blockchain technology can protect information from attackers during the exchange of information. There are various classical techniques to protect electronic medical records. These techniques can exchange information correctly but cannot resist collective and coherent attacks and have more communication and computation costs. In this article, we introduced a privacy-preserving quantum blockchain technique to protect electronic medical records. This technique can resist the intercept, intercept-resend, entangle-measure, man-in-the-middle, collective, and coherent attacks with less communication and computation costs.
Parametrized Hyperbolic Tangent Induced Banach Space Valued Multivariate Multi Layer Neural Network Approximations

Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We research also the case of approximation by iterated operators of the last four types, that is multi hidden layer approximations.
The Impact of Computer Assisted Language Learning (CALL) Technology on Indonesian Learners' Speaking Skills

The use of technology in learning English-speaking skills has grown rapidly. One such technology is the computer-aided learning media. The use of computer media in the field of education has many advantages as they are easier to access, disseminate and store. The aim of present study is to investigate the impact of computer assisted language learning (CALL) technology on Indonesian learners' speaking skills. This study applied quantitative research. The results of study found that the use of information technology in learning is marked by the interaction between students and materials through internet technology, the existence of programmed teaching, and flexibility in the process. learning both in terms of time and place. It can be concluded that it is very possible for us to be easily literate through various media, with this we can improve the quality of speaking.
The Effect of Syntactic Form on Simple Belief Revisions and Updates

In this paper we report preliminary results on how people revise or update a previously held set of beliefs. When intelligent agents learn new things which conflict with their current belief set, they must revise their belief set. When the new information does not conflict, they merely must update their belief set. Various AI theories have been proposed to achieve these processes. There are two general dimensions along which these theories differ: whether they are syntactic-based or model-based, and what constitutes a minimal change of beliefs. This study investigates how people update and revise semantically equivalent but syntactically distinct belief sets, both in symbolic-logic problems and in quasi-real-world problems. Results indicate that syntactic form affects belief revision choices. In addition, for the symbolic problems, subjects update and revise semantically-equivalent belief sets identically, whereas for the quasi-real-world problems they both update and revise differently. Further, contrary to earlier studies, subjects are sometimes reluctant to accept that a sentence changes from false to true, but they are willing to accept that it would change from true to false.
PROVERB– A System Explaining Machine-Found Proofs

This paper outlines an implemented system called PROVERB that explains machine -found natural deduction proofs in natural language. Different from earlier works, we pursue a reconstructive approach. Based on the observation that natural deduction proofs are at a too low level of abstraction compared with proofs found in mathematical textbooks, we define first the concept of so-called assertion level inference rules. Derivations justified by these rules can intuitively be understood as the application of a definition or a theorem. Then an algorithm is introduced that abstracts machine-found ND proofs using the assertion level inference rules. Abstracted proofs are then verbalized into natural language by a presentation module. The most significant feature of the presentation module is that it combines standard hierarchical text planning and techniques that locally organize argumentative texts based on the derivation relation under the guidance of a focus mechanism. The behavior of the system is demonstrated with the help of a concrete example throughout the paper.
Combining Simulative and Metaphor-Based Reasoning about Beliefs

An unprecedented combination of simulative and metaphor-based reasoning about beliefs is achieved in an AI system, ATT-Meta. Much mundane discourse about beliefs uses conceptual metaphors (e.g., MIND AS CONTAINER) productively, and ATT-Meta’s metaphor-based reasoning accordingly leads to crucial discourse comprehension decisions. ATT-Meta’s non-metaphorical mode of belief reasoning includes simulative reasoning (SR). In ATT-Meta, metaphor-based reasoning can block and otherwise influence the course of SR. Also, ATT-Meta can nest SR and metaphor-based reasoning within themselves and each other. As well as currently allowing ATT-Meta to simulatively reason about beliefs about beliefs the nesting will in the near future allow the system to handle chained metaphors, ascribe its own metaphor-based reasoning to other agents, and apply simulative reasoning to purely metaphorical agents.
On the Security of a Variant of ElGamal Encryption Scheme

Recently, based on the Paillier cryptosystem [1] , Yi et al. outline a distributed ElGamal cryptosystem which allows for both a much simpler distributed key generation procedure and a more efficient distributed decryption of messages from a large plaintext domain [2] . In this paper, we analyze the security of their proposed variant of ElGamal encryption scheme and demonstrate that their proposed variant is not secure as claimed. Thus, whether an additively homomorphic cryptosytem satisfying the desired properties exists remains an open question.