Reviewerly/exHarmony huggingface dataset:
5G and Edge Computing Enabling Experience Delivery Network (XDN) for Immersive Media

With 5G and Edge Computing, media services observe a very big revolution nowadays. LTE led to media services evolution through enabling video streaming anytime, anywhere, and to any connected mobile device. CDN offered HTTP-based solutions for media streaming services, however real-time communication (RTC) and interactive traffic are not fully supported. 5G and Edge Computing are now enabling new experience through an Experience Delivery Network (XDN), where content creation is in real-time, on the fly and equally uses upstream and downstream links. Real-time Communication (RTC) with 5G and Edge Computing allows negligible latency between the communicating parties and is promising not only for immersive media, but also for Cloud Gaming and Tele-health applications. In this paper, we present a 360 Immersive Media solution using Intel-incubated Open WebRTC Toolkit (OWT) and edge computing platforms, while allowing media ingestion over 5G networks from multiple cameras, media control and 360 media distribution over 5G networks. Microservices architecture is used for media ingestion, control, and distribution functions, allowing the solution deployment across multiple edge computing clusters and sites. We implemented a prototype for our solution, and we present a performance evaluation showing the gain in terms of bandwidth and latency besides the modular deployment.
Multimodal Patient-Specific Registration for Breast Imaging Using Biomechanical Modeling with Reference to AI Evaluation of Breast Tumor Change

Background: The strategy to combat the problem associated with large deformations in the breast due to the difference in the medical imaging of patient posture plays a vital role in multimodal medical image registration with artificial intelligence (AI) initiatives. How to build a breast biomechanical model simulating the large-scale deformation of soft tissue remains a challenge but is highly desirable. Methods: This study proposed a hybrid individual-specific registration model of the breast combining finite element analysis, property optimization, and affine transformation to register breast images. During the registration process, the mechanical properties of the breast tissues were individually assigned using an optimization process, which allowed the model to become patient specific. Evaluation and results: The proposed method has been extensively tested on two datasets collected from two independent institutions, one from America and another from Hong Kong. Conclusions: Our method can accurately predict the deformation of breasts from the supine to prone position for both the Hong Kong and American samples, with a small target registration error of lesions.
Towards Easy Vocabulary Drafts with Neologism 2.0

Shared vocabularies and ontologies are essential for many applications. Although standards and recommendations already cover many areas, adaptations are usually necessary to represent concrete use-cases properly. Domain experts are unfamiliar with ontology engineering, which creates special requirements for needed tool support. Simple sketch applications are usually too imprecise, while comprehensive ontology editors are often too complicated for non-experts. We present Neologism 2.0 – an open-source tool for quick vocabulary creation through domain experts. Its guided vocabulary creation and its collaborative graph editor enable the quick creation of proper vocabularies, even for non-experts, and dramatically reduces the time and effort to draft vocabularies collaboratively. An RDF export allows quick bootstrapping of any other Semantic Web tool.
Multidimensional Analysis of NewSQL Database Systems

Collecting, transforming and analyzing data is seeing an all time high interest over the last few years. Due to the large amount of emerging or enhanced data management systems, as well as the high amount of differences in features and options they support, this paper is focused on reviewing the existing systems that are part of the NewSQL category. By looking at the top nine most used and researched implementations this work investigates how these compare in terms of supported data models, transactionality features and querying abilities. Furthermore, it provides an overview of scalability implementations and functionality as well as security assets and liabilities. It also includes an analysis of costs, by looking at both licensing as well as infrastructural support. This work can be used as a guiding analysis for providing insights into what features are supported and offered by the top NewSQL systems available over multiple dimensions.
Homogeneous Architecture Augmentation for Neural Predictor

Neural Architecture Search (NAS) can automatically design well-performed architectures of Deep Neural Networks (DNNs) for the tasks at hand. However, one bottleneck of NAS is the prohibitively computational cost largely due to the expensive performance evaluation. The neural predictors can directly estimate the performance without any training of the DNNs to be evaluated, thus have drawn increasing attention from researchers. Despite their popularity, they also suffer a severe limitation: the shortage of annotated DNN architectures for effectively training the neural predictors. In this paper, we proposed Homogeneous Architecture Augmentation for Neural Predictor (HAAP) of DNN architectures to address the issue aforementioned. Specifically, a homogeneous architecture augmentation algorithm is proposed in HAAP to generate sufficient training data taking the use of homogeneous representation. Furthermore, the one-hot encoding strategy is introduced into HAAP to make the representation of DNN architectures more effective. The experiments have been conducted on both NAS-Benchmark-101 and NAS-Bench-201 dataset. The experimental results demonstrate that the proposed HAAP algorithm outperforms the state of the arts compared, yet with much less training data. In addition, the ablation studies on both benchmark datasets have also shown the universality of the homogeneous architecture augmentation. Our code has been made available at https://github.com/lyq998/HAAP.
Handwritten Data Digitization Using an Anchor based Multi-Channel CNN (MCCNN) Trained on a Hybrid Dataset (h-EH)

To develop a holistic system for handwritten English character recognition for manually filled forms by systematically synthesising a robust handwritten textual character dataset for acceptable representation of handwriting. As part of this study, 572 copies of a form were filled by over 200 different individuals to introduce demographic variation. These forms were then scanned and each handwritten character in the forms was labelled and extracted using standard image processing techniques. The dataset of 84,712 character images created by this method (HW-dataset) comprised of both alphabetical and numerical characters. Three hybrid datasets (h-EH) were then formed by combining EMNIST datasets and the HW-dataset based on Digits (h-EHd - 329,668 character images), Alphabets (h-EHa - 163,085 character images) and a mixture of Digits and Alphabets (h-EHm - 189,586 character images). An anchor based image extraction technique was used in conjunction with a Multi-Channel CNN (MCCNN) model which was trained on three versions of h-EH, to automate the process of digitization of handwritten forms. The classification accuracies of the MCCNN for h-EHa, h-EHd and h-EHm are 93%, 96% and 93% respectively for test data. Models trained on only the EMNIST dataset perform poorly on test data. An anchor based object detection method used in conjunction with MCCNN trained on h-EH produces excellent results in digitising hand filled forms. Touch free solutions will gain prevalence due to the emergence of threat of fomites in the world. In such a space, manual handling of forms for the purpose of data entry, digitization and information handling will be considered as potential health and safety hazards. The solution presented in the current work uses a combination of models which is trained on a hybrid handwritten data set with high demographic variability. The model developed as part of this study is well suited for enabling touch free handling of documents.
An English Learning System Based on Mobile Edge Computing Constructs a Wireless Distance Teaching Environment

The development of information technology and the progress of the educational information process provide technical support for the construction of a distance learning environment for learning English. Online teaching mode is a new thing for people, and it is more and more accepted by people familiar with it. We summarize and analyze the texts of short messages generated by students’ online learning, so as to fully understand students, make effective adjustments to the classroom, guide teachers’ teaching practices, optimize the classroom, and improve students’ learning, practical, and innovative abilities, in order to study the effect of online teaching on improving the ability and skills of learning English efficiently. This paper builds wireless distance teaching based on the English learning system of mobile edge computing and uses the method of case analysis and literature analysis to collect data from the database. A model was built based on online teaching, and a large number of relevant documents were read and analyzed through the literature survey method. The results of the study show that online teaching can effectively improve students’ oral and reading abilities. Students’ oral and reading abilities are about 30% higher than traditional teaching methods, and the awareness of cooperation among students has also increased by 80% compared to before, which solves the problem of popularization and improvement. The contradiction and the resolution of polarization and the transformation of underachievers, so that students’ personalities can be brought into full play and development, and college education will be on the track of quality education. This shows that the wireless long-distance teaching environment constructed by the English learning system based on mobile edge computing can play an excellent role in improving English ability.
HatchEnsemble: an efficient and practical uncertainty quantification method for deep neural networks

Abstract Quantifying predictive uncertainty in deep neural networks is a challenging and yet unsolved problem. Existing quantification approaches can be categorized into two lines. Bayesian methods provide a complete uncertainty quantification theory but are often not scalable to large-scale models. Along another line, non-Bayesian methods have good scalability and can quantify uncertainty with high quality. The most remarkable idea in this line is Deep Ensemble, but it is limited in practice due to its expensive computational cost. Thus, we propose HatchEnsemble to improve the efficiency and practicality of Deep Ensemble. The main idea is to use function-preserving transformations, ensuring HatchNets to inherit the knowledge learned by a single model called SeedNet. This process is called hatching, and HatchNet can be obtained by continuously widening the SeedNet. Based on our method, two different hatches are proposed, respectively, for ensembling the same and different architecture networks. To ensure the diversity of models, we also add random noises to parameters during hatching. Experiments on both clean and corrupted datasets show that HatchEnsemble can give a competitive prediction performance and better-calibrated uncertainty quantification in a shorter time compared with baselines.
Patterns of online repeat victimisation and implications for crime prevention

This paper explores the characteristics of repeat victimisation (RV) in relation to fraud and computer misuse (F&CM) crimes recorded in Wales, United Kingdom (UK). The wider study included mixed-methods analysis of a sample of cases (n = 10,001) reported by individuals in Wales, over a period of two years (ending in September 2016). In this paper, key results from the quantitative part of this study concerning RV are presented. This paper contributes to an empirically grounded understanding RV with respect to F&CM and its insights are of direct relevance to theoretical understandings of victimisation and the formulation of interventions within the ‘Protect’ strand of policing in the UK. It suggests that older age is associated with RV for these crime types, that a repeat report is likely to be of the same general type as the crime which preceded it and that interventions to protect individuals from being re-victimised are best targeted within two weeks to one month of the first report. The paper also highlights the extent to which RV analysis is constrained by the rules which shape crime recording and identifies avenues for improvement of data collection and further research. Furthermore, it suggests the need to develop a framework of F&CM vulnerability which goes beyond risk of re-victimisation and better accounts for and enables a response to victims’ wider support needs.
A case study in the use of attack graphs for predicting the security of cyber-physical systems

Cyber-attacks increasingly threaten Cyber-Physical Systems (CPS). Recent examples are attacks on a Florida water plant control system and firewalls protecting several Western U.S. electricity grid operators. Techniques for rigorously analysing the cybersecurity of CPS without the risk of disrupting their operations are therefore of increasing interest to CPS operators and regulators. Meanwhile, attack graphs have long been studied by researchers as a means of analysing the cybersecurity of both information and control systems. In this paper, we present a case study on the use of attack graphs for predicting the cybersecurity of a CPS within the critical national infrastructure. It explains how the attack graph was developed and analysed using existing system documentation, computer-aided techniques and human analysis. In this case study human cyber analysts with good knowledge of the CPS considered the automated predictions of the most exploitable attack paths to be credible. This enabled a detailed and evidenced analysis of the minimum level of cyber-attacker sophistication needed to compromise the CPS. The case study has evidenced that this style of CPS analysis could be used either during system design or whilst in operational use.
Simple security proof of coherent-one-way quantum key distribution

Coherent-one-way quantum key distribution (COW-QKD), which requires a simple experimental setup and has the ability to withstand photon-number-splitting attacks, has been not only experimentally implemented but also commercially applied. However, recent studies have shown that the current COW-QKD system is insecure and can only distribute secret keys safely within 20 km of the optical fiber length. In this study, we propose a practical implementation of COW-QKD by adding a two-pulse vacuum state as a new decoy sequence. This proposal maintains the original experimental setup as well as the simplicity of its implementation. Utilizing detailed observations on the monitoring line to provide an analytical upper bound on the phase error rate, we provide a high-performance COW-QKD asymptotically secure against coherent attacks. This ensures the availability of COW-QKD within 100 km and establishes theoretical foundations for further applications.
Great Expectations: On the Design of Predictive Motion Cues to Alleviate Carsickness

Motion sickness has gained renewed interested in the context of the developments in vehicle automation in which we are witnessing a transition from a driver-centric to passenger-centric design philosophy. As a corollary, motion sickness can be expected to become considerably more prevalent which creates a hurdle towards the successful introduction of vehicle automation and its ultimate socio-economic and environmental benefits. We here review early proof-of-concept studies into the beneficial effects of providing passenger with predictive motion cues as an elegant and effective method to reduce motion sickness in future vehicles. Future design parameters are discussed to finetune such cues not only for optimum effectiveness but, importantly, also for acceptance including sensory modality, timing, information detailing, and personalization.
A Decision Matrix for Implementing AR, 360° and VR Experiences into Mining Engineering Education

The use of Mixed Reality (MR) technologies is expected to address the current challenges in mining engineering education. Including hands-on experience often conflicts with limited resources for field trips and excursions. Various studies report that MR can support students in fostering knowledge transfer and enhancing the development of professional skills. However, the success of MR technologies in teaching depends significantly on the teachers, who are rarely supported in the introduction and selection of MR technologies. In order to support teachers in the selection of suitable media and technologies, we present a multidimensional matrix to support the decision towards the usage of three different MR experiences in mining engineering education: Augmented Reality (AR), 360°, and Virtual Reality (VR) experiences. This matrix was built based on the result of several conducted interviews with the technology developers and users. The decision matrix differentiates between methodological aspects of teaching, infrastructural factors, individual prerequisites and knowledge of the teacher, all of which play a role in the selection of the right MR technology. In addition, examples of the use of the respective technology in teaching are discussed.
Strong-Diameter Network Decomposition

Network decomposition is a central concept in the study of distributed graph algorithms. We present the first polylogarithmic-round deterministic distributed algorithm with small messages that constructs a strong-diameter network decomposition with polylogarithmic parameters.
New Image Encryption Scheme Based on Dynamic Substitution and Hill Cipher

In this work, we propose a new color image encryption technique. After transiting of the original image into a vector and decomposing it into blocks of three pixels, along with modifying of a seed block by an initialization vector computed from the plain image, a preliminary confusion will be handled by a substitution matrix developed under the control of the two chaotic maps used in the system. The achieved block will be injected in affine transformation provided by an invertible matrix accompanied by a dynamic translation vector to surmount the problem of null or uniform blocks. The encrypted block will be linked to the original block to set up diffusion and avalanche effect to protect the system from differential attacks. Simulations applied to on a large number of color images prove the robustness of the proposed approach against known attacks.
Data-driven estimation using an Echo-State Neural Network equipped with an Ensemble Kalman Filter

This paper considers the problem of data-driven estimation with sparse measurements for a complex nonlinear system. While model-based nonlinear estimation methods are well known, state estimation from partial observations with unmodeled dynamics is less understood. Here we use a method for model-free estimation based on an echo-state network (ESN) where a reasonably accurate set of training data is available during the training period and some sparse measurements are obtained during the testing phase. The measurements are assimilated by an ensemble Kalman filter (EnKF) to improve the predictor's performance when compared to a free-running neural network architecture. The proposed method is applied to three systems: a low-dimensional chaotic system, a high-dimensional chaotic system, and a set of real-time traffic data.
An E-learning Portal Quality Model: from Al-Zaytoonah University Students’ Perspective

The standard of e-learning is a compound of different quality factors quality dimensions. The researchers looked at the efficiency of e-learning from various angles and viewpoints. However, few works of literature study, specifically, the quality of e-learning portals or software. Besides, the students' or learners' opinions about this issue. In this research, we only focused on the quality of e-learning portals and technology and what factors and impact they have on e-learning in general from the students' perspectives. The results show that the students are more concerned about the usability and related factors because their learning process become depends fully on the e-learning portal as a result of the COVID-19 pandemic.
Performance comparison of Extreme Learning Machines and other machine learning methods on WBCD data set

Breast cancer is one of the most common forms of cancer among women in our country and the world.Artificial intelligence studies are growing in order to reduce the mortality and early diagnosis needed for appropriate treatment.The Excessive Learning Machines (ELM) method, one of the machine learning approaches, is applied to the Wisconsin Breast Cancer Diagnostic (WBCD) dataset in this study, and the findings are compared to those of other machine learning methods.For this purpose, the same dataset is also classified using Multi-Layer Perceptron (MLP), Sequential Minimum Optimization (SMO), Decision Tree Learning (J48), Naive Bayes (NB), and K-Nearest Neighbor (KNN) methods.According to the results of the study, the ELM approach is more successful than other approaches on the WBCD dataset.It's also worth noting that as the number of neurons in the ELM grows, so does the learning ability of the network.However, after a certain number of neurons have passed, test performance begins to decline sharply.Finally, the ELM's performance is compared to the results of other studies in the literature.
FuBiNFS – fuzzy biclustering neuro-fuzzy system

In data sets some attributes may have higher or lower importance. One of the tools used for data analysis of such datasets are subspace neuro-fuzzy systems. They elaborate fuzzy rules to describe data sets. In subspace neuro-fuzzy systems fuzzy rules exist in subspaces defined with subsets of attributes. In the paper we propose a novel fuzzy biclustering algorithm that groups both objects and attributes in fuzzy clusters. In that way we create a subspace fuzzy rule base for a subspace fuzzy system. The paper is accompanied with numerical examples that show this approach can lead to better generalisation (and thus lower data prediction errors) with preserved interpretation of fuzzy models.
Receiver-Anonymity in Rerandomizable RCCA-Secure Cryptosystems Resolved

In this work we resolve the open problem raised by Prabhakaran and Rosulek at CRYPTO 2007, and present the first anonymous, rerandomizable, Replayable-CCA (RCCA) secure public-key encryption scheme. This solution opens the door to numerous privacy-oriented applications with a highly desired RCCA security level. At the core of our construction is a non-trivial extension of smooth projective hash functions (Cramer and Shoup, EUROCRYPT 2002), and a modular generic framework developed for constructing rerandomizable RCCA-secure encryption schemes with receiver-anonymity. The framework gives an enhanced abstraction of the original Prabhakaran and Rosulek's scheme (which was the first construction of rerandomizable RCCA-secure encryption in the standard model), where the most crucial enhancement is the first realization of the desirable property of receiver-anonymity, essential to privacy settings. It also serves as a conceptually more intuitive and generic understanding of RCCA security, which leads, for example, to new implementations of the notion. Finally, note that (since CCA security is not applicable to the privacy applications motivating our work) the concrete results and the conceptual advancement presented here, seem to substantially expand the power and relevance of the notion of rerandomizable RCCA-secure encryption.