Reviewerly/exHarmony huggingface dataset:
An Instance Segmentation Based Framework for Large-Sized High-Resolution Remote Sensing Images Registration

Feature-based remote sensing image registration methods have achieved great accomplishments. However, they have faced some limitations of applicability, automation, accuracy, efficiency, and robustness for large high-resolution remote sensing image registration. To address the above issues, we propose a novel instance segmentation based registration framework specifically for large-sized high-resolution remote sensing images. First, we design an instance segmentation model based on a convolutional neural network (CNN), which can efficiently extract fine-grained instances as the deep features for local area matching. Then, a feature-based method combined with the instance segmentation results is adopted to acquire more accurate local feature matching. Finally, multi-constraints based on the instance segmentation results are introduced to work on the outlier removal. In the experiments of high-resolution remote sensing image registration, the proposal effectively copes with the circumstance of the sensed image with poor positioning accuracy. In addition, the method achieves superior accuracy and competitive robustness compared with state-of-the-art feature-based methods, while being rather efficient.
Probabilistic learning vector quantization on manifold of symmetric positive definite matrices

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data , and motor imagery electroencephalogram (EEG) data demonstrate the superior performance of the proposed method.
Formation and development of digital transformation of domestic education on the basis of systemic convergence of pedagogical science and technology

This paper justifies and defines the terms "digital transformation of education" and "digital paradigm of education". It reveals the essence of the system approach in the formation of the digital paradigm of education, and presents the conditions needed to form the values of domestic education in the digital paradigm period. The work also shows how to form a digital paradigm of domestic education based on a systematic convergence of pedagogical science and technology.
Interval Extended Kalman Filter—Application to Underwater Localization and Control

The extended Kalman filter has been shown to be a precise method for nonlinear state estimation and is the facto standard in navigation systems. However, if the initial estimated state is far from the true one, the filter may diverge, mainly due to an inconsistent linearization. Moreover, interval filters guarantee a robust and reliable, yet unprecise and discontinuous localization. This paper proposes to choose a point estimated by an interval method, as a linearization point of the extended Kalman filter. We will show that this combination allows us to get a higher level of integrity of the extended Kalman filter.
Activity Detection from Electricity Consumption and Communication Usage Data for Monitoring Lonely Deaths

As the number of single-person households grows worldwide, the need to monitor their safety is gradually increasing. Among several approaches developed previously, analyzing the daily lifelog data generated unwittingly, such as electricity consumption or communication usage, has been discussed. However, data analysis methods in the domain are currently based on anomaly detection. This presents accuracy issues and the challenge of securing service reliability. We propose a new analysis method that finds activities such as operation or movement from electricity consumption and communication usage data. This is evidence of safety. As a result, we demonstrate better performance through comparative verification. Ultimately, this study aims to contribute to a more reliable implementation of a service that enables monitoring of lonely deaths.
Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Literacy rate of deaf students is very less in India. So there is a need to build an effective academic prediction model for identifying weak deaf students. Many machine learning techniques like Decision tree, Support Vector Machine, Neural Network are used to build prediction models. But the most preferred technique is neural network. It is found out that regression model build with neural networks takes more time to converge and the error rate is quite high. To solve the problems of neural network, we use Particle Swarm Optimization (PSO) for weight adjustment in the neural network. But, one of the main drawback of PSO lies in setting the initial parameters. So, a new PSO algorithm which determines the initial weight of the neural network using regression equation is proposed. The results show that neural network build with the proposed PSO algorithm performs well than neural network build with basic PSO algorithm. The Mean Square Error (MSE) achieved in this work is 0.0998, which is comparatively less than many existing models.
Addressing topic modeling with a multi-objective optimization approach based on swarm intelligence

Topic modeling is a growing field within the area of text analysis that extracts underlying topics from document collections. Several objectives can be simultaneously considered when designing an approach for topic modeling. A multi-objective optimization approach based on the swarm intelligence of a bee colony (MOABC, Multi-Objective Artificial Bee Colony) has been designed, implemented, and tested. This new approach has been evaluated by using documents from the Reuters-21578 and TagMyNews datasets. Three objective functions (coherence, coverage, and perplexity) and three multi-objective metrics (hypervolume, set coverage, and distance to the ideal point) have been considered in two topic scenarios. Results show that MOABC provides relevant improvements with respect to LDA (Latent Dirichlet Allocation, the most used approach for topic modeling) and MOEA/D (Multi-Objective Evolutionary Algorithm based on Decomposition, the only multi-objective approach published to date). This demonstrates that the multi-criteria nature of topic modeling should be exploited with multi-objective optimization approaches.
Did they answer? Subjective acts and intents in conversational discourse

Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences.At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations.However, current discourse data and frameworks ignore the social aspect, expecting only a single ground truth.We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents.We carefully analyze our dataset and create computational models to (1) confirm our hypothesis that taking into account the bias of the interpreters leads to better predictions of the interpretations, (2) and show disagreements are nuanced and require a deeper understanding of the different contextual factors.We share our dataset and code at http://github.com/ elisaF/subjective_discourse.
Posthumanism and the digital university: texts, bodies and materialities

"Posthumanism and the digital university: texts, bodies and materialities." Learning, Media and Technology, 46(3), pp. 366–367
3D Environmental Perception Modeling in the Simulated Autonomous-Driving Systems

Self-driving vehicles require a number of tests to prevent fatal accidents and ensure their appropriate operation in the physical world. However, conducting vehicle tests on the road is difficult because such tests are expensive and labor intensive. In this study, we used an autonomous-driving simulator, and investigated the three-dimensional environmental perception problem of the simulated system. Using the open-source CARLA simulator, we generated a CarlaSim from unreal traffic scenarios, comprising 15000 camera-LiDAR (Light Detection and Ranging) samples with annotations and calibration files. Then, we developed Multi-Sensor Fusion Perception (MSFP) model for consuming two-modal data and detecting objects in the scenes. Furthermore, we conducted experiments on the KITTI and CarlaSim datasets; the results demonstrated the effectiveness of our proposed methods in terms of perception accuracy, inference efficiency, and generalization performance. The results of this study will faciliate the future development of autonomous-driving simulated tests.
Towards Foundations of Categorical Cybernetics

We propose a categorical framework for processes which interact bidirectionally with both an environment and a 'controller'.Examples include open learners, in which the controller is an optimiser such as gradient descent, and an approach to compositional game theory closely related to open games, in which the controller is a composite of game-theoretic agents.We believe that 'cybernetic' is an appropriate name for the processes that can be described in this framework.
Learning Local Regularization for Variational Image Restoration

In this work, we propose a framework to learn a local regularization model for solving general image restoration problems. This regularizer is defined with a fully convolutional neural network that sees the image through a receptive field corresponding to small image patches. The regularizer is then learned as a critic between unpaired distributions of clean and degraded patches using a Wasserstein generative adversarial networks based energy. This yields a regularization function that can be incorporated in any image restoration problem. The efficiency of the framework is finally shown on denoising and deblurring applications.
Secure Smart Green House Farming using Blockchain Technology

The evolving agricultural technologies used mostly for remote access and modernization in farming connected via the Internet of Things (IoT) have been grown rapidly. However because of the wide size of all its broadcaster's propagandizing existence, it has some significant concerns with respect to security and privacy. We utilize blockchain to address such security breaches, allowing the development of a decentralized distributed blockchain system that's also exchanged between the IoT cluster heads. This article's major focus is provide smart greenhouse farmlands with a portable blockchain-based infrastructure which offers integrity and confidentiality. Where, green-house IoT sensor nodes are function as a blockchain centrally controlled to optimize the energy consumption by utilizing secure immutable ledgers. Furthermore, we present a significant solution that integrates blockchain technology via IoT devices to offer Smart Greenhouse cultivation with an enhanced secure communication.
Squeezing Value of Cross-Domain Labels: A Decoupled Scoring Approach for Speaker Verification

Domain mismatch often occurs in real applications and causes serious performance reduction on speaker verification systems. The common wisdom is to collect cross-domain data and train a multi-domain PLDA model, with the hope to learn a domain-independent speaker subspace. In this paper, we firstly present an empirical study to show that simply adding cross-domain data does not help performance in conditions with enrollment-test mismatch. Careful analysis shows that this striking result is caused by the incoherent statistics between the enrollment and test conditions. Based on this analysis, we present a decoupled scoring approach that can maximally squeeze the value of cross-domain labels and obtain optimal verification scores in the enrollment-test mismatch condition. When the statistics are coherent, the new formulation falls back to the conventional PLDA. Experimental results on cross-channel test show that the proposed approach is highly effective and is a principal solution to domain mismatch.
Application of Virtual Reality Technology in Psychotherapy

With the progress of the times, the social attention to mental health has been continuously improved, which has led to the continuous development of psychotherapy technology. Some newly developed treatment technologies have made up for the limitations of traditional psychotherapy technology and in many aspects made major breakthroughs. Virtual reality technology is a psychotherapy technology developed in recent years. It combines with traditional psychotherapy technology to adjust psychological disorders and improve the therapeutic effect.Virtual reality system has good immersion, interactivity and conception. At present, clinical psychologists abroad have applied virtual reality technology to the treatment of anxiety disorder, post-traumatic stress disorder, eating disorder, sexual dysfunction and other psychological disorders, and achieved remarkable results. This paper introduces the related research and treatment of these aspects, summarizes the advantages and disadvantages of virtual reality technology in psychotherapy, and looks forward to the future.
Efficient Ciphertext-Policy Attribute-Based Encryption Constructions with Outsourced Encryption and Decryption

The invention of the Ciphertext-Policy Attribute-Based Encryption scheme opened a new perspective for realizing attribute-based access control systems without being forced to trust the storage service provider, which is the case in traditional systems where data are sent to the storage service provider in clear and the storage service provider is the party that controls the access to these data. In the Ciphertext-Policy Attribute-Based Encryption model, the data owner encrypts data using an attribute-based access structure before sending them to the storage service, and only users with authorized sets of attributes can successfully decrypt the generated ciphertext. However, Ciphertext-Policy Attribute-Based Encryption schemes employ expensive operations (i.e., bilinear pairings and modular exponentiations) and generate long ciphertexts and secret keys, which makes them hard to implement in real-life applications especially for resource-constrained devices. In this paper, we propose two Ciphertext-Policy Attribute-Based Encryption Key Encapsulation Mechanisms that can be provided as services in the cloud, minimizing the user’s encryption and decryption costs without exposing any sensitive information to the public cloud provider. In the first scheme, the ABE Service Provider is considered fully untrusted. On the other hand, the second scheme requires the ABE Service Provider to be semi-trusted (Honest-but-Curious) and does not collude with illegitimate users. Both schemes are proved to be selectively CPA-secure in the random oracle. The theoretical and experimental performance results show that both our first and second schemes are more efficient than the reviewed outsourced CP-ABE schemes in terms of user-side computation, communication, and storage costs.
Developing Intelligent Interviewers to Collect the Medical History: Lessons Learned and Guidelines

Background: Physicians spend a lot of time in routine tasks, i.e. repetitive and time consuming tasks that are essential for the diagnostic and treatment process. One of these tasks is to collect information on the patient’s medical history. Objectives: We aim at developing a prototype for an intelligent interviewer that collects the medical history of a patient before the patient-doctor encounter. From this and our previous experiences in developing similar systems, we derive recommendations for developing intelligent interviewers for concrete medical domains and tasks. Methods: The intelligent interviewer was implemented as chatbot using IBM Watson assistant in close cooperation with a family doctor. Results: AnCha is a rule-based chatbot realized as decision tree with 75 nodes. It asks a maximum of 44 questions on the medical history, current complaints and collects additional information on the patient, social details, and prevention. Conclusion: When developing an intelligent digital interviewer it is essential to define its concrete purpose, specify information to be collected, design the user interface, consider data security and conduct a practice-oriented evaluation.
Data Augmentation and Faster RCNN Improve Vehicle Detection and Recognition

Vehicle detection is a technique that uses photographs to identify and label vehicles. Images for their learning may be taken in various scenarios, such as focus ranges or different lighting. Lower yields may have arisen from the photographs taken under normal conditions. Better accuracy can be achieved by improving the original frames by correcting the point of view conditions. The simulation can test machine learning performance by simulating different scenarios, such as whether the images are good. One method of simulating the initial image conditions is to use data augmentation. Several techniques for machine learning for image recognition have been developed, such as Neural Networks and Deep Learning, which can be used in large image datasets indefinitely. An attribute from each type or class's images is used to classify the type or classes in Deep Learning. Faster R-CNN with Data Augmentation was proposed as a tool for classifying images learned from a large dataset. Data augmentation was used to replicate the images that could be produced to improve precision.
Text Selection in AR-HMD Using a Smartphone as an Input Device

Text selection is a common task while reading a PDF file or browsing the web. Efficient text selection techniques exist on desktops and touch devices, but are still under-explored for Augmented Reality Head Mounted Display (AR-HMD). Performing text selection in AR commonly uses hand-tracking, voice commands, and eye/head-gaze, which are cumbersome and lack precision. In this poster paper, we explore the use of a smartphone as an input device to support text selection in AR-HMD because of its availability, familiarity, and social acceptability. As an initial attempt, we propose four eyes-free, uni-manual text selection techniques for AR-HMD, all using a smartphone - continuous touch, discrete touch, spatial movement, and raycasting.
Block-hash of blockchain framework against man-in-the-middle attacks

Payload authentication is vulnerable to Man-in-the-middle (MITM) attack. Blockchain technology offers methods such as peer to peer, block hash, and proof-of-work to secure the payload of authentication process. The implementation uses block hash and proof-of-work methods on blockchain technology and testing is using White-box-testing and security tests distributed to system security practitioners who are competent in MITM attacks. The analyisis results before implementing Blockchain technology show that the authentication payload is still in plain text, so the data confidentiality has not minimize passive voice. After implementing Blockchain technology to the system, white-box testing using the Wireshark gives the result that the authentication payload sent has been well encrypted and safe enough. The percentage of security test results gets 95% which shows that securing the system from MITM attacks is relatively high. Although it has succeeded in securing the system from MITM attacks, it still has a vulnerability from other cyber attacks, so implementation of the Blockchain needs security improvisation.
Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model

Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performance, this paper explores the probabilistic approach to improve the prediction of the photovoltaic rate of power output per hour. This research conducted a variable correlation analysis with output values and a specific EM algorithm (expectation and maximization) made from 6054 observations. A comparison was made between the performance of the EM algorithm with five different machine learning algorithms. The EM algorithm exhibited the best performance compared to other algorithms with an average of 0.75 accuracies. Notably, there is the benefit of performance, stability, the goodness of fit, lightness, and avoiding overfitting issues using the EM algorithm. According to the results, the EM algorithm improves photovoltaic power output prediction with simple weather forecasting services.
A proof-of-concept augmented reality system in oral and maxillofacial surgery

The advent of digital medical imaging, medical image analysis and computer vision has opened the surgeon horizons with the possibility to add virtual information to the real operative field. For oral and maxillofacial surgeons, overlaying anatomical structures to protect (such as teeth, sinus floors, inferior and superior alveolar nerves) or to remove (such as cysts, tumours, impacted teeth) presents a real clinical interest.Through this work, we propose a proof-of-concept markerless augmented reality system for oral and maxillofacial surgery, where a virtual scene is generated preoperatively and mixed with reality to reveal the location of hidden anatomical structures intraoperatively. We devised a computer software to process still video frames of the operating field and to display them on the operating room screens.Firstly, we give a description of the proposed system, where virtuality aligns with reality without artificial markers. The dental occlusion plan analysis and cusps detection allow us to initialise the alignment process. Secondly, we validate the feasibility with an experimental approach on a 3D printed jaw phantom and an ex-vivo pig jaw. Thirdly, we evaluate the potential clinical benefit on a patient.this proof-of-concept highlights the feasibility and the interest of augmented reality for hidden anatomical structures visualisation without artificial markers.
Blind Estimation of Self-Synchronous Scrambler in DSSS Systems

Self-synchronous scramblers are more difficult to blindly estimate than synchronous scramblers because their input sequence affects the state of the scrambler's linear feedback shift register.There has been much research on the estimation of synchronous scramblers and relatively little on the estimation of self-synchronous scramblers.For non-cooperative contexts, this paper proposes a method for the estimation of a self-synchronous scrambler in direct sequence spread spectrum systems.We blindly estimate the self-synchronous scrambling parameter, the feedback polynomial, using the repeated patterns by a spreading code, the linearity between the bits in a scrambling sequence, and the coidentity of the self-synchronous scrambled sequence and the linear feedback shift register states.To validate the proposed method, we show the estimation performance in terms of the computational complexity, required minimum scrambled sequence length, execution time, and detection probability.