Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ordinal Approach to Modeling and Visualizing Phishing Susceptibility

Phishing is a significant, ongoing cybersecurity threat facing both individuals and organizations, and the consequences of falling victim to phishing can be dire, particularly in terms of financial loss. While much research has focused on understanding or predicting user susceptibility to phishing with the aim of preventing it, little of this research has focused on modeling the process of being phished holistically. Specifically, while the outcome of interacting with a phishing website may be thought of as binary (e.g., "Were you phished or notƒ"), the actual process typically involves a sequence of stages spanning from the choice to visit (or not visit) a link all the way to transacting with a website and giving away valuable personal or financial information. To better understand the variables that influence phishing website traversal, we conducted a controlled lab experiment with a large sample of 908 participants. In this experiment, each participant was given a task such as opening an online checking account and presented with a series of simulated search results that included both legitimate and phishing websites. By monitoring participants’ interactions with these websites and collecting additional information via surveys, we evaluated which variables were the most likely to result in behavior that could lead to greater phishing exposure using a multi-model comparison approach. The results of our analyses shed light on the key variables that can lead to a greater propensity for being phished and may prove invaluable to researchers interested in designing new interventions.
User-Centered Design of e-Learning User Interfaces: A Survey of the Practices

Designing an effective user interface (UI) of e-Learning system that has excellent usability is a crucial activity in e-Learning development. Nowadays, user-centered design (UCD) is a popular method used for developing the user interfaces of e-Learning systems and modules. This systematic literature review offers a helicopter view of the state-of-the-art applications of UCD for designing the UI of e-Learning systems. This study reviewed 17 works of literature from 2015 until early 2020 to identify how research in the development of e-Learning system applied UCD and its impacts on the usability of the developed systems. This study found that many e-Learning system development projects applied UCD differently in terms of methods and user involvement. The most popular methods integrated with the UCD are questionnaires, interviews, high-fidelity prototyping, and usability testing. The study also found that some of the projects that used UCD with involving the users in multiple phases produced a design with good usability. Furthermore, this study suggests that both learners and subject matter experts should be involved in the design process of e-Learning system to acquire both excellent UI and effective learning process.
A SOIL CLASSIFICATION SYSTEM FOR DESCRIBING WATER AND CHEMICAL TRANSPORT

The authors present an initial version of a soil classification system based on selected soil properties believed to be useful in describing water and chemical transport through soils. The system is based primarily on surface texture, clay mineralogy, and nature of soil structure. We now have developed this system for the more extensive soil series in South Carolina, and these soils have been grouped into eight different classes. While variability of some soil properties within a given class certainly exists, a class is distinct from each of the other seven classes in water and chemical transport characteristics. A classification system such as this can help to identify those soil properties that most affect transport processes and will help to provide the framework for development of mathematical transport models that incorporate these same soil properties.
Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

It has become a mainstream to realize the accurate classification of diseases by using deep learning. However, due to the opaque learning and diagnosis mechanism of these models, it is difficult for doctors to believe their diagnosis results and obtain more useful information from these models. According to the brain structural Magnetic Resonance Imaging (sMRI) data of autistic patients, this article proposes an Autism Spectrum Disorders (ASD) classification model based on Recurrent Attention Model (RAM). On this basis, a detailed data analysis is made according to the focus area of the model. To solve the poor convergence of the Policy Gradient (PG) algorithm used in traditional RAM, a Deep Deterministic Policy Gradient Recurrent Attention Model (DDPG-RAM) model and a priority experience replay algorithm based on Gaussian sampling are proposed. The internal information such as Time Difference (TD) error and reward value of training samples is fully utilized. Experimental results show that the algorithm improves the classification accuracy, convergence and stability. Finally, according to the attention block and attention center from the output results of the model, the subcortical tissue of autism group and control group were analyzed. Attention block mainly concentrated in the right thalamus and right caudate nucleus, while the attention center mainly focused on the right white matter and right cerebral cortex. There were significant differences in volume and voxel values of the above regions between the autistic group and the control group. Most of these regions are related to behavioral decision-making, self-learning, communication and some cognitive functions, which provides the possibility that brain structural abnormalities lead to behavioral abnormalities in autistic patients. The model proposed in this article can provide a new method for doctors to find biomarkers.
Analysis of Continuance Use of Video On Demand Applications by Using The Hedonic Motivation System Adoption Model

Hedonic-motivation systems (HMS) are systems used mainly to fulfill users' intrinsic motivations, such as online shopping, education/learning, online gaming, social networking, and general gamification. The hedonic-motivation system adoption model (HMSAM) is a native information systems theory to improve the understanding of HMS adoption. This study evaluated a continuance intention used of Video on Demand (VoD) ─ an application of HMS - using an extended HMSAM model. Using the quantitative approach, the valid data of 165 respondents that obtained from distributed questionnaire to the target sample, it was analyzed using SmartPLS 3.2. 8 as a tool for the PLS-SEM method. The results indicated that VoD applications have sufficient empirical evidence for increasing intrinsic user motivation. Furthermore, based on PLS-SEM reliability and hypothesis analysis, the results showed that Joy's variable has a high value with (0.109) and β value of (0.329). The benefits obtained from this research include the application of VoD developers to improve information systems based on control variables and curiosity in VoD applications.
Types and configurations of domain walls in ferroelectric Bi4Ti3O12 single crystals

In the ferroelectric Bi4Ti3O12, the major component Ps(a) of spontaneous polarization Ps lies along the a axis; the component Ps(c) along the c axis is small. The two switchable components are expected to make up various types of domain walls (DWs). According to group-theory analysis, 11 permissible types of DWs are predicted to exist theoretically, and they include five types of ferroelectric DWs, one type of anti-phase boundary (APB) and five types of APB-combined ferroelectric DWs. The five types of ferroelectric DWs are Ps(a)-90° DWs, Ps(a)-180° DWs, Ps(c)-180° DWs, Ps(a)-90°/Ps(c)-180° DWs and Ps-180° DWs. In Bi4Ti3O12 single crystals, just the five types of ferroelectric DWs were observed using transmission electron microscopy, with no trace of APBs or APB-combined ferroelectric DWs seen. The Ps(a)-90° domains are lamellar and do not have even thickness. Both the Ps(a)-90° DWs and Ps(a)-90°/Ps(c)-180° DWs lie mainly on the (110) plane, but often fold to the (001) plane. The Ps(a)-180° domains are predominantly flaky. Both the Ps(a)-180° DWs and Ps-180° DWs lie mainly on the (001) plane. The Ps(c)-180° DWs observed are irregularly curved.
Wearable motion sensors and artificial neural network for the estimation of vertical ground reaction forces in running

Biomechanical load assessments are becoming increasingly important in the sporting community; however, there are still numerous difficulties in monitoring them in a field environment outside of specialized biomechanical monitoring laboratories. Inertial Measurements Units (IMUs) have been showing promising results in the modeling of biomechanical variables. This study explores the application of an artificial neural network (ANN) in the estimation of runners’ vertical ground reaction forces (GRFs) based on the accelerometry collected from two wearable motion sensors developed in-house and attached on the shanks. Data collected from fourteen runners running at three different speeds (8, 10, 12 km/h) were used to train and validate the ANN. Predictions were compared against gold-standard measurements from a pair of pressure in-soles. Root mean square error (RMSE) was used to evaluate the performance of the models. Further investigations, e.g., the use of principal components analysis (PCA) and the impact on the estimation of several GRF-related variables, were carried out to provide useful insights regarding the portability of the model to low-power resource-constrained devices. Findings indicate that ANNs in conjunction with accelerometry may be used to compute vertical ground reaction forces (RMSE: 0.148 BW) and related loading metrics in running accurately.
Speech Emotion Recognition by Combining a Unified First-Order Attention Network With Data Balance

In the domain of speech emotion recognition (SER), generally there is an unbalanced data distribution of emotional samples in existing emotional datasets. Moreover, different fragment areas in an utterance contribute diversely to SER. To address these two issues, this paper proposes a new SER method by combining a unified first-order attention network with data balance. The proposed method firstly utilizes the strategy of data balance to augment and balance the training data. Then, a pre-trained convolutional neural network (CNN) model (i.e., VGGish) is fine-tuned on target emotional datasets to learn segment-level speech features from the extracted Log Mel-spectrograms. Next, the unified first-order attention mechanism, including different feature-pooling strategies such as sum, min, max, mean, and standard deviation (std), is embedded into the output of a bi-directional long short-term memory (Bi-LSTM) network. This is used for learning high-level discriminative segment-level features, and simultaneously aggregating the learned segment-level features into fixed-length utterance-level features for SER. Finally, based on utterance-level features, the softmax layer in a Bi-LSTM network is adopted to conduct final emotion classification task. Extensive experiments are implemented on three public datasets such as BAUM-1s, AFEW5.0, and CHEAVD2.0, demonstrate the advantage of the proposed method.
Enhancement of Omni-Directional EMAT Signal Using a Optimized Magnetic Circuit Design

Electromagnetic ultrasonic transducers (EMATs) are crucial for the effective excitation of ultrasonic signals. However, the transducers suffer from low signal-to-noise ratio. To improve this situation, this work presents a new configuration of omni-directional magnetic-circuit-optimized EMATs. The new optimized design increases the static magnetic field strength and therefore helps to enhance the generation of Lamb wave. The coupling model applied to characterize and analyze the new design indicates that the optimized magnetic field is generated with an increasing intensity compared to the traditional magnetic configuration of EMAT. Furthermore, the simulation verification is performed and the results show that the signal of magnetic-circuit-optimized EMATs design is dramatically improved.
Structuring Distributed Control Applications for Adaptability

In modern industrial applications, the complexity of control software is increasing while its execution may be distributed to numerous small devices. The standard IEC 61499 fosters designing distributed control applications by defining a platform-independent application model that can be mapped to numerous hardware components. Applications though need to be designed for distribution, as comparing three implementation designs of a running example indicated. We propose the usage of a distributed architectural pattern for applications with parallel paths. Metrics confirm a reduced complexity compared to purely hierarchical designs.
Color Degradation Kinetics of Carrot (Daucus carota L.) Slices during Hot Air Drying

The effect of temperature on color change kinetics of carrot slices was investigated during hot air drying. The color parameters for the color change of the foods were quantified by Hunter L (whiteness/darkness), a (redness/greenness) and b (yellowness/blueness) values. These values were also used for calculation of the total color change (ΔE). The color values (L, a and b) decreased, while ΔE increased during drying. Zero- and first-order kinetic models were applied to describe color change. Mathematical modeling color change kinetics indicated that both models were found to describe the L, a and b values adequately. However, ΔE followed zero-order kinetics. PRACTICAL APPLICATIONS In this study, a hot air dryer was used. The effect of air temperatures (45, 55 and 65C) on color change kinetics of carrot slices was investigated. The color of carrot slices was measured during hot air drying. Zero- and first-order kinetic models were applied color change. The first-order kinetic model adequately described evolution of L, a and b values derived for carrot slices during drying. Dried carrots are used as an ingredient in instant soups or meals and an excellent candidate for developing oil-free, healthy snack food if the nutritional value can be well preserved.
Artificial Neural Network-Based Method for Identifying Under-Inflated Tire in Indirect TPMS

Tire pressure monitoring is essential to driving safety. Indirect tire pressure monitoring system (TPMS) is a cost-effective alternative to direct tire pressure monitoring system. Its performance depends on the algorithm for data pre-processing and analysis which is normally complicated, sensitive to initial calibration with limited working range. In this work, four tests were carried out with Baojun 530 with a different deflated tire in each case. Speed data read through ABS CAN bus was analyzed and traditional frequency based method was employed to identify the deflated tire. To simplify the data pre-processing and improve response speed and working range, a new artificial neural network (ANN) based method was also proposed to identify deflated tire based on speed data point collected through antilock brake system (ABS) sensors in tests. A long short-term memory (LSTM) network was developed to locate the deflated tire with an accuracy of 0.83 after training for individual data points. And performance of this method can be further improved by employing a soft voting mechanism with 3 LSTM networks. In proposed ANN based method, benchmark data from properly inflated tire is not required, which makes it a promising solution for multiple deflated tires cases which is challenging for traditional frequency-based method.
Relationships among behavior, climate and milk production in a dairy cattle farm in Northern Italy

Knowledge on the effect of behavior and welfare on dairy cattle’s health and production has been increasing, highlighting the need of satisfactory animals’ living conditions. Among others, the lying time represents a signal for health and welfare status as well as for milk production. In this study, a one-year test on primiparous cows on a dairy cattle farm in Northern Italy was carried out in order to evaluate the effect of lying time on other variables and vice versa. Accelerometers and environmental sensors were installed on animals and in the barn. Weather data were collected from a data station on farm. Statistical analyses were carried out to identify the effect among the monitored variables, showing interesting results on behavior and production of dairy cattle. In particular, the behavior in the first weeks of the lactation period affects the behavior and milk production during the whole lactation.
Learning Computer Network Through WebQuest

This study describes students’ perceptions of the WebQuest for computer network course because it can be translated into class success. A poll was managed to gather understudies’ viewpoints on this movement. In the WebQuest, a detailed description of procedures for completing the project and useful links to web resources, as well as an evaluation rubric, they were a scaffolding aid for students. Project tasks that have been completed namely designing and planning computer networks in schools with the determination of minimum specifications. The results indicated positive responses from students to the WebQuest activity in completing the project.
Measuring the Effects of Summarization in Cluster-based Information Retrieval

Summarization is an integral part of modern Internet. In social networks, which have become primary information sources, users have grown accustomed to condense their writing. Content providers routinely publish short textual excerpts to these platforms as well. However, with larger quantities of small documents becoming constantly available, search engines now have less data to index, classify and retrieve relevant information. In this regard, more research is needed to show how reliable the current Information Retrieval (IR) algorithms are when confronted to collections of exclusively short documents, such as the ones arising from social media.This paper explores the semantic proximity between human summaries and queries through cluster analysis, and how it relates to IR. Roughly, the k-means algorithm was used to cluster two collections of summaries by their semantic similarity: one in English and one in Spanish. This, to measure how summarization may affect information content in cluster-based IR. Furthermore, the same algorithm was used to measure how documents grouped around a set of artificially generated queries.The results show that, regardless of the language, providing the algorithm with previous category knowledge may contribute to increase the accuracy of cluster-based document classification. Furthermore, some evidences points to the effect of summary quality in retrievability: summaries created by specialized summarizers induced more distinguishable clusters than summaries created by university students. Future work in this area may serve to adapt existing algorithms to big collections of short documents, improving IR performance in cases where machine learning techniques are not available.
A modified coating method for preparing a mono-valent perm-selective cation exchange membrane: I. The evolution of membrane property corresponding to different preparing stages

Abstract In this work, enlightened by the preparation of the Tokuyama BP-1 bipolar membrane, a modified coating method is attempted to prepare the mono-valent perm-selective cation exchange membrane (CEM). That is, a surface-roughening step is introduced into conventional coating technology to increase the contacting area and strengthen the interaction between modification layer and base membrane. Furthermore, a post-quaternization is carried out after the formation of the cross-linked chitosan layer to weaken the effect of the electrostatic repulsion among the cationic charged macromolecules on the compactness of the modification layer. The SEM images and the diffusion experiments of solute confirm the formation of a dense and smooth cationic modification layer. The electrodialysis experiments of the CEMs corresponding to different preparing stages with respect to H+/Zn2+ system and Na+/Mg2+ system show that the mono-valent perm-selectivity can be endowed successfully to the ordinary CEM. For example, th.
An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively.
Multi-Agent Systems to Implement Industry 4.0 Components

The fast-changing market conditions, the increased global competition and the rapid technological developments demand flexible, adaptable and reconfigurable manufacturing systems based on Cyber-Physical Systems (CPS). Aligned with CPS, the adoption of production system architectures is suitable to reduce complexity and achieve interoperability in the industrial applications. In this context, the Reference Architecture Model for Industry 4.0 (RAMI4.0) provides the guidelines to develop Industry 4.0 (I4.0) compliant solutions, considering the existing industrial standards. The so-called I4.0 components implement this model in practice, combining the physical asset with its digital representation, named Asset Administration Shell (AAS). This paper explores the use of Multi-Agent Systems (MAS) to implement the AAS functionalities, taking advantage of their inherits characteristics, e.g., autonomy, intelligence, decentralization and reconfigurability. In this context, the mapping between AAS functionalities and MAS characteristics is provided, as well as the challenges for this implementation. The applicability is illustrated by digitalizing an inspection cell comprising an UR3 robot and several console products by using MAS technology.