Reviewerly/exHarmony huggingface dataset:
A Mobile Application Model for Differently Abled Using CNN, RNN and NLP

Inability to hear, speak and see are considered some of the major disabilities which make it difficult for the people with these disabilities (differently abled people) to communicate with others. These differently abled people make use of other modes of communication, such as sign language, for communicating efficiently. But often the differently abled people face difficulty even when they try to communicate using sign language, mainly due to 2 reasons: firstly, many people do not understand sign language and secondly, even if the people understand sign language, long distance communication is still difficult for the differently abled. So, a sign language converter is being developed to address these issues. In the modern world, sign language converters play a vital role in bridging the communication gap between the differently abled and the others. This sign language converter is being developed as an android application to make it more convenient for people to use and to reach a wide range of audience. This application comes with a sign language conversion framework, which is built using deep neural networks. The conversion model will be able to translate sign language to text (words or sentences)/speech and vice-versa, as per the user's request. The sign language conversion model will be trained with the help of CNN using a sign language dataset to accurately predict the output. RNN model is also being used here to reduce the time complexity involved and for handling speech inputs (audio files). The sign language conversion system will use a microphone (in case of speech input) and a camera (in case of sign language video input). The converter will involve image capture, binarization, classification, hand shape edge recognition and feature extraction.
Non-Fungible Tokens’ Marketplace: A Secured Blockchain-Based Decentralized Framework for Online Auction

In the current modern world of computation and technology, the Internet plays a very important role. Majority of the things on the Internet work on the concept of centralized networks which offer good accessibility and security along with innovations. But, centralized networks are vulnerable to a fair share of cyberattacks and security issues. Decentralized networks form the basis of blockchain technology. Blockchain offers a simple solution to ever-increasing cyber and security attacks on networks by removing the concept of a sole point of failure from networks. The advantages of decentralized networks are that it keeps more information from becoming a separate process. As a result, it is quite difficult for malicious hackers to steal or misuse information from the network. The work presented is an effort to secure digital currency transactions for online auction, and application developed is a secured decentralized platform using blockchain. Model comprises DApp development, a Non-Fungible Tokens (NFTs) Marketplace which is a platform to acquire NFTs, sell NFTs, display NFT status, store them, and even mint new ones. Work done also employs technologies like IPFS, hardhat, Ethereum smart contract functionalities, Next JS and other technologies for implementation of DApp. Application developed provides an extremely secured environment with non-fungible tokens’ (NFTs) for implementing online auction and exchange of digital currency with decentralized blockchain at the backend. Application was developed on Solsea platform, virtually deployed, and tested with 22 bidder’s input. On auction time completion, prototype provides the address of the leading bidder and in addition also provides the margin by which the leading bidder claims the bid. Platform designed is a solution that is secure, reliable, and transparent technique for online auctions equipped with multipurpose smart contract that can address to the broader requirements of auctioneers and bidders.
Breast Cancer Classification Using a Novel Image Processing Pipeline and a Two-Stage Deep Learning Segmentation and Classification Approach

Mammography is used as a primary method for the X-ray imaging of breast regions. These mammogram images are utilized by radiologists for localization and prognosis of the mass regions present in the breast region, as either malignant or benign. We present a similar approach in our work, where a two-stage system is proposed for the localization and classification of breast mass regions using the mammogram images. First, these mammograms are passed through an image processing pipeline for the initial processing. Secondly, these processed images are fed into the proposed two-stage system for segmentation and classification. For the segmentation stage, we use the UNET segmentation architecture with EfficientNetB0, ResNet50, and MobileNet encoders without any pre-trained weights. For the classification stage, we use the VGG16 and ResNet50 pre-trained models for our task where we feed in the segmented region of interest of tumors as input and the output of the model being the pathology of the tumor. The results obtained show good accuracy in determining the pathology of the mass region in the mammogram images, with results obtained at low latency with good precision, recall, specificity, and sensitivity rates.
Surrogate model for memetic genetic programming with application to the one machine scheduling problem with time-varying capacity

Surrogate evaluation is a useful, if not the unique, technique in population-based evolutionary algorithms where exact fitness calculation is too expensive. This situation arises, for example, in Genetic Programming (GP) applied to evolve scheduling priority rules, since the evaluation of a candidate rule amounts to solve a large number of problem instances acting as training set. In this paper, a simplified model is proposed that relies on finding and then exploiting a small set of small problem instances, termed filter, such that the evaluation of a rule on the filter may help to estimate the performance of the same rule in solving the training set. The problem of finding the best filter is formulated as a variant of the optimal subset problem, which is solved by means of a Genetic Algorithm (GA). The surrogate evaluation of a new candidate rule consist in solving the instances of the filter. This model is exploited in combination with a Memetic Genetic Program (MGP); the resulting algorithm is termed Surrogate Model MGP (SM-MGP). An experimental study was performed on the problem of scheduling a set of jobs on a machine with varying capacity over time, denoted (1,Cap(t)||∑Ti). The results of this study provided interesting insights into the problems of filter and rules calculation, and showcase that the priority rules evolved by SM-MGP outperform those evolved by MGP.
Preparing quantum states by measurement-feedback control with Bayesian optimization

The preparation of quantum states is crucial for enabling quantum computations and simulations. In this work, we present a general framework for preparing ground states of many-body systems by combining the measurement-feedback control process (MFCP) with machine learning techniques. Specifically, we employ Bayesian optimization (BO) to enhance the efficiency of determining the measurement and feedback operators within the MFCP. As an illustration, we study the ground state preparation of the one-dimensional Bose–Hubbard model. Through BO, we are able to identify optimal parameters that can effectively drive the system towards low-energy states with a high probability across various quantum trajectories. Our results open up new directions for further exploration and development of advanced control strategies for quantum computations and simulations.
Trust Model for Cloud Using Weighted KNN Classification for Better User Access Control

The majority of the time, cloud computing is a service-based technology that provides Internet-based technological services. Cloud computing has had explosive growth since its debut, and it is now integrated into a wide variety of online services. These have the primary benefit of allowing thin clients to access the resources and services. Even while it could appear favorable, there are a lot of potential weak points for various types of assaults and cyber threats. Access control is one of the several protection layers that are available as part of cloud security solutions. In order to improve cloud security, this research introduces a unique access control mechanism. For granting users access to various resources, the suggested approach applies the trust concept. For the purpose of predicting trust, the KNN model was recently proposed, however the current approach for categorizing options is sensitive and unstable, particularly when an unbalanced data scenario occurs. Furthermore, it has been discovered that using the exponent distance as a weighting system improves classification performance and lowers variance. The prediction of the user’s trust levels using weighted K-means closest neighbors is presented in this research. According to the findings, the suggested approach is more effective in terms of throughput, cost, and delay.
FaaSOnto: A Semantic Model for Enabling Function-as-a-Service Platform Selection

Serverless computing shifts the responsibilities of provisioning and managing cloud infrastructure resources from developers to cloud service providers, allowing developers to focus solely on their applications. Function-as-a-Service (FaaS) is a serverless computing approach that enables developers to develop their applications as event-driven functions. There are many FaaS platforms available through public cloud providers or open-source distributions. Understanding the differences in these platforms and keeping up to date with their latest developments is challenging. Hence, it is necessary to systematically model the information about FaaS Platforms to allow practitioners to select the platform most suited for realizing their use cases. This paper presents the FaaSOnto ontology, a semantic model that represents the characteristics of FaaS platforms. We developed the ontology systematically following the NeOn methodology. We fully implemented the ontology using OWL2 and created a knowledge base with information about ten different FaaS platforms. The knowledge base is semi-automatically populated. On top of the knowledge base, we developed a minimal decision support system to enable the sorting and filtering of FaaS platforms based on their characteristics to facilitate an interactive platform selection process.
A Development Example: From Conceptual Specification to OO Specification

We illustrate, by using a practical example, how to map a Conceptual Specification (CS) systematically to an OO-implementation, using formal mapping rules or “semi-automatic” guidelines. Special attention will be paid to the “transfer” of constraints from the Conceptual Specification to the software, since programming languages usually lack constructs to express constraints. We separate Model from View and Controller (MVC pattern) and create a default class diagram (statics/data) and default methods (dynamics/processes) from a CS. Our enhanced class diagrams inherit the constraints from the CS, which are subsequently worked out in the OO-specification in a systematic way. We generate pseudo-code such that a programmer in a specific (OO) language can easily translate this into real code.
Synonyms, Antonyms and Factual Knowledge in BERT Heads

In recent years, many studies have been devoted to discovering the inner workings of Transformer-based models, such as BERT, for instance, attempting to identify what information is contained within them. However, little is known about how these models store this information in their millions of parameters and which parts of the architecture are the most important. In this work, we propose an approach to identify self-attention mechanisms, called heads, that contain semantic and real-world factual knowledge in BERT. Our approach includes a metric computed from attention weights and exploits a standard clustering algorithm for extracting the most relevant connections between tokens in a head. In our experimental analysis, we focus on how heads can connect synonyms, antonyms and several types of factual knowledge regarding subjects such as geography and medicine.
A new approach to software vulnerability detection based on CPG analysis

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Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.
An Image Encryption Transmission Scheme Based on a Polynomial Chaotic Map

Most existing chaotic systems have many drawbacks in engineering applications, such as the discontinuous range of chaotic parameters, weak chaotic properties, uneven chaotic sequence outputs, and dynamic degradation. Therefore, based on the above, this paper proposes a new method for the design of a three-dimensional chaotic map. One can obtain the desired number of positive Lyapunov exponents, and can also obtain the desired value of positive Lyapunov exponents. Simulation results show that the proposed system has complex chaotic behavior and high complexity. Finally, the method is implemented into an image encryption transmission scheme and experimental results show that the proposed image encryption scheme can resist brute force attacks, correlation attacks, and differential attacks, so it has a higher security.
Evaluating Object (Mis)Detection From a Safety and Reliability Perspective: Discussion and Measures

We argue that object detectors in the safety critical domain should prioritize detection of objects that are most likely to interfere with the actions of the autonomous actor. Especially, this applies to objects that can impact the actor’s safety and reliability. To quantify the impact of object (mis)detection on safety and reliability in the context of autonomous driving, we propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. To achieve this, we build an object criticality model to reward the detection of the objects based on proximity, orientation, and relative velocity with respect to the subject vehicle. Then, we apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.
Revocable identity‐based matchmaking encryption in the standard model

Abstract Identity‐based Matchmaking Encryption (IB‐ME) is an extension notion of matchmaking encryption (CRYPTO 2019), where a sender and a receiver can specify an access policy for the other party. In IB‐ME, data encryption is performed by not only a receiver identity but also a sender's encryption key. Nevertheless, previous IB‐ME schemes have not considered the problem of efficient revocation . Hence, the authors introduce a new notion of revocable IB‐ME (RIB‐ME) and formalise the syntax and security model of RIB‐ME. In particular, the authors give an effective and simple construction of RIB‐ME in the standard model, whose security is reduced to the hardness of decisional bilinear Diffie—Hellman problem and computational Diffie—Hellman problem. In addition, the authors show two extensions of our RIB‐ME scheme to consider chosen‐ciphertext security and forward privacy.
Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.
Exploring Virtual Machine Scheduling Algorithms: A Meta-Analysis

This review paper provides a comprehensive assessment of scheduling methods for cloud computing, with an emphasis on optimizing resource allocation in cloud computing systems. The PRISMA methodology was utilized to identify 2,487 articles for this comprehensive review of scheduling methods in cloud computing systems. Following a rigorous screening process, 30 papers published between 2018 and 2023 were selected for inclusion in the review. These papers were analyzed in-depth to provide an extensive overview of the current state of scheduling methods in cloud computing, along with the challenges and opportunities for improving resource allocation. The review evaluates various scheduling approaches, including heuristics, optimization, and machine learning-based methods, discussing their strengths and limitations and comparing results from multiple studies. The paper also highlights the latest trends and future directions in cloud computing scheduling research, offering insights for practitioners and researchers in this field.
A ranking prediction strategy assisted automatic model selection method

With the development of booming AutoML systems, modeling processes have become more automatic for researchers. However, AutoML systems may struggle to identify the optimal surrogate type, find the best combination of the hyper-parameters or establish a high-fidelity ensembled surrogate model for certain datasets. To address these issues and further improve the warm-start procedure of AutoML, a Ranking Prediction Strategy assisted Automatic Model Selection (RPS-AMS) method is proposed. In the suggested method, an integration of evolutionary algorithms (EA-based) and feature-based driven model selection strategy selects the best or the best combination models for prediction. Based on the proposed criteria, an XGBoost regression model is trained to determine the rankings from the candidate surrogate models and then build an ensembled surrogate model to further enhance accuracy. We evaluate RPS-AMS using 13 mathematical functions, 14 public datasets, and a real engineering problem. Compared with the popular modeling tools, such as Auto-Sklearn and EvalML, the RPS-AMS outperforms in term of accuracy while maintaining the performances of ergodic methods of all surrogate models. The accuracies of RPS-AMS rival EvalML in most tested datasets, although RPS-AMS may be slightly less efficient. Given that EvalML is a masterpiece of the AutoML systems, the performances of RPS-AMS are promising. This code is available at: https://github.com/HnuAiSimOpt/RPS-AMS.
Green consumption behavior prediction based on fan-shaped search mechanism fruit fly algorithm optimized neural network

Predicting consumption behavior is very important for adjusting supplier production plans and enterprise marketing activities. Conventional statistical methods are unable to accurately predict green consumption behavior because it is characterized by multivariate nonlinear interactions. The paper proposes an optimized fruit fly algorithm (FOA) and extreme learning machine (ELM) model for consumption behavior prediction. First, to address the problem of uneven search direction of FOA leading to insufficient search ability and low efficiency, the paper proposes a sector search mechanism instead of a random search mechanism to improve the global search ability and convergence speed of FOA. Second, to address the issue that the initial weights and hidden layer bias values of the ELM are randomly generated, which affects the learning efficiency and generalization of the ELM, the paper uses an improved FOA to optimize the weights and bias values of ELM for improving the prediction accuracy. Taking the green vegetable consumption behavior of Beijing residents as an example, the results show the optimization of the initial weight and threshold of ELM by the GA, PSO, FOA, and SFOA, the prediction accuracy of the GA-ELM, PSO-ELM, FOA-ELM, and SFOA-ELM models all surpass those of ELM. Compared with BPNN, GRNN, ELM, GA-ELM, PSO-ELM, and FOA-ELM models, the RMSE value of SFOA-ELM was decreased by 9.45%, 8.40%, 11.89%, 5.84%, 2.22%, and 2.69%, respectively. These findings demonstrate the effectiveness of the SFOA-ELM model in green consumption behavior prediction and provide new ideas for the accurate prediction of consumption behaviors of other green products with similar characteristics.