Reviewerly/exHarmony huggingface dataset:
A Hybrid Scene Text Script Identification Network for regional Indian Languages

Authors:
raw_author_name: Veronica Naosekpam
raw_affiliation_string: Artificial Intelligence Lab, Indian Institute of Information Technology Guwahati, Guwahati, India
raw_author_name: Nilkanta Sahu
raw_affiliation_string: Indian Institute of Information Technology Guwahati, Guwahati, India

Abstract: In this work, we introduce WAFFNet, an attention-centric feature fusion architecture tailored for word-level multi-lingual scene text script identification. Motivated by the limitations of traditional approaches that rely exclusively on feature-based methods or deep learning strategies, our approach amalgamates statistical and deep features to bridge the gap. At the core of WAFFNet, we utilized the merits of Local Binary Pattern —a prominent descriptor capturing low-level texture features with high-dimensional, semantically-rich convolutional features. This fusion is judiciously augmented by a spatial attention mechanism, ensuring targeted emphasis on semantically critical regions of the input image. To address the class imbalance problem in multi-class classification scenarios, we employed a weighted objective function. This not only regularizes the learning process but also addresses the class imbalance problem. The architectural integrity of WAFFNet is preserved through an end-to-end training paradigm, leveraging transfer learning to expedite convergence and optimize performance metrics. Considering the under-representation of regional Indian languages in current datasets, we meticulously curated IIITG-STLI2023, a comprehensive dataset encapsulating English alongside six under-represented Indian languages: Hindi, Kannada, Malayalam, Telugu, Bengali, and Manipuri. Rigorous evaluation of the IIITG-STLI2023, as well as the established MLe2e and SIW-13 datasets, underscores WAFFNet’s supremacy over both traditional feature-engineering approaches as well as state-of-the-art deep learning frameworks. Thus, the proposed WAFFNet framework offers a robust and effective solution for language identification in scene text images.
Video stabilization based on low‐rank constraint and trajectory optimization

Authors:
raw_author_name: Zhenhong Shang
raw_affiliation_string: Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China
raw_author_name: Zhishuang Chu
raw_affiliation_string: Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Abstract: Abstract Video stabilization plays a pivotal role in enhancing video quality by eliminating unwanted jitter in shaky videos. This paper introduces a novel video stabilization algorithm that leverages low‐rank constraint and trajectory optimization to effectively eliminate undesirable motion and generate stabilized videos. In the proposed algorithm, a low‐rank constraint regularization term is incorporated to enhance the smoothness of motion trajectories. Additionally, a predictive path smoothness term is integrated to ensure the consistency of motion across neighbouring frames. To address the problem of excessive cropping resulting from aggressive smoothing, a flexible local window strategy that emphasizes local motion relationships within the trajectories is introduced. The experimental results show that, compared to other excellent video stabilization algorithms, the proposed algorithm improves the stability metric by approximately 2.3%. Furthermore, in the stabilized videos generated by the algorithm, an approximate improvement of 2.18 dB in average image temporal fidelity and a 5.7% increase in average structural similarity between adjacent frames are achieved. The code that implements the proposed method is publicly accessible at https://github.com/CZS0319/VS_Low‐Rank_Constraint_Trajectory_Optimization .
An assessment of heterogenous ensemble classifiers for analyzing change‐proneness in open‐source software systems

Authors:
raw_author_name: Megha Khanna
raw_affiliation_string: Sri Guru Gobind Singh College of Commerce University of Delhi  Delhi India
raw_author_name: Ankita Bansal
raw_affiliation_string: Netaji Subhas University of Technology  Delhi India

Abstract: Abstract Software managers constantly look out for methods that ensure cost effective development of good quality software products. An important means of accomplishing this is by allocating more resources to weak classes of a software product, which are prone to changes. Therefore, correct prediction of these change‐prone classes is critical. Though various researchers have investigated the performance of several algorithms for identifying them, the search for an optimum classifier still persists. To this end, this study critically investigates the use of six Heterogenous Ensemble Classifiers (HEC) for Software Change Prediction (SCP) by empirically validating datasets obtained from 12 open‐source software systems. The results of the study are statistically assessed using three robust performance indicators (AUC, F‐measure and Mathew Correlation Coefficient) in two different validation scenarios (within project and cross‐project). They indicate the superiority of Average Probability Voting Ensemble, a heterogenous classifier for determining change‐proneness in the investigated systems. The average AUC values of software change prediction models developed using this ensemble classifier exhibited an improvement of 3%‐9% and 3%‐11% respectively when compared with its base learners and homogeneous counter parts. Similar observations were inferred using other investigated performance measures. Furthermore, the evidence obtained from the results suggests that the change in number of base learners or type of meta‐learner does not exhibit significant change in the performance of corresponding heterogenous ensemble classifiers.
Adversities in Abstract Interpretation

Authors:
raw_author_name: Roberto Giacobazzi
raw_affiliation_string: University of Arizona - Dept. of Computer Science, Tucson, US
raw_author_name: Isabella Mastroeni
raw_affiliation_string: University of Verona - Dept. of Computer Science, Verona, Italy
raw_author_name: Elia Perantoni
raw_affiliation_string: University of Verona - Dept. of Computer Science, Verona, Italy

Abstract: Robustness is a key and desirable property of any classifying system, in particular, to avoid the ever-rising threat of adversarial attacks. Informally, a classification system is robust when the result is not affected by the perturbation of the input. This notion has been extensively studied, but little attention has been dedicated to how the perturbation affects the classification. The interference between perturbation and classification can manifest in many different ways, and its understanding is the main contribution of the present paper. Starting from a rigorous definition of a standard notion of robustness, we build a formal method for accommodating the required degree of robustness — depending on the amount of error the analyst may accept on the classification result. Our idea is to precisely model this error as an abstraction . This leads us to define weakened forms of robustness also in the context of programming languages, particularly in language-based security — e.g., information-flow policies — and in program verification. The latter is possible by moving from a quantitative (standard) model of perturbation to a novel qualitative model, given by means of the notion of abstraction. As in language-based security, we show that it is possible to confine adversities, which means to characterize the degree of perturbation (and/or the degree of class generalization) for which the classifier may be deemed adequately robust. We conclude with an experimental evaluation of our ideas, showing how weakened forms of robustness apply to state-of-the-art image classifiers.
An optimal linear-combination-of-unitaries-based quantum linear system solver

Authors:
raw_author_name: Sander Gribling
raw_affiliation_string: Université Paris Cité, Paris, France
raw_author_name: Iordanis Kerenidis
raw_affiliation_string: Université Paris Cité and QC Ware, Paris, France
raw_author_name: Dániel Szilágyi
raw_affiliation_string: Université Paris Cité, Paris, France

Abstract: Solving systems of linear equations is one of the most important primitives in many different areas, including in optimization, simulation, and machine learning. Quantum algorithms for solving linear systems have the potential to provide a quantum advantage for these problems. In this work, we recall the Chebyshev iterative method and the corresponding optimal polynomial approximation of the inverse. We show that the Chebyshev iteration polynomial can be efficiently evaluated both using quantum singular value transformation (QSVT) as well as linear combination of unitaries (LCU). We achieve this by bounding the 1-norm of the coefficients of the polynomial expressed in the Chebyshev basis. This leads to a considerable constant-factor improvement in the runtime of quantum linear system solvers that are based on LCU or QSVT (or, conversely, a several orders of magnitude smaller error with the same runtime/circuit depth).
Enhancing corporate brands through service robots: The impact of anthropomorphic design metaphors on corporate brand perceptions

Authors:
raw_author_name: Nancy V. Wünderlich
raw_affiliation_string: Technische Universität Berlin  Berlin Germany
raw_author_name: Markus Blut
raw_affiliation_string: Durham University Business School, Durham University Durham UK
raw_author_name: Christian Brock
raw_affiliation_string: University of Rostock Rostock Germany

Abstract: Abstract The increasing introduction of intelligent, interactive robots in the service industry raises concerns about the potential dehumanization of service provision and its influences on corporate brand perceptions. To avoid adverse effects, new service development (NSD) managers seemingly favor service robots that feature anthropomorphic design metaphors, so they appear more human‐like. The current research investigates explicitly how customers' perception of a robot's anthropomorphic design metaphors might spill over to affect corporate brand perceptions. Study 1, a picture‐based scenario study with 109 participants, reveals the impact of anthropomorphic design metaphors on untested corporate brand outcomes, such as brand trust and brand experience. Then Study 2, a video‐based scenario study with 530 participants, addresses whether these effects depend on the service context. In Study 3, a field study of 393 participants, the authors examine how anthropomorphic design metaphors influence other firm‐related outcomes (e.g., shopping enjoyment, sales). The combined results confirm that anthropomorphic design metaphors strongly affect brand trust and brand experience, as well as other critical firm‐related outcomes; they also reveal notable context effects, such that customers of people‐processing (e.g., care services) and mental‐stimulus‐processing (e.g., shopping assistance) services appear more likely to use anthropomorphic design metaphors as corporate brand cues. Our research encourages NSD managers and scholars to consider the effects of introducing anthropomorphic service robots on corporate brands.
Intelligent Situation Awareness Based on the Fractal Dimension and Multidimensional Reconstruction

Authors:
raw_author_name: Xin Guan
raw_affiliation_string: Naval Aviation University Yantai Shandong 264001 China

Abstract: Abstract In order to realize autonomy and intelligence for situation awareness, this paper proposes an intelligent situation awareness model based on fractional dimension information mining and multidimensional information reconstruction. First, some new concepts are proposed, the spatial situation perception is established by 3D reconstruction of the input fusion information, 4D reconstruction completes the situation comprehension and 5D reconstruction seeks the situation prediction. The three‐level situation estimation model is optimized to a more robust flexible model. Combined with the database system, the reasoning learning mechanism, and the diversified human‐machine interface concept, a basic framework of intelligent situation awareness is constructed. Second, the basic process of intelligent situation awareness is provided. Third, based on the flexible configuration method of the situation awareness system and the concept of unmanned weight determination for the situation awareness agents, an efficiency measurement model for the intelligent situation awareness model is also constructed, and an evaluation method for the consistency of multinode intelligent situation awareness is also provided. Fourth, the paper gives a typical electromagnetic situation estimation example for a drilling platform to explain and validate the concepts. Finally, several suggestions are put forward for the next construction of an intelligent situation awareness system.
Multi-bit per cycle true random number generator based on XOR-XNOR ring oscillator unit

Authors:
raw_author_name: Qitian Fan
raw_affiliation_string: Microelectronics Research and Development Center, Shanghai University, Shanghai, 200444, China
raw_author_name: Feng Ran
raw_affiliation_string: Microelectronics Research and Development Center, Shanghai University, Shanghai, 200444, China
raw_author_name: Limin Yan
raw_affiliation_string: Microelectronics Research and Development Center, Shanghai University, Shanghai, 200444, China

Abstract: To achieve a fast and resource-efficient entropy source, we present a novel oscillator unit, called XOR-XNOR ring oscillator (XXRO) which consists of an XOR gate and an XNOR gate. The XXRO unit has an internal feedback loop that allows the accumulation of jitter within the unit. Connecting multiple XXRO units forms a new entropy source. We utilize the differences between the XXRO units in the entropy sources to output two bits pre cycle, increasing the throughput of the TRNG while optimizing the use of hardware resources. In this paper, we use four proposed entropy source to form a TRNG, by sampling the two outputs of each entropy source individually and performing an XOR computation based on the corresponding positions to obtain random numbers. Experimental results indicate that when working at a throughput of 250 Mbps under a 125 MHz sampling clock, both outputs of the proposed TRNG pass the randomness tests and other performance tests.
An Improved <i>k</i>‐Nearest Neighbor Algorithm for Recognition and Classification of Thyroid Nodules

Authors:
raw_author_name: Xiang Han
raw_affiliation_string: School of Mathematics and Information Science Henan Polytechnic University  Jiaozuo China

Abstract: To complete the task of automatic recognition and classification of thyroid nodules and solve the problem of high classification error rates when the samples are imbalanced.An improved k-nearest neighbor (KNN) algorithm is proposed and a method for automatic thyroid nodule classification based on the improved KNN algorithm is established. In the improved KNN algorithm, we consider not only the number of class labels for various classes of data in KNNs, but also the corresponding weights. And we use the Minkowski distance measure instead of the Euclidean distance measure.A total of 508 ultrasound images of thyroid nodules, including 415 benign nodules and 93 malignant nodules, were used in the paper. Experimental results show the improved KNN has 0.872549 accuracy, 0.867347 precision, 1 recall, and 0.928962 F1-score. At the same time, we also considered the influence of different distance weights, the value of k, different distance measures on the classification results.A comparison result shows that our method has a better performance than the traditional KNN and other classical machine learning methods.
Military camouflaged object detection with deep learning using dataset development and combination

Authors:
raw_author_name: Kyo-Seong Hwang
raw_affiliation_string: Department of Defense Science, Korea National Defense University, Republic of Korea
raw_author_name: Jungmok Ma
raw_affiliation_string: Department of Defense Science, Korea National Defense University, Republic of Korea

Abstract: Camouflaged object detection (COD) is one of the emerging artificial intelligence technologies. COD identifies objects that require attention and time to detect with human eyes due to the similarity in texture or color to the surrounding environment. Despite the importance of camouflage and its detection in military, there is a lack of military camouflaged object detection research. Previous studies point out that the general COD has not been well studied due to the lack of camouflaged datasets, and the situation is worse in the military domain. This study aims at tackling the challenge in two directions. First, we carefully assemble the military camouflaged object (MCAM) dataset, including camouflaged soldiers and people as well as camouflaged military supplies for military COD. The experiment shows that MCAM can generate better performance results than the other benchmark datasets (CAMO, COD10K). Second, military (MCAM) and nonmilitary camouflage datasets (benchmark datasets) are combined and tested to overcome data scarcity. The experiment shows that the nonmilitary camouflage datasets are effective for military COD at a certain level, and a proper combination of military and nonmilitary camouflage datasets can improve the detection performance.
A Dirichlet Process Mixture regression model for the analysis of competing risk events

Authors:
raw_author_name: Edwin R. van den Heuvel
raw_affiliation_string: Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands

Abstract: We develop a regression model for the analysis of competing risk events. The joint distribution of the time to these events is flexibly characterized by a random effect which follows a discrete probability distribution drawn from a Dirichlet Process, explaining their variability. This entails an additional layer of flexibility of this joint model, whose inference is robust with respect to the misspecification of the distribution of the random effects. The model is analysed in a fully Bayesian setting, yielding a flexible Dirichlet Process Mixture model for the joint distribution of the time to events. An efficient MCMC sampler is developed for inference. The modelling approach is applied to the empirical analysis of the surrending risk in a US life insurance portfolio previously analysed by Milhaud and Dutang (2018). The approach yields an improved predictive performance of the surrending rates.
Multimodal learning with only image data: A deep unsupervised model for street view image retrieval by fusing visual and scene text features of images

Authors:
raw_author_name: Shangyou Wu
raw_affiliation_string: School of Geography and Information Engineering China University of Geosciences Wuhan China
raw_author_name: Wenhao Yu
raw_affiliation_string: National Engineering Research Center for Geographic Information System China University of Geosciences Wuhan China; School of Geography and Information Engineering China University of Geosciences Wuhan China
raw_author_name: Yifan Zhang
raw_affiliation_string: School of Geography and Information Engineering China University of Geosciences Wuhan China
raw_author_name: Mengqiu Huang
raw_affiliation_string: School of Geography and Information Engineering China University of Geosciences Wuhan China

Abstract: Abstract As one of the classic tasks in information retrieval, the core of image retrieval is to identify the images sharing similar features with a query image, aiming to enable users to find the required information from a large number of images conveniently. Street view image retrieval, in particular, finds extensive applications in many fields, such as improvements to navigation and mapping services, formulation of urban development planning scheme, and analysis of historical evolution of buildings. However, the intricate foreground and background details in street view images, coupled with a lack of attribute annotations, render it among the most challenging issues in practical applications. Current image retrieval research mainly uses the visual model that is completely dependent on the image visual features, and the multimodal learning model that necessitates additional data sources (e.g., annotated text). Yet, creating annotated datasets is expensive, and street view images, which contain a large amount of scene texts themselves, are often unannotated. Therefore, this paper proposes a deep unsupervised learning algorithm that combines visual and text features from image data for improving the accuracy of street view image retrieval. Specifically, we employ text detection algorithms to identify scene text, utilize the Pyramidal Histogram of Characters encoding predictor model to extract text information from images, deploy deep convolutional neural networks for visual feature extraction, and incorporate a contrastive learning module for image retrieval. Upon testing across three street view image datasets, the results demonstrate that our model holds certain advantages over the state‐of‐the‐art multimodal models pre‐trained on extensive datasets, characterized by fewer parameters and lower floating point operations. Code and data are available at https://github.com/nwuSY/svtRetrieval .
Harnessing generative artificial intelligence to support nature‐based solutions

Authors:
raw_author_name: Sandra Lavorel
raw_affiliation_string: Laboratoire d'Ecologie Alpine Université Grenoble Alpes‐CNRS‐Université Savoie Mont Blanc  Grenoble France; Manaaki Whenua–Landcare Research  Lincoln New Zealand

Abstract: Abstract The ongoing biodiversity and climate change crises require society to adopt nature‐based solutions that integrate and enhance ecosystems. To achieve successful implementation of nature‐based solutions, it is vital to communicate scientific information about their benefits and suitability. This article explores the potential of generative artificial intelligence (GenAI) as a tool for automating and scaling up science communication, outreach, and extension for nature‐based solutions. To illustrate the potential of GenAI, we present three case study examples; (1) reporting scientific information on ecosystem services, future land use options, and nature‐based solutions for farms (2) interactively providing guidance in response to homeowner questions about biodiversity‐friendly garden design and (3) visualising potential future scenarios of landscape change that incorporate diverse nature based and technological solutions. These examples demonstrate potential applications which may be relevant to other systems and types of nature‐based solutions. While GenAI for nature‐based solutions offers significant opportunities, this new technology brings risks of bias, false information, data privacy, mistrust, and high energy usage. Alongside technological development, we require integrated social research into ethics, public acceptability, and user experience, to maximise the benefits of GenAI while limiting these risks. GenAI offers an opportunity to accelerate the dissemination of nature‐based design strategies and reach a broader audience, by synthesising information and producing tailored content for specific users and locations. By harnessing the power of GenAI alongside human expertise, we can support nature‐based solutions to tackle the complex challenges of future sustainability. Read the free Plain Language Summary for this article on the Journal blog.