Reviewerly/exHarmony huggingface dataset:
Multi-Objective Modular Strategic Planning Framework for Low Altitude Missions Within the Urban Air Mobility Ecosystem

This paper introduces a multi-metric multi-constraint strategic path planning framework applicable to unstructured urban airspace. The planner is based on a modular and scalable approach to handle several information sources and aspects characterizing urban flight scenarios, such as risk and weather maps, landing site locations, navigation requirements, and mobile and fixed obstacle characteristics. This information is coupled with dynamic constraints and UAV specifications to derive a flyable and safe path connecting a start position and a destination. Strategies for data gathering and synthesis, used to keep a reduced computational burden, are described along with the path planner algorithm. The latter consists in three steps specifically developed to handle both static and time-varying information. A multi-objective cost function with variable weighting coefficients has been implemented so that the most relevant factors for the considered applications can be selected in an adaptive fashion. The performance of the developed algorithms is tested by investigating the planner behavior when changing its inputs as well as the cost function weighting coefficients, demonstrating the ability of the planner in returning an efficient and safe trajectory.
Laryngeal Cancer Detection and Classification Using Aquila Optimization Algorithm With Deep Learning on Throat Region Images

Laryngeal cancer detection on throat area images is a vital application of medical image diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and detection of cancerous or abnormal tissues from the larynx, an essential part of the respiratory and vocal systems. Several machine learning (ML) and deep learning (DL) systems are executed for classifying the extraction features as both cancerous and healthy tissue. Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) have shown promise in this context. With this motivation, this study designs a new Laryngeal Cancer Detection and Classification using the Aquila Optimization Algorithm with Deep Learning (LCDC-AOADL) technique on neck region images. The purpose of the LCDC-AOADL technique is to examine the histopathological images for the recognition and classification of Laryngeal Cancer. In the presented LCDC-AOADL technique, the Inceptionv3 model is used for the feature extraction process. Besides, the LCDC-AOADL technique employed a deep belief network (DBN) model for the identification and classification of LC. Moreover, the AOA is utilized for the hyperparameter tuning of the DBN model which results in improved detection rate. The simulation analysis of the LCDC-AOADL method is validated on the benchmark Laryngeal dataset. The experimental results pointed out the enhanced detection results of the LCDC-AOADL technique over other recent approaches with a maximum accuracy of 96.02%, precision of 92.10%, recall of 91.87%, and F-score of 91.86%.
SARGAN: A Novel SAR Image Generation Method for SAR Ship Detection Task

Deep learning-based synthetic aperture radar (SAR) ship detection methods are significant in signal processing and radar imaging. However, these approaches always require large-scale SAR ship images with labels to train the model. Due to the inaccessibility of SAR sensors, it is difficult to acquire enough SAR images. Annotating ship targets also demands resources and manpower. To tackle this issue, we propose a novel SAR image generation method named SARGAN for SAR ship detection task. Given the position and category, SARGAN can generate realistic SAR images with SAR ship targets, land, and background in the desired location. In the SARGAN, there are five components: target encoder, scene constructor, SAR image generator, and target and image discriminators. The target encoder is introduced to predict the latent vector for each target, while the scene constructor integrates all targets in the entire scene using convolutional LSTM. We improve the structure of the SAR image generator by adding operations to generate high-quality images. The image and target discriminators are responsible for distinguishing between real and fake samples, with the latter also predicting the category. To promote the generation of diverse and realistic SAR ship images, multiple loss functions are employed for training. Additionally, we have annotated the lands and background in the high-resolution SAR images dataset (HRSID) and combined them with labeled ships to create a new dataset for training and testing of SARGAN. Extensive experiments demonstrate that SARGAN outperforms other SAR image generation methods, and the generated SAR ship images are highly conducive for SAR ship detection task.
Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)

Serverless computing has seen rapid growth, thanks to its adaptability, elasticity, and deployment agility, embraced by both cloud providers and users. However, this surge in serverless adoption has prompted a reevaluation of security concerns and thus, searchable encryption has emerged as a crucial technology. This paper explores the Searchable Encryption as a Service (SEaaS) and introduces an innovative privacy-preserving Multiple Keyword Searchable Encryption (MKSE) scheme within a serverless cloud environment, addressing previously unmet security goals. The proposed scheme employs probabilistic encryption and leverages fully homomorphic encryption to enable operations on ciphertext, facilitating searches on encrypted data. Its core innovation lies in the use of probabilistic encryption for private multi-keyword searches. To validate its practicality, we deploy the scheme on the public cloud infrastructure, "Contabo," and conduct rigorous testing on a real-world dataset. The results demonstrate that our novel scheme successfully preserves the privacy of search queries and access patterns, achieving robust security. This research contributes to the field of serverless cloud security, particularly in the context of searchable encryption, by providing a refined solution for safeguarding data while maintaining usability in a serverless computing landscape.
Safety and Privacy in Immersive Extended Reality: An Analysis and Policy Recommendations

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Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation.The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately.In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC).The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve.Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.
Rusty Links in Local Chains✱

Rust successfully applies ownership types to control memory allocation. Unfortunately, Rust’s ownership restricts the programs’ topologies to the point where doubly-linked lists cannot be programmed in Safe Rust. We sketch how more flexible “local” ownership could be added to Rust, permitting multiple mutable references to objects, provided each reference is bounded by the object’s lifetime. To maintain thread-safety, locally owned objects must remain thread-local; to maintain memory safety, local objects must remain allocated until their owner’s lifetime expires.
Activation of referents in the bilingual mind

This paper investigates reference production by bilingual and monolingual children. We focus on the degree to which the activation of referents is encoded by different types of referring expressions among bilinguals and monolinguals. The study is based on forty-six story retellings produced in German by twenty-five Greek-German bilingual children and twenty-one monolingual children, respectively. The activation of referents is assessed based on a multi-factorial analysis of cognitive and linguistic factors that are involved in the use of referring expressions. The results show that pronouns produced by bilingual children tend to encode a lower degree of activation of referents and to be underspecific. This may be an effect of reduced processing speed experienced by bilinguals in the mapping of the referent’s activation onto the use of a certain referring expression. More in general, we account for the observed differences between bilinguals and monolinguals in terms of cognitive mechanisms underlying bilingual language production.
Blockchain-based public key encryption with keyword search for medical data sharing in cloud environment

The cloud-based sharing of electronic health data (EHDs) has great significance in researching diseases, doctors' diagnoses and various fields. In this paper, we applied the potentiality of both blockchain and public key searchable encryption to build a medical data-sharing platform. In recent years, blockchain has emerged as a promising solution for medical data sharing with security and privacy preservation due to its immutability, decentralization, anonymity and verifiability advantages. Two kinds of blockchains, private blockchain and consortium blockchain, are used in our model. In our model, we have designed a consortium blockchain of preselected users. To achieve data security, access control, privacy preservation and secure search, public key encryption with keyword search (PEKS) technology is used here. According to the security proof, our proposed scheme is indistinguishable under the keyword guessing attack. The outcomes of the performance analysis and comparison simulations reveal that the proposed scheme performs better than other related schemes.
Multimaster Node Byzantine Fault-Tolerant Consensus Algorithm Based on Consistent Hash Algorithm

We propose a consensus mechanism based on the consistent hash algorithm for multiple master nodes. The consensus mechanism uses a consistent hash algorithm to select consensus group nodes and common nodes and further selects multiple master nodes on this basis, and conduct one-way communication with the final leader node.
Focusing on Refinement Typing

We present a logically principled foundation for systematizing, in a way that works with any computational effect and evaluation order, SMT constraint generation seen in refinement type systems for functional programming languages. By carefully combining a focalized variant of call-by-push-value, bidirectional typing, and our novel technique of value-determined indexes, our system generates solvable SMT constraints without existential (unification) variables. We design a polarized subtyping relation allowing us to prove our logically focused typing algorithm is sound, complete, and decidable. We prove type soundness of our declarative system with respect to an elementary domain-theoretic denotational semantics. Type soundness implies, relatively simply, the total correctness and logical consistency of our system. The relative ease with which we obtain both algorithmic and semantic results ultimately stems from the proof-theoretic technique of focalization.
Transpiler-Based Architecture Design Model for Back-End Layers in Software Development

The utilization of software architectures and designs is widespread in software development, offering conceptual frameworks to address recurring challenges. A transpiler is a tool that automatically converts source code from one high-level programming language to another, ensuring algorithmic equivalence. This study introduces an innovative software architecture design model that integrates transpilers into the back-end layer, enabling the automatic transformation of business logic and back-end components from a single source code (the coding artifact) into diverse equivalent versions using distinct programming languages (the automatically produced code). This work encompasses both abstract and detailed design aspects, covering the proposal, automated processes, layered design, development environment, nest implementations, and cross-cutting components. In addition, it defines the main target audiences, discusses pros and cons, examines their relationships with prevalent design paradigms, addresses considerations about compatibility and debugging, and emphasizes the pivotal role of the transpiler. An empirical experiment involving the practical application of this model was conducted by implementing a collaborative to-do list application. This paper comprehensively outlines the relevant methodological approach, strategic planning, precise execution, observed outcomes, and insightful reflections while underscoring the the model’s pragmatic viability and highlighting its relevance across various software development contexts. Our contribution aims to enrich the field of software architecture design by introducing a new way of designing multi-programming-language software.
Obstacle Avoidance for Automated Guided Vehicles in Real-World Workshops Using the Grid Method and Deep Learning

An automated guided vehicle (AGV) obstacle avoidance system based on the grid method and deep learning algorithm is proposed, aiming at the complex and dynamic environment in the industrial workshop of a tobacco company. The deep learning object detection is used to detect obstacles in real-time for the AGV, and feasible paths are generated by the grid method, which ultimately finds an AGV obstacle avoidance solution in complex dynamic environments. The experimental results showed that the proposed system can effectively identify and avoid obstacles in a simulated tobacco production workshop environment, resulting in the average obstacle avoidance success rate of 98.67%. The transportation efficiency of cigarette factories is significantly improved with the proposed system, reducing the average execution time of handing tasks by 27.29%. This paper expects to provide a reliable and efficient solution for AGV obstacle avoidance in real-world industrial workshops.
An Elementary Emulator Based on Speech-To-Text and Text-to-Speech Technologies for Educational Purposes

The article examines two innovative technologies Text-to-Speech (TTS) and Speech-to-Text (STT). They have been used to create a tool to facilitate the preparation of learning documentation in English, such as: lectures, laboratory exercises, course projects, tests. The article provides a brief description of TTS and STT technologies; analysis of existing developments; presentation of development trends, synthesizer technologies; a comparison of acoustic and language modeling; description of models that use STT technology; algorithm presentation and description of a simple application based on TTS and STT technologies. In summary, the results of the application testing are presented and requests for future development are indicated.
A Survey of Evolutionary Algorithms

Evolutionary algorithm is a kind of according to the selection and evolution of biological evolution model and the evolution of a kind of random search technology. Evolutionary algorithm is a blend of genetics, and cell biology evolution, therefore has a comparatively complete theoretical basis. Evolutionary algorithm has a good robustness, parallelism and flexible structure and so on. At present, widely used in path planning, feature selection, image processing, etc. This paper introduces the four parts, first is the basic theory of algorithm and the characteristics of the general framework, algorithm research, mathematical model and algorithm of improvement.
A Measurement-Referenced Error Vector Magnitude for Counterfeit Cellular Device Detection

Standard formulations of error vector magnitude compare a wireless device's symbol constellation to an ideal reference constellation. In this work, we utilize the residual error vector magnitude, which uses measurements of a wireless device to define a reference constellation. We apply this formulation to the problem of identifying a device's manufacturer from over-the-air measurements of the wireless device and show that the residual error vector magnitude outperforms standard error vector magnitude formulation in this task.
Risk Assessment Model of Aquatic Product Cold Chain Logistics Based on PSO-BP Neural Network

The risk assessment model of cold chain logistics of aquatic products was established by PSO-BP neural network, and the risk index of cold chain logistics of aquatic products was determined. The model training results show that the risk of refrigerated truck transport is the highest and the risk of public event is the lowest in the cold chain logistics of aquatic products. Strengthen the updating of refrigerated truck equipment technology, do a good job in the maintenance and maintenance of vehicles and chillers, avoid damage caused by improper operation, and promote the stable development of cold chain logistics.
Fight detection with spatial and channel wise attention‐based <scp>ConvLSTM</scp> model

Abstract An automated detection of aggressive and violent behaviour in videos has immense potential. It enables efficient online content filtering by restricting access to extreme content and also, when integrated with security systems, helps to monitor violence in surveillance videos. In this work, a convolutional neural network is combined with the proposed Spatial and Channel wise Attention‐based ConvLSTM encoder (SCan‐ConvLSTM). The proposed architecture performs an efficient spatiotemporal fusion of the features extracted from the video sequences containing fight scenes. In order to focus selectively on regions of utmost importance, this blended attention mechanism adjusts the weights of outputs in different locations and across different channels. This recurrent attention mechanism enhances the sequential refinement of activation maps and boosts the model performance. Finally, the experimental results have been presented that show the proposed architecture achieves superior results on the benchmark datasets (RWF‐2000, Violent‐flow, Hockey‐fights, and Movies).
Hiding Information in a Well-Trained Vector Quantization Codebook

Data hiding schemes can generally be categorized into four types: spatial-domain schemes, frequency-domain schemes, compression-domain schemes, and encryption-domain schemes. VQ or VQ variant compression techniques have been widely used by scholars to design data hiding approaches in the compression domain. Over the past two decades, the index tables generated by VQ or VQ variant-based compression techniques have served as the primary carriers for concealing confidential data.
Multimodal Sentiment Analysis Method Based on Multi-task Learning

Multimodal sentiment analysis aims to predict the overall sentiment polarity from multimodal signals, an essential task for many applications. A central part of this task is designing a suitable fusion model to integrate heterogeneous information from different modalities. In this paper, we propose a multi-task learning-based approach to multimodal sentiment analysis and emotion recognition, where previous research approaches treat sentiment analysis and emotion recognition as two separate tasks, ignoring the correlation between sentiment and emotion. In this paper, the two tasks are modeled using a shared-private model in a multi-task framework. First, sentiment analysis and emotion recognition extract their respective features using separate private layers. Secondly, the feature-sharing layer uses a shared Bi-LSTM network and an inter-sentence attention network to encode each sentence to obtain a sentence-level semantic representation. Finally, private and fused features are used for sentiment analysis and recognition to enhance multimodal sentiment analysis results. Experimental results on the dataset CMU-MOSEI show that the model performs well and demonstrates the effectiveness of multi-task learning in multimodal sentiment analysis.
Emotionally Enhanced Talking Face Generation

Several works have developed end-to-end pipelines for generating lip-synced talking faces with real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely depends on the faces in the training dataset, which means they may not perform well on unseen faces. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing. With a broad range of six emotions, i.e., happiness, sadness, fear, anger, disgust, and neutral, we show that our model can adapt to arbitrary identities, emotions, and languages. Our proposed framework has a user-friendly web interface with a real-time experience for talking face generation with emotions. We also conduct a user study for subjective evaluation of our interface's usability, design, and functionality. Project page: \hrefhttps://midas.iiitd.edu.in/emo/ https://midas.iiitd.edu.in/emo/ .
Comparative Performance of Algorithms for Inductive Modeling the Cooling Process of a Metal Casting

Inductive modeling methods have repeatedly demonstrated their effectiveness in various problems of building models based on experimental data. Many different GMDH algorithms of the sorting and iterative types are known which have already proved their efficiency, but they are constantly being improved. To date, some new effective GMDH algorithms have been developed, for example, combinatorial-genetic, correlation-rating and hybrid iterative-genetic algorithms. The paper compares the results of applying various GMDH algorithms with the classical and Lasso regression methods to the problem of modeling the cooling process of a metal casting.