Reviewerly/exHarmony huggingface dataset:
Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

Contemporary deep learning multi-scale deblurring models suffer from many issues: (I) They perform poorly on non-uniformly blurred images/videos; (II) Simply increasing the model depth with finer-scale levels cannot improve deblurring; (III) Individual RGB frames contain a limited motion information for deblurring; (IV) Previous models have a limited robustness to spatial transformations and noise. Below, we propose several mechanisms based on the multi-patch network to address the above issues: (I) We present a novel self-supervised event-guided deep hierarchical Multi-patch Network (MPN) to deal with blurry images and videos via fine-to-coarse hierarchical localized representations; (II) We propose a novel stacked pipeline, StackMPN, to improve the deblurring performance under the increased network depth; (III) We propose an event-guided architecture to exploit motion cues contained in videos to tackle complex blur in videos; (IV) We propose a novel self-supervised step to expose the model to random transformations (rotations, scale changes), and make it robust to Gaussian noises. Our MPN achieves the state of the art on the GoPro and VideoDeblur datasets with a 40 $$\times $$ faster runtime compared to current multi-scale methods. With 30 ms to process an image at 1280 $$\times $$ 720 resolution, it is the first real-time deep motion deblurring model for 720p images at 30 fps. For StackMPN, we obtain significant improvements over 1.2 dB on the GoPro dataset by increasing the network depth. Utilizing the event information and self-supervision further boost results to 33.83 dB.
Issues of education in the school informatics course on the basic level

The article discusses the possibilities of the school informatics course on the basic level in solving the problems of education and various aspects of the organization of the educational activities of the teacher in the implementation of the school informatics course in order to solve the problem of achieving by students the personal results of mastering the subject "Informatics" at the level of basic general education. The author reveals the content of each of the four thematic sections of the informatics course for 7th–8th grades ("Digital Literacy", "Theoretical Foundations of Informatics", "Algorithms and Programming", "Information Technologies") from the point of view of providing the possibility of solving the problems of education necessary for the student to achieve personal results in mastering the subject. The possible forms of achieving educational results by means of the subject "Informatics" at the basic level are given. The practical experience of domestic teachers in the implementation of the educational opportunities of the school informatics course is considered.
Towards a Model of Information Audit in the Document Management of Public Institutions

For an improvement of the processes and activities related to document management in health institutions, the appropriate and pertinent application of various administrative methodologies is required to verify and validate the institutional information requirements carried out through a formal process of auditing of information systems. document management. This article intends, by means of a comparison of the existing models, to determine the level of compliance of the existing models with world standards and the current Ecuadorian legal regulations, in order to demonstrate the need to generate a new information audit model, based on ISO 27001 and ISO 30301 standards, and that complies with Ecuadorian regulations; obtaining as a result that there are shortcomings in the existing models compared to the needs in health institutions, and that therefore, an adaptable model that adjusts to the reality of each environment is necessary.
PhysiQ

Physical therapy (PT) is crucial for patients to restore and maintain mobility, function, and well-being. Many on-site activities and body exercises are performed under the supervision of therapists or clinicians. However, the postures of some exercises at home cannot be performed accurately due to the lack of supervision, quality assessment, and self-correction. Therefore, in this paper, we design a new framework, PhysiQ, that continuously tracks and quantitatively measures people's off-site exercise activity through passive sensory detection. In the framework, we create a novel multi-task spatio-temporal Siamese Neural Network that measures the absolute quality through classification and relative quality based on an individual's PT progress through similarity comparison. PhysiQ digitizes and evaluates exercises in three different metrics: range of motions, stability, and repetition.
A Survey of Low-light Image Enhancement

With the higher requirements of computer vision image enhancement of low-light image has become an important research content of computer vision. Traditional low-light image enhancement algorithms can improve image brightness and detailed visibility to varying degrees, but due to their strict mathematical derivation, such methods have bottlenecks and are difficult to break through their limits. With the development of deep learning and the birth of large-scale data sets, low-light image enhancement based on deep learning has become the mainstream trend. In this paper, first of all, the traditional low-light image enhancement algorithms are classified, summarized the improvement process of the traditional method, then the image enhancement method based on the deep learning are introduced, at the same time on the network structure and is suitable for the method of combing the network part, after the introduction to the experiment database and enhance image evaluation criteria. Based on the discussion of the above situation, combined with the actual situation, this paper points out the limitations of the current technology, and predicts its development trend.
Exploration of University Laboratory Safety Education Platform Based on WeChat official account

As a new thing in the field of information technology, WeChat public platform has a huge impact on people's social communication life. According to the functions and advantages of the platform, this paper proposes an online and offline college laboratory safety education model based on WeChat official account, which provides a reference for effective laboratory safety education and emergency prevention ability training.
Realtime blur simulation of varifocal spectacle lenses in virtual reality

Virtual reality is a promising tool to study the influence of ophthalmic lenses on human perception. Independent of the actual refractive errors of the users’ eye, vision through different types of lenses can be simulated while allowing free eye and head movements and easy measurement of behavioural parameters. Especially for presbyopia, existing solutions often lead to visual discomfort. Multifocal spectacle lenses have a spatially varying optical power distribution leading to changing strength and orientation of blurred vision. Understanding the contribution of blur to visual discomfort and studying behavioral changes under the influence of lens blur is a promising application of a spectacle lens simulator. Here we present our framework to simulate spherical and astigmatic blur of corrective spectacle lenses. Size and orientation of a location dependent blur ellipse is calculated live in virtual reality using the depth information of the scene and precalculated power distribution of the lens.
On the Sampling Size for Inverse Sampling

In the Big Data era, sampling remains a central theme. This paper investigates the characteristics of inverse sampling on two different datasets (real and simulated) to determine when big data become too small for inverse sampling to be used and to examine the impact of the sampling rate of the subsamples. We find that the method, using the appropriate subsample size for both the mean and proportion parameters, performs well with a smaller dataset than big data through the simulation study and real-data application. Different settings related to the selection bias severity are considered during the simulation study and real application.
The integration of psychology and artificial intelligence in e-learning systems to guide the learning path according to the learner's style and thinking

Traditional e-learning systems fall short in many respects when it comes to delivering content to learners in the most effective way. Research shows that e-learning systems are not accommodative of learners’ thinking and learning styles, which leads to poor performance. This paper proposes a way through which this problem can be addressed. The researcher believes that the technology of Artificial Intelligence can be integrated with the learning and thinking styles (Psychology) of learners in an e-learning system to provide an enriched learning experience. No attempts have been made so far to integrate Artificial intelligence and Psychology in an e-learning environment, making this paper unique. The paper explores this subject by designing a system that will be termed a “smart e-learning system.” The paper sought to propose Artificial Intelligence algorithms that will be applied to the learning and thinking styles of learners to come up with highly adaptive models for each student that enhances their learning experience. The significant difference in the performance of the control group and experimental group confirms that if psychology and AI are integrated, there is a significant improvement in the student learning experience in an e-learning system. This shows that Artificial Intelligence can work well with Psychology to enhance the learning experience in the e-learning environment.
Fast Shape Recognition Method Using Feature Richness Based on the Walking Minimum Bounding Rectangle over an Occluded Remote Sensing Target

Remote sensing target recognition has always been an important topic of image analysis, which has significant practical value in computer vision. However, remote sensing targets may be largely occluded by obstacles due to the long acquisition distance, which greatly increases the difficulty of recognition. Shape, as an important feature of a remote sensing target, plays an important role in remote sensing target recognition. In this paper, an occluded shape recognition method based on the local contour strong feature richness (contour pixel richness, contour orientation richness, and contour distance richness) to the walking minimum bounding rectangle (MBR) is proposed for the occluded remote sensing target (FEW). The method first obtains the local contour feature richness by using the walking MBR; it is a simple constant vector, which greatly reduces the cost of feature matching and increases the speed of recognition. In addition, this paper introduces the new concept of strong feature richness and uses the new strategy of constraint reduction to reduce the complex structure of shape features, which also speeds up the recognition speed. Validation on a self-built remote sensing target shape dataset and three general shape datasets demonstrate the sophisticated performance of the proposed method. FEW in this paper has both higher recognition accuracy and extremely fast recognition speed (less than 1 ms), which lays a more powerful theoretical support for the recognition of occluded remote sensing targets.
Design and implement a mobile-based system to teach traffic signs to illiterate people

Abstract Nowadays, most information and communication technologies target literate users and neglect the 800 million illiterate individuals around the world. Although there have been several attempts to develop systems that help illiterate people and try to reduce the digital gap between them and those who are literate, it is still challenging to find accessible systems for illiterate people. This research aims to reduce the digital gap between illiterate and literate people in Iraq through developing a mobile application for learning traffic signs. Firstly, a requirement elicitation study was conducted to explore a set of design considerations and build an accessible interface for illiterate people. The study resulted in several design principles, such as using voice instructions, focusing on graphic content, and using the local language. Then, the mobile application was developed based on these design principles. Finally, an evaluation study was conducted to measure the usability of the developed application. The results revealed that the built application is efficient and effective, and the users were extremely satisfied in several aspects, such as easy usage and learnability, and declared that they would use it in the future and recommend it to others.
Virtual eating experience of poisonous mushrooms using TTTV2

VR content does not have to follow reality entirely, thus creating a variety of experiences, and the same should be true for content for taste displays. It may be worthwhile to create experiences that cannot be experienced in the real world. For example, many VR games create thrill and excitement through experiences that would be life-threatening in the real world. In this context, we developed taste content that allows users to safely experience the taste of poisonous mushrooms.
A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment

This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed algorithm takes the optimistic approach of the navigation problem. It does not look for problems to avoid, but rather for solutions to follow. This human-like decision-making behaviour distinguishes the new algorithm from all the other navigation algorithms. Furthermore, the new algorithm utilises newly developed tracking and prediction algorithms, to safely navigate mobile robots. This is further supported by a fuzzy logic controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. This paper also includes physical experimental results combined with bench-marking against other recent methods. The reported results verify the algorithm’s successful advantages in navigating robots in both static and dynamic environments.
A Quantum Algorithm for Pricing Asian Options on Valuation Trees

We develop a novel quantum algorithm for approximating the price of a discrete floating-strike Asian option based on an underlying valuation tree. The paths of the tree are encoded in bit-representation into a qubit register, where quantum state preparation is used to load the corresponding distribution onto the states. We implement the expectation value of the option pricing formula as a composition of the price probabilities, the payout and an indicator function, mapping their respective values to amplitudes of additional qubits. Thus, the underlying no longer has to be discretized into the same bit values for different times, resulting in smaller quantum circuits. The algorithm may be used with quantum amplitude estimation, enabling a quadratic speed-up over classical Monte Carlo methods.
A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques

Abstract In this work, advanced facial emotions are recognized using Neural network‐based (NN) PCA methodology. The earlier models are cannot detect facial emotions with moving conditions but the CCTV and other advanced applications are mostly depending on moving object‐based emotion recognition. The blurring, mask, and moving object‐based facial image are applied to the training process, and at the testing condition, real‐time facial images are applied. The PCA is extracting features and pre‐processing the images with NN deep learning process. The proposed facial emotion recognition model is most useful for advanced applications. The design is finally verified through confusion matrix computations and gets measures like accuracy 98.34%, sensitivity 98.34% Recall 97.34%, and F score 98.45%. These output results compete with present models and outperformance the methodology.
Omni-Directional Semi-Global Stereo Matching with Reliable Information Propagation

High efficiency and accuracy of semi-global matching (SGM) make it widely used in many stereo vision applications. However, SGM not only struggles in dealing with pixels in homogeneous area, but also suffers from streak artifacts. In this paper, we propose a novel omni-directional SGM (OmniSGM) with a cost volume update scheme to aggregate costs from paths along all directions and to encourage reliable information to propagate across entire image. Specifically, we perform SGM along four tree structures, namely trees in the left, right, top and bottom of root node, and then fuse the outputs to obtain final result. The contributions of pixels on each tree can be recursively computed from leaf nodes to root node, ensuring our method has linear time computational complexity. Moreover, An iterative cost volume update scheme is proposed using aggregated cost in the last pass to enhance the robustness of initial matching cost. Thus, useful information is more likely to propagate in a long distance to handle the ambiguities in low textural area. Finally, we present an efficient strategy to propagate disparities of stable pixels along the minimum spanning tree (MST) for disparity refinement. Extensive experiments in stereo matching on Middlebury and KITTI datasets demonstrate that our method outperforms typical traditional SGM-based cost aggregation methods.
A semantic blocks model for human activity prediction in smart environments using time-windowed contextual data

Abstract Complex human activity prediction is a difficult problem for computer science. Simple behaviours can be mapped to sequence prediction algorithms with good results; however, real-world examples of activity are generally stochastic and much more computationally difficult to infer. One method for solving this problem is to utilise contextual data—clues surrounding the actual activity—to decipher what is about to happen next; in much the same way humans do. In this paper, we present the semantic blocks model (SBM), a method for using contextual data to infer the next activity in a smart home environment by augmenting the inference with contextual data, but also segmenting it into time-windowed sections of activity—or semantic blocks. Our proof-of-concept produces 74.55% accuracy on the CASAS smart home dataset, an increase on the comparable CRAFFT algorithm which produces 66.91% on the same dataset. We detail how our experimental prototype works using intersecting contextual data, and explore opportunities for further work by the research community.
A multi-dimensional extensible cloud-native service stack for enterprises

Abstract With the widespread acceptance of the cloud-native concept and the emergence of a large number of dedicated cloud-native applications, the service stacks of cloud-native applications have received extensive attention in the industry. To analyze the extensibility problems of service stacks, a cloud-native light-cone model is proposed, which focuses on the dimensions of application, infrastructure, tenant and workflow, and provides a perspective view that reflects the concerns of stakeholders. Based on this model, various challenges in designing extensible cloud-native service stacks are identified by classification. To solve these challenges, a holistic architecture and a set of key technologies are designed, involving unified runtime abstraction, cluster bootstrapped creation, application-specific controllers, etc. Furthermore, the OMStack (Oriental Mind Stack) is implemented, which integrates these technologies and provides a group of PaaS and SaaS services for container cluster (OMCC), artificial intelligence (OMAI), big data (OMBD) and so on. Experimental analysis and production applications demonstrate the practicality, efficiency and reliability of the proposed architecture, stack and services.
Information preservation of two qubits in a structured environment

Abstract The environment-induced decoherence of a quantum open system makes it fundamentally import to preserve the initial quantum information of the system in its steady state. Here we study information preservation of two maximally entangled qubits lying inside a photonic-crystal waveguide with semi-infinite cavity-array structure. We generalize our study to arbitrary position and arbitrary frequency detuning of the qubits. We find that for weak qubits-waveguide couplings, the information preservation greatly depends on the position and the frequency detuning of the qubits, while for strong couplings, both of these dependence is significantly weakened. Interestingly, by suitably choosing the position and the frequency of the qubits, high information preservation could be achieved for both weak and strong couplings, irrespective to Markovian or non-Markovian dynamics. Physically, we analytically verify that the ability of information preservation is indeed determined by the existence of the bound states of the entire system, but the probability of information preservation is closely related to the probability of the initial state of the qubits in the bound states. Our results provide an alternative route getting high information preservation without any external controls of the system.
Profiling Attack on WiFi-based IoT Devices using an Eavesdropping of an Encrypted Data Frames

The rapid advancement of the Internet of Things (IoT) is distinguished by heterogeneous technologies that provide cutting-edge services across a range of application domains.However, by eavesdropping on encrypted WiFi network traflc, attackers can infer private information such as the types and working status of IoT devices in a business or residential home.Moreover, since attackers do not need to join a WiFi network, such a privacy attack is very easy for attackers to conduct while at the same time invisible and leaving no trace to the network owner.In this paper, we extend our preliminary work originally presented at the CCNC'22 conference by using a new set of time series monitored WiFi data frames with extended machine learning algorithms.We instrument a testbed of 10 IoT devices and conduct a detailed evaluation using multiple machine learning techniques for fingerprinting, achieving high accuracy up to 95% in identifying what IoT devices exist and their working status.Compared with our previous work in , the new approach could achieve IoT device profiling much quicker while maintaining the same level of classification accuracy.Moreover, the experimental results show that outside intruders can significantly harm the IoT devices without joining a WiFi network and can launch the attack within a minimum time without leaving any detectable footprints.