Reviewerly/exHarmony huggingface dataset:
Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions.Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions.The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches.This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images.The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly.To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model.In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs.The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion.Finally, the fused features are passed into bounding box and class prediction networks.For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem.The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.3118
Light-Weighted Decision Support Framework for Selecting Cloud Service燩roviders

Multi-criteria decision making (MCDM) is a technique used to achieve better outcomes for some complex business-related problems, whereby the selection of the best alternative can be made in as many cases as possible.This paper proposes a model, the multi-criteria decision support method, that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling.The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider.Each consumer seeks a service provider based on various preferences, such as price, service quality, and time to complete the tasks.In the literature, the problem is viewed from different perspectives, such as investigating how to enhance task scheduling and the resource allocation process, improve consumers' trust, and deal with network problems.This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer.First, the model adopts the best-worst method (BWM) to gather and prioritize tasks based on consumers' and service providers' preferences.Then, the model calculates and matches similarities between the sets of tasks from the consumer's side with the sets of tasks from the provider's side to select the best service provider for each consumer using the two proposed algorithms.The complexity of the two algorithms is found to be O(n 3 ).
Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment

Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly, user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search (CS) and seagull optimization algorithm (SOA) had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization (MO-ACO), ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%, 4.2%, and 2.4% for makespan and had reduced the overall cost to the extent of 1.74%, 3.93% and 2.77% when compared with PSO, ACO, IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.
Improved Multileader Optimization with Shadow Encryption for Medical Images in IoT Environment

Nowadays, security plays an important role in Internet of Things (IoT) environment especially in medical services' domains like disease prediction and medical data storage.In healthcare sector, huge volumes of data are generated on a daily basis, owing to the involvement of advanced health care devices.In general terms, health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis.At the same time, it is also significant to maintain the delicate contents of health care images during reconstruction stage.Therefore, an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data.The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security (IMLOSIE-MIS) technique for IoT environment.The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively.To do so, the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image.Besides, shadow image encryption process takes place with the help of Multileader Optimization (MLO) with Homomorphic Encryption (IMLO-HE) technique, where the optimal keys are generated with the help of MLO algorithm.On the receiver side, decryption process is initially carried out and shadow image reconstruction process is conducted.The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.3134 CMC, 2023, vol.74, no.2The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.
Data De-identification Framework

As technology develops, the amount of information being used has increased a lot.Every company learns big data to provide customized services with its customers.Accordingly, collecting and analyzing data of the data subject has become one of the core competencies of the companies.However, when collecting and using it, the authority of the data subject may be violated.The data often identifies its subject by itself, and even if it is not a personal information that infringes on an individual's authority, the moment it is connected, it becomes important and sensitive personal information that we have never thought of.Therefore, recent privacy regulations such as GDPR (General Data Protection Regulation) are changing to guarantee more rights of the data subjects.To use data effectively without infringing on the rights of the data subject, the concept of de-identification has been created.Researchers and companies can make personal information less identifiable through appropriate de-identification/pseudonymization and use the data for the purpose of statistical research.De-identification/pseudonymization techniques have been studied a lot, but it is difficult for companies and researchers to know how to de-identify/pseudonymize data.It is difficult to clearly understand how and to what extent each organization should take deidentification measures.Currently, each organization does not systematically analyze and conduct the situation but only takes minimal action while looking at the guidelines distributed by each country.We solved this problem from the perspective of risk management.Several steps are required to secure the dataset starting from pre-processing to releasing the dataset.We can analyze the dataset, analyze the risk, evaluate the risk, and treat the risk appropriately.The outcomes of each step can then be used to take appropriate action on the dataset to eliminate or reduce its risk.Then, we can release the dataset under its own purpose.These series of processes were reconstructed to fit the current situation by analyzing various standards such as ISO/
Natural Language Processing with Optimal Deep Learning-Enabled Intelligent Image Captioning System

The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on.If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step, the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario, computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops a Natural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System (NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this, the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially, at the encoding side, the proposed NLPODL-IICS model makes use of Hunger Games Search (HGS) with Neural Search Architecture Network (NASNet) model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides, during decoding phase, Chimp Optimization Algorithm (COA) with deeper Long Short Term Memory (LSTM) approach is followed to concatenate the description sentences 4436 CMC, 2023, vol.74, no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.A widespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.
Generic Construction of 1-out-of-&lt;i&gt;n&lt;/i&gt; Oblivious Signatures

In a 1-out-of-n oblivious signature scheme, a user provides a set of messages to a signer for signatures but he/she can only obtain a valid signature for a specific message chosen from the message set. There are two security requirements for 1-out-of-n oblivious signature. The first is ambiguity, which requires that the signer is not aware which message among the set is signed. The other one is unforgeability which requires that the user is not able to derive any other valid signature for any messages beyond the one that he/she has chosen. In this paper, we provide a generic construction of 1-out-of-n oblivious signature. Our generic construction consists of two building blocks, a commitment scheme and a standard signature scheme. Our construction is round efficient since it only asks one interaction (i.e., two rounds) between the user and signer. Meanwhile, in our construction, the ambiguity of the 1-out-of-n oblivious signature scheme is based on the hiding property of the underlying commitment, while the unforgeability is based on the binding property of the underlying commitment scheme and the unforgeability of the underlying signature scheme. Moreover, our construction can also enjoy strong unforgeability as long as the underlying building blocks have strong binding property and strong unforgeability respectively. Given the fact that commitment and digital signature are well-studied topics in cryptography and numerous concrete schemes have been proposed in the standard model, our generic construction immediately yields a bunch of instantiations in the standard model based on well-known assumptions, including not only traditional assumptions like Decision Diffie-Hellman (DDH), Decision Composite Residue (DCR), etc., but also some post-quantum assumption like Learning with Errors (LWE). As far as we know, our construction admits the first 1-out-of-n oblivious signature schemes based on the standard model.
Tracking Stuffed Toy for Naturally Mapped Interactive Play via a Soft-Pose Estimator

Have you ever picked up a stuffed toy and pretended to play with it in your childhood? We are motivated by the novel use of stuffed toys in enhancing extended reality interaction. A key goal of extended reality is to induce the feeling of presence in its users. Naturally mapped control interface has been shown to enhance presence. The literature also indicates that a high degree of freedom tracking is important to extended reality. Based on these observations, we show that a free-form naturally mapped control interface is well-motivated via a theoretical contextualization. We explore the possibility of building such a controller in the form of stuffed toys. To realize stuffed toys as controllers, a novel soft-pose estimator empowered by cage-based deformation is proposed. It is shown to be effective in tracking the poses and deformations of real soft objects even by training with synthetic data only. Three gameplay prototypes are developed to demonstrate that interactive play can be enabled by the soft-pose estimator. They also form the basis for two user studies that validate the success of tracking stuffed toys with the soft-pose estimator for interactive play.
Effects of PCG on Creativity in Playful City-Building Environments in VR

The use of procedural content generation (PCG) in the context of video games has increased over the years as it provides an economical way to generate game content whilst enhancing their variety and replayability. For city-building games, this approach is often utilized to predefine map layouts, terrains, or cityscapes for the player. One core aspect of facilitating enjoyment in these games comes from creative expressivity. PCG, in this context, may support creativity by lowering the technical complexity for content creation, or it may hinder creativity by taking away control and freedom from the user. To examine these potential effects, this paper investigates if PCG has an impact on players' creativity in the context of VR city-building games. We present a VR prototype that provides varying degrees of procedural content: No PCG, terrain generation, city generation, and full (city + terrain) generation. In a remote user study, these conditions were compared regarding their capability to support creativity. Statistical tests for equivalence revealed that the presence of PCG did not affect creativity in any way. Our work suggests that PCG can be a useful integration into city-building games without notably decreasing players' ability to express themselves creatively.
Fractional resources in unbounded separation logic

Many separation logics support fractional permissions to distinguish between read and write access to a heap location, for instance, to allow concurrent reads while enforcing exclusive writes. Fractional permissions extend to composite assertions such as (co)inductive predicates and magic wands by allowing those to be multiplied by a fraction. Typical separation logic proofs require that this multiplication has three key properties: it needs to distribute over assertions, it should permit fractions to be factored out from assertions, and two fractions of the same assertion should be combinable into one larger fraction. Existing formal semantics incorporating fractional assertions into a separation logic define multiplication semantically (via models), resulting in a semantics in which distributivity and combinability do not hold for key resource assertions such as magic wands, and fractions cannot be factored out from a separating conjunction. By contrast, existing automatic separation logic verifiers define multiplication syntactically, resulting in a different semantics for which it is unknown whether distributivity and combinability hold for all assertions. In this paper, we present a novel semantics for separation logic assertions that allows states to hold more than a full permission to a heap location during the evaluation of an assertion. By reimposing upper bounds on the permissions held per location at statement boundaries, we retain key properties of separation logic, in particular, the frame rule. Our assertion semantics unifies semantic and syntactic multiplication and thereby reconciles the discrepancy between separation logic theory and tools and enjoys distributivity, factorisability, and combinability. We have formalised our semantics and proved its properties in Isabelle/HOL.
Monadic and comonadic aspects of dependency analysis

Dependency analysis is vital to several applications in computer science. It lies at the essence of secure information flow analysis, binding-time analysis, etc. Various calculi have been proposed in the literature for analysing individual dependencies. Abadi et. al., by extending Moggi’s monadic metalanguage, unified several of these calculi into the Dependency Core Calculus (DCC). DCC has served as a foundational framework for dependency analysis for the last two decades. However, in spite of its success, DCC has its limitations. First, the monadic bind rule of the calculus is nonstandard and relies upon an auxiliary protection judgement. Second, being of a monadic nature, the calculus cannot capture dependency analyses that possess a comonadic nature, for example, the binding-time calculus, λ ∘ , of Davies. In this paper, we address these limitations by designing an alternative dependency calculus that is inspired by standard ideas from category theory. Our calculus is both monadic and comonadic in nature and subsumes both DCC and λ ∘ . Our construction explains the nonstandard bind rule and the protection judgement of DCC in terms of standard categorical concepts. It also leads to a novel technique for proving correctness of dependency analysis. We use this technique to present alternative proofs of correctness for DCC and λ ∘ .
Design and Development of New Interactive Chatbot System for Mobile Service Providers through Heuristic-Based Ensemble Learning

In recent times, the human agents for chat services are used to be more in the communication system among human users according to artificial intelligence. These chatbots seem to be cost-efficient and time-saving paradigm, and so it is widely developed for usage. Still, it suffers from satisfying the customer requirements, which reduces the users being less liable to conform to desires through the chatbot. The designed interactive system has to process the queries and gets a suitable reply in the chatbot. The data collection related to diverse queries is gathered. Then, the pre-processing is carried out. Further, the word to vector is conducted for extracting the features. Then, the weighted feature vector is attained through optimizing the weight by hybrid optimization named Hybrid Water Wave Barnacle Mating Optimization (HWW-BMO). Finally, detection is performed through Heuristic-based Extreme learning + Long Short-Term Memory (H-ExtLSTM) for predicting the intent related to the queries asked by mobile service providers, thus can extract the answers based on word similarity. Here, the parameters of ELM and LSTM are optimized by the HWW-BMO algorithm. Experiments show that the proposed joint models have achieved competitive results in different datasets in multiple scenarios compared with other models.
A novel Deep-Learning model for RDTS signal denoising based on graph neural networks

• Based on the spatial relationship of signals, a graph neural network signal noise reduction method for RDTS is proposed. • The significant advantage of this method is that it reduces the model size by segmenting the input data while improving the model performance. • The method can greatly reduce the temperature bias and improve the smoothness of the temperature curve. Focusing on the problem of increasing the deviation of temperature measurement for Raman-based distributed temperature sensor (RDTS) caused by random noise, a new method of applying a three-layer GraphSAGE-based graph neural network (3L-GraphSAGE) to noise reduction is proposed, where the spatial relationship between each signal is first constructed, and then the effective denoised results are obtained from the developed 3L-GraphSAGE model. First, an experimental setup is built for collecting fiber signals. Then, the datasets are input into the 3L-GraphSAGE to train the model. Finally, the test datasets are input into the well-trained 3L-GraphSAGE model to obtain effective denoised signals. To evaluate the performance of 3L-GraphSAGE, three evaluation indexes are calculated, including maximum deviation (MD), root mean square error (RMSE) and smoothness. The experimental results show that it can efficiently suppress the random noise and reduce the temperature measurement deviation in RDTS compared with direct demodulation of the raw data, and significantly improve the curve smoothness compared with wavelet transform by soft threshold function (WT-soft) and fast waveform type (FWT). Therefore, 3L-GraphSAGE model can provide an available method for improving the performance of RDTS.
Efficient subsampling of realistic images from GANs conditional on a class or a continuous variable

To improve image quality, subsampling or refining images generated from (unconditional) generative adversarial networks (GANs) has received much attention recently. These methods are less effective or inefficient, however, for conditional GANs (cGANs) that either condition on a class (i.e., class-conditional GANs) or a continuous variable (i.e., continuous cGANs or CcGANs). Thus, we introduce a novel subsampling scheme for cGANs: conditional density ratio rejection sampling (cDR-RS). Specifically, cDR-RS comprises an improved feature extraction mechanism and a conditional Softplus loss (cSP). We also derive an error bound for the density ratio model trained with the cSP loss. Fake images are accepted or rejected based on the improved estimated conditional density ratio. An additional filtering scheme further increases fake images’ label consistency without losing diversity when sampling from CcGANs. We extensively test the effectiveness and efficiency of cDR-RS in sampling from both class-conditional GANs and CcGANs on six benchmark datasets. Experimental results demonstrate that the proposed method can achieve state-of-the-art performances in subsampling both types of conditional GANs.