Reviewerly/exHarmony huggingface dataset:
PhishGuard - An Automatic Web Phishing Detection System

People are being compelled to adopt a "work from home" strategy because of the COVID-19 outbreak. In today’s world, the Internet has evolved into a powerful tool for social connection. People’s reliance on digital platforms creates opportunities for deception. Phishing websites are one of the types of internet security issues that target human vulnerabilities rather than software flaws. It is defined as the act of impersonating someone else to steal sensitive information such as usernames and passwords from online users. In this research, we provide an intelligent system for identifying phishing websites that work as an extension to an internet browser and automatically warn the user when a phishing website is detected.
Design principles for internet skills education: results from a design-based research study in higher education

The current generation of students lives in a globally connected world where Internet technologies are ubiquitous. As a result, they learn how to use various digital tools from a very young age, although, for the most part, the skills they develop are not adequate to use internet technologies in academic settings effectively. Additionally, while digital skills are essential in Higher Education (HE), developing programs for the effective use of internet technologies is still an issue in question. Research lacks empirical investigations regarding the design of such programs. To address this gap, the authors applied a design-based research (DBR) methodology to empirically explore an instructional intervention that aimed to enhance undergraduate students' digital skills for the effective use of the Internet during their studies. Specifically, the authors drew from multiple sources and utilized a triangulation approach to interpreting the findings emphasizing the aspects of digital skills development and assessment and learning design. The results clearly show that the project-based learning intervention and the proposed design principles can positively impact digital skills development and support learning in academic settings. The authors conclude with implications for further research in the field focusing on digital skills frameworks, assessment instruments, instructional approaches, and learning content.The online version contains supplementary material available at 10.1007/s43545-022-00428-2.
Exposing DeepFakes using Siamese Training

Developments in the deep learning domain have led to its usage in solving problems ranging from robotics to computer vision and healthcare. However, due to easy access to large public datasets, technologies based on deep learning have been utilized to create software that can be used for immoral purposes. One such application of deep learning is facial manipulation in videos also known as deepfakes. If used maliciously, deepfakes can have devastating consequences on society such as cyberbullying, scamming, and spreading false and vindictive news which pose serious threats to individuals as well as to the nation by disrupting its peace and security, etc. Hence, developing techniques to detect manipulated media is of utmost urgency and importance. Using a siamese network architecture, this research proposes an ensemble-based metric learning approach in which several models are created beginning from a base network to identify face manipulation. The technique has been evaluated against publicly available datasets: DFDC, FaceForensics++, and Celeb-DF (v2) and shows promising results in detecting manipulations during self and cross-testing.
IFChatbot: Convolutional Neural Network based chatbot for Image Forgery Detection and Localization

With the quick adoption of the internet, social media, and the widespread availability of unsophisticated image manipulation tools, the adverse consequences of image forgery are taking a toll on society. Visual inspection is often ineffective in determining a forgery in an image, making it potentially perilous for all the illicit means of its application. To overcome this problem, we analyze the effectiveness of a deep convolutional neural network for the detection and localization of the area of manipulation in forged images, bearing forgeries of simple and complex nature. Further along, we interface the trained model with a web application for users to interact with the model in a simple and effective manner. Finally, we also developed a chatbot to further ease the interaction process with the model and effectively tackle the problem of fake news forwards on popular internet messaging platforms such as WhatsApp.
Intelligently driven performance management: an enabler of real-time research forecasting for innovative commercial agriculture

Research in commercial agriculture is instrumental to achieve the targets set under the second Sustainable Development Goal (SDG), i.e., 'Zero Hunger by 2030.' Execution of research for the success of commercial agriculture becomes a tedious task and research organizations have been long struggling to assess their performance unequivocally in the face of COVID-19. Any evacuation plan in place to improve the performance, monitoring, and evaluation of a research institute must guarantee that the institute is on the right momentum and let it evades metrics-obsessed research drives during this pandemic. A survey was conducted through the participation of the topmost administrators attached to key research institutes working on agriculture in Sri Lanka to explore the current performance management practices deeply. The conclusions derived from a thematic analysis of the survey data were used to propose a set of solutions that facilitate a well-thought research agenda in a digitally transformed performance management system. The solutions imply that intelligently driven key performance measurements worked by artificial intelligence and big data could be used with policy innovations to support research integrity and assessment security within the coexistence of humans and machines for the well-being of research development in the commercial agriculture sector.The online version contains supplementary material available at 10.1007/s43545-022-00484-8.
DL-Based Anomaly Detection at the Physical-Layer of Cognitive Radio by Deep Support Vector Data Description

Aiming at achieving anomaly detection at the physical-layer of cognitive radio, a Deep Support Vector Data Description (Deep SVDD) based anomaly detection scheme is proposed in this paper. Specifically, two novel techniques with different sensitivities related to Signal-to-Noise Ratio (SNR)-walls are proposed for two typical application scenarios. On one hand, for studying the optimal boundary of the desired signal in the feature space, an Improved Deep SVDD (IDSVDD) is conceived for extracting low-dimensional features of the samples represented in the time-frequency domain. On the other hand, a joint algorithm of Deep SVDD and Modulation Classification (JDSMC) is developed, which is capable of extracting low-dimensional features utilizing deep Convolutional Neural Networks (CNN). Extensive simulation and experimental results demonstrate that: 1) the detection performance and the real-time performance of the proposed techniques outperform conventional control algorithms. 2) The proposed techniques can effectively detect abnormal signals in complex electromagnetic environments and effectively improve the self-aware ability of Cognitive Radio Network (CRN). 3) Over-the-Air (OTA) measurements using Software Defined Radio (SDR) in the lab validate the proposed techniques’ anomaly detection performance.
Combined Local and Global Features for Action Recognition from Motion Sensors

Hand gestures are becoming one of the most convenient means of communication between human and machine. In this paper, we propose a method for hand gesture recognition using wearable motion sensors. The method composes of three main steps. The first step removes noise using smoothing filters. Then features extracted from each sliding window will be inputted into a machine learning model for recognition. While deep learning models have achieved impressive results on many tasks, some hand-crafted features-based techniques are still preferable due to their lightweight in terms of memory requirement and computational times. Mostly, they are easier to be deployed on edge or low-resource computers. We follow the hand-crafted features approach which extracts statistical features from the given signal. Extracting features from the whole signal can characterize the global information but not local ones. We then propose to split the original signal into several non-overlapped segments and compute the same features on each of the local segments. The final feature vector is the combination of both local and global features. We have evaluated the proposed method on three datasets (CMDFALL, C-HMAD, and DaLiAc) and show that the proposed method achieves better performance than using the global features. This shows the potential to apply such a technique in practical applications.
Modeling and Prediction of User Stability and Comfortability on Autonomous Wheelchairs With 3-D Mapping

Traditional manual wheelchairs have a fixed seat with no movement or angle adjustment, which can seriously affect the user's comfort and greatly limit user experience. However, the electric wheelchair relies on strong intelligence and automatic features; it can not only realize the multidegree freedom adjustment of the human body and the seat but also has a rich and powerful man–machine control interface, which greatly facilitates and improves the user experience. This study upgraded a Permobil C400-powered wheelchair with multisensor data fusion technology to enrich its terrain recognition, tipping stability, and comfortability prediction. The tipping stability modeling of the wheelchair dummy system is carried out using multibody dynamics and vibration mechanics to obtain the tipping stability limit and the comfort evaluation of the wheelchair vibration acceleration on the human body during travel. Based on the elevation mapping method, the wheelchair can estimate the terrain from the local point of view at any point in time. At the same time, the RGB-D depth camera is connected to the robot operating system (ROS) system, and the open-source algorithm package RTAB-MAP is used to complete the MAP construction and collect the 3-D point-cloud terrain data. Then, the real 3-D terrain files are generated through the point-cloud stitching technology for stability simulation of the wheelchair–human system. The tipping stability and comfort indexes of the wheelchair–human system when passing over different physical terrains can be obtained. The experimental results show that the IMU data located on the human chest agree well with the simulation analysis data and are suitable for a variety of complex real-terrain conditions, verifying the accuracy of the wheelchair–human system dynamics model and the feasibility of the simulation analysis process. Thus, this modeling and simulation method can predict wheelchair stability and user comfortability well and ensure a high-performance experience.
Link Prediction With Contextualized Self-Supervision

Link prediction aims to infer the link existence between pairs of nodes in networks/graphs. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges&#x2014;<i>link sparsity</i>, <i>node attribute noise</i> and <i>dynamic changes</i>&#x2014;that are faced by many real-world networks. To address these challenges, we propose a <u>C</u>ontextualized <u>S</u>elf-<u>S</u>upervised <u>L</u>earning (CSSL) framework that fully exploits structural context prediction for link prediction. The proposed CSSL framework learns a link encoder to infer the link existence probability from paired node embeddings, which are constructed via a transformation on node attributes. To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance. Two types of structural context are investigated, <i>i.e.</i>, context nodes collected from random walks <i>vs.</i> context subgraphs. The CSSL framework can be trained in an end-to-end manner, with the learning of model parameters supervised by both the link prediction and self-supervised learning tasks. The proposed CSSL is a generic and flexible framework in the sense that it can handle both attributed and non-attributed networks, and operate under both transductive and inductive link prediction settings. Extensive experiments and ablation studies on seven real-world benchmark networks demonstrate the superior performance of the proposed self-supervision based link prediction algorithm over state-of-the-art baselines, on different types of networks under both transductive and inductive settings. The proposed CSSL also yields competitive performance in terms of its robustness to node attribute noise and scalability over large-scale networks.
Adaptive non-uniform partition algorithm based on linear canonical transform

This paper proposes a new signal representation method of adaptive non-uniform partition algorithm based on linear canonical transform, which can partition the signal in independent regions to further improve the reconstruction quality of the signal thus extending its application range. That means the partial signal in each sub-region can be approximated by a determined linear canonical transform series after applying the least squares approximation. The partition process is guided by a set control error so that it can more effectively reflect the regional characteristics of the signal. Compared with the signal reconstructed effect by both the original adaptive non-uniform partition algorithm and Fourier transform through simulation experiments, the results prove that the proposed algorithm has better performance in signal representation and reconstruction under the same approximation condition. On the other hand, through the comparison of simulation experiments with wavelet transform and the original rectangular non-uniform partition algorithm for one-dimensional signal denoising, it can be concluded that the proposed method has certain potential in signal denoising.
Improving reconstruction-based coding methods for image classification: a visual dictionary refining method

The reconstruction-based coding methods utilize a dictionary to approximate an input feature as a sparse representation in image classification tasks by solving a least-squares problem with constraints. The input feature can be a handcrafted feature calculated over a small patch or a deep feature extracted by a pre-trained convolutional neural network model. As known, the contribution degrees of features extracted within different regions are distinct to the same classification task. Given this, we argue that for features with a large contribution degree, if their reconstruction errors induced by a reconstruction-based coding method can be decreased, more discriminative information will be retained to improve classification accuracy. Considering that different coding methods have the same lower bound of reconstruction error, we propose a visual dictionary refining method that adjusts only the lower bounds of the errors of features according to their contribution degrees. Concretely, given an initial dictionary used by a reconstruction-based coding method, the indexes (coding indexes) of the words from the dictionary for encoding each feature are obtained. Then, the initial dictionary is updated according to the contribution degrees and coding indexes of features. The highlight of our method is that it can be applied to many reconstruction-based coding methods without damaging their original constraints. Experimental results on 15-Scenes, Caltech-101, and UIUC-Sports show that the classification accuracies of four representative coding methods are improved by 0.17% to 1.5% after applying our method. These results exhibit the effectiveness and universality of our method.
Atom Search Optimization with Deep Learning Enabled Arabic Sign Language Recognition for Speaking and Hearing Disability Persons

Sign language has played a crucial role in the lives of impaired people having hearing and speaking disabilities. They can send messages via hand gesture movement. Arabic Sign Language (ASL) recognition is a very difficult task because of its high complexity and the increasing intraclass similarity. Sign language may be utilized for the communication of sentences, letters, or words using diverse signs of the hands. Such communication helps to bridge the communication gap between people with hearing impairment and other people and also makes it easy for people with hearing impairment to express their opinions. Recently, a large number of studies have been ongoing in developing a system that is capable of classifying signs of dissimilar sign languages into the given class. Therefore, this study designs an atom search optimization with a deep convolutional autoencoder-enabled sign language recognition (ASODCAE-SLR) model for speaking and hearing disabled persons. The presented ASODCAE-SLR technique mainly aims to assist the communication of speaking and hearing disabled persons via the SLR process. To accomplish this, the ASODCAE-SLR technique initially pre-processes the input frames by a weighted average filtering approach. In addition, the ASODCAE-SLR technique employs a capsule network (CapsNet) feature extractor to produce a collection of feature vectors. For the recognition of sign language, the DCAE model is exploited in the study. At the final stage, the ASO algorithm is utilized as a hyperparameter optimizer which in turn increases the efficacy of the DCAE model. The experimental validation of the ASODCAE-SLR model is tested using the Arabic Sign Language dataset. The simulation analysis exhibit the enhanced performance of the ASODCAE-SLR model compared to existing models.
Design of Chinese Grammar Recognition and Error Correction Model Based on the Deep Neural Network

In order to further improve the performance of the automatic grammar error detection system, a new Chinese grammar recognition and correction model is proposed in this paper. Based on the transformer attention mechanism, the bias matrix of Gaussian distribution is added to improve the attention of the model to local text and strengthen the information extraction of wrong words and surrounding words in the wrong text. In addition, the ON_LSTM model is used to extract grammatical information from the special grammatical structure of error text. The experimental results show that the two methods can effectively improve the accuracy and recall rate, and the fused model achieves the highest F1 value. Finally, the Chinese text error correction system is designed to expand the application scope of the model, which helps to reduce the human cost in language learning.
Hybrid recommender system with core users selection

Recommender system plays an increasingly important role in identifying the individual’s preference and accordingly makes a personalized recommendation. Matrix factorization is currently the most popular model-based collaborative filtering (CF) method that achieves high recommendation accuracy. However, similarity computation hinders the development of CF-based recommendation systems. Preference obtained only depends on the explicit rating without considering the implicit content feature, which is the root cause of preference bias. In this paper, the content feature of items described by fuzzy sets is integrated into the similarity computation, which helps to improve the accuracy of user preference modeling. The importance of a user is then defined according to preferences, which serves as a baseline standards of the core users selection. Furthermore, core users based matrix factorization model (CU-FHR) is established, then genetic algorithm is used to predict the missing rating on items. Finally, MovieLens is used to test the performance of our proposed method. Experiments show CU-FHR achieves better accuracy in prediction compared with the other recommendation methods.
Augmented Reality (AR) for Biology Learning: A Quasi-Experiment Study with High School Students

In recent years, Augmented Reality (AR) has been adopted at both formal and informal learning settings. It has been found useful in training learners’ core competencies, such as spatial ability, practical skills, and conceptual understanding. Various studies have highlighted its positive impact on enhancing learning outcomes (e.g., learning engagement and performance). Its impact on learners’ academic emotion, however, is yet to be comprehensively investigated. The aim of this study is to explore high school students’ perception of AR in biology learning, measure the impact of AR on their learning performance, and systematically examine the extent to which AR affects their academic emotion. A quasi-experiment was conducted among 103 students from a high school in mainland China. An AR learning material was developed for participants in the experiment group, while the control group used traditional learning approaches solely. Questionnaire surveys and focus-group interviews were conducted to understand participating students’ perception towards AR and/or the subject of biology as well as their academic emotion during the quasi-experiment. Their learning performance before and after the intervention was also measured and compared. The findings indicate a general willingness to use AR applications in learning among the participating students. The capacity of AR was confirmed in terms of facilitating acquisition of conceptual knowledge in biology and in stimulating positive academic emotions towards the subject. These findings suggested that AR can serve as an effective means in science education to sustain students’ positive learning emotion and engagement in both formal and informal learning environments.
The Safe Financial Processing Method for Realizing Supply Chain Integrated Business Intelligence Using Blockchain Application Scenarios

In order to improve the financial processing effect of supply chain integrated business intelligence, this study applies blockchain technology to the financial processing of supply chain integrated business intelligence and conducts an evolutionary game analysis on the factors that affect the selection of information-sharing strategies for logistic service supply chain nodes. By establishing the evolutionary game model of logistic service integrators and logistic service providers under the strategy of information sharing and information nonsharing, the value of information sharing in the logistic service supply chain is discussed from a qualitative and quantitative perspective. In addition, this study constructs the expected profit mathematical model of a two-level logistic service supply chain composed of logistic service integrators and logistic service providers. The experimental research results show that the safe financial processing method for realizing supply chain integrated business intelligence using blockchain application scenarios has good results.