Reviewerly/exHarmony huggingface dataset:
Ciao AI: the Italian adaptation and validation of the Chatbot Usability Scale

Abstract Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67; p &lt; .001) by also offering specific insights regarding the chatbots’ characteristics. The Italian BUS-11 can be confidently used by chatbot designers to assess the satisfaction of their users during formative or summative tests.
Enhanced descriptive captioning model for histopathological patches

Abstract The interpretation of medical images into a natural language is a developing field of artificial intelligence (AI) called image captioning. This field integrates two branches of artificial intelligence which are computer vision and natural language processing. This is a challenging topic that goes beyond object recognition, segmentation, and classification since it demands an understanding of the relationships between various components in an image and how these objects function as visual representations. The content-based image retrieval (CBIR) uses an image captioning model to generate captions for the user query image. The common architecture of medical image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem. We aim in this paper to build an optimized model for histopathological captions of stomach adenocarcinoma endoscopic biopsy specimens. For the image feature extraction subsystem, we did two evaluations; first, we tested 5 different vision models (VGG, ResNet, PVT, SWIN-Large, and ConvNEXT-Large) using (LSTM, RNN, and bidirectional-RNN) and then compare the vision models with (LSTM-without augmentation, LSTM-with augmentation and BioLinkBERT-Large as an embedding layer-with augmentation) to find the accurate one. Second, we tested 3 different concatenations of pairs of vision models (SWIN-Large, PVT_v2_b5, and ConvNEXT-Large) to get among them the most expressive extracted feature vector of the image. For the caption generation lingual subsystem, we tested a pre-trained language embedding model which is BioLinkBERT-Large compared to LSTM in both evaluations, to select from them the most accurate model. Our experiments showed that building a captioning system that uses a concatenation of the two models ConvNEXT-Large and PVT_v2_b5 as an image feature extractor, combined with the BioLinkBERT-Large language embedding model produces the best results among the other combinations.
Voice Visual based Login Authentication using Machine Learning

Within the past few a long time, face and voice acknowledgment are drawing in much consideration. Combination of face and voice verification in a framework will guarantee secure human-machine intuitive instead of the conventional secret word. In this age of lockdown due to the Covid-19widespread, more exchanges take put online and it is critical to embrace these exchanges safely and securely. Conventional secret word frameworks and single confirmation frameworks can be amplified to the combination of confront acknowledgment and voice discovery. This paper presents quick login confirmation based on voice-visual combination.
A computer-aided feature-based encryption model with concealed access structure for medical Internet of Things

One of the Internet of Things (IoT) security issues is the secure sharing and granular management of data access. This study recommends a feature-based encryption scheme with a hidden access structure for medical IoT data security. While establishing fine-grained access control of ciphertext data, the system can guarantee clinical client data privacy. First, it is recommended to convert identity-based encryption (IBE) into a feature-based encryption model (FBEM) using a universal conversion technique that supports multi-valued attributes and gates. IBE characteristics could be inherited by the converted FBEM. The conversion method is then used to change the receiver anonymous IBE scheme into the FBEM scheme with concealed access structure. The FBEM model is then used to construct the IoT scenario for the smart medical application. Theoretical analysis and experimental findings reveal that the suggested system provides advantages over prominent systems regarding computing efficiency, storage load, and security when the access structure is disguised.
Underwater plenoptic cameras optimized for water refraction

By inserting a microlens array (MLA) between the main lens and imaging sensor, plenoptic cameras can capture 3D information of objects via single-shot imaging. However, for an underwater plenoptic camera, a waterproof spherical shell is needed to isolate the inner camera from the water, thus the performance of the overall imaging system will change due to the refractive effects of the waterproof and water medium. Accordingly, imaging properties like image clarity and field of view (FOV) will change. To address this issue, this paper proposes an optimized underwater plenoptic camera that compensates for the changes in image clarity and FOV. Based on the geometry simplification and the ray propagation analysis, the equivalent imaging process of each portion of an underwater plenoptic camera is modeled. To mitigate the impact of the FOV of the spherical shell and the water medium on image clarity, as well as to ensure successful assembly, an optimization model for physical parameters is derived after calibrating the minimum distance between the spherical shell and the main lens. The simulation results before and after underwater optimization are compared, which confirm the correctness of the proposed method. Additionally, a practical underwater focused plenoptic camera is designed, further demonstrating the effectiveness of the proposed model in real underwater scenarios.
Privacy Protection of Cloud Computing Based on Strong Forward Security

Cloud computing is a new information technology. It is the product of the scientific and technological development of the times and plays an important role in the development of this country. In order to effectively solve the security problem of cloud computing data access, an identity-based privacy protection algorithm for cloud computing is proposed. The user information is stored in the cloud server at the registration stage, and the user identity is verified by signature when the information is obtained. The strong forward secure signature scheme can ensure that the signature is both forward secure and backward secure. At present, most signature schemes based on lattice focus on forward security. Therefore, this article constructs a strong forward secure signature scheme based on lattice and applies this signature scheme to cloud user authentication to ensure security.
Privacy-Aware Intelligent Healthcare Services with Federated Learning Architecture and Reinforcement Learning Agent

The convergence of digital health systems, Internet of Healthcare Things (IoHT), and Deep Learning (DL) offers intelligent healthcare services to deploy in medical software development with extended decision-making modules. However, the communication reliability and information privacy of data-driven model integration remain a challenging topic to further discuss by researchers and standard organizations. From a real-time communication perspective, the standard of Quality-of-Service requirements in smart healthcare is within real-time services, which permits extremely low packet delay budget and error loss rate. To jointly generate a delay-aware, reliability-aware, and privacy-aware approaches in intelligent healthcare services (e.g. DL-based medical software), there are three key essential aspects, namely distributed edge learning, resource virtualization, and model slicing orchestration. This paper presents optimized Federated Learning (FL) architecture based on autonomous agent policies (resource placement and slicing orchestration) using reinforcement learning. The system architecture outlines large-scale IoHT deployment in wireless cellular networks, network functions virtualization-enabled edge computing placement, and converged FL components. The communication and computation models tackle the utilization efficiencies of bandwidth, energy, and computing capacities. This proposed optimization approach strengthens privacy and reliability by orchestrating through different weights of slicing prioritization in multi-intelligent healthcare services such as non-real-time/real-time modelling, telemedicine, and remote operation (e.g. telesurgery).
Modeling global distribution for federated learning with label distribution skew

Federated learning achieves joint training of deep models by connecting decentralized datasources, which can significantly mitigate the risk of privacy leakage. However, in a more general case, the distributions of labels among clients are different, called “label distribution skew”. Directly applying conventional federated learning without consideration of label distribution skew issue significantly hurts the performance of the global model. To this end, we propose a novel federated learning method, named FedMGD, to alleviate the performance degradation caused by the label distribution skew issue. It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art on several public benchmarks. Code is available at https://www.github.com/Sheng-T/FedMGD.
Differentially Private Data Release over Multiple Tables

We study synthetic data release for answering multiple linear queries over a set of database tables in a differentially private way. Two special cases have been considered in the literature: how to release a synthetic dataset for answering multiple linear queries over a single table, and how to release the answer for a single counting (join size) query over a set of database tables. Compared to the single-table case, the join operator makes query answering challenging, since the sensitivity (i.e., by how much an individual data record can affect the answer) could be heavily amplified by complex join relationships. We present an algorithm for the general problem, and prove a lower bound illustrating that our general algorithm achieves parameterized optimality (up to logarithmic factors) on some simple queries (e.g., two-table join queries) in the most commonly-used privacy parameter regimes. For the case of hierarchical joins, we present a data partition procedure that exploits the concept of uniformized sensitivities to further improve the utility.
Review on Sentiment Analysis Using Supervised Machine Learning Techniques

Sentiment analysis is a technique used to identify information by analyzing the subjectivities presented in a sentence. In sentiment analysis, text analytics which combines deep learning and machine learning with natural language processing, is used to develop classification models and calculate sentiment scores. Text can be divided as positive, neutral, or negative, and is often represented with a score to reflect the strength of the sentiment. Social media tracking, customer support control, and comments analysis are examples of popular sentiment analysis applications. Machine learning algorithms can classify unstructured textual content based on emotion and opinion using natural language processing techniques. Sentiment analysis in business allows companies to identify negative or positive views toward their product or service and take the necessary steps to address such concerns. The goal of sentiment analysis is to assess customers or users’ opinion on a target object by analyzing a vast volume of text from many sources. Due to the rapid evolution of sites, interpersonal organizations, web journals, online entries, surveys, sentiments, ideas, evaluations, and criticism, the amount of data on the internet is growing by the day. It is challenging to discover the appropriate sentiment polarity and analyze proper sentiment meaning from large amounts of data. The objective of this paper is to give a quick overview of sentimental analysis and its techniques, as well as a comparison of sentiment analysis research.
Computer Vision-Based Waste Detection and Classification for Garbage Management and Recycling

Waste management systems and their inherent problems are still a matter of great concern amid this cutting edge of science and technology. Pile of untreated waste in open environment aids to a wide range of problems such as air and water pollution, untidy and unhealthy surroundings, wastage of recyclable materials, potential health risk of waste management workers and many more. The root cause of this problem points to a single fact that is too much manual labour involved in the garbage treatment process (collection, separation and recycling)—cannot keep up to the pace with which garbage generation happens. An efficient recycling method is imperative to solve this problem, which can be achieved by a fast, real-time garbage detection and classification system. In this research, we will propose a novel deep learning-based approach for automatic detection and classification of five kinds of waste materials, namely, kitchen waste, glass waste, metal waste, paper waste, plastic waste, from the garbage dump for an efficient recycling process, which not only improves the efficiency of the current manual approach but also provides a scalable solution to the problem. The contributions of this paper include a fully human labelled data set consists of 2200 images of garbage dump with 135,541 annotated objects from aforementioned categories and a real-time garbage object localization and classification framework based on a lightning fast, fairly accurate and end-to-end deep learning algorithm. For the baseline, we have articulated a single-stage object detection and classification framework and initialised the detector training process with pre-trained weights (trained on MS COCO data set) of the feature extractor. Then, with some fine-tuning and employing a few transfer learning tricks, we have proposed a waste object detection framework, that yields a mAP of 66.08% at an IoU threshold of 0.35 with an inference speed of roughly 55–58 ms in a single-GPU environment on both images and videos. It surpasses the performances of all the contemporary frameworks which deal with waste separation task.
Adaptation of Blockchain Technology in the Mobile Banking System in the Context of Bangladesh

The mobile banking system is now very interested in blockchain technology because of its security mechanisms, data availability, increased transaction processing speed, and decentralized a database. Users of mobile financial institutions (MFIs) can use a mobile device to deposit money, withdraw cash, and send and receive money from their accounts. Mobile banking allows users to access account information, payments, deposits, withdrawals, and transfers, as well as investments, ATM assistance, and content services. The distributed ledger technology (DLT), often known as a blockchain, is a tamper-proof digital ledger that can store data and distribute it among connected nodes. Many scholars have contributed to the blockchain technology and consensus mechanism in their earlier studies. The process of integrating consortium blockchain technology and the Proof of Authority (POA) consensus protocol into Bangladesh's mobile banking system is the subject of this research. A model of blockchain architecture with its classification and consensus protocol is also discussed. Moreover, the 51% security level of POA consensus protocol is justified. This initiative will have a huge impact on the financial industry, as well as improve the security of mobile banking.
Snowed autoencoders are efficient snow removers

Snow is a harsh natural phenomenon that greatly affects the performance of advanced computer vision tasks. Recently, most image desnowing methods rely on complex model structures, leading to increased carbon emissions and impossibilities to deploy on lightweight computing devices. Additionally, most deep learning-based methods do not effectively utilize the position information of snow particles, which will limit the performance of the model. To address these issues, considering that snow particles have block-shaped shape distribution and color uniformity similar to a mask, we propose a snowed autoencoder (SAE) desnowing method. Specifically, the SAE is composed of four parts: snowed masking process, SAE encoder, SAE decoder, and prediction stage. First, a snowy image is passed through the snowed masking process to generate a mask that exactly covers the snow particles and output the coordinates of the four vertices of the mask and the index of each segmented image patch. The input image and each mask coordinate and index are passed through the SAE encoder based on the snow particle attention module to generate an image representation for identifying typical features of snow particles. Then, the generated typical features of snow particles are fed into the SAE decoder to reconstruct image patches without snow. Finally, all the patches will be passed through the prediction stage to reconstruct the original size of the snow-free image. A large number of experiments show that the proposed SAE desnowing method achieves the state-of-the-art desnowing performance on three synthetic and one real-world desnowing datasets. The images after desnowing by SAE have better color details and are more consistent with human visual habits. In addition, SAE has faster desnowing speed and fewer parameters. The code is available at https://github.com/awhitewhale/SAE.
RGBT tracking based on prior least absolute shrinkage and selection operator and quality aware fusion of deep and handcrafted features

Object tracking has always been one of the most challenging topics in machine vision. In recent years, trackers have used RGB-T datasets to overcome the limitations of single-modality data. This paper introduces a Prior Least Absolute Shrinkage and Selection Operator (PLASSO)-based cost function optimized by the Alternating Direction Method of Multipliers (ADMM) for a Discriminative Correlation Filter (DCF)-based tracker. Detailed closed-form solutions for these optimization problems are also given in this paper. The proposed PLASSO-based approach adaptively determines the scale, parameters, and features of the PLASSO-ADSPF tracker. PLASSO- ADSPF first finds the response map in the Fourier domain for the input images and then uses an efficient adaptive function to fuse the results. Based on the response maps, the proposed tracker adaptively and consciously extracts features (handcraft and Deep Features (DF)) to improve the speed of the tracking algorithm. PLASSO-ADSPF extracts DF from the convolution layer output of a modified pre-trained deep network via SoftMax Tsallis Entropy (TsEn) as a proposed cost function in the last layer. Also, a graph is proposed to determine longitudinal and transverse scales independently and accurately. Moreover, a new bounding box regression is proposed based on PLASSO and Cholesky factorization to refine the target box. Extensive experiments have been performed for the PLASSO-ADSPF tracker on five popular datasets. The results show the superiority of the proposed approach compared to the state-of-the-art trackers. Our code is available at PLASSO-ADSPF-tracker.
Transforming Numerical Feature Models into Propositional Formulas and the Universal Variability Language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling. Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.
A novel virtual sample generation method to improve the quality of data and the accuracy of data-driven models

Small data volume and data imbalance often lead to statistical failure and seriously restrict the accuracy of data-driven models, which has become a bottleneck problem, needing to be solved, in small sample modeling. The data expansion method has become the main way to solve small sample modeling. However, the randomness in the process of virtual sample generation and combination leads to many invalid data, resulting in poor consistency between the expanded data and the original data. For this reason, this paper proposes a virtual sample generation method based on acceptable area and joint probability distribution sampling (APS-VSG) to limit the randomness in the data expansion method, reduce the proportion of invalid data, improve data consistency after expansion, and improve the accuracy of the data-driven model under the condition of small samples. Firstly, the concept of “compact range of interaction (CRI)” was proposed, which further limits the domain estimation range of data to approximate the valid area of the data. Secondly, the prior knowledge was used to improve mega-trend-diffusion (MTD), and the CRI is delineated according to the trend dispersion to obtain the acceptable area of the virtual data. Finally, a joint probability distribution was constructed based on the true values of small samples in the acceptable area, and data sampling was conducted based on the probability distribution to generate virtual data. The experimental results of standard function datasets show that the virtual samples generated by the proposed method can ensure validity of more than 85%. The experimental results of the NASA li-ion battery dataset show that, compared with Interpolation, Noise, MD-MTD, GAN, and GMM-VSG methods, the error of the data-driven model trained with virtual data generated by the proposed method is significantly reduced. Compared with GAN and GMM-VSG, MSE, RMSE, MAE, and MAPE are reduced by at least 19.3%, 10.6%, 15.4%, and 16.7%, respectively.