Reviewerly/exHarmony huggingface dataset:
How Consumers Process Online Reviews for Purchase Decision: A Grounded Theory Approach: An Abstract

For marketers, online consumer reviews are a double-edged weapon that customers rely on to make purchasing decisions. The past OCRs literature suggested that consumers frequently depend on OCRs to make online purchase decisions (Gottschalk & Mafael, 2017) and profoundly affect a firm’s success (Garnefeld et al., 2021). The past OCRs researchers documented OCRs research in several ways. However, a scant the literature remains on how consumers process online reviews. This study attempts to fill this gap, how consumers process online reviews for purchase decisions and relevant review evaluation touchpoints they consider at the hour of purchase. In order to explore the research question, an in-depth qualitative investigation was conducted, and a grounded theory approach was applied. Diversity is critical in GT, and we identified the participants from LinkedIn India with different demographic profiles. A detailed interview protocol was developed to carry out the interview, and the medium was English. This study saturated in 24th interview and continued upto 26th to ensure the saturation point in GT. Each interview lasted approximately 30-40 minutes, and 158 pages of the transcribed document were generated in total. The interviews were transcribed and analysed using the open, axial, and selective coding strategies. This exploratory research contributes to the existing literature by highlighting a workable model explaining how consumers process online reviews for purchase decisions concerned with different review evaluation methods, touchpoints, and sub-components. The review process model is divided into three; precedence (critical conditions influencing the selection of review evaluation methods), review processors (review evaluation touchpoints and sub-components) and action/outcome (purchase or purchase abandonment). Theoretically, this study contributes novel literature by providing a workable model, and tentative hypotheses lend for further refining and empirical testing. This qualitative research clarifies the relevant review evaluation touchpoints that an online consumer considers whilst making an optimal online buying decision by addressing the void in the past OCRs literature. The findings of this study provide insights to managers to understand how consumers process OCRs according to product involvement, product category, brand familiarity (precedence), and how methods, touchpoints and sub-components change according to that, further leading to action/outcome.
FED6G: Chameleon Learning for Network Slice Management in Beyond 5G Systems

A cornerstone of wireless connectivity involves trust and privacy in the data shared between users and network elements as wireless connectivity becomes an integrated, fundamental element of society. With a large influx of data in Beyond 5G (B5G) systems from end-users and network elements, it is imperative to understand how data is collected and used for real-time data processing operations. The current wireless network learning involves centralizing the training data, which is inefficient as it continuously requires end devices to send their collected data to a central server. Federated Learning (FL) effectively allows end devices to train ground-truth data on-device, and only model update parameters are sent back to the federated server. This work proposes a Chameleon FL model, FED6G, for network slicing in 5G and Beyond systems to solve complex resource optimization problems without collecting sensitive, confidential information from end devices. The evaluation results reflect more than 39% improvement in Mean Squared Error (MSE), 46% better model accuracy, and more than 23% reduced energy cost for training the proposed FED6G against the traditional deep learning neural network model.
Analysis of Students' Satisfaction Level on iLearn Quality during COVID-19 Pandemic with WebQual 4.0 and PLS-SEM

Andalas University implements a Learning Management System (LMS) named iLearn as an online learning facility to carry out the learning process during the COVID-19 pandemic. This research analyzes the influence of iLearn quality on students' satisfaction levels. iLearn quality is measured by WebQual 4.0 instrument, which consists of three variables, i.e. Usability, Information Quality, and Service Interaction Quality. To analyze the relationship between the WebQual 4.0 variables and students' satisfaction, we used Partial Least Squares - Structural Equation Modeling (PLS-SEM) method. The research sample is 100 students of the Mathematics Department of Andalas University who were enrolled in iLearn. Based on data analysis, the structural equation model for the students' satisfaction level is obtained. From the model, the variables that significantly affect the Student Satisfaction Level are Usability and Service Interaction Quality, with a P-value of 0.001. In contrast, the Information Quality has a low significance in influencing students' satisfaction levels in iLearn quality with a P-value of 0.420. Improvements on iLearn quality can be made by reviewing these measured indicators.
Digital research environments: a requirements analysis

Physical science depends on historical records as well as new ideas, results, and opportunities.
An Efficient Source Printer Identification Model using Convolution Neural Network (SPI-CNN)

Document forgery detection is becoming increasingly important in the current era, as forgery techniques are available to even inexperienced users.Source printer identification is a method for identifying the source printer and classifying the questioned document into one of the printer classes.According to what we know, most earlier studies segmented documents into characters, words, and patches or cropped them to obtain large datasets.In contrast, in this paper, we worked with the document as a whole and a small dataset.This paper uses three techniques dependent on CNN to find the document source printer without segmenting the document into characters, words, or patches and with small datasets.Three separate datasets of 1185, 1200, and 2385 documents are used to estimate the performance of the suggested techniques.In the first technique, 13 pre-trained CNN were tested, and they were only used for feature extraction, while SVM was used for classification.In the second technique, a pre-trained neural network is retrained using transfer learning for feature extraction and classification.In the third technique, CNN is trained from scratch and then used for feature extraction and SVM for classification.Many experiments are done in the three techniques, showing that the third technique gives the best result.This technique achieved 99.16%, 99.58%, and 98.3% accuracy for datasets 1, 2, and 3.The three techniques are compared with some previously published papers, and found that the third technique gives better results.
Eye Contact as a New Modality for Man-machine Interface

In daily life, people use many appliances, where different machines and tools should be operated with their specialized interfaces.These specialized interfaces are often not intuitive and thus require considerable time and effort to master.On the other hand, human communications are rich in modalities and mostly intuitive.One of them is eye contact.This study proposes eye contact for enriching modalities for humanmachine interface.The proposed interface modality, based on a neural network for object detection, allows humans to initiate machine operations by looking at them.In this paper, the hardware framework for building this interface is elaborated and the results of usability assessment through users' experiments are reported.
An Automated Framework to Detect Emotions from Contextual Corpus

The emotion extraction or opinion mining is one of the key tasks for any text processing frameworks.In recent times, the use of opinion mining has gained a lot of potential due to the application of the potential customized aspects of the consumer relations and other customized applications.However, the application of sentiment analysis or opinion mining is highly challenging as the accuracy of the sentiment analysis depends on the input text corpus.The input text corpus can be highly fluctuating due to the inclusion of emojis or local language influences and finally the use of a wide variety of the regional languages.A good number of parallel research outcomes have aimed to solve these challenges in the recent time.However, most of the parallel research outcomes have primarily three challenges kept unsolved as firstly, the emojis in the text corpus is mainly removed but not translated into sentiment scores, secondly, the translation of the texts from various regional languages and the translation is mainly true translations rather than the contextual translation.Finally, the use of the dictionaries in the actual translation tasks takes a lot of time to process and must be reduced.Henceforth, in order to solve these challenges, this work proposed a framework to automate the weighted emoji-based sentiment analysis, Unicode based translation process to reduce the time complexity and finally use the collaborative sentiment analysis scores to build the final sentiment models.This work results into nearly 97% accuracy and nearly 50% reduction in the time complexity.
Mammography Image Abnormalities Detection and Classification by Deep Learning with Extreme Learner

Breast cancer has emerged as a leading killer of women worldwide in recent decades.Mammography is a useful tool for detecting abnormalities and doing screenings.The primary factors in the early identification of breast cancer are the quality of mammogram image and the radiologist's appraisal of the mammography.The extensive use of deep learning (DL) as well as other image-processing technologies in recent times has tremendously aided in the categorization of breast cancer images.Image processing and classification methods may help us find breast cancer earlier, increasing the likelihood of a positive outcome from therapy and the likelihood of survival.employ picture segmentation methods on the datasets to draw attention to the area of interest, and then classify the findings as malignant or benign.In an effort to minimize the mortality rate from breast cancer among females, this research seeks to discover novel approaches to illness classification and detection, as well as new strategies for preventing the disease.In order to correctly categorize the results, the best possible feature optimization is carried out utilizing deep learning technology.The Proposed deep CNN (Convolutional Neural Network) is improved using two classification models such as SVM (Support Vector Machine) and ELM (Extreme Learning Machine).In the proposed deep learning model, the feature extraction with AlexNet is accomplished using deep CNN.Subsequently, different parameters are fine-tuned to enhance accuracy with various optimizers and learning rates.
Incremental Diversity: An Efficient Anonymization Technique for PPDP of Multiple Sensitive Attributes

Data collected at the organizations such as schools, offices, healthcare centers and e-commerce websites contain multiple sensitive attributes.The sensitive information from these organisations such as marks obtained, salary, disease, treatment and traveling history are personal information that an individual dislikes to disclose to the public as it may lead to privacy threats.Therefore, it is necessary to preserve privacy of the data before publishing.Privacy Preserving Data Publishing(PPDP) algorithms aim to publish the data without compromising the privacy of individuals.In the recent years several algorithms have been designed for PPDP multiple sensitive attributes.The major limitations are, firstly among several sensitive attributes these algorithms consider one of them as primary sensitive attribute and anonymize the data, however there may be other dominant sensitive attributes that need to be preserved.Secondly, there is no consistent way to categorize multiple sensitive attributes.Lastly, increased proportion of records are generated due to usage of generalization and suppression techniques.Hence, to overcome these limitations the current work proposes an efficient approach to categorize the sensitive attributes based their semantics and anonymize the data using an anatomy technique.This reduces the residual records as well as categorizes the attributes.The results are compared with popular techniques like Simple Distribution of Sensitive Values (SDSV) and (l, e) diversity.Experiments prove that our method outperforms the existing methods in terms of categorization of multiple sensitive attributes, reducing the percentage of residual records and preventing the existing privacy threats.
Automatic Detection of Software Defects based on Machine Learning

Defects in software are one of the critical problems in software engineering community because they provide inaccurate results and negatively affect the quality and reliability of the software.These defects must be detected in the early stages of software development.Researchers had used Software Defect Detection (SDD) techniques to allow predicting module faultproneness.By implementing the hyperparameter optimization techniques and exploiting data imbalances in predicting defects, this paper proposes and develops an SDD model with high performance and generalization capability.To classify defects in software modules, machine learning algorithms and ensemble learning techniques are used on the balanced datasets.The balanced datasets are obtained through using a hybrid of synthetic minority oversample (SMOTE) and Support Vector Machine (SVM).To obtain the optimal hyperparameters needed for the used classifiers and for the dataset balanced algorithms, Non-dominated Sorting Genetic Algorithm II (NDSGA-II) is used.To reduce the time and save other used resources, Hyperband technique, which is a multi-fidelity optimization, is used in NDSGA-II.A 10-fold Cross Validation (CV) is applied to overcome the overfitting and underfitting problems.The accuracy, recall, F-measure, and ROC AUC metrics are used to evaluate the SDD model.The results show that the proposed model predicts defects more accurately than the compared studies.
Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services.Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements.Recent research has focused on combining unique and looselycoupled services into a preferred system.An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS).In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously.Service composition fulfils a variety of user needs in a variety of scenarios.The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds.Service composition combines atomic services from multiple clouds into a single service.Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task.This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints.Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.
Online Signature Verification for Forgery Detection

The increasing trend of using e-versions of document transmission and storage requires the electronic verification of sender/author.This research presents an efficient and robust online handwritten signature verification system targeting verification rates better than the available state-of-theart systems in the presence of skilled forgeries.Fourier analysis is employed on the signatures to represent feature vectors in higher dimensional space followed by Local Fisher Discriminant Analysis to obtain compress representation while enhancing inter-class scatter between signature patterns.Signature modeling is performed using m-mediod-based modeling approach where m-mediods are put on to represent data distribution in each class.Connected component labeling is applied to binarized images of Urdu text to extract ligatures which are separated into primary ligatures and diacritics.Fast Euclidean Distance is used as dis(similarity).A total of 2414 signature samples including skilled forgeries are considered in our study.The evaluation of the proposed system on Japanese signature dataset provided by SigWiComp2013 realized promising results than the competitors.
Efficient Handwritten Signatures Identification using Machine Learning

Any agreement or contract between two or more parties requires at least one party to employ a signature as evidence of the other parties' identities and as a means of establishing the parties' intent.As a result, more people are curious about Signature Recognition than other biometric methods like fingerprint scanning.Utilizing both Fourier Descriptors and histogram of oriented gradients (HOG) features, this paper presents an efficient algorithms for signature recognition.The use of Local binary patterns (LBP) features in a signature verification technique has been proposed.Using morphological techniques, the signature is encapsulated within a curve that is both symmetrical and a good match.Measured by the frequency with which incorrect patterns are confirmed by a given system, false acceptance rate (FAR) provides an indication of the effectiveness and precision of the proposed system.Using a local dataset of 60 test signature patterns, this investigation found that 10% were incorrectly accepted for FAR of 0.169.Experiments are conducted on signature photos from a local dataset.Verification of signatures has previously made use of KNN classifier.KNN classifier produced higher FARs and recognition accuracies than prior techniques.
A Robust Steganographic Algorithm based on Linear Fractional Transformation and Chaotic Maps

The fundamental objectives of a steganographic technique are to achieve both robustness and high-capacity for the hidden information.This paper proposes a steganographic algorithm that satisfies both of these objectives, based on enhanced chaotic maps.The algorithm consists of two phases.In the first phase, a cryptographic substitution box is constructed using a novel fusion technique based on logistic and sine maps.This technique overcomes existing vulnerabilities of chaotic maps, such as frail chaos, finite precision effects, dynamical degradation, and limited control parameters.In the second phase, a frequency-domain-based embedding scheme is used to transform the secret information into ciphertext by employing the substitution boxes.The statistical strength of the algorithm is assessed through several tests, including measures of homogeneity, correlation, mean squared error, information entropy, contrast, peak signal-to-noise ratio, energy, as well as evaluations of the algorithm's performance under JPEG compression and image degradation.The results of these tests demonstrate the algorithm's robustness against various attacks and provide evidence of its high-capacity for securely embedding secret information with good visual quality.
Digital Signature Algorithm: A Hybrid Approach

Security is one of the most important issues in layout of a Digital System.Communication these days is digital.Consequently, utmost care must be taken to secure the information.This paper specializes in techniques used to defend the facts from thefts and hacks the use of quit-to-cease encryption and decryption.Cryptography is the important thing technique related to Encrypting and Decrypting messages.We use Digital Signature preferred (DSS) and the Digital Signature Algorithm (DSA).The code for this algorithm is written in MATLAB.The DSA Algorithm is commonly used in cryptographic applications to provide services such as entity authentication, key transit, and key agreement in an authenticated environment.This structure is related with steady Hash Function and cryptographic set of rules the government groups in USA as it is taken into consideration to be one of the safest approaches of protection system.This fashion-able could have a top notch effect on all of the Government Agencies and Banks for protective the facts.
Auto JSON: An Automatic Transformation Model for Converting Relational Database to Non-relational Documents

In recent days, the demand for dealing large set of distributed data obsoletes the relational database and its structured query language (SQL) solutions in practice and paves the way for novel solutions in the name of non-relational database as not-only SQL (NoSQL).The NoSQL offers dynamic, flexible, scalable, highly available, greater performance and near real-time access to the distributed nature of voluminous data used for current industrial applications.Apart from these giant features of NoSQL, the SQL is still found to be in operation because of its popularity and standard.This paper projected an algorithm to convert the relational documents of MySQL into any document oriented NoSQL databases automatically without destructing the existing relational database setup and installing the NoSQL from scratch in the core machines.Java Script Object Notation (JSON) is a human readable data interchange format, being used in web development.The characteristics of JSON widened its use cases from web development to database storage.The Mongo DB, one of the most popular document oriented NoSQL adapts JSON format for its storage.The proposed algorithm is built based on its schema definition and the performance is captured through evaluating it against a sample database from hospital management system.The findings are discussed with great interest of addressing the challenges and revealing the scope for improvement.
A Comparative Study of Twofish, Blowfish, and Advanced Encryption Standard for Secured Data Transmission

Now-a-days, network security is becoming an increasingly significant and demanding research area of interest.Threats and attacks on information and Internet security are getting increasingly difficult to detect.As a result, encryption has emerged as a solution and now plays a critical role in information security systems.Many techniques are required to safeguard shared data.In this work, the encryption, decryption times, and throughput (speed) of the three most commonly used block cipher algorithms: Twofish, Blowfish, and AES were investigated using different file types.Comparison of symmetric encryption techniques of experiments on these types of algorithms uses a lot of computer resources including CPU time, memory, and battery power.Previous research has yielded diverse results in terms of time complexity, speed, space complexity, power consumption, and security.However, this research evaluated the effectiveness of each algorithm based on the following parameters: process time and speed.An application was developed for data simulation to test different file formats and for the encryption process and speed using Python 3.10.