Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Education

Reimagining our future engagement with learners through an augmented reality (AR) lens offers a range of possibilities, on a continuum from rigid materials, created with generic learning outcomes, to learner-centred, personalised and emancipatory practice. Educational paradigms are shifting to include alternatives to physical classrooms and the controlled virtual learning spaces that support traditional content delivery. Augmented reality is part of a broader mixed reality where varying degrees of virtual enhancement to the real world can be integrated into traditional delivery practice but also allow learning spaces to be explored more imaginatively and collaboratively. For educators, there are significant challenges to utilise the potential of technology to meet the increasing demands of students, institutions, industry and the expectations of society. By carefully scaffolding educators into reframing their curricula to encourage, inspire and motivate a diverse student body, technology can act as a mediator – a proxy for what Vygotsky termed the ‘more capable peer’ (Cook 2010). AR applications in education, training, marketing, medicine and other industries demonstrate both the capability of the technology and the need for designers to be aware of the possibilities. Karakus et al (2019), in their bibliometric study of augmented reality in education, identify the work by Wu et al (2013) as the most influential across the sector. This paper suggests that AR not only bridges virtual and real worlds but alsocreates an enhanced reality through a creative process. They argue that the educational values of AR are not solely based on the use of technologies but are closely related to how AR is designed, implemented and integrated into formal and informal learning settings. The recent McKinsey report (Bughin et al, 2018) modelled skills shifts in automation and artificial intelligence going forward to 2020 and found a sharp acceleration in demand for these technologies that will transform the workplace, as humans work with ever smarter machines. Transferring technology for educational use, re-use and re-purpose are key emergent themes in research as employers demand ever more technology enabled graduates, with high-level cognitive skills.
LARIISA

LARIISA is an intelligent framework for decision-making in public health systems. The project had its initial ideas conceived in 2009. Since then it has evolved in the academic and market perspective, becoming a product in 2018 called GISSA. This article presents the architectural evolution of LARIISA, the functionalities implemented, the scientific and commercial results achieved with GISSA. Ontology and Data Mining (DM) are technologies that support their inference mechanisms. A semantic portal is proposed for GISSA and a DM application is presented.
High Responsive Plug-In for Malicious URL Detection

Malicious URL Spamming is one of the major issues, which is still being faced by our society. Many techniques or solutions have being found out in the past years but still many cyber attacks have being taking place. The aim of this work is to close the existing loopholes, which are being used by attackers to perform URL Spamming. The detection technique used is a plug-in, which is being run using machine learning algorithm. Until now, no method is being found out to block the malicious URL in a real time scenario. Through our work, we aim to block Malicious URL in real time through a plug-in. We use feature extraction and tokenization in the preliminary stage and the result of the feature extraction of URL is given to the machine-learning algorithm, namely Logistic Regression. Using the training data given to the algorithm the test data is being tested and output is produced. Logistic Regression Algorithm thus classifies the test data to be a malicious one or genuine one. In addition, if it is a genuine one through the plug-in used it will be tagged as good and if it is a malicious URL it will be tagged as bad. Moreover, on the spot an error message will be given to the user to indicate that the URL is malicious in nature thereby safeguarding the user from any kind of cyber-attack or fraud.
School Children Educational Culture as a Russian Pedagogical Education Development Factor

The article discusses the patterns and reasons of the development of pedagogical initiative and schoolchildren’s creative work; develops the idea of the influence of the pedagogical culture of schoolchildren on national education, and raises the issue of the pedagogical talent of students. The authors note that the Russian pedagogical education today faces challenges that are connected with the new quality of the teacher’s pedagogical culture, based on the ability to transform the traditional practices of the industrial education model into pedagogical solutions that meet the challenges of the 21st century. It is concluded that at present pedagogical initiatives of schoolchildren are emerging and developing in Russian education. There is a need for a comprehensive understanding, scientific substantiation and scientific and methodological support of creative proposals of schoolchildren in the field of education, since the development of the pedagogical culture of the younger generation can not only create the basis for the development of school education, but also become a valuable and technological foundation for the modernization of national education.
Super-resolution restoration of spaceborne HD videos using the UCL MAGiGAN system

We developed a novel SRR system, called Multi-Angle Gotcha image restoration with Generative Adversarial Network (MAGiGAN), to produce resolution enhancement of 3-5 times from multi-pass EO images. The MAGiGAN SRR system uses a combination of photogrammetric and machine vision approaches including image segmentation and shadow labelling, feature matching and densification, estimation of an image degradation model, and deep learning approaches, to retrieve image information from distorted features and training networks. We have tested the MAGiGAN SRR using the NVIDIA® Jetson TX-2 GPU card for onboard processing within a smart-satellite capturing high definition satellite videos, which will enable many innovative remote-sensing applications to be implemented in the future. In this paper, we show SRR processing results from a Planet® SkySat HD 70cm spaceborne video using a GPU version of the MAGiGAN system. Image quality and effective resolution enhancement are measured and discussed.
Performance Testing of Different Configurations of Hybrid Proposed Models for Solar Radiation Prediction Using WNN and ANN

The solar energy resource is important because it offers a response to polluted energy challenges. Solar radiation forecasting is important to deal with as the overall all power generated depends on the forecasted model. In this paper Saudi Arabia is used as a case study. Several meteorological variables will be used to find better result of hourly Global Solar Radiation (hGSR) prediction in terms of error.Based on previous studies, the most effective weather variables in GSR prediction for Saudi Arabia are cloud cover, relative humidity and temperature. In this paper we propose four models based on different combination of these variables used as inputs to the GSR prediction models. These models involve different combinations of Wavelet and Artificial Neural Networks (ANN) prediction methods. The best model among these models will be applicable to be used in different locations and conditions.The proposed hybrid models of WNN and ANN are highly accurate and outperform alternatives.
An Efficient Semantic Retrieval Method for Network Education Information Resources

To improve the performance of network education information resources system, this paper proposes ontologybased similarity algorithm and semantic query expansion method. We mainly study ontology reasoning and define reasoning rules based on knowledge point ontology. Combining with various conceptual relationships in knowledge point ontology, we define corresponding rules and verify them by experiments, which lays a foundation for semantic extension. Then, a domain ontology-based information retrieval model is proposed based on the above content. By separating the relationship between concepts, the semantic similarity and correlation are calculated by using the conceptual hierarchy and association relationship and considering various factors, the semantic similarity and correlation are synthetically quantified into the semantic association degree for semantic retrieval. Finally, the prototype system of semantic retrieval is designed and validated, and the simple retrieval of resources is realized.
Designing A Low-Cost Surface Plasmon Resonance (SPR) Instrument For Monitoring Degradation Of Milk Quality

Abstract A design of a low-cost SPR instrument has been made to monitor the degradation of milk quality. The cost-production of the SPR instruments is much more economical, namely around IDR 10,000,000 where the price of commercial SPR instruments such as nanoSPR6 is $ 9,950. The main components of the instrument are consist of an Arduino Uno microcontroller, red laser, polarizer, stepper motor, prism, gold chip, and a cell phone camera. The light detection system of the instruments is using image processing that utilizes recorded data from the cell phone camera programmed with python software. The sample in this research used is pasteurized milk. The measurement results obtained from the SPR instruments are compared with nanoSPR6. The sensorgram result from the monitoring of pasteurized milk using both SPR instruments for 12 hours and nanoSPR6 for 4 hours has experienced a decrease in light intensity. The light intensity had decreased up to 8 hours from the time of observation and remained flat for the next 4 hours. This decreased intensity states that there is a shift in the angle dip during the monitoring process. This explanation is supported by track mode measurement data by nanoSPR6 that run for 2 hours, showing a change in the angle dip from 67.34° to 67.6°. The increase in angle dip during the monitoring process is because of increasement in the dielectric constant and the density of the milk during the process of quality degradation. In addition, it is also suspected to be due to the appearance of fatty deposits in milk which results in an increase in the milk refractive index. The change of the milk refractive index within 2 hours is 0.0023 obtained by a fitting process using the Winspall software.
Client-Aided Two-Party Secure Interval Test Protocol

Secure interval test protocol checks if an integer is within some interval in a privacy-preserving manner. A natural application is geological location hiding, where we can check whether a person is in a certain territory without revealing any information. In addition, secure interval test protocol enables us to do arithmetic over private values with rounding errors. Therefore, it allows servers to obtain an approximation of a complicated function. In this work, we present an efficient secure interval test protocol that checks whether a shared value is within the range of two plain values. We also show that the interval test protocol can be used as a building block to construct protocols with richer functionality such as the approximation of exponential functions or logarithmic functions. Our protocol is constructed in the client-aided model, which is briefly mentioned in some previous work on constructing practical MPC frameworks such as SecureML (S&P’17), in which any number of clients can not only create shares of their inputs but also generate some necessary correlated randomness used in the online phase and distribute them to servers. Such correlated randomness generated by clients serves efficient protocols since servers don’t have to jointly generate randomness by themselves, which can avoid costly computation/communication. In this paper, we improve the state-of-the-art secure interval test protocol by Nishide and Ohta (PKC’07) based on a secret sharing scheme. We use the client-aided model and tree-based techniques, which contribute to reducing communication rounds. Our proposed protocol has only 4 communication rounds regardless of the bit length of inputs. This is about 3 times fewer rounds than existing protocols. Using the proposed protocol, we further introduce a secure look-up table technique that can be utilized to securely compute some richer functions.
A computational model for ubiquitous intelligent services in indoor agriculture

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, accurate sensors and specific protocols for an IoT. One of the trends in this research area is the use of context awareness. In agriculture, the context can be related to the environment, for example, the conditions found inside a greenhouse. Recently a series of studies proposed the use of sensors to monitor the production or the use of cameras to obtain crop information, providing data, reminders and alerts to farmers. This paper proposes a computational model for Indoor Agriculture called IndoorPlant that uses the contexts history analysis to provide intelligent services such as predict the productivity, indicate the problems that the crop may suffer, give suggestions for improvements in the parameters in the greenhouse, among others. IndoorPlant was tested on cucumber prediction using simulated data that was approved by three farmers with more than 10 years of experience each. The results obtained in the prediction of cucumber, with a coefficient of determination (R2) of 0.9912 for root mean square error (RMSE) of 8,06 units of cucumber.
Benchmarking Dynamic Searchable Symmetric Encryption with Search Pattern Hiding

Searchable symmetric encryption (SSE) is an important technique for cloud computing. SSE allows encrypted critical data stored on an untrusted cloud server to be searched using keywords, returning correct data, but the keywords and data content are unknown by the server. However, an SSE database is not practical because the data is generally frequently modified even when stored on a remote server, since the server cannot update the encrypted data without decryption. Dynamic searchable symmetric encryption (DSSE) is designed to support this requirement. DSSE allows adding or deleting encrypted data on the server without decryption. Many DSSE systems have been proposed, based on link-list structures or blind storage (a new primitive). Each has advantages and drawbacks regarding function, extensibility, and efficiency. For a real system, the most important aspect is the tradeoff between performance and security. Therefore, we implemented several DSSE systems to compare their efficiency and security, and identify the various disadvantages with a view to developing an improved system.
Ways to merge two secret sharing schemes

A secret sharing scheme implemented in an organisation is designed to reflect the power structure in that organisation. When two organisations merge, this usually requires a number of substantial changes and, in particular, changes to their secret sharing schemes which have to be merged in the way which reflects a new role of each of the organisations. This study looks at the ways secret sharing scheme can be modified when organisational changes occur. The authors restrict themselves with the class of ideal linear secret sharing schemes and describe how matrices of these linear schemes have to be modified when they take the sum, the product or the composition of two linear access structures.
Practical Use Cases of Anomaly Detection

In this chapter, you will learn how anomaly detection can be used in several industry verticals. You will explore how anomaly detection techniques can be used to address practical use cases and address real-life problems in the business landscape. Every business and use case is different, so while we cannot copy-paste code to build a successful model to detect anomalies in any dataset, this chapter will cover many use cases to give an idea of the possibilities and concepts behind the thought processes.
An Enhanced Method for Topic Modeling using Concept-Latent

In this digital era, Topic Modeling plays a vital role in clustering of documents. In topic modeling, each document is represented as a collection of words. Labeling appropriate word distributions to the defined topic is a challenging task. Topic modeling using unsupervised algorithm does not align topics with those as it exists in the real world metaphor. This problem of aligning topic with real world objects can be carried out by two different methods-either to use a supervised learning algorithm that assigns individual topic with a label acquired from existing data or to use a predefined set of topics and to match them with the distribution of words. The above said methods have some drawbacks as the former method may match a label which does not convey a semantic meaning with the word distributions whereas the latter lacks the knowledge required to detect the topic from the closed set of topics. In this paper an algorithm called Concept-Latent Dirichlet Allocation (Concept-LDA) is proposed which incorporates reinforcement learning for topic modeling which inturn improves the accuracy of resulting topics and of the topic labeling. Experiments were conducted using LDA and Concept-LDA for topic modeling and it is shown that Concept-LDA showing better accuracy than LDA.
Noble Authentication Protocol with Privacy Preservation Policy for Public Auditing on Shared Data

The cloud based services today offers a large amount of services irrespective of the geographical location to individuals or a group. This opens the way to new security dangers to the protection and also the integrity of the information put away. Public Key Infrastructure mechanism, advanced endorsements, computerized marks are existing techniques to accomplish numerous security prerequisite sites, however late dangers make them powerless against genuine assaults when utilized without earlier trust-point establishments. At the time of open reviewing of the information by Third Party Auditor, it is quite possible that it might trade off in protecting client’s privacy. This outcome in two separate issues which represents the dangers of authentication and additionally the trustworthiness of the information that is being shared. In this paper, we propose a Noble Authentication Protocol with privacy preservation policy cloud that gives solid common verification framework utilizing the Pedersen Commitment scheme alongside using ring signature algorithm calculations that will authenticate the TPA and just give the essential data to checking the trustworthiness of the mutual information with keeping the privacy of the clients covered up. The proposed technique helps in staying away from conceivable hash crashes, approving the chain of trust to certificate authorities and different denial records.
Digital selves, material bodies, and participant research tools: towards material semiotic video ethnography

Analysis of social action has increasingly considered the place of ‘things’ in recent years, including seeing objects (both physical and digital) as co-participants in social interaction, systems, and the research process. This article argues that video ethnography is well positioned to address such considerations including, with some methodological shifts, digital activity. Drawing on the methodology and findings of an 8-month video ethnography in a US classroom, I discuss a material semiotic approach to examining digital practices, including methods that followed interaction across the online and offline, approached laptops themselves as interlocutors, and examined participant interaction with research instruments as part of student meaning-making practices.
Single Image Super-Resolution Based on Modified Interpolation Method Using MLP and DWT

Nowadays Super Resolution (SR) is a trending term in many fields of image processing area where resolution describes the degree of details an image holds like, pixel count, sharpness and clarity. In many applications captured images are of low quality known as low resolution (LR) image, these images hold very less details. Therefore there is a need to convert such LR images to High resolution (HR) images which can be obtained by various SR methods like, interpolation methods, frequency domain based methods, reconstruction based methods and learning based methods. HR images obtained from interpolation methods contain reconstruction artifacts like edge blurs, edge halos and ringing effects, whereas learning based methods produce good results but predict unknown HR pixel values based only on input LR/HR image pairs. Thus, to overcome above mentioned disadvantages we proposed modified interpolation method for obtaining HR image, which predicts unknown HR pixel values from interpolated LR patch and corresponding HR patch using multilayer perceptron (MLP) and discrete wavelet transform (DWT). Experimental results subjectively and objectively show that for considered test images corresponding HR images obtained by our proposed method are of better quality than classical bicubic, Chopade et al. and Man et al. method.
Sensor selection for target tracking based on single dimension information gain

In sensor management for target tracking, the existing sensor selection methods based on information entropy generally select the sensor that brings the most information gain for target; however, for multi-dimension state vector of target, information gain for target in the specified dimension cannot be guaranteed. For the ambiguity problem in the measure for multi-dimension state vector using information entropy, this study proposes a sensor selection method based on single dimension information gain of the target, which uses particle filter to compute the marginal probability density of target state in the specified dimension and exploits the Kullback–Leibler divergence to measure the information gain of target in the specified dimension, in such a way, by selecting the sensor that brings the most information gain for target in the specified dimension. The proposed method can guarantee higher tracking accuracy gain of target in the specified dimension and obtain more reliable tracking results. Experiment results illustrate that the proposed method has a higher track accuracy of position and velocity in the specified dimension than sensor selection method based on target state entropy.
Enhanced graph processing in PIM accelerators with improved queue management

Processing-in-memory (PIM) proposes to move computational components inside memory units to alleviate the high cost of data movement in big data processing. This approach has been recently utilized to reach high performance and energy-efficiency in large-scale graph processing. This paper analyzes a state-of-the-art PIM accelerator for graph processing and identifies message queue management as a significant bottleneck for system efficiency. Two metrics were introduced for representing the waiting time and processor utilization. We then present a lightweight solution for reducing waiting time caused by the message queue while increasing resource utilization in the system. Our simulation results on a set of real-world graph examples indicate that the enhanced graph processing system achieves 40% reduction in the overall execution time and 15% system energy savings over the baseline PIM based accelerator.