Reviewerly/exHarmony huggingface dataset:
Lawless space theory for online child sexual exploitation material offending

Deterrence, treatment and legal response to online child sexual exploitation material (CSEM) offenders is enhanced by the joint consideration of technological behaviours and cognitions. CSEM offenders choose an environment based on both psychosexual needs and utility, and in turn that environment shapes future behaviour and reinforces cognitive distortions. This paper introduces lawless space theory, a theory of cyber criminality which posits that offenders will primarily choose and utilize a perceived lawless space that best meets their psychosocial and criminogenic needs in the most frictionless way; habituation and differential association in the lawless space will reduce the perceived risk; normalization will increase comfort in a particular lawless space, increasing friction costs that must be overcome to switch technologies; and additional countermeasures will only be implemented by offenders to reduce perceived risk and lower cognitive dissonance, but not at the expense of utility. The theory is explored through the exemplar of CSEM offences and offers explanatory power for the lifecycle of a lawless space, the use of legacy spaces in the presence of objectively more capable options, the simultaneous use of multiple spaces to meet different psychosexual needs, and the adoption of new technologies by offenders. Additionally, the gateway choice and progression of lawless space usage informs investigations, risk assessments, and deterrence efforts and provides behavioural treatment targets.
Implications of Using Mobile Technologies in Higher Education Amidst the COVID-19 Pandemic

The purpose and objectives of this case study are to explore the effective use of mobile technologies by first year students amid the COVID-19 coronavirus pandemic, using a situational analysis and active theory. Moreover, this study will unveil intersectional problems and educational inequality among ‘digital natives' and ‘digital immigrants' at the Rundu campus of the University of Namibia (UNAM). The study aims at examining how the interventions of distance and/or online learning are exclusive, exposing many, especially first year, students to educational inequality. Finally, the implications of sustainable development in higher education in terms of teaching, learning, and assessment will be considered.
IPD-Net: Infrared Pedestrian Detection Network via Adaptive Feature Extraction and Coordinate Information Fusion

Infrared pedestrian detection has important theoretical research value and a wide range of application scenarios. Because of its special imaging method, infrared images can be used for pedestrian detection at night and in severe weather conditions. However, the lack of pedestrian feature information in infrared images and the small scale of pedestrian objects makes it difficult for detection networks to extract feature information and accurately detect small-scale pedestrians. To address these issues, this paper proposes an infrared pedestrian detection network based on YOLOv5, named IPD-Net. Firstly, an adaptive feature extraction module (AFEM) is designed in the backbone network section, in which a residual structure with stepwise selective kernel was included to enable the model to better extract feature information under different sizes of the receptive field. Secondly, a coordinate attention feature pyramid network (CA-FPN) is designed to enhance the deep feature map with location information through the coordinate attention module, so that the network gains better capability of object localization. Finally, shallow information is introduced into the feature fusion network to improve the detection accuracy of weak and small objects. Experimental results on the large infrared image dataset ZUT show that the mean Average Precision (mAP50) of our model is improved by 3.6% compared to that of YOLOv5s. In addition, IPD-Net shows various degrees of accuracy improvement compared to other excellent methods.
Explainable-AI in Automated Medical Report Generation Using Chest X-ray Images

The use of machine learning in healthcare has the potential to revolutionize virtually every aspect of the industry. However, the lack of transparency in AI applications may lead to the problem of trustworthiness and reliability of the information provided by these applications. Medical practitioners rely on such systems for clinical decision making, but without adequate explanations, diagnosis made by these systems cannot be completely trusted. Explainability in Artificial Intelligence (XAI) aims to improve our understanding of why a given output has been produced by an AI system. Automated medical report generation is one area that would benefit greatly from XAI. This survey provides an extensive literature review on XAI techniques used in medical image analysis and automated medical report generation. We present a systematic classification of XAI techniques used in this field, highlighting the most important features of each one that could be used by future research to select the most appropriate XAI technique to create understandable and reliable explanations for decisions made by AI systems. In addition to providing an overview of the state of the art in this area, we identify some of the most important issues that need to be addressed and on which research should be focused.
A3C System: One-Stop Automated Encrypted Traffic Labeled Sample Collection, Construction and Correlation in Multi-Systems

Encrypted traffic classification can essentially support network QoS (Quality of Service) and user QoE (Quality of Experience). However, as a typical supervised learning problem, it requires sufficiently labeled samples, which should be frequently updated. The current gateway-based labeled sample acquisition methods can only be carried out under TLS traffic. It relies on the Server Name Indication, a confused optional field that can be tampered with. The current end-based methods carried out manually or automatically have low efficiency and lack sample integrity, category purity, and label authenticity. In addition, they may have colossal packet loss and violate device security and user privacy. To solve these problems, we propose a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C. First, we carry out the automated process-isolated traffic collection and labeled sample construction in the mixed application scenario, which can be used on Windows, Linux, and Android systems. Then, we propose the Segmented Entropy Distribution Capsule Neural Network (SED-CapsNet) to validate the encryption of the collected samples. We also propose optional authenticity validation and context flow correlation methods. Experimental results show that the system can effectively achieve one-stop encrypted traffic labeled dataset acquisition. It is superior to the existing methods.
Fair recommendation by geometric interpretation and analysis of matrix factorization

Matrix factorization-based recommender system is in effect an angle preserving dimensionality reduction technique. Since the frequency of items follows power-law distribution, most vectors in the original dimension of user feature vectors and item feature vectors lie on the same hyperplane. However, it is very difficult to reconstruct the embeddings in the original dimension analytically, so we reformulate the original angle preserving dimensionality reduction problem into a distance preserving dimensionality reduction problem. We show that the geometric shape of input data of recommender system in its original higher dimension are distributed on co-centric circles with interesting properties, and design a paraboloid-based matrix factorization named ParaMat to solve the recommendation problem. In the experiment section, we compare our algorithm with 8 other algorithms and prove our new method is the most fair algorithm compared with modern day recommender systems such as ZeroMat and DotMat Hybrid.
Open Science Practices in Communication Sciences and Disorders: A Survey

Open science is a collection of practices that seek to improve the accessibility, transparency, and replicability of science. Although these practices have garnered interest in related fields, it remains unclear whether open science practices have been adopted in the field of communication sciences and disorders (CSD). This study aimed to survey the knowledge, implementation, and perceived benefits and barriers of open science practices in CSD.An online survey was disseminated to researchers in the United States actively engaged in CSD research. Four-core open science practices were examined: preregistration, self-archiving, gold open access, and open data. Data were analyzed using descriptive statistics and regression models.Two hundred twenty-two participants met the inclusion criteria. Most participants were doctoral students (38%) or assistant professors (24%) at R1 institutions (58%). Participants reported low knowledge of preregistration and gold open access. There was, however, a high level of desire to learn more for all practices. Implementation of open science practices was also low, most notably for preregistration, gold open access, and open data (< 25%). Predictors of knowledge and participation, as well as perceived barriers to implementation, are discussed.Although participation in open science appears low in the field of CSD, participants expressed a strong desire to learn more in order to engage in these practices in the future. Supplemental Material and Open Science Form: https://doi.org/10.23641/asha.21569040.
Reversible data hiding scheme using deep learning and visual cryptography for medical image communication

Due to a growth in information sharing and the use of multiple digital technologies, human lifestyles have been delivered into the virtual world. This digital world has seen the use of images in special fields increase dramatically, especially in healthcare. Healthcare services can be delivered remotely through telemedicine, a well-known method of providing quality healthcare for people worldwide. There is the risk of illegal exploitation of medical data when telemedicine applications involve exposing data over open networks. In particular, medical experts should exercise more caution when sharing a patient’s private information. In the proposed model, reversible data concealment is combined with visual cryptography to provide a secure method of exchanging medical images. A cover image is divided into nonoverlapping secret shares using Hadamard matrix. Secret digital imaging communication in medicine (DICOM) image is encoded by a deep learning model and embedded into secret shares for secure medical image sharing. Finally, the DICOM image is fully reversibly extracted with the cover image. In this case, a visual cryptographic design is used to secure the embedded secret shares. Furthermore, the metrics, such as mean squared error, peak-signal–to-noise ratio, and normalized correlations, are evaluated in the suggested scheme and are compared with various research outcomes to determine the performance of the nominated model.
Bayesian Parameter Estimation for Continuous-Variable Quantum Key Distribution

The effectiveness and accuracy of parameter estimation are guarantees for the high-performance and high-safety operation of practical continuous-variable quantum key distribution systems. A scheme based on Bayesian estimation is proposed to tackle the fluctuations of channels. In free-space channel, the Bayesian random-effects model is employed to construct complete prior information, and the channel state information extracted through pilot signals is applied to rectify the prior information. Compressed sensing technology is applied due to the sparsity of the free-space channel to reduce the cost of the system. Experimental results show that the proposed scheme has higher estimation accuracy under the free space, and the system performance is also enhanced. Moreover, the results under fiber show higher estimation accuracy and better stability than that of traditional methods. In conclusion, the scheme can create practical conditions for the construction of future global quantum networks.
Podcasting Library Services Post COVID-19 Pandemic Era: Its Potential and Challenges for Distant Learners

The study investigated the willingness of students to remotely tune into podcast programmes aired by libraries and the associated challenges. The study employed a descriptive survey research design. The sample included 245 Adeleke University undergraduates. The data was analyzed using descriptive statistics. Findings indicated that most patrons rarely use podcasts, with Spotify being the most popular among students. Students indicated that the campus news and information programme, library seminars, and user education would be their top choices for frequent listening. Students may be discouraged from utilizing library podcast programmes due to issues with internet access, a lack of finances to obtain data, and a time constraint. The study concluded that podcasts had potential for distant learners and that students would be ready to tune into podcast programmes aired by libraries, and therefore recommended that libraries in Nigeria begin to employ podcasts in airing library programmes and marketing library services.
Detecting code smells in React-based Web apps

Facebook’s React is a widely popular JavaScript library to build rich and interactive user interfaces (UI). However, due to the complexity of modern Web UIs, React applications can have hundreds of components and source code files. Therefore, front-end developers are facing increasing challenges when designing and modularizing React-based applications. As a result, it is natural to expect maintainability problems in React-based UIs due to suboptimal design decisions. To help developers with these problems, we propose a catalog with twelve React-related code smells and a prototype tool to detect the proposed smells in React-based Web apps. The smells were identified by conducting a grey literature review and by interviewing six professional software developers. We also use the tool in the top-10 most popular GitHub projects that use React and conducted a historical analysis to check how often developers remove the proposed smells. We detect 2,565 instances of the proposed code smells. The results show that the removal rates range from 0.9% to 50.5%. The smell with the most significant removal rate is Large File (50.5%). The smells with the lowest removal rates are Inheritance Instead of Composition (IIC) (0.9%), and Direct DOM Manipulation (14.7%). The list of React smells proposed in this paper as well as the tool to detect them can assist developers to improve the source code quality of React applications. While the catalog describes common problems with React applications, our tool helps to detect them. Our historical analysis also shows the importance of each smell from the developers’ perspective, showing how often each smell is removed.
Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor

The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMFO) for the efficient parameter estimation of induction motors. A steady-state equivalent circuit of the induction motor is employed for the simulation. The proposed technique handles the parameter estimation problem better than moth flame optimization (MFO), particle swarm optimization (PSO), the flower pollination algorithm (FPA), the tunicate swarm algorithm (TSA), and the sine cosine algorithm (SCA). The anticipated IMFO reduces the cost function by 49.38% as compared with the basic version of MFO.
A Novel ML-Aided Methodology for SINS/GPS Integrated Navigation Systems during GPS Outages

To improve the navigation accuracy for land vehicles during global positioning system (GPS) outages, a machine learning (ML) aided methodology to integrate a strap-down inertial navigation system (SINS) and GPS system is proposed, as follows. When a GPS signal is available, an online sequential extreme learning machine with a dynamic forgetting factor (DOS-ELM) algorithm is used to train the mapping model between the SINS’ acceleration, specific force, speed/position increments outputs, and the GPS’ speed/position increments. When a GPS signal is unavailable, GPS speed/velocity measurements are replaced with prediction output of the well-trained DOS-ELM module’s prediction output, and information fusion with the SINS reduces the degree of system error divergence. A land vehicle field experiment’s actual sensor data were collected online, and the DOS-ELM-aided methodology for the SINS/GPS integrated navigation systems was applied. The simulation results indicate that the proposed methodology can reduce the degree of system error divergence and then obtain accurate and reliable navigation information during GPS outages.
Detection of Image Level Forgery with Various Constraints Using DFDC Full and Sample Datasets

The emergence of advanced machine learning or deep learning techniques such as autoencoders and generative adversarial networks, can generate images known as deepfakes, which astonishingly resemble the realistic images. These deepfake images are hard to distinguish from the real images and are being used unethically against famous personalities such as politicians, celebrities, and social workers. Hence, we propose a method to detect these deepfake images using a light weighted convolutional neural network (CNN). Our research is conducted with Deep Fake Detection Challenge (DFDC) full and sample datasets, where we compare the performance of our proposed model with various state-of-the-art pretrained models such as VGG-19, Xception and Inception-ResNet-v2. Furthermore, we perform the experiments with various resolutions maintaining 1:1 and 9:16 aspect ratios, which have not been explored for DFDC datasets by any other groups to date. Thus, the proposed model can flexibly accommodate various resolutions and aspect ratios, without being constrained to a specific resolution or aspect ratio for any type of image classification problem. While most of the reported research is limited to sample or preview DFDC datasets only, we have also attempted the testing on full DFDC datasets and presented the results. Contemplating the fact that the detailed results and resource analysis for various scenarios are provided in this research, the proposed deepfake detection method is anticipated to pave new avenues for deepfake detection research, that engages with DFDC datasets.
Petrophysical Database for European Pegmatite Exploration—EuroPeg

Granitic pegmatites contain natural concentrations of a variety of raw materials invaluable for modern technologies and a green and sustainable society. The most abundant ones are silicon for high-purity quartz applications, and indispensable lithium for today’s batteries. However, the exploration of these target materials in Europe is underdeveloped, causing high dependencies on non-European supply chains. The European Commission Horizon 2020 project GREENPEG (GA no. 869274) is addressing the exploration of buried, small-scale pegmatite deposits in Europe through the development of innovative new exploration toolsets. One component of these toolsets is petrophysical data of pegmatite ores and their wall rock. These data are essential to supplement and ground-truth non-invasive geophysical investigations and deposit modeling. Both important tools in mineral exploration can then be used in a more targeted and cost-effective way. Petrophysical parameters measured on drill core and field samples and acquired through geophysical borehole logging are compiled in the first database for European Pegmatite deposits: EuroPeg_PetroDB. Samples are supplemented with meta-information, and the database is comprehensively structured in an easy-to-use format. Supporting the initiative of FAIR data, EuroPeg is freely accessible on an open data repository. The sample content and petrophysical measurements are described, followed by the structure and usability of the database.
A Comparative Study of Speaker Role Identification in Air Traffic Communication Using Deep Learning Approaches

Automatic spoken instruction understanding (SIU) of the controller-pilot conversations in the air traffic control (ATC) requires not only recognizing the words and semantics of the speech but also determining the role of the speaker. However, few of the published works on the automatic understanding systems in air traffic communication focus on speaker role identification (SRI). In this article, we formulate the SRI task of controller-pilot communication as a binary classification problem. Furthermore, the text-based, speech-based, and speech-and-text-based multi-modal methods are proposed to achieve a comprehensive comparison of the SRI task. To ablate the impacts of the comparative approaches, various advanced neural network architectures are applied to optimize the implementation of text-based and speech-based methods. Most importantly, a multi-modal speaker role identification network (MMSRINet) is designed to achieve the SRI task by considering both the speech and textual modality features. To aggregate modality features, the modal fusion module is proposed to fuse and squeeze acoustic and textual representations by modal attention mechanism and self-attention pooling layer, respectively. Finally, the comparative approaches are validated on the ATCSpeech corpus collected from a real-world ATC environment. The experimental results demonstrate that all the comparative approaches worked for the SRI task, and the proposed MMSRINet shows competitive performance and robustness compared with the other methods on both seen and unseen data, achieving 98.56% and 98.08% accuracy, respectively.
A Trusted Paradigm of Data Management for Blockchain-Enabled Internet of Vehicles in Smart Cities

Internet of Vehicles(IoV) enables vehicles to generate and share messages to improve transportation safety and efficiency, especially in a smart city scenario where modern communication technology is utilized. The current IoV, however, faces three main issues: (1) existing frameworks fail to build a complete data management system, (2) received messages in an untrusted environment are challenging to assess for credibility, and (3) the centralized ways to store data are causing severe security and efficiency problems. Blockchain-enabled IoV (BIoV) provides an opportunity for addressing these issues. This paper proposes a trusted paradigm of data management based on a vehicle–road–cloud architecture. A few-shot learning model, Wasserstein Generative Adversarial Network (WGAN) with Synthetic Minority Oversampling Technique (SMOTE) sampling is designed to evaluate whether the uploading message is malicious. This paper also proposes the novel group-weighted-decay Practical Byzantine Fault Tolerance (PBFT) consensus algorithm, an improved version of PBFT to store data, and provides a comprehensive review of its viability and data management procedures. By employing the joint gwd-PBFT and Proof of Trust (PoT) consensus, the method mitigates the issue of excessive incentives. According to simulation results, the system's overall efficiency can be increased while retaining security and availability.