Reviewerly/exHarmony huggingface dataset:
Aligning Education with Vision 2030 Using Augmented Reality

Vision 2030 requires a new generation of people with a wide variety of abilities, talents, and skills.The adoption of augmented reality (AR) and virtual reality is one possible way to align education with Vision 2030.Immersive technologies like AR are rapidly becoming powerful and versatile enough to be adopted in education to achieve this goal.Technologies such as AR could be beneficial tools to enhance maintainable growth in education.We reviewed the most recent studies in augmented reality to check its appropriateness in aligning with the educational goals of Vision 2030.First, the various definitions, terminologies, and technologies of AR are described briefly.Then, the specific characteristics and benefits of AR systems are determined.There may be a significance of the pedagogical method used by adapting the AR scheme and the consistency of the equipment and learning experiences.Therefore, three kinds of instructional methods that stress roles, location, and tasks were evaluated.The kind of learning that is offered by the distinct kinds of AR approaches is elaborated upon.The technological, pedagogical, learning problems experienced with AR are described.The potential solutions for a few of the issues experienced and the topics for subsequent research are presented in this article.
Library 4.0 and Sustainable Development: Opportunities and Challenges

Given the speed at which technologies are reshaping economies and societies, libraries are called to embrace these changes while maintaining their role in the achievement sustainable development goals. This chapter aims to highlight how libraries can embrace the sustaining and disruptive technologies brought by the fourth industrial revolution on the library role in advancing sustainable development. The chapter highlights the role of libraries in sustainable development, and the unveils how the fourth industrial revolution technologies, that is, maker spaces, block chain technology, augmented reality, cloud computing, and artificial intelligence are used to enhance the library role in advancing sustainable development. Major challenges affecting libraries in embracing Library 4.0, that is, chronic financial constraints, inadequate infrastructure, resistance to change, and technical skills deficiency and possible recommendations to overcome these challenges are highlighted. This chapter is based on desktop review of relevant literature and the authors' views.
RETRACTED: Motion image processing system based on multi core FPGA processor and convolutional neural Network

The moving image processing can now be applied to the economic analysis of the imaging target's movement by the high-speed shooting. This method is, consider some of the common approaches. For this reason, the acknowledgement of the movement recognition and movement following are talked about. These projects, not successive autonomous moreover. As of late, Field-Programmable Gate Array (FPGA) is wide, particularly in the versatile and installed gadgets have been utilized in equipment quickening agent for the execution of Convolutional Neural Network (CNN). A CNN dependent on FPGA has been proposed. It is intended to assemble a neural organization convolution with the streamlining and advancement of the memory of the profoundly reusable quickening agent work is low equipment asset utilization. Movement, position, speed, and key data of the camera's objective can catch any ideal data from the caught casing can be shipped off the framework's investigation parts. To follow the moving objects by recognizing movement location is one of these smart frameworks. Even though there is another technique for identifying a moving there, these strategies, there are a few impediments for constant applications. Hence, to give precise outcomes with this strategy, the foundation deduction technique is reasonable for constant applications.
Memory-efficient architecture for FrWF-based DWT of high-resolution images for IoMT applications

This paper proposes a simple low memory architecture for computing discrete wavelet transform (DWT) of high-resolution (HR) images on low-cost memory-constrained sensor nodes used in visual sensor networks (VSN) or Internet of Multimedia Things (IoMT). The main feature of the proposed architecture is the novel data scanning technique that makes memory requirement independent of the image size. The proposed architecture needs only (30S) words of memory, where S is the number of parallel processing units and a critical path delay (CPD) equal to the delay of a multiplier (Tm). Furthermore, a multiplierless version of this architecture is also proposed which reduces the CPD to Ta<Tm (where Ta is the delay of an adder). In order to evaluate their effectiveness, the proposed architectures are coded in HDL and implemented on same FPGA board. Their performance is also compared with other state-of-the-art low memory DWT architectures. The experimental results show the superiority of the proposed architectures in terms of memory and CPD compared to existing DWT architectures. Moreover, the reduction in CPD to Ta indicates that the operating frequency can be scaled up by several factors and can be chosen depending upon the application. Compared to one of the best state-of-the-art DWT architecture, proposed multiplierless architecture (with S = 4) needs 57.37% less LUT’s and 64.39% less flip-flops for HR image of dimension 2048 × 2048. Moreover, the proposed architecture needs no LUTRAM and DSP, whereas the existing architecture requires 3264 LUTRAM and 24 DSP’s. Thus the proposed multiplierless architecture is superior to the existing state-of-the-art architecture and is suitable for IoMT/VSNs.
Flight Situation Recognition Under Different Weather Conditions

The weather conditions affect the approach and departure routings of aircraft. The detection of flight status of aircraft faces two major issues under adverse weather conditions. They are how to select an approximate and effective feature combination from different flight parameters and how to identify the status of an aircraft via a reasonable flight parameter combination. This article presents a solution to the flight status recognition problem of an aircraft. The time-domain and wavelet-domain features are extracted from a complete flight dataset and a typical flight dataset, respectively. ARMA coefficients entropy is also extracted to represent dynamic behavior of flight data. A new deep sparse learning network with an optimized Gaussian process classifier is proposed to detect the aircraft status. Experiments are executed via a practical flight dataset under different weather. The feasibility of the selection scheme of flight parameters and the proposed deep learning method to detect abnormal flight situation are verified by competitive experimental results, which presents widespread conclusions on feature selection methods and abnormality flight status detection.
Sense Inventories for Arabic Texts

Word sense disambiguation is the process of determining the proper meaning of a word according to its context. In this study, we represent the impact of word embedding on building Arabic sense inventory by an unsupervised approach. Three pre-trained embeddings are tested to investigate their effect on the resulting sense inventory and their efficiency in word sense disambiguation for Arabic context. Sense inventories are constructed using a fully unsupervised method based on graph-based word sense induction algorithm. The results show that Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50-neighbors and a close accuracy to the Fasttext inventory for 200-neighbors.
The Performance of Byzantine Fault Tolerant Blockchains

Blockchains have captured the attention of many, resulting in an abundance of new systems available for use. However, selecting an appropriate blockchain for an application is challenging due to the lack of comparative information discussing core metrics such as throughput, latency and scalability. Although a number of efforts have been devoted to performance evaluation, there is limited work dedicated to blockchains that are both efficient, due to avoiding complex Proof-of-Work cryptopuzzles, and secure, because they solve consensus deterministically despite Byzantine failures. In this paper, we evaluate the performance of three blockchains that cope with such malicious behaviors, namely Burrow, Quorum and Red Belly Blockchain. To this end, we modified the Hyperledger Caliper benchmark to solve three main limitations: unnecessary overheads, online cryptographic signatures and centralized clients. Our results identify the maximum send rate that Burrow and Quorum can process, and that Red Belly Blockchain can offer an 8-times higher throughput than the other blockchains.
Application of Algorithmic Cognitive Decision Trust Modeling for Cyber Security Within Organisations

Cybercrime continues to cause increasing threat to business processes, eroding stakeholders’ trust in Internet technologies. In this article, we explore how six dominant algorithmic trust positions facilitate cognitive processing, which, in turn, can influence an organization's productivity and align its values and support structures for combating cybercrimes. This conceptual paper uses a cognitive perspective described as a throughput model. This modeling perspective captures several dominant algorithmic trust positions for organizations, providing a new, and powerful approach which seeks to enhance our understanding of the cognitive representation of decision-making processes. These trust positions are rational-based trust, rule-based trust, category-based trust, third-party based trust, role-based trust, and knowledge-based trust. Finally, we provide conclusion and implications for future research.
Superpixel-Based Local Features for Image Matching

Image matching is the research basis of many computer vision problems, such as intelligent driving, object recognition and structure from motion. However, the traditional feature-based image-matching results are usually very sparse and unevenly distributed for wide baseline or weakly textured images. Implementing an efficient and robust image-matching technology is a challenging task. To solve these problems, we propose an efficient extractor and binary descriptor based on superpixels and a modified binary robust independent elementary features (BRIEF) descriptor called FSRB. FSRB can improve the computational efficiency, number of matches, feature distribution and robustness of feature-based image matching. In theory, FSRB is rotation-, scale-, affine-, distorted-, and intensity-invariant. A comprehensive performance evaluation of FSRB is performed. The experimental results show that our method can effectively obtain many matches for different types of images. Compared with state-of-the-art algorithms, our method performed very well in terms of the number of correct matches (which increased by 2-5 times), time consumption, matching accuracy, matching success rate and feature repetition rate. In addition, our method is applied to sparse 3D reconstruction of multiview images, and satisfactory results are obtained.
Application of Cloud Edge Collaboration Architecture in Power IoT

In order to solve the problem of network resource redundancy and overload caused by the widespread use of chimney-type independent service access in the current power IoT architecture, this paper proposes a model of cloud edge collaborative architecture applied to the power IoT. In this mode, edge computing devices or networks are set up around the perception layer devices of the power IoT to process massive power / non-power data locally. This model combines the centralized nature of cloud computing and the decentralized nature of edge computing to meet the requirements of big data computing and real-time analysis of local data, and fits the complex network and data access and management requirements of the power IoT.
Cluster Optimized Proximity Scaling

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An Evolution of CNN Object Classifiers on Low-Resolution Images

Object classification is a significant task in computer vision. It has become an effective research area as an important aspect of image processing and the building block of image localization, detection, and scene parsing. Object classification from low-quality images is difficult for the variance of object colors, aspect ratios, and cluttered backgrounds. The field of object classification has seen remarkable advancements, with the development of deep convolutional neural networks (DCNNs). Deep neural networks have been demonstrated as very powerful systems for facing the challenge of object classification from high-resolution images, but deploying such object classification networks on the embedded device remains challenging due to the high computational and memory requirements. Using high-quality images often causes high computational and memory complexity, whereas low-quality images can solve this issue. Hence, in this paper, we investigate an optimal architecture that accurately classifies low-quality images using DCNNs architectures. To validate different baselines on low-quality images, we perform experiments using webcam captured image datasets of 10 different objects. In this research work, we evaluate the proposed architecture by implementing popular CNN architectures. The experimental results validate that the MobileNet architecture delivers better than most of the available CNN architectures for low-resolution webcam image datasets.
A matrix inequality for entanglement distillation problem

The pure entangled state is of vital importance in the field of quantum information. The process of asymptotically extracting pure entangled states from many copies of mixed states via local operations and classical communication is called entanglement distillation. The entanglement distillability problem, which is a long-standing open problem, asks whether such process exists. The 2-copy undistillability of 4×4 Werner states has been reduced to the validity of a conjecture for the following matrix inequality with d=4supX∈Xd⁡σ12(X)+σ22(X)⩽3d−4d2, where σ1(X) and σ2(X) are the largest two singular values of X, Xd is the set of all matrices X=A⊗I+I⊗B with A,B∈Cd×d(d⩾4), Tr(A)=Tr(B)=0 and ‖A‖F2+‖B‖F2=1/d. The latest progress, made by Pankowski et al. (2010) [21], shows that this conjecture holds when both matrices A and B are normal. In this paper, we prove that the conjecture holds when one of matrices A and B is normal and the other one is arbitrary via optimization techniques. Our work makes solid progress towards this conjecture and thus the distillability problem.
Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints

Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated ( r = 0.78 ), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods.
Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks

Human Papilloma Virus (HPV) is a major risk factor for the development of oropharyngeal cancer. Automatic detection of HPV in digitized pathology tissues using in situ hybridisation (ISH) is a difficult task due to the variability and complexity of staining patterns as well as the presence of imaging and staining artefacts. This paper proposes an intelligent image analysis framework to determine HPV status in digitized samples of oropharyngeal cancer tissue micro-arrays (TMA). The proposed pipeline mixes handcrafted feature extraction with a deep learning for epithelial region segmentation as a preliminary step. We apply a deep central attention learning technique to segment epithelial regions and within those assess the presence of regions representing ISH products. We then extract relevant morphological measurements from those regions which are then input into a supervised learning model for the identification of HPV status. The performance of the proposed method has been evaluated on 2009 TMA images of oropharyngeal carcinoma tissues captured with a ×20 objective. The experimental results show that our technique provides around 91% classification accuracy in detecting HPV status when compared with the histopatholgist gold standard. We also tested the performance of end-to-end deep learning classification methods to assess HPV status by learning directly from the original ISH processed images, rather than from the handcrafted features extracted from the segmented images. We examined the performance of sequential convolutional neural networks (CNN) architectures including three popular image recognition networks (VGG-16, ResNet and Inception V3) in their pre-trained and trained from scratch versions, however their highest classification accuracy was inferior (78%) to the hybrid pipeline presented here.
Game Input with Delay – A Model of the Time Distribution for Selecting a Moving Target with a Mouse

Computer game player performance can degrade with delays from both the local system and network. Analytic models and simulations have the potential to enable exploration of player performance with delay as an alternative to time-intensive user studies. This paper presents an analytic model for the distributions of elapsed times for players doing a common game action – selecting a moving target with a mouse with delay – derived from results from prior user studies. We develop and validate our model, then demonstrate the use of our model via simulation, exploring player performance with different game configurations and delays.
Cloud Pricing: The Spot Market Strikes Back

Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance or for users with long-term contracts). A natural idea, which should intuitively increase the provider's profit, is to sell these idle instances on a secondary market, for example, via a preemptible spot market. However, this ignores possible "market cannibalization" effects that may occur in equilibrium as well as the additional costs the provider experiences due to preemptions. To study the viability of offering a spot market, we model the provider's profit optimization problem by combining queuing theory and game theory to analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which a provider can simultaneously achieve a profit increase and create a Pareto improvement for the users by offering a spot market (using idle resources) alongside a fixed-price market. Finally, we illustrate our results numerically to demonstrate the effects that the provider's costs and her strategy have on her profit.
IoT-Based Healthcare and Monitoring Systems for the Elderly: A Literature Survey Study

The world is turning to Internet of Things (IoT) technologies to remotely monitor and control things faster and more accurately. IoT can be very beneficial in the medical and healthcare fields as it enables remote follow-up of chronic illnesses, monitoring of vital signs, emergency detection, diagnosis and prognosis of patient status or illnesses. IoT becomes evidently useful in the case of elderly healthcare and monitoring, which, in many cases, are tasks requiring full attendance of a caregiver or medical staff. The aim of this survey study is to shed light on the importance and classification of IoT-based elderly healthcare systems. The paper includes a review of research studies that report developments and utilization of IoT-based healthcare systems of elderly. The paper also includes a description of basic IoT-based healthcare systems architecture and ways of its implementation, including wearables, at hospitals and at home. The paper concludes with a presentation of the security challenges imposed by the nature of IoT systems implementation that require remote access to personal data and data storage. It is recommended from the surveyed studies that IoT will continue to expand as a technology trend that allows for development of inexpensive, reliable and secure elderly healthcare and monitoring systems.
Permission Blockchain based Smart Contract Utilizing Biometric Authentication as a Service: A Future Trend

Smart contracts are the triggered self executing contracts which are termed and conditioned among the trusted parties. Being independent of trusted third party, smart contract rely on blockchain and the underlying consensus submitted by participants for the security. Hyper ledger fabric provides peer-to-peer-network based distributed operating environment. This scenario highlights the need of identification of peer, more precisely on the `who the user is' rather than `what does the user possess'. This paper proposes an architecture of the integration of multimodal biometric authentication system incorporation with the permissioned blockchain based smart contract. Every peer has to be authenticated before getting involved in smart contract. Paper also informs about the surveyed ways of multimodal recognition which ensures the major concern of various applications like insurance settlement, market predictions and internet on things, etc. i.e. liveliness detection.
Supratentorial ependymoma with <scp><i>YAP1:FAM118B</i></scp> fusion: A case report

We report a case of a 26‐year‐old Chinese man who had experienced three grand mal seizures in the past two months. Magnetic resonance imaging revealed a relatively well‐circumscribed lesion in the left frontal lobe. A craniotomy with total excision of the tumor was performed. Histopathological investigations confirmed a grade 2 ependymoma according to the World Health Organization classification. Genetic analysis revealed a tumor harboring FAM118B fusion to YAP1 , and no other genetic alterations or methylation of the O 6 ‐methylguanine‐DNA methyltransferase gene promoter were detected. This is the second case report of ependymoma with YAP1:FAM118B fusion.