Reviewerly/exHarmony huggingface dataset:
Tracking-by-Trackers with a Distilled and Reinforced Model

Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with real-time state-of-the-art trackers.
A Computational Model of Motion Sickness Considering Visual and Vestibular Information

Interest for motion sickness is increasing with increasing opportunities to view digital devices in transportation systems, including automated vehicles. Hence, technology for predicting and estimating motion sickness is essential. As one such technology, computational models for estimating motion sickness from head movements have been proposed and are used for motion sickness evaluation. However, a model capable of handling the effects of visual input and the visual-vestibular interaction for motions with six-degrees-of-freedom has yet to be developed. In this study, therefore, a computational model of motion sickness with visual-vestibular inputs is proposed by extending a model of the subjective vertical conflict theory of motion sickness for a vestibular input only. The proposed model inputs are the acceleration and angular velocity of the head and the visual perception of the angular velocity. A simulation conducted by inputting 1 h of sinusoidal oscillations demonstrated that the results are consistent with those of similar experiments conducted with human participants. In addition, the results of a simulation experiment conducted by changing the visual input demonstrated that motion sickness increases significantly when a conflict occurs between the visual and vestibular signals, which imitates the reading of a book in a moving car. Furthermore, we developed a method to calculate the predicted MSI using experimental data measured by IMU and camera image by approximating the visual perception of the angular velocity through an optical flow analysis. The results of inputting camera images and inertial measurement unit signals obtained by sine-wave-like pitching motion into the proposed model demonstrate that the proposed method, can describe the difference in motion sickness by the changes in the visual environment.
Image decomposition and denoising using fractional‐order partial differential equations

In this study, the authors propose a fractional derivative-based image decomposition and denoising model which decomposes the image into the cartoon component (the component formed by homogeneous regions and with sharp boundaries) and the texture (or noise) component. The cartoon component is modelled by a function of the fractional-order total bounded variation, while the texture component is modelled by an oscillatory function, bounded in the negative Sobolev space norm. The authors give the corresponding minimisation functional, after some transformations, and then the resulting fractional-order partial differential equation can be solved using the Fourier transform. By symmetry and asymmetry of the fractional-order derivative, some generalisations and variants of the proposed model are also introduced. Finally, the authors implement the algorithm by the fractional-order finite difference in the frequency-domain. The experimental results demonstrate that the proposed models make objective and visual improvements compared with other standard approaches in the task of decomposition and denoising.
Assessing digital nativeness in pre-service teachers: Analysis of the Digital Natives Assessment Scale and implications for practice

(2020). Assessing digital nativeness in pre-service teachers: Analysis of the Digital Natives Assessment Scale and implications for practice. Journal of Research on Technology in Education. Ahead of Print.
Face Recognition Login Authentication for Digital Payment Solution at COVID-19 Pandemic

On March 11, 2020 the World Health Organization has announced the status of a global pandemic of corona virus disease 2019 or also called corona virus disease 2019 (COVID-19).The World Health Organization defines this disease as a pandemic because all citizens of the world are potentially exposed to COVID-19 infection.With the establishment of the global pandemic status, WHO also confirmed that COVID-19 was an international emergency.The trend of digitalization is becoming a new business trend to develop and survive in the midst of a crisis due to this pandemic.The online buying and selling market, digital payments and electronic health services, from online training classes to consulting with doctors via the internet, continue to increase.Some companies in the offline market also continue to operate by implementing health protocols.The form of digital payments continues increasing, this is because according to WHO the surface of objects can be a medium in the spread of the covid-19 virus.However, some digital payment media still require a card and enter a Personal Identification Number (PIN) in the Electronic Data Capture machine.For a solution so that the buyer does not need to bring a card and touch the Electronic Data Capture machine, face recognition authentication can be developed instead of PIN.
Gaussian process-based nonlinear predictive control for visual servoing of constrained mobile robots with unknown dynamics

In this paper, a Gaussian process-based nonlinear model predictive control (GP-based NMPC) algorithm is presented to deal with the visual servoing problem for constrained mobile robots. Firstly, a GP-enhanced model is established by incorporating a GP model and a visual servoing kinematic model where the GP-model is used to capture the robot dynamics with on-line updating. Then, a nonlinear model predictive control (NMPC) strategy is proposed to transform the visual servoing task into a nonlinear optimization problem with robot-physical and camera-visibility constraints. Subsequently, a variant iterative linear quadratic regulator algorithm is presented to solve the constrained NMPC problem in real time. Finally, simulations and experiments are conducted to show the effectiveness of the presented method.
Part 2: The Phase-oriented Advice and Review Structure (PARS) for digital forensic investigations

This work forms the second part of a two part series providing the necessary scaffolding for the digital forensic discipline to conduct effective peer review in their laboratories and units. The first part articulated the need for a structured approach to peer review in digital forensic investigations (Horsman and Sunde, 2020). Here in part two, the Phase-oriented Advice and Review Structure (PARS) for digital forensic investigations is offered. PARS is the first documented peer review methodology for the digital forensics field, a six staged approach designed to formally support organisations and their staff in their goal of facilitating effective peer review of DF work, from investigative tasks to forensic activities and forensic analysis processes (Pollitt et al., 2018). This article discusses how the PARS methodology can be implemented, and the available options and mechanisms available to ease the interpretation of this model into existing practices. Both the early 'Advisor' and later 'Reviewer' roles in PARS are discussed and their requirements and expectations are defined. Three template documents are provided and explained: The PARS Advisors template, the PARS Advisor Brief template and the PARS Peer Review Hierarchy template, for direct use by organisations seeking to adopt the PARS methodology.
Beyond Fermi’s golden rule in free-electron quantum electrodynamics: acceleration/radiation correspondence

Abstract In this article, we present a unified reciprocal quantum electrodynamics (QED) formulation of free-electron and quantum–light interaction. For electron–light interactions, we bridge the underlying theories of photon-induced near-field electron microscopy, laser-induced particle accelerators, and radiation sources, such as quantum free electron laser, transition radiation and Smith-Purcell effect. We demonstrate an electron–photon spectral reciprocity relation between the electron energy loss/gain and the radiation spectra. This ‘acceleration/radiation correspondence’ (ARC) conserves the electron energy, and photon number exchanged, that is, Δ E / ℏω + Δ ν q = 0, and in the representation of a quantum electron wavepacket, displays explicit dependence on the history-dependent phase and shape of the wavepacket configuration. It originates from an interaction-induced quantum interference term that is usually ignored in Fermi’s golden rule analyses, but is kept in our combined quantum free electron–photon state formulation. We apply this formulation to both stimulated interaction and spontaneous emission of classical and quantum light by the quantum-featured electrons. The ‘spontaneous’ emissions of coherent states (‘classical’ light) are remarked and squeezed states of quantum light is shown to be enhanced with squeezing. This reciprocal QED formulation has promise for extensions to other fundamental research issues in quantum light and quantum matter interactions.
Word embeddings for biomedical natural language processing: A survey

Abstract Word representations are mathematical objects that capture the semantic and syntactic properties of words in a way that is interpretable by machines. Recently, encoding word properties into low‐dimensional vector spaces using neural networks has become increasingly popular. Word embeddings are now used as the main input to natural language processing (NLP) applications, achieving cutting‐edge results. Nevertheless, most word‐embedding studies are carried out with general‐domain text and evaluation datasets, and their results do not necessarily apply to text from other domains (e.g., biomedicine) that are linguistically distinct from general English. To achieve maximum benefit when using word embeddings for biomedical NLP tasks, they need to be induced and evaluated using in‐domain resources. Thus, it is essential to create a detailed review of biomedical embeddings that can be used as a reference for researchers to train in‐domain models. In this paper, we review biomedical word embedding studies from three key aspects: the corpora, models and evaluation methods. We first describe the characteristics of various biomedical corpora, and then compare popular embedding models. After that, we discuss different evaluation methods for biomedical embeddings. For each aspect, we summarize the various challenges discussed in the literature. Finally, we conclude the paper by proposing future directions that will help advance research into biomedical embeddings.
Planning Three-Dimensional Collision-Free Optimized Climbing Path for Biped Wall-Climbing Robots

Biped wall-climbing robots well adapt for carrying out tasks in large-scale 3-D environments such as skeleton-facade structures, due to their good abilities to transit between walls and surmount obstacles. However, to plan 3-D collision-free climbing paths in such environments is challenging and significant for biped wall-climbing robots. In this article, we propose a novel hierarchical framework for planning 3-D biped climbing navigation, including three levels, namely the global route, the foothold, and the single-step motion. We also present three effective planners incorporated in the framework to solve this challenging planning problem efficiently. A global route planner is designed on top, for rapidly analyzing the possibility for the robot to transit between walls, and searching walls that must be passed through and key footholds for performing transition, in order to optimize the route length globally. A local foothold planner is implemented in the middle, to search and optimize footholds on each via wall, considering the robot's ability to step over, step on, and bypass obstacles. A single-step motion planner is deployed at the bottom, for planning collision-free movement swinging the robot suction module from a foothold to another. Simulations are conducted to fully verify the effectiveness and performance of the presented planners. Experiments with W-Climbot are carried out to demonstrate that the proposed framework and planners succeed in planning 3-D collision-free optimized climbing paths for biped wall-climbing robots.
A partial encryption algorithm for medical images based on quick response code and reversible data hiding technology

Abstract Background Medical image data, like most patient information, have a strong requirement for privacy and confidentiality. This makes transmitting medical image data, within an open network, problematic, due to the aforementioned issues, along with the dangers of data/information leakage. Possible solutions in the past have included the utilization of information-hiding and image-encryption technologies; however, these methods can cause difficulties when attempting to recover the original images. Methods In this work, we developed an algorithm for protecting medical image key regions. Coefficient of variation is first employed to identify key regions, a.k.a. image lesion areas; then additional areas are processed as blocks and texture complexity is analyzed. Next, our novel reversible data-hiding algorithm embeds lesion area contents into a high-texture area, after which an Arnold transformation is utilized to protect the original lesion information. After this, we use image basic information ciphertext and decryption parameters to generate a quick response (QR) code used in place of original key regions. Results The approach presented here allows for the storage (and sending) of medical image data within open network environments, while ensuring only authorized personnel are able to recover sensitive patient information (both image and meta-data) without information loss. Discussion Peak signal to noise ratio and the Structural Similarity Index measures show that the algorithm presented in this work can encrypt and restore original images without information loss. Moreover, by adjusting the threshold and the Mean Squared Error, we can control the overall quality of the image: the higher the threshold, the better the quality and vice versa. This allows the encryptor to control the amount of degradation as, at appropriate amounts, degradation aids in the protection of the image. Conclusions As shown in the experimental results, the proposed method allows for (a) the safe transmission and storage of medical image data, (b) the full recovery (no information loss) of sensitive regions within the medical image following encryption, and (c) meta-data about the patient and image to be stored within and recovered from the public image.
Security analysis and modified attack algorithms for a nonlinear optical cryptosystem based on DRPE

In this paper, security of a nonlinear optical cryptosystem based on double random phase encoding (DRPE) is analyzed. In the cryptosystem, a time-varying nonlinear term is combined with the classical DRPE technique to enhance security. The advantages of the nonlinear term are its easy distribution and no effect on the decrypted image. Also, it is confirmed that the method is resistant to conventional plaintext attacks such as known-plaintext attack, chosen-plaintext attack, and ciphertext only attack. However, our security analysis shows that the method is vulnerable to the modified chosen-plaintext, and known-plaintext attacks. The original image can be recovered successfully using the proposed attack algorithms. Numerical simulation results are presented to validate the proposed attack methods and test effectiveness of our analysis.
CoVer: Collaborative Light-Node-Only Verification and Data Availability for Blockchains

Validating a blockchain incurs heavy computation, communication, and storage costs. As a result, clients with limited resources, called light nodes, cannot verify transactions independently and must trust full nodes, making them vulnerable to security attacks. Motivated by this problem, we ask a fundamental question: can light nodes securely validate without any full nodes? We answer affirmatively by proposing CoVer, a decentralized protocol that allows a group of light nodes to collaboratively verify blocks even under a dishonest majority, achieving the same level of security for block validation as full nodes while only requiring a fraction of the work. In particular, work per node scales down proportionally with the number of participants (up to a log factor), resulting in computation, communication, and storage requirements that are sublinear in block size. Our main contributions are light-node-only protocols for fraud proofs and data availability.
Semi-supervised blockwisely architecture search for efficient lightweight generative adversarial network

In the field of computer vision, methods that use fully supervised learning and fixed deep network structures need to be improved. Currently, many studies are devoted to designing neural architecture search methods to use neural networks in a more flexible way. However, most of these methods use fully supervised learning at the cost of extraordinary GPU training time. In view of the above problems, we propose a semi-supervised generative adversarial network and search network architecture based on block structure. Use real pictures and generated pictures with corresponding real tags and pseudo tags for training, to achieve the purpose of semi-supervised learning. By setting the layer’s hyperparameters to a variable and flexible stacking block structure, network architecture search is achieved. The proposed method realizes image generation and extends to image classification. In the experimental results in Section 4, the training time is greatly reduced and the model performance is improved, which illustrates the efficiency of our method. The code can be found in https://github.com/AICV-CUMT/STASGAN.
Accurate and Efficient Stereo Matching by Log-Angle and Pyramid-Tree

Efficient and fast stereo matching is a challenging task due to the presence of occlusion and low texture areas. In stereo matching, the correspondence between left and right images may be difficult owing to the lack of matching information. The cost metrics proposed before are not robust enough or are computationally expensive. In this work, we propose a novel generic tree structure, Pyramid-tree, which improves the single mode of traditional tree, and can achieve cross-regional connection between different regions with similar colors and similar depths. This unique structure can achieve cross-regional cost smoothing, significantly reducing the possibility of mismatch due to occlusion or lack of corresponding matching information, and has stronger robustness to occlusion and low texture regions. In addition, we also propose a new bearings-only cost metric, Log-angle, which is not affected by occlusion, low texture, illumination and other factors. Log-angle combines with traditional metrics can show better performance. We show that the Pyramid-tree structure and Log-angle are very important as it efficiently expands the state-of-the-art stereo matching methods and leads to significant improvements. Qualitative and quantitative experiments on Middlebury data sets verify the superior performance of the algorithm, and very effective compromise between the accuracy and computation load is achieved.
Segmentation of White Blood Cells using Image Segmentation Algorithms

Segmentation of images plays a key role in Image Processing as it simplifies further processing by separating a broad image into many parts. White Blood Cells (WBC) microscopic images allow haematologists to predict vulnerability to several diseases. Our intension is to identify Nucleus of White Blood cells. Colour is a significant reference point for discerning segmented WBCs from microscopic images. Results reveal that in identifying the nucleus of various types of WBC: including Neutrophils, Lymphocytes, Eosinophils, Monocytes, Basophils. In this study, we will evaluate the efficiency of the Edge detection Algorithm (log & Canny) methods, k-Means Algorithm, Linear Transformed Image, Color-based technique, and will compare the results with the original image. This will allow haematologists for clear identification of WBC. Analysis of the results using Algorithms shows that Edge detection and k-means based segmentation is the most suitable approach for segmenting WBC cells.
Traffic control system for vehicles on Indian roads using raspberry Pi

Abstract In India at the roads the vehicles were growing because of the increase of population and controlling of traffic is one of the tough duties for the individuals who control the traffic. The everyday traffic congestion at crucial junctions becomes more issues for the emergency automobiles and ought to wait for until inexperienced signal. This results the growth in pollution ranges and wastage of time, pollutants stages may boom to a huge scale. Previously the traffic control strategies used like magnetic loop detectors, induction loop detectors are buried on the street aspect provide the constrained visitors information and necessitate separate tracking structures for site traffic counting and for traffic surveillance. Right here the undertaking proposes to enforce a synthetic density traffic manipulate gadget using photo processing and Raspberry pi.
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Recently, several infinite families of minimal and optimal linear codes are obtained from simplicial complexes or down-sets by Hyun and Wu et al. Motivated by their work, we give two new constructions of codes over the ring Fp+uFp,u2=u by using down-sets. We compute the Lee weight distributions of two classes of codes when the down-sets are generated by a single maximal element. Moreover, by using the Gray map, we obtain two classes of codes, which are minimal and distance optimal.
Pre-training Without Natural Images

Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven Supervised Learning. We automatically generate image patterns and their category labels by assigning fractals, which are based on a natural law existing in the background knowledge of the real world. Theoretically, the use of automatically generated images instead of natural images in the pre-training phase allows us to generate an infinite scale dataset of labeled images. Although the models pre-trained with the proposed Fractal DataBase (FractalDB), a database without natural images, does not necessarily outperform models pre-trained with human annotated datasets at all settings, we are able to partially surpass the accuracy of ImageNet/Places pre-trained models. The image representation with the proposed FractalDB captures a unique feature in the visualization of convolutional layers and attentions.