Reviewerly/exHarmony huggingface dataset:
Adversarial Attacks Against Detecting Bot Generated Text

Contemporary text generation models have produced neural text that is increasingly indistinguishable from human text. This is a threat if used maliciously to produce misinformation or extremist content. Recent work has explored building detectors to identify neural text. In this paper, I present an approach to generate adversarial text that fools detectors whilst remaining fluent to humans. This approach decreases recall on detecting GPT-2 generated text from 99 to 0.4% with an average of 6.4 words perturbed and recall on bot generated tweets from 93 to 33.9% with an average of 4.2 words perturbed. My findings also suggest that such attacks can be performed randomly and at low cost and still achieve a significant success rate. This indicates that present detectors are not robust and many real-world systems are vulnerable to augmented neural text. Finally, I experiment with possible defences, including training with adversarial examples, and using TF-IDF and stylometric features.
Manga Layout Analysis via Deep Learning

Manga Layout Analysis is an end-to-end system integration of instance segmentation models, novel algorithms, and optical character recognisers as a complete solution for the adaptation of manga into other media. As an innovative and unified approach, this system combines the aforementioned deep learning tools into one process to contribute to document layout analysis concerning manga on which only limited research has been conducted and aid Japanese companies adapt manga into anime and other media. This integrated system hence consists of instance segmentation models trained with the Manga109 dataset, algorithms leveraging the results of the instance segmentation models to analyse and evaluate the structure and semantics of manga, and Japanese hiragana and kanji optical character recognisers trained with Kuzushiji-MNIST datasets. The instance segmentation models for text and frames built using Mask R-CNN and PointRend achieved mAP values of 0.93 and 0.95, respectively (IoU = 0.5); for text, frames, and faces, the Mask R-CNN model achieved an mAP value of 0.90 (IoU = 0.5). The ResNet-50 and ResNet-101 optical character recognisers achieved high F1 scores of 0.89 and 0.90, respectively.
Image Geolocation from Alternative Cues

This paper presents an investigative analysis into the use of alternative cues, such as the metadata of images, for image geolocation purposes. Firstly, the metadata was retrieved, pre-processed and converted into a form that can be used for the analysis. A combination of this metadata is then used to make predictions of where the image was taken, with the help of machine learning algorithms through the use of a Support Vector Regression (SVR) model. The results of the geolocation using alternative cues were compared to those from existing image geolocation methods based on image features. The results proved that the alternative cues performed better at the higher country and continent levels, while existing geolocation methods prevail at the lower street, city and regional levels. An approach to combining the use of alternative cues and existing geolocation methods, which could help to increase the accuracy of image geolocation, is then proposed.
Path Planning Method of Unmanned Surface Vessel Based on Strategy Integration

In the task of path planning, the unmanned surface vessels (USV) are required to reach the destination while avoiding obstacle. However, it is difficult for USV to prioritize the two sub-target tasks of destination arrival and obstacle avoidance in a complex dynamic environment using traditional path planning methods. Based on the above considerations, a path planning method for USVs based on strategy integration is proposed. Firstly, the path planning scenario containing dynamic obstacles and two sub-target scenarios are set up. In order to strengthen its ability to reach the destination and avoid dynamic obstacles, the dynamic obstacles and the destination are removed separately in the two sub-target scenarios. Furthermore, the model for strategy selection is trained on action decisions in sub-target scenarios through double deep Q-Network with prioritized experience replay. Finally, a framework for strategy integration is designed to optimize strategy selection for USV. The simulation results show that the proposed method achieves a good overall performance and a higher path planning success rate than the traditional path planning method.
Web API Service to RDF Mapping Method for Querying Distributed Data Sources

The paper describes the method for querying spatio-temporal data across several data sources presented in form of ontology. Data from a single source is often cannot be used for complex analysis tasks. On the other hand, it is difficult to combine several data sources and use them together in a single system. If all data are stored in relational databases, it is possible to use distributed SQL. If the data are in several sources and the task is to obtain new knowledge from existing sources and flexibly connect new sources, it is better to use knowledge graphs and tools such as RDF for data representation and SPARQL for data querying. In case of full access to each knowledge source, the only suitable solution is federated SPARQL. The paper describes a method of working with Web services, so that for users it was like a federated SPARQL. The article proposes an algorithm of the method and provide an example of its use on the system of analysis of accident cards.
Machine Learning Approach for Detection of Mental Health

Mental health issues are the most prevalent between the ages of 15 and 60. People are unaware of this health issue. The identification of mental health made use of numerous machine-learning approaches. Artificial intelligence (AI) called machine learning enables computers to automatically learn from their experiences and advance over time without explicit programming. In this work, ANN, regression, linear regression, random forest, and decision tree models were utilized to identify mental health problems precisely. This was because machine-learned technology was an effective method for evaluating whether a person had a mental health issue. For the detection of mental diseases, machine learning is crucial. There are ANN and Linear Regression used for better results. There were key indicators as to whether an episode was imminent. These crises could be predictable if we could detect a pattern of stress, isolation, or exposure to triggers. The model data used is a survey based on a company that gave information about how many people had gone through mental health problems. Our proposed ANN model gives an accuracy of around 64% as we increase epochs, the model performs better, and accuracy increases.
U-Net as a Tool for Adjusting the Velocity Distributions of Rheomagnetic Fluids

Hydrodynamics of viscous fluids deals with Navier-Stokes equation - a partial differential equation with unknown distributions for velocity and pressure in a flow domain. It is difficult to find its analytical solution, especially in cases of unsteady flows, flows of non-Newtonian or rheomagnetic fluids. It is usually solved numerically using finite difference, finite element, or control volume methods. The goal of this research is application of proposed physics-based loss to rheomagnetic fluids flows modeling. The basic network architecture is U-Net. The network receives an image of the flow domain and calculates the fluid velocity distribution in a form of an image of the stream function distribution. The network was tested for the asymptotic case, the results were compared with numerical solution and known analytical solution. Proposed tool allows modeling 2D flows of rheomagnetic fluids. The proposed method is general and allows modeling 3D flows.
Abnormal Event Detection Method Based on Spatiotemporal CNN Hashing Model

With the development of public security awareness, anomaly detection has become a crucial demand in surveillance videos. To improve the accuracy of abnormal events detection, this paper proposes a novel spatio-temporal architecture called spatio-temporal CNN hashing model. In this paper, we propose a novel deep CNN learning framework that can exploit the differential binary motion image data to learn informative hash codes, to accurately classify abnormal events. However, we exploit the feature learning capabilities of CNN architectures to learn representative hash codes to obtain compact binary codes to solve the domain adaptation problem. In order to attain adequate computation for feature distance, we include hashing learning into a deep network. Exactly, a hashing layer is inserted after the last fully connected layer to move to the high-dimension and real-value features into low-dimension and binary features. Extensive experiments on a public available dataset have demonstrated the effectiveness of our framework, which achieves the state-of-the-art performance.
Leveraging Sequence Mining for Robot Process Automation

The automation of sequences of repetitive actions performed by human operators in interacting with software applications is crucial to prevent work from being perceived as alienating and boring. Robot applications can automatise these sequences once they have been identified. In this paper, we propose a two-step approach to mine sequences of actions that could be automated from log data produced by the interactions of a human operator with specific software applications. Since the number of possible sequences may be very high and not all the sequences are interesting to be automatised, we focus our mining process on sequences that meet precise patterns. First, Frequent Episode Mining algorithms are applied for extracting all the sequences of actions that occur with at least a minimum frequency. Then, we exploit fuzzy string matching based on the Levenshtein distance for filtering out the sequences that do not match established patterns. We evaluate the effectiveness of the approach using a benchmark dataset and present a case study on a real-world dataset of activity logs generated in the context of the AUTOMIA project.
Context-Aware QoS Prediction for Web Services Using Deep Learning

Quality of Service (QoS) is a non-functional property that reflects the extent to which services provided by the providers meet the needs of the users. As the application of IoT and web services in real world is increasing, QoS prediction is becoming important to predict which service is suitable for a particular user. QoS information is not readily available to providers while providing service recommendation to the users. Thus, there is no certainty in providing the right services to the users. Recommendation systems need better strategies for recommending and managing services according to the user requirements. So, QoS prediction is highly essential for recommending the most suitable service for a user at that instant. In this work, Long short-term memory network (LSTM), Bidirectional long-short term memory network (BiLSTM), convolutional neural network (CNN) and Gated Recurrent Unit Network(GRU) have been used to perform QoS prediction. For this task, data from WS-Dream dataset is used. Initially, Fuzzy C-Means (FCM) algorithm is used to cluster similar users and services. Neural network algorithms have been implemented for accomplishing the prediction task. Prediction is performed in terms of response time and throughput properties. The performance of these algorithms is compared using Mean Absolute Error and Root mean squared error metrices.
Bayesian Particle Filtering

This chapter defines Bayesian particle filtering and applies it to tracking problems that are difficult to solve by Kalman filter methods. In addition, it shows that particle filters can be applied to solve problems other than tracking. The process of computing the posterior distribution on the target’s path given the measurements received in a fixed time interval $$\left[ {0,T} \right]$$ is called fixed interval smoothing and the resulting posterior distribution is the smoothed solution. This chapter presents repeated filtering, a simple and general method for smoothing particle filters. The chapter closes with examples of applying repeated filtering to find smoothed solutions to particle filter problems that are difficult to solve by any other method.
Spatial Correlations in the Qubit Properties of D-Wave 2000Q Measured and Simulated Qubit Networks

We show strong positive spatial correlations in the qubits of a D-Wave 2000Q quantum annealing chip that are connected to qubits outside their own unit cell. By simulating the dynamics of spin networks, we then show that correlation between nodes is affected by a number of factors. The different connectivity of qubits within the network means that information transfer is not straightforward even when all the qubit-qubit couplings have equal weighting. The similarity between connected nodes is further changed when the couplings’ strength is scaled according to the physical length of the connections (here to simulate dipole-dipole interactions). This highlights the importance of understanding the architectural features and potentially unprogrammed interactions/connections that can divert the performance of a quantum system away from the idealised model of identical qubits and couplings across the chip.
Design of Photovoltaic Power Generation Servo System Based on Discrete Adaptive Network Dynamic Surface Control Technology

In recent years, under the development of the dual carbon goal, the energy crisis has become increasingly serious, and China has also experienced serious power rationing. However, the research on dynamic surface control technology in solar tracking systems in nonlinear control systems is mostly based on continuous-time systems, while adaptive dynamic surface control based on discrete-time nonlinear control systems can describe an actual control system more accurately in the production process. It can effectively suppress interference with extremely high stability and safety. To solve the problem of low efficiency in photovoltaic power generation, this research first built a photovoltaic power generation servo system model based on the parameter of uncertainty. Then, a discrete adaptive neural network dynamic surface (DANNDS) controller was designed to solve the problems in the design of the traditional backstepping method. Finally, based on the designed method of a dynamic surface controller, a discrete adaptive neural network quantization controller (DANNQC) for the photovoltaic power generation servo system was designed by introducing external disturbance. The control parameters and their studied ranges were as follows: The reference signals were or1=sin(0.1t) and or2=cos(0.1t). The parameters of the virtual control law and the final control law were m11=0.01, m22=0.01, m12=0.02, m13=0.02, and m23=0.02. The time constant of the low-pass filter was ζ12=ζ13=ζ22=ζ23=0.005. The parameters of the parameter regulation law were ρ12=ρ13=ρ22=ρ23=0.0005 and a12=a13=20, a22=a23=22. The research results show that the MTE, RMSTE, and 2NTE scores of the height angle servo motor of the DANNDS control method were 0.0026, 7.0279 × 10−4, and 0.3552, respectively. The scores for each index of the azimuth servo motor were 0.0028, 8.9237 × 10−4, and 0.4511, respectively. The height angle tracking error for the DANNQC control method was [−0.02,0.022]. The azimuth tracking error was [−0.03,0.03]. In summary, the photovoltaic power generation servo system based on the DANNQC has a better control performance. By controlling the height angle and azimuth angles, it can better track the position of the sun and adjust the position of the photovoltaic panel in real time. The sun’s rays illuminate the photovoltaic panel at an appropriate angle to achieve maximum power generation efficiency, which is of great practical significance for the development of solar technology.
Camera-irrelevant Feature Invariance Learning for Unsupervised Person Re-identification

Abstract Unsupervised person re-identification (re-ID) aims to match pictures of target pedestrians from some other cameras without any labels. The existing methods usually adopt a cluster-based pipeline to generate false labels for training. However, because of the differences in camera configuration, taking pictures Angle, illumination intensity and pedestrian background, unlabeled pedestrian training samples captured by different cameras are hard to classify into a cluster. To solve this problem, the paper proposes a camera-irrelevant feature learning framework, which learns camera-irrelevant features and camera-irrelevant distances through network training and clustering. Specifically, during training, images of other camera styles are generated, which enriches cross-camera learning. During clustering, we propose a camera-irrelevant distance matrix, which reduces the inter-class variance caused by camera variance.
Exploring the Robustness of Magnetic Ring Arrays Reservoir Computing with Linear Field Calibration

One of the challenges for reservoir computing is the robustness of the implementation in the face of fabrication error. If a system is too sensitive to fabrication error, then each manufactured reservoir becomes a unique artefact with unique computational properties. Under most circumstances, this is undesirable as it makes reproduction of results, or useful systems, complicated. This paper uses simulation to examine the properties of nano-scale magnetic ring arrays as reservoir computers under parameters corresponding to a wide variety of physically derived parameters, and investigates the effectiveness of linear field calibration to minimise the difference in unexpected behaviour of the systems.
State of Development and Research

So, what might an initial thought framework of digital psychology look like? Systemic adaptations, the complexity of digital spaces, research on artificial intelligence, logic, thinking and human-machine interactions – these are some topics worth looking at.
Privacy Preserving Data Fusion

Data fusion combines multiple datasets to make inferences that are more accurate, generalizable, and useful than those made with any single dataset alone. However, data fusion poses a privacy hazard due to the risk of revealing user identities. We propose a privacy preserving data fusion (PPDF) methodology intended to preserve user-level anonymity while allowing for a robust and expressive data fusion process. PPDF is based on variational autoencoders and normalizing flows, together enabling a highly expressive, nonparametric, Bayesian, generative modeling framework, estimated in adherence to differential privacy – the state-of-the-art theory for privacy preservation. PPDF does not require the same users to appear across datasets when learning the joint data generating process and explicitly accounts for missingness in each dataset to correct for sample selection. Moreover, PPDF is model-agnostic: it allows for downstream inferences to be made on the fused data without the analyst needing to specify a discriminative model or likelihood a priori. We undertake a series of simulations to showcase the quality of our proposed methodology. Then, we fuse a large-scale customer satisfaction survey to the customer relationship management (CRM) database from a leading U.S. telecom carrier. The resulting fusion yields the joint distribution between survey satisfaction outcomes and CRM engagement metrics at the customer level, including the likelihood of leaving the company’s services. Highlighting the importance of correcting selection bias, we illustrate the divergence between the observed survey responses vs. the imputed distribution on the customer base. Managerially, we find a negative, nonlinear relationship between satisfaction and future account termination across the telecom carrier’s customers, which can aid in segmentation, targeting, and proactive churn management. Overall, PPDF will substantially reduce the risk of compromising privacy and anonymity when fusing different datasets.
Efficient inverse Z-transform: sufficient conditions

We derive several sets of sufficient conditions for applicability of the new efficient numerical realization of the inverse Z-transform. For large n, the complexity of the new scheme is dozens of times smaller than the complexity of the trapezoid rule. As applications, pricing of European options and single barrier options with discrete monitoring are considered; applications to more general options with barrier-lookback features are outlined. In the case of sectorial transition operators, hence, for symmetric L\'evy models, the proof is straightforward. In the case of non-symmetric L'evy models, we construct a non-linear deformation of the dual space, which makes the transition operator sectorial, with an arbitrary small opening angle, and justify the new realization. We impose mild conditions which are satisfied for wide classes of non-symmetric Stieltjes-L\'evy processes.
Gaining Insight into Adoption of Immersive Technologies in Higher Education

Immersive technologies such as virtual, augmented and mixed reality, also referred as extended reality (XR), can be integrated into education in several disciplines to facilitate learning experiences that might be difficult or unfeasible at a particular time or place. Our group manages an XR lab that supports educators in integrating immersive technologies in their courses. We investigated how and why those educators used immersive technologies to inform how to improve further adoption. The results show the value of having a contact person that actively communicates the available resources to educators, the need for training in immersive technologies, the need for technical support and importance of logistics for accessing hardware as well as organising educational sessions with students. We propose that the results from this work can inform understanding of technology adoption in higher education and help to design strategies to reduce the digital divide.
Delta Operator-Based Modelling and Control of High Power Induction Motor Using Novel Chaotic Gorilla Troop Optimizer

New chaotic gorilla troop optimizer is developed in this study. Different one-dimensional chaotic maps are used to alter two parameters of the parent algorithm. Thus, the development of new chaotic algorithms is based on ten well-known and widely cited chaotic maps. The suggested method is tested using two varieties of benchmark functions, namely unimodal and multi-modal. Using this proposed algorithm, a 500 hp ac motor model is diminished. Further, the controller is also developed taking advantage of the unified domain method. The controller implementation is done with the help of approximate model matching technique. The methods proposed outclass manifold cutting-edge approaches with respect to the optimal solutions as well as convergence graphs. In order to account for the stability of the proposed algorithms, the standard deviation of the optimal values is examined. Selected results of the proposed methods are displayed in this work. All of the experiments conducted have yielded promising results.
Rhizome Theory and Twenty-First Century Learning

French philosopher Gilles Deleuze and psychoanalyst Félix Guattari introduced the rhizomeRhizome concept and its principles and their legacy influenced many academic disciplines including educationEducation. The studies have been conducted in education, teaching, and learningLearning using the rhizome concept, and other theories and philosophy introduced by Deleuze and GuattariDeleuze and Guattari as a theoretical lens. The studies show that the concept and principles can apply not only to network-based learning but also to other divergent learning environments. This introduction provides an overview of the chapters contained in this book.