Reviewerly/exHarmony huggingface dataset:
A robust medical image steganography approach based on particle swarm optimization algorithm and quantum walks

Abstract Medical information plays an essential task in our everyday lives, in which medical data privacy and security constitute an important issue. The confidentiality of medical data can be achieved by applying one or more encryption and data hiding methods. Amidst the development of quantum computers, most medical data confidentiality techniques may be hacked because their construction is based on mathematical models. Most medical data have a long lifetime exceeding 25 years. Therefore, it is an important issue to design a new medical data hiding technique that has the capability to withstand the probable attacks from the side of quantum or digital devices. In this article, we aim to present a novel medical image steganography strategy based on quantum walks, chaotic systems, and particle swarm optimization algorithm. A 3-D chaotic system and quantum walks are utilized for operating particle swarm optimization algorithm, in which the generated velocity sequence is utilized for substituting the confidential data, and the position sequence is utilized for selecting which position in the hosting image will be employed to host the substituted confidential data. The payload capacity of the suggested mechanism is 2 bits per 1 byte, and the average value for PSNR is 44.1, which is big enough for the naked eye to not differentiate the difference between the carrier image and its stego one.
Semantic-enhanced neural collaborative filtering models in recommender systems

Recommendation systems or recommender systems (RSs) are very popular in entertainment websites. With the combination of neural networks and collaborative filtering, Neural Collaborative Filtering (NCF) recommendation methods have shown their outperformance in making item suggestions. However, the lack of semantic relationships between objects makes the NCF unable to capture the complex user-item interactions. Moreover, traditional NCF is unable to capture the dynamic user preference over time. To address these issues, in this paper, we propose novel semantic-enhanced NCF models which are applied to movie rating prediction and movie recommendation. Therefore, MovieLens and IMDB datasets are taken into account as case studies. The proposed models are the integration of ontology-like modeling and deep learning for recommendation tasks into two parts:(1) building the semantic knowledge base for movies and (2) building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from user sessions, input into the NCF module for making predictions or recommendations. Several experiments have been conducted to show their better recommendation performance than the traditional NCF model. • Building the semantic knowledge base for enhancing deep learning models. • Building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from sequential data, input into the Neural Collaborative Filtering module for making predictions and recommendations.
An Efficient RGB-D Hand Gesture Detection Framework for Dexterous Robot Hand-Arm Teleoperation System

Aiming at the problems of accurate and fast hand gesture detection and teleoperation mapping in the hand-based visual teleoperation of dexterous robots, an efficient hand gesture detection framework based on deep learning is proposed in this article. It can achieve an accurate and fast hand gesture detection and teleoperation of dexterous robots based on an anchor-free network architecture by using an RGB-D camera. First, an RGB-D early-fusion method based on the HSV space is proposed, effectively reducing background interference and enhancing hand information. Second, a hand gesture classification network (HandClasNet) is proposed to realize hand detection and localization by detecting the center and corner points of hands, and a HandClasNet is proposed to realize gesture recognition by using a parallel EfficientNet structure. Then, a dexterous robot hand-arm teleoperation system based on the hand gesture detection framework is designed to realize the hand-based teleoperation of a dexterous robot. Our method achieves high accuracy with fast speed on public and custom hand datasets and outperforms some state-of-the-art methods. In addition, the application of the proposed method in the hand-based teleoperation system can control the grasping of various objects by a dexterous hand-arm system in real time and accurately, which verifies the efficiency of our method.
Learning Features of Intra-Consistency and Inter-Diversity: Keys Toward Generalizable Deepfake Detection

Public concerns about deepfake face forgery are continually rising in recent years. Most deepfake detection approaches attempt to learn discriminative features between real and fake faces through end-to-end trained deep neural networks. However, the majorities of them suffer from the problem of poor generalization across different data sources, forgery methods, and/or post-processing operations. In this paper, following the simple but effective principle in discriminative representation learning, i.e., towards learning features of intra-consistency within classes and inter-diversity between classes, we leverage a novel transformer-based self-supervised learning method and an effective data augmentation strategy towards generalizable deepfake detection. Considering the differences between the real and fake images are often subtle and local, the proposed method firstly utilizes Self Prediction Learning (SPL) to learn rich hidden representations by predicting masked patches at a pre-training stage. Intra-class consistency clues in images can be mined without deepfake labels. After pre-training, the discrimination model is then fine-tuned via multi-task learning, including a deepfake classification task and a forgery mask estimation task. It is facilitated by our new data augmentation method called Adjustable Forgery Synthesizer (AFS), which can conveniently simulate the process of synthesizing deepfake images with various levels of visual reality in an explicit manner. AFS greatly prevents overfitting due to insufficient diversity in training data. Comprehensive experiments demonstrate that our method outperforms the state-of-the-art competitors on several popular benchmark datasets in terms of generalization to unseen forgery methods and untrained datasets.
Knowledge-graph-based explainable AI: A systematic review

In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships. The construction of large KGs can enable the integration of heterogeneous information sources and help Artificial Intelligence (AI) systems be more explainable and interpretable. This systematic review examines a selection of recent publications to understand how KGs are currently being used in eXplainable AI systems. To achieve this goal, we design a framework and divide the use of KGs into four categories: extracting features, extracting relationships, constructing KGs, and KG reasoning. We also identify where KGs are mostly used in eXplainable AI systems (pre-model, in-model, and post-model) according to the aforementioned categories. Based on our analysis, KGs have been mainly used in pre-model XAI for feature and relation extraction. They were also utilised for inference and reasoning in post-model XAI. We found several studies that leveraged KGs to explain the XAI models in the healthcare domain.
Identifying algorithm in program code based on structural features using CNN classification model

Abstract In software, an algorithm is a well-organized sequence of actions that provides the optimal way to complete a task. Algorithmic thinking is also essential to break-down a problem and conceptualize solutions in some steps. The proper selection of an algorithm is pivotal to improve computational performance and software productivity as well as to programming learning. That is, determining a suitable algorithm from a given code is widely relevant in software engineering and programming education. However, both humans and machines find it difficult to identify algorithms from code without any meta-information. This study aims to propose a program code classification model that uses a convolutional neural network (CNN) to classify codes based on the algorithm. First, program codes are transformed into a sequence of structural features (SFs). Second, SFs are transformed into a one-hot binary matrix using several procedures. Third, different structures and hyperparameters of the CNN model are fine-tuned to identify the best model for the code classification task. To do so, 61,614 real-world program codes of different types of algorithms collected from an online judge system are used to train, validate, and evaluate the model. Finally, the experimental results show that the proposed model can identify algorithms and classify program codes with a high percentage of accuracy. The average precision, recall, and F-measure scores of the best CNN model are 95.65%, 95.85%, and 95.70%, respectively, indicating that it outperforms other baseline models.
Multi-Object Tracking and Segmentation Via Neural Message Passing

Abstract Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and Multiple Object Tracking and Segmentation (MOTS) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a model that can operate on such structured domain is not trivial. In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks. By operating directly on the graph domain, our method can reason globally over an entire set of detections and exploit contextual features. It then jointly predicts both final solutions for the data association problem and segmentation masks for all objects in the scene while exploiting synergies between the two tasks. We achieve state-of-the-art results for both tracking and segmentation in several publicly available datasets. Our code is available at https://github.com/ocetintas/MPNTrackSeg
Extending the unified theory of acceptance and use of technology to investigate determinants of acceptance and adoption of learning management systems in the post-pandemic era: a structural equation modeling approach

Despite the disruptive effects of COVID-19 on higher education institutions, the pandemic has enforced the intensive and sustained integration of new digital technologies and platforms into Education, a case that has instigated new contexts for online learning research. A considerable proportion of empirical literature advocates that perceived fear associated with the pandemic is positively related to protection motivation and, hence, online learning acceptance. The current study aims to extend one of the prominent technology acceptance theories, namely, the Unified Theory of Acceptance and Use of Technology (UTAUT), and diffusion of innovation of theory (DIT) to investigate the acceptance and adoption of learning management systems (LMS) among students and faculty. A cross-sectional survey study has been conducted in a higher education institution in Oman, involving a sample of undergraduate students and faculty. The findings have indicated that perceived corona fear has significantly influenced perceived effort expectancy and performance expectancy, whereas facilitating conditions and technological innovativeness, contrary to the forecasts, have not significantly affected intention. Based on its findings, the current study suggests several practical implications for promoting the pedagogical efficacy of online learning elements within comprehensive blended learning models in higher education institutions in the post-pandemic era.
Refactoring Codes to Improve Software Security Requirements

Refactoring is one of the most widely used techniques in practice to improve the quality of software, such as maintainability, testability, and understandability. However, there is a lack of studies investigating the effect of refactoring on security. The effect of refactoring on security is poorly understood and understudied. A limited number of studies provide the categorization of refactoring techniques based on their effect on quality attributes to assist developers in achieving their design objectives by selecting the most beneficial techniques and applying them at the right places with respect to specific software quality attributes. However, security was not considered in these studies. Therefore, this study aims to investigate the effect of refactoring techniques on security in terms of information hiding. The aforementioned objectives were achieved by conducting several steps starting with selecting suitable refactoring techniques, selecting five case studies, selecting security metrics, applying the refactoring techniques, and conducting multi-case analysis. Then, the chosen refactoring techniques were categorized based on their effect on security. The results of this study identify and analyze the effect of the refactoring techniques on security metrics and then propose a categorization of the refactoring techniques based on their effect on security metrics. The finding will help the developers select appropriate refactoring techniques to improve existing software security.
Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain

Abstract This study proposes a primary node election method based on probabilistic linguistic term set (PLTS) for the practical Byzantine fault tolerance (PBFT) consensus mechanism to effectively enhance the efficiency of reaching consensus. Specifically, a novel concept of the probabilistic linguistic term set with a confidence interval (PLTS-CI) is presented to express the uncertain complex voting information of nodes during primary node election. Then, a novel score function based on the exponential semantic value and confidence approximation value for the PLTS-CI, called Score-ESCA, is used to solve the problems of comparing different nodes with various voting attitudes. This method helps select the node with the highest score by utilizing complex decision attitudes, making it an accurate primary node election solution. Furthermore, the feasibility of our proposed method is proved by both theoretical analysis and experimental evaluations.
Impact of Blockchain Technology on Smart Grids

Energy systems are transforming due to the incorporation of multiple distributed energy resources, such as renewable energy and battery storage systems. This transformation has triggered a need to shift power distribution from a low efficiency centralized model with high coordination costs to a decentralized distribution system comprising smart grids. Researchers have discovered a number of uses for blockchain technology in the energy sector because of its decentralized structure and possibility for safe transactions. In order to pinpoint current trends and important research directions in this area, this article thoroughly examines the effects of blockchain technology on smart grids and distributed energy resources. The aim of this paper is also to identify research gaps and future research initiatives in the area of blockchain-based energy distribution. To do this, 92 research publications were subjected to a comprehensive literature review based on predetermined criteria. Transactive Energy, Electric Vehicle Integration, Privacy and Security, and Demand Response, together with some other relatively fresh and unexplored topics, were, therefore, highlighted as four major focal areas of blockchain energy research. We have also drawn attention to the gaps in the research that has already been done and the constraints imposed by present systems that must be removed before blockchain technology can be widely used.
DISNet: A sequential learning framework to handle occlusion in human action recognition with video acquisition sensors

Human action recognition (HAR) requires a clear line of sight for the video acquisition system to properly collect the data. However, the data acquisition is hampered due to presence of the obstructions in front of the action happening in real time environment. In recently reported HAR techniques, the widely used multi-stream sequential networks are having two major limitations. First, the performance of the existing algorithms degrades comprehensively in the presence of partial loss of action data due to obstruction. Second, the performance of the existing networks is restricted by not exploiting the dependency relationship between different streams of a multi-stream network. To handle these problems, a novel double input sequential network (DISNet) is proposed which takes care the HAR algorithm in the presence of partial loss of action data. The DISNet, which learns inter-stream information, is jointly trained on the normal data and the artificially created obstructed data of a single video to provide immunity to the HAR network against obstructions. The proposed DISNet is evaluated on publicly available datasets like KTH, UCF-sports, JHMDB and UCF101, which provides performance accuracy of 83.57%, 82.27%, 50.24% and 54.96% respectively.
BehavePassDB: Public Database for Mobile Behavioral Biometrics and Benchmark Evaluation

Mobile behavioral biometrics have become a popular topic of research, reaching promising results in terms of authentication, exploiting a multimodal combination of touchscreen and background sensor data. However, there is no way of knowing whether state-of-the-art classifiers in the literature can distinguish between the notion of user and device. In this article, we present a new database, BehavePassDB, structured into separate acquisition sessions and tasks to mimic the most common aspects of mobile Human-Computer Interaction (HCI). BehavePassDB is acquired through a dedicated mobile app installed on the subjects devices, also including the case of different users on the same device for evaluation. We propose a standard experimental protocol and benchmark for the research community to perform a fair comparison of novel approaches with the state of the art1. We propose and evaluate a system based on Long-Short Term Memory (LSTM) architecture with triplet loss and modality fusion at score level.
Digital resources as an aspect of teacher professional digital competence: One term, different definitions – a systematic review

The term 'digital resources' is increasingly used in educational research to describe the specific knowledge and skills that constitute teachers' professional digital competence. Educational policy documents, including the European Framework for the Digital Competence of Educators (DigCompEdu), deploy the term to reaffirm teachers' need for special skills in using digital resources. However, educational research literature presents inconsistent views of the term, restricting its effective use in further research and the promotion of associated skills among pre-service and in-service teachers. To clarify the term 'digital resources' and support future research related to its application especially in empirical research on teachers' professional digital competence, this systematic review aims to analyse the definitions of digital resources as a scientific term in 23 articles and to examine and compare the facets and aspects of digital resources. Finally, we derive a definition from the various perspectives and discuss the implications for the definition of digital resources as an aspect of teachers' professional digital competence.
Logic tensor network with massive learned knowledge for aspect-based sentiment analysis

Aspect-based sentiment analysis assists service providers to better understand users’ opinions expressed in massive amounts of online posts, because it automatically infers users’ sentiments towards the aspect terms of interest. Recently, several researchers have attempted to apply first-order logic (FOL) rules to deep neural networks via the posterior constraint method. However, existing methods simply apply a priori constraints to represent the FOL with coefficients selected by hand, which requires improvements in incorporating and adapting abstract knowledge in data. In this study, we propose a novel logic tensor network with massive rules (LTNMR) for aspect-based sentiment analysis, which is constructed by incorporating FOL. Specifically, we integrate two types of knowledge into the logic tensor network: (1) dependency knowledge, which improves the efficiency of the capture of aspect-related words and (2) the human-defined knowledge rule, which helps the classifier understand the sentiment of the extracted aspect-related words. Furthermore, to achieve high inferring accuracy, we propose a mutual distillation structure knowledge injection (MDSKI) strategy. MDSKI transfers dependency knowledge from teacher Bert to LTNMR, which acts as the student network. Experiments demonstrate that the proposed LTNMR, combined with the MDSKI strategy, substantially outperforms state-of-the-art results for aspect-based sentiment analysis.
Contamination vertical distribution and key factors identification of metal(loid)s in site soil from an abandoned Pb/Zn smelter using machine learning

Soil heterogeneity makes the vertical distribution of metal(loid)s in site soil vary considerably and poses a challenge for identifying the key factors of metal(loid)s migration in site soil profiles. In this study, a machine learning (ML) model was developed to study a typical abandoned Pb/Zn smelter using 267 site soils from 46 drilling points. Results showed that a well-trained ML model could be used to identify the key factors in determining the contamination vertical distribution and predict the metal(loid)s contents in subsurface soil. As, Cd, Pb, and Zn were the primary pollutants and their vertical migration depth arrived to 4-6 m. Based on the predictive performance of different ML algorithms, the extreme gradient boosting (XGB) was selected as the best model to produce accurate predictions for the most metal(loid)s content. Contents of As, Cd, Pb, and Zn in the heavily contaminated zones declined with an increase of soil depth. The metal(loid) contents in surface soil of 0-2 m could be readily used to predict the content of Cd, Cr, Hg, and Zn in subsurface soil from 2 m to 10 m. Based on the metal-specific XGB models, sulfur content, functional area, and soil texture were identified as key factors affecting the vertical distribution of As, Cd, Pb, and Zn in site soil. Results suggested the ML method is helpful to manage the potential environmental risks of metal(loid)s in Pb/Zn smelting site.