Reviewerly/exHarmony huggingface dataset:
Profiling Darkweb Child Sexual Exploitation Material Forum Members Using Longitudinal Posting History Data

Darkweb fora dedicated to the illegal exchange of child sexual exploitation material (CSEM) continue to thrive. Profiling forum members based on their communication patterns will increase our insights in the dynamics of online CSEM and may aid law enforcement to identify those members who are most influential and pose the highest risk. The current study uses data from a large English language Darkweb CSEM forum that was active between 2010 and 2014, containing over 400,000 posts. Posts were time stamped, categorized based on subforum topic, and linked to individual forum members by nickname. Group-based trajectory modeling was subsequently applied to derive forum member profiles based on members’ posting history. Analyses show that over the course of the observation period, overall activity levels—in terms of total number of posting members and the average number of posts per month per member—fluctuate substantially and that multiple developmental pathways—in terms of monthly patterns in the frequency of posts by individual members—can be distinguished. Theoretical and practical ramifications of these findings are discussed.
Hand-Gesture-Based Touchless Exploration of Medical Images with Leap Motion Controller

Hand gesture recognition has become one of the most interesting means of contactless human-computer interaction. There is significant importance for commanding medical images during surgical procedures by the mean of touchless hand gestures for reducing the time of surgery and the risk of contamination. In this work, we used the Leap Motion Controller as an acquisition device, with different classification methods, to recognize 11 hand gestures dedicated to manipulating medical images through a touchless graphical user interface. This framework was trained and tested on a benchmark dataset called LeapGestureDB. We worked with statistical features calculated from fingers and hand data, then normalized and fed into various classifiers such as the support vector machine, the nearest neighbor, the decision tree, the random forest, the AdaBoost, the linear discriminant analysis and the multi-layer perceptron. The highest accuracy was 91.73% and 89.91% using the cubic SVM and the multilayer perceptron, respectively. We developed a contactless interface based on the best recognition rate in order to facilitate the way of interaction with medical images in the operating room.
Two viewpoints based real‐time recognition for hand gestures

It is extremely challenging to accomplish excellent accuracy for gesture recognition using an approach where complexity in computation time for recognition is less. This study compares accuracy in hand gesture recognition of a single viewpoint set-up with proposed two viewpoint set-up for different classification techniques. The efficacy of the presented approach is verified practically with various image processing, feature extraction and classification techniques. Two camera system make geometry learning and three-dimensional (3D) view feasible compared to a single camera system. Geometrical features from additional viewpoint contribute to 3D view estimation of the hand gesture. It also improves the classification accuracy. Experimental results demonstrate that the proposed method show escalation in recognition rate compared to the single-camera system, and also has great performance using simple classifiers like the nearest neighbour and decision tree. Classification within 1 s is considered as real-time in this study.
Speech Audio Splicing Detection and Localization Exploiting Reverberation Cues

Manipulating speech audio recordings through splicing is a task within everyone's reach. Indeed, it is very easy to collect through social media multiple audio recordings from well-known public figures (e.g., actors, politicians, etc.). These can be cut into smaller excerpts that can be concatenated in order to generate new audio content. As a fake speech from a famous person can be used for fake news spreading and negatively impact on the society, the ability of detecting whether a speech recording has been manipulated is a task of great interest in the forensics community. In this work, we focus on speech audio splicing detection and localization. We leverage the idea that distinct recordings may be acquired in different environments, which are typically characterized by distinctive reverberation cues. Exploiting this property, our method estimates inconsistencies in the reverberation time throughout a speech recording. If reverberation inconsistencies are detected, the audio track is tagged as manipulated and the splicing point time instant is estimated.
Utilizing blockchain technology for IoT-based healthcare systems

Abstract Recent years have witnessed a widespread interest in healthcare issues and the search for faster and safer service for patients. Electronic health record was established to provide advanced health services. Researchers seek to provide permanent and simplified ways to monitor patients remotely using remote patient monitors. One of these methods is the use of Internet of Things (IoT) devices, where the healthcare provider can monitor the patient remotely. However, with the current centralized electronic patient record system and increase in number of IoT devices, security and privacy issues have arisen, as some patients may not want to disclose their data, in addition to particular concerns or risks that affect the patient’s life. To reduce these issues, several studies have presented the use of blockchain technology as a trustworthy network of solutions to ensure patient information security and secure the transfer of IoT devices’ data. In this regard, this research is an attempt to present a new framework that facilitates the storage and transfer of patient data in the blockchain by merging the electronic health record and remote patient monitoring techniques into a single framework using Django. This framework allows the transfer of data from IoT devices to multiple peers, making use of the smart contract provided by the Ethereum platform.
Deep‐features with Bayesian optimized classifiers for the breast cancer diagnosis

Abstract To improve the state‐of‐the‐art works in breast cancer diagnosis, this paper proposes ensemble‐based deep‐transfer learning with classifiers namely optimizable K‐nearest neighbors, optimizable naïve Bayes, optimizable ensemble, and optimizable support vector machine algorithms for the auto‐feature extraction, detection, and severity classification of input mammograms. The hyperparameters of these classification algorithms are optimized by using the Bayesian optimization technique. The extracted robust features are normalized and then classified using the Bayesian optimized classifiers. Thus, the work throws a light of research on detecting whether the input mammogram is normal or abnormal. Afterward, it further focuses on the severity classification of abnormalities that is, benign or malignant. The aforesaid algorithms are trained and tested for the three‐class classification problem, thus achieving a maximum performance using the Bayesian optimized SVM algorithm applied with ResNet18 deep features providing classification accuracy of 99.689% for MIAS and 98.883% for INbreast dataset.
Topic modeling and its applications in materials science and engineering

Topic modeling is an unsupervised task which helps to capture hidden semantics structure of words in a document. It is significant area of research due to its diverse applications in multidisciplinary fields such as materials sciences, chemical engineering, industrial aspects, social media analysis, bioinformatics and many more. In materials science and engineering, with the help of topic modeling methods, clustering of keywords related to material synthesis steps can be performed which further leads to the detection of topics related to various steps. e.g. keywords related to synthesis procedures can be clustered into topics such as centrifuging, grinding, heating, and dissolving. Classification technique may be used on these topics to make groups of materials based upon their synthesis steps such as hydrothermal, or solid-state. In this paper, an overview and evolution of prominent topic modeling techniques is presented. Also, applications of topic modeling in materials science are discussed.
Influence of Directional Sound Cues on Users’ Exploration Across 360° Movie Cuts

Virtual reality (VR) is a powerful medium for $360^{\circ }$360∘ storytelling, yet content creators are still in the process of developing cinematographic rules for effectively communicating stories in VR. Traditional cinematography has relied for over a century on well-established techniques for editing, and one of the most recurrent resources for this are cinematic cuts that allow content creators to seamlessly transition between scenes. One fundamental assumption of these techniques is that the content creator can control the camera; however, this assumption breaks in VR: Users are free to explore $360^{\circ }$360∘ around them. Recent works have studied the effectiveness of different cuts in $360^{\circ }$360∘ content, but the effect of directional sound cues while experiencing these cuts has been less explored. In this work, we provide the first systematic analysis of the influence of directional sound cues in users’ behavior across $360^{\circ }$360∘ movie cuts, providing insights that can have an impact on deriving conventions for VR storytelling.
Time Efficient Federated Learning with Semi-asynchronous Communication

With the explosive growth of massive data generated by smart Internet of Things (IoT) devices, federated learning has been envisioned as a promising technique to provide distributed machine learning services while protecting training data privacy. However, conventional federated learning protocols have shown significant drawbacks in regards of efficiency and scalability. First, since the synchronous communication model of federated learning and the computation capability of each device is different, the straggled users could severely desegregate the efficiency. Second, in synchronous communication, there is no effective client selection mechanism to make the model perform better in the early stage. Third, how to coordinate the communication of various nodes to accelerate global convergence is also one of the issues that need to be considered. To solve the above-mentioned problems, we propose a semi-asynchronous federated learning mechanism where a data expansion method is used to effectively reduce the stragglers existing in both synchronous and asynchronous communication models. Moreover, we also designed a priority function to make the accuracy increase rapidly in the early stage. Experimental results demonstrate that our proposed method have higher accuracy and faster convergence time compared with existing synchronization methods.
Inversion Based on a Detached Dual-Channel Domain Method for StyleGAN2 Embedding

A style-based generative adversarial network (StyleGAN2) yields remarkable results in image-to-latent embedding. This work proposes a Detached Dual-channel Domain Encoder as an effective and robust method to embed an image to a latent code, i.e., GAN inversion. It infers a latent code from two aspects: a) a detached dual-channel design to support faithful image reconstruction; and b) a local skip connection that allows conveying pieces of information with image details. We further introduce a hierarchical progressive training strategy that allows the proposed encoder to separately capture different semantic features. The qualitative and quantitative experimental results show that the well-trained encoder can embed an image into a latent code in StyleGAN2 latent space with less time than its peers while preserving facial identity and image details well.
Information space of multi-sensor networks

It is a challenging problem to explore the capability of multi-sensor networks due to the identity of the underlying information space across modalities. In this paper, the information space for multi-sensor networks is developed from information geometry. The relationship between information space and the performance of multi-sensor networks is investigated. Different sensor information obtained by multi-sensor networks is represented, analyzed and fused concisely. The structure of the information space is studied such as geodesic, Ricci tensor and the information metric matrix. The structural properties of the information space are introduced: i) the symmetry; ii) the connection between information space’s curvature and Einstein’s field equation; iii) noise essence conjecture. The proposed analysis techniques are validated in many scenarios. The theoretical demonstration and numerical results indicate that the information described in different coordinate systems is equivalent.
A reversible extended secret image sharing scheme based on Chinese remainder theorem

In a secret image sharing (SIS) scheme, a secret image is divided into n shadow images. Then, any t or more shadow images can recover the secret image. Different from pure SIS, extended SIS (ESIS) further embeds shadow images into a cover image to generate stego images. In this way, ESIS can be more secure because shadow images are always noise-like images which might arouse suspicion of attackers, while stego images are meaningful images. Furthermore, if both secret image and cover image can be recovered from enough stego images in an ESIS scheme, the scheme is called reversible ESIS (RESIS). This paper utilizes a new secret sharing scheme based on Chinese remainder theorem for polynomial ring to design a RESIS scheme, which uses polynomials over F2[x] to divide secret image. Besides, least significant bit substitution technology is employed to hide shadow images and generate stego images. The proposed RESIS scheme can guarantee both the secret and cover image totally lossless reconstruction. Besides, experimental results show that the shadow images have satisfactory quality which is not affected by different cover images.
Estimation of Individual Device Contributions for Incentivizing Federated Learning

Federated learning (FL) is an emerging technique used to collaboratively train a machine-learning model using the data and computation resources of mobile devices without exposing private or sensitive user data. Appropriate incentive mechanisms that motivate the data and mobile-device owner to participate in FL is key to building a sustainable platform. However, it is difficult to evaluate the contribution levels of participants to determine appropriate rewards without large computation and communication overhead. This paper proposes a computation- and communication-efficient method of estimating participants contribution levels. The proposed method requires a single FL training process, which significantly reduces overhead. Performance evaluations are done using the MNIST dataset, showing that the proposed method estimates participant contributions accurately with 46-49% less computation overhead and no communication overhead, as compared to a naive estimation method.
Fake Review Classification Using Supervised Machine Learning

The revolution of social media has propelled the online community to take advantage of online reviews for not only posting feedback about the products, services, and other issues but also assists individuals to analyze user's feedback for making purchase decisions, and companies for improving the quality of manufactured goods. However, the propagation of fake reviews has become an alarming issue, as it deceives online users while purchasing and promotes or demotes the reputation of competing brands. In this work, we propose a supervised learning-based technique for the detection of fake reviews from the online textual content. The study employs machine learning classifiers for bifurcating fake and genuine reviews. Experimental results are evaluated against different evaluation measures and the performance of the proposed system is compared with baseline works.
Rapid Cyber-bullying detection method using Compact BERT Models

Nowadays, many people use their social media platform to spread hate online and that is why the problem of cyber-bullying detection has been the focus of many researchers over the past decade. In this work, we tackle this problem with transfer learning. We use various compact BERT models and fine-tune them with hate-speech data. We incorporate Focal Loss function to handle class imbalance in the data. Using this approach, we were able to achieve state-of-the-art results of 0.91 precision, 0.92 recall and 0.91 F1-score on the hate-speech dataset. Additionally, using our transfer learning pipeline, we show that the more compact BERT models are significantly faster in detection and are suitable for real-time applications of cyber-bullying detection.
Learning with virtual humans: Introduction to the special issue

Virtual humans are embodied agents with a human-like appearance. In educational contexts, virtual humans are often designed to help people learn. In this special issue, we see six representations of this field from different perspectives, including system analysis and evaluation, value-added studies, and reviews of the literature. This diversity of research exemplifies the exciting field of virtual humans. In this introduction to the special issue, we discuss virtual humans in educational contexts, highlight the articles in this special issue, and outline productive future research directions.
Navigation in GPS Spoofed Environment Using M-Best Positioning Algorithm and Data Association

Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique's performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm's efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE).
Effects of Panoramic Image Virtual Reality on the Workplace English Learning Performance of Vocational High School Students

Digital learning has become an inevitable trend with many benefits for students’ learning performance and motivation. However, Virtual Reality, a pervasive mode of digital learning nowadays, has incurred ambivalence among teachers over the years due to its high cost and other technical concerns. Panoramic image VR (PIVR) is a cost-efficient alternative to conventional VR in classroom settings, and is featured in this quasi-experimental research as a digital means to simulate language situations in future workplaces for a total of 106 vocational high school students majoring in auto mechanics. All student participants were divided into an experimental and a control group. The results showed that learning performance, motivation, and satisfaction were better in the experimental group than in the control group thanks to the use of PIVR. Furthermore, it was found that the learning performance of the experimental group students with medium prior knowledge was significantly better than that of the control group students of the same level of prior knowledge. However, no such difference was found for the remaining participants. It is thus inferred that PIVR-based digital learning might be more effective for students with medium prior knowledge than for those with either high or low prior knowledge.
Revisiting the performance of evolutionary algorithms

The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research and application demonstrates improved performance for domain-specific challenges. However, developing a new algorithm or comparison and selection of existing EAs for challenges in the field of optimization is relatively unexplored. The performance of different well-established algorithms is, therefore, investigated in this work. The selection of algorithms using nonparametric tests encompasses different categories to include- Genetic Algorithm, Particle Swarm Optimization, Harmony Search Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Firefly algorithm, Differential Evolution, and Artificial Bee Colony. These algorithms are applied to solve test functions, including unconstrained, constrained, industry specific problems, CEC 2011 real world optimization problems and selected CEC 2013 benchmark test functions. The three distinct performance metrics, namely, efficiency, reliability, and quality of solution derived using the quantitative attributes are provided to evaluate the performance of the employed EAs. The categorical assignment of performance attributes helps to compare different algorithms for a specific optimization problem while the performance metrics are useful to provide the common platform for new or hybrid EA development.