Social Engineering Threats (SETs) exploit human vulnerabilities in information system security. Due to its ability to bypass technical security, these threats have become a common concern today. However, Social engine...
详细信息
Context: Chatbots are intelligent agents that mimic human behavior to carry on meaningful conversations. The conversational nature of chatbots poses challenges to designers since their development is different from ot...
详细信息
ISBN:
(纸本)9798400712241
Context: Chatbots are intelligent agents that mimic human behavior to carry on meaningful conversations. The conversational nature of chatbots poses challenges to designers since their development is different from other software and requires investigating new practices in the context of human-AI interaction and their impact on user experience. Since human dialogue involves several variables beyond verbalizing words, it is vital to design well-thought dialogues for chatbots to provide a humanized and optimal interaction. Objective: The main objective of this work is to unveil textual, visual, or interactive design practices from text-based chatbot interactions and how they can potentiate or weaken some perceptions and feelings of users, such as satisfaction, engagement, and trust, for the creation of the Guidelines for Chatbot Conversational Design (GCCD) guide. Method: We used multiple research methods to generate and validate the guide. First, we conducted a Systematic Literature Review (SRL) to identify conversational design practices and their impacts. These practices were inserted into the GCCD guide through qualitative analysis and coding of SLR results. Then, the guide was validated quantitatively through a survey and qualitatively through a case study. The survey aimed to assess the guide’s clarity and usefulness based on the reading of the guide by the participants and their responses to a questionnaire adapted from the Technology Acceptance Model. The case study aimed to assess the guide’s usefulness based on its practical application by participants in a situation that simulates a real scenario and follow-up interviews. Results: The survey showed that software developers with different levels of experience strongly agreed that the guide could induce greater user satisfaction and engagement. Furthermore, they also strongly agreed that the guide is clear, understandable, flexible, and easy to use. Although participants suggested some improvements, they repo
The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering tech...
详细信息
The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones;it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, which is used to cluster genes. FCM allows an object to belong to two or more clusters with a membership grade between zero and one and the sum of belonging to all clusters of each gene is equal to one. This paradigm is useful when dealing with microarray data. The total time required to implement the first model is 22.2589 s. The second model combines FCM and particle swarm optimization (PSO) to obtain better results. The hybrid algorithm, i.e., FCM– PSO, uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–PSO method is effective. The total time of implementation of this model is 89.6087 s. The third model combines FCM with a genetic algorithm (GA) to obtain better results. This hybrid algorithm also uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–GA method is effective. Its total time of implementation is 50.8021 s. In addition, this study uses cluster validity indexes to determine the best partitioning for the underlying data. Internal validity indexes include the Jaccard, Davies Bouldin, Dunn, Xie–Beni, and silhouette. Meanwhile, external validity indexes include Minkowski, adjusted Rand, and percentage of correctly categorized pairings. Exp
Spatial databases store objects with their locations and certain types of attached items.A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects,suc...
详细信息
Spatial databases store objects with their locations and certain types of attached items.A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects,such as searching points of interest,hot topics,or users’attitude in specified spatial *** many scenarios,the high and low-frequency items in a spatial region are worth noticing,considering they represent the majority’s interest or eccentric users’***,existing works have yet to identify such items in an interactive manner,despite the significance of the endeavor in decision-making *** study recognizes a novel type of analytical query,called top/bottom-k fraction query,to discover such items in spatial *** achieve fast query response,we propose a multilayered data summary that is spread out across the main memory and external memory.A memory-based estimation method for top/bottom-k fraction queries is *** maximize the use of the main memory space,we design a data summary tuning method to dynamically allocate memory space among different spatial *** proposed approach is evaluated with real-life datasets and synthetic datasets in terms of estimation *** results demonstrate the effectiveness of the proposed data summary and corresponding estimation and tuning algorithms.
The ensemble is a technique that strategically combines basic models to achieve better accuracy ***,combination methods,and selection topology are the main factors determining ensemble ***,it is a challenging task to ...
详细信息
The ensemble is a technique that strategically combines basic models to achieve better accuracy ***,combination methods,and selection topology are the main factors determining ensemble ***,it is a challenging task to design an efficient ensemble *** though numerous paradigms have been proposed to classify ensemble schemes,there is still much room for *** paper proposes a general framework for creating ensembles in the context of ***,the ensemble framework consists of four stages:objectives,data preparing,model training,and model *** is comprehensive to design diverse *** proposed ensemble approach can be used for a wide variety of machine learning *** validate our approach on real-world *** experimental results show the efficiency of the proposed approach.
The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system comb...
详细信息
This study employs data mining techniques and clustering algorithms to analyze life expectancy factors and discover influential elements. Using World Health Organization data, including life expectancy figures for var...
详细信息
Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding t...
详细信息
ISBN:
(数字)9798350364910
ISBN:
(纸本)9798350364927
Managing data has changed significantly because of cloud computing, which offers scalabe, flexible and reasonably priced solutions to enterprises and to people as well such as Amazon, Google, and Microsoft expanding their infrastructure to meet the demand from the customer. With such advantages there will also be disadvantages such as security problems. This paper will discuss about security problems such as encryption, access control, multi-factor authentication, regular audits, secure configurations, incident response planning. This paper will also talk about security measures to deal with threats such as malicious insiders, data abuse, unsecured interfaces and APIs, shared technology complications, data loss or leakage, hijacking, and enigmatic risk profiles. This study examines and evaluates a range of cloud computing security concerns and issues through a systematic analysis of the literature. The goal of the research is to raise public awareness of the need for cloud computing security and to offer possible remedies. Identify and classify network security threats and vulnerabilities in cloud computing by doing an extensive examination of the body of research and empirical investigations is one of the answer to the questions from the paper. In the end, this take a look at unearths vulnerabilities and their practical implications through very well reading network safety in cloud computing.
This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly bo...
详细信息
Cloud computing, renowned for its on-demand resource provisioning and reliability, offers virtualized and distributed resources. Despite its benefits, the challenge of load balancing arises due to the large-scale data...
详细信息
暂无评论