Reviewerly/exHarmony huggingface dataset:
Distributed Deep Variational Information Bottleneck

This paper deals with a distributed version of the information bottleneck method. For this problem, we develop a variational bound on the optimal tradeoff between relevance and complexity that generalizes the evidence lower bound (ELBO) to the distributed setting. Furthermore, we also provide a variational inference type algorithm that allows to compute this bound and in which the mappings are parametrized by neural networks and the bound approximated by Markov sampling and optimized with stochastic gradient descent. Experimental results are provided to support the efficiency of the approaches and algorithms which we develop in this paper.
Work-in-Progress—Design and Evaluation of an Augmented and Virtual Reality Flipped-Learning Course for K-12 Educators

In this article we describe the design of a blended training course on AR and VR for K-12 teachers. The overall aim of the course is to facilitate the creation of communities of practice that will inspire, motivate, and empower educators to apply innovative, pedagogically informed learning scenarios using AR and VR. This eight-week professional development course is one of the first of its kind to be developed and offered nation-wide to all educators of a European country. We also outline an elaborate evaluation scheme based on pilot runs and mixed research methods, interconnected with the European Framework for the Digital Competence of Educators. In this work-in-progress study we demonstrate how best practices from previous digital learning and educational technology paradigms, such as Open and Distance Learning and Flipped Learning, can be applied to facilitate teacher training and the mass adoption of Immersive Technologies by teachers in the primary and secondary education.
Analysis of Mixed Reality Tools for Learning Math in Primary and Secondary School

In our study, we provide a state of the art on Mixed Reality (MR) learning tools for teaching math in primary and secondary school. Through a detailed analysis of eight representative applications, we provide an overview of the MR applications currently used, their educational objectives, the augmentations and interactions they offer, the technologies they use, their advantages and their limitations. We conclude by identifying several remaining challenges that need to be addressed in order to benefit from the full educational potential of MR for teaching math in schools.
Types and Abstract Interpretation for Authorization Hook Advice

Authorization hooks are access control checks that prevent unauthorized principals from interacting with some protected resource, and are used extensively in critical software such as operating systems, middleware, and server programs. They are often intended to mediate information flow between subjects (e.g., file owners), but typically in an ad-hoc manner. In this paper we present a static type and effect system for detecting whether authorization hooks in programs properly defend against undesired information flow between subjects. A significant novelty of our approach is an integrated abstract interpretation-based tool that guides system clients through the information flow consequences of access control policy decisions.
Rely/Guarantee Reasoning for Noninterference in Non-Blocking Algorithms

Noninterference characterizes a security property in which an attacker cannot determine the inputs to a system based on outputs of a lower classification. Value-dependent noninterference enables the analysis of systems in which these classifications may depend on the system's state and evolve throughout execution. Existing approaches to enforcing such a property for concurrent systems are constrained in their capability to express how the concurrent components modify shared variables and, therefore, the value-dependent classifications. Such approaches typically make use of externally verified annotations or coarse locking primitives to express limited constraints on variables, such as read and write permissions. Consequently, these techniques are insufficient for the analysis of programs that feature complex concurrent behaviours or require fine-grained synchronisation, as seen in non-blocking algorithms. This paper presents a compositional logic for enforcing value-dependent noninterference properties for complex concurrent algorithms, including non-blocking algorithms. It uses rely/guarantee reasoning to establish how classifications may be modified by concurrent components. Additionally, the logic allows for the specification of security policies at a component level and ensures their valid composition. These results have been formalised in Isabelle/HOL.
Learn to Optimize: Adaptive VNF Provisioning in Mobile Edge Clouds

Machine learning (ML) has been penetrating into our daily life by facilitating many daily applications, e.g., self-driving, cloud gaming, product fault detection and drones. Meanwhile, there is an emerging trend that adopts ML methods into network optimization problems, such as flow classification, traffic engineering, routing, and etc. Conventional ML methods need careful training for a specific application of a given network structure, and the trained model normally cannot be applied to other applications and network structures. In this paper, we aim to design adaptive ML methods for network optimization problems, with the trained models having the ability of being deployed to any similar problems. In particular, we consider the virtualized network function (VNF) provisioning problem as our target optimization problem. We first propose a deep Q-learning-based optimization framework for VNF provisioning in a mobile edge network with network capacity constraints, by devising an adaptive graph feature embedding method. We then propose a series of deep Q-learning based learning algorithms for the problems of service chaining and the throughput maximization, based on the proposed learning-based optimization framework. We also propose a novel design of master-slave dual neural network that enables the decisions on both cloudlet selections and routing path finding. To stabilize and accelerate the convergence of the proposed methods, we devise a novel environment generation and termination strategy and a new structure for the replay buffer. We also evaluate the performance of the proposed framework and algorithms by extensive simulations. Results show that the proposed algorithms outperform existing methods by around 12%, and the trained model in a network can be directly adapted to other network structures and settings.
The Sequential Recommendation Strategy for the Intelligent Learning of Chinese Vocabulary

The purposes of this study are to implement a new sequential recommendation strategy in a mobile-device supported Elementary Chinese Vocabulary Learning System (ECVLS) and evaluate its effect upon the learning. Experiment results appeared that the strategy combining with word levels, word frequencies and word relations can make an effective impact on the vocabulary learning with the support of ECVLS.
Recognition and Application of Learner’s Cognitive Ability for Adaptive E-learning

In adaptive learning system, the key to promote personalized learning is the learner model. The Elo model has great potential in online learning environment, so based on it, we propose the EELO which is an improved extension of Elo rating system, in view of polychotomously scored items and different granularity evaluations that the Elo rating system do not cover. The performance of the EELO estimating learners' abilities and predicting their future performances are evaluated on two large data set, which demonstrates that the EELO is better-performing. Then, we apply it in a real online learning environment to provide an analysis report for different user roles, which also can be used as the reference for the development of adaptive learning applications in the future.
Improvised Progressive Neural Network (iPNN) for Handling Catastrophic Forgetting

Learning is a technique which is used to solve a complex sequence of tasks like catastrophic forgetting. Transfer of learning starts from a pre-trained model in the source domain and learning gets transferred to a target domain. A progressive neural network is a technique in the eld of transfer learning, to solve the problem of catastrophic forgetting. Catastrophic forgetting is an area of machine learning, in which machines forget the previously known data while training a new model in transfer learning. The contribution of this paper is a novel technique called improvised Progressive Neural Network (iPNN) to handle the problem of catastrophic forgetting. The proposed technique reduces the loss of information while transferring knowledge from one model to another model in transfer learning. In this technique, an improvised progressive neural network is presented such that the storage constraints are significantly reduced The basic idea of the technique is to remove those models whose knowledge is no longer required in other models for a long period It also removes that model whose knowledge is already transferred in another model, completely or partially. In this case, the target model already captures the complete knowledge of the source model. With the removal of such source models, performance in terms of computation costs is significantly enhanced.
Shuttlecock Detection and Fall Point Prediction using Neural Networks

The prediction of moving objects has always been an interesting area of research. Lots of prediction algorithms have been developed in the past to predict the trajectory of moving objects. This paper deals with the prediction of shuttlecock trajectory in two dimensions. This work is a part of the shuttle badminton playing robot that is under development in our laboratory. The focus of this work is to carry out detection and prediction processes using single camera input only. Based on the obtained results, the work will be extended to stereoscopic cameras. The first part of the work deals with the detection of the shuttlecock using deep learning techniques and the rest of the paper deals with the prediction of the shuttlecock using Neural Networks. The obtained prediction is around 80 percent accurate and can be convincingly used as the target point for the navigation of the robot.
Load Balancing Algorithm to Reduce Make Span in Cloud Computing by Enhanced Firefly Approach

Cloud computing provides essential environments and services to the organizations through cloud service and its often splendid choice for individual to enterprises. Cloud Servers places an important role to integrate the environment may remotely located in several areas owned by somebody else and own. These are fully managing environment to execute applications in easy, elasticity and dynamic way, either remotely located or employed by users keep with their requirements. Today researchers provide numerous ways to unravel additional challenges in Resource sharing and load Balancing. Load Balancing allows distributing the work load among all the cloud servers evenly to form sure optimization of resource allocation. Load Balancing algorithm developed by using Bio-inspired intelligent firefly approach through the strong ability of worldwide searching. Firefly approach used to develop a solution for Meta heuristic problem like load balancing. Enhanced Firefly algorithm developed by adopting the essential concepts of Firefly approach. Search capability increased to understand global optimized solution by enhanced firefly feedback mechanism and communications. Enhanced Firefly Approach (EFA) evaluated in Cloudsim simulation environment to retrieve execution time, make span time and number of migrations etc. less variance value proves its efficiency. The Results are compared with existing traditional firefly load balancing algorithms, supported the result value of objective function meets the user's requirements and proved its performance.
Loss-Size and Reliability Trade-Offs Amongst Diverse Redundant Binary Classifiers

Many applications involve the use of binary classifiers, including applications where safety and security are critical. The quantitative assessment of such classifiers typically involves receiver operator characteristic (ROC) methods and the estimation of sensitivity/specificity. But such techniques have their limitations. For safety/security critical applications, more relevant measures of reliability and risk should be estimated. Moreover, ROC techniques do not explicitly account for: 1) inherent uncertainties one faces during assessments, 2) reliability evidence other than the observed failure behaviour of the classifier, and 3) how this observed failure behaviour alters one's uncertainty about classifier reliability. We address these limitations using conservative Bayesian inference (CBI) methods, producing statistically principled, conservative values for risk/reliability measures of interest. Our analyses reveals trade-offs amongst all binary classifiers with the same expected loss – the most reliable classifiers are those most likely to experience high impact failures. This trade-off is harnessed by using diverse redundant binary classifiers.
Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors

We analyzed a volume of software project data and found significant user satisfaction in several software project factors. statistical significance A analysis (logistic regression) a collinearity analysis and determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. Eight prediction models were used to test the prediction potential of these factors: Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were significant in predicting user satisfaction: client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. They provided 82.71% prediction accuracy when used with a neural network prediction model. These findings may directly benefit software development managers.
Sentinel-2 Image Dehazing using Visible and Infrared Band Correlation Based on NDVI Classification

Due to the penetration of the electromagnetic (EM) wave in infrared are higher than the visible bands. The satellite image details from these bands are obviously than others. Most of the visible bands directly varied with the infrared bands, especially the non-water area. We can employ these properties to generate synthetic visible bands for decreasing the haze effect. However, some area has a more complex relation between visible and infrared bands. To overcome this problem, we proposed the dehazing technique for Sentinel-2 imagery by using visible and infrared band correlation based on the Normalized Difference Vegetation Index (NDVI) classification. The synthetic visible bands are constructed from the linear combination of the infrared bands. The multiple linear regression is applied to determine the linear coefficients of each formula. The NDVI is employed to classify the group of considered samples. The haze-free images with the nearest sensing date were employed to compare probably realistic images, both visual and metric comparisons. The results show that our proposed techniques can be well applied to reduce the haze effects, especially the uniform thin haze. Moreover, there give both visual and metrics results superior to the traditional method. In the case of thick haze, our proposed methods give more obvious vision. However, our methods have a disadvantage in the water areas. It had more artifact results due to its NDVI failure in the corrected classes.
Smartphone-based Risky Traffic Situation Detection and Classification

Although the number of traffic accidents occurring in Japan is decreasing, there still happen approximately 400,000 traffic accidents annually. Behind such accidents, there are frequent minor incidents (near-miss incidents) that may lead to such serious accidents. Analyzing such minor incidents is effective to reduce accidents, but the challenge is to design and deploy a method to collect and analyze such incident information. Drive recorders may be useful for such a purpose, but they cannot collect information from those vehicles without recorders. In this study, we propose the design and development of a platform that aggregates behavioral data from pedestrians and vehicle drivers using their smartphones, and automatically estimates risky traffic situations from the aggregated data. We present our preliminary result of detecting and classifying those events in a controlled environment and have achieved F-value 0.89 for four categories classification.
Gated Recurrent Attention for Multi-Style Speech Synthesis

End-to-end neural network-based speech synthesis techniques have been developed to represent and synthesize speech in various prosodic style. Although the end-to-end techniques enable the transfer of a style with a single vector of style representation, it has been reported that the speaker similarity observed from the speech synthesized with unseen speaker-style is low. One of the reasons for this problem is that the attention mechanism in the end-to-end model is overfitted to the training data. To learn and synthesize voices of various styles, an attention mechanism that can preserve longer-term context and control the context is required. In this paper, we propose a novel attention model which employs gates to control the recurrences in the attention. To verify the proposed attention’s style modeling capability, perceptual listening tests were conducted. The experiments show that the proposed attention outperforms the location-sensitive attention in both similarity and naturalness.
A Computation-Efficient Solution for Acceleration of Generative Adversarial Network

Nowadays, Generative Adversarial Network (GAN) has been developed rapidly and has found variety of applications. Convolutional (CONV) and deconvolutional (DeCONV) layers are typical components of GANs. There existe a few researches on acceleration of deconvolution implementations. However, the previous works on accelerating DeCONV layers generally incur several issues, such as resource under-utilization, large memory requirements, and complex data access. In this paper, we propose an efficient method, namely fast transformation algorithm, to accelerate DeCONV layers. The algorithm improves the computational efficiency significantly and solves the problems of resource under-utilization and large memory requirements. Based on the algorithm, a reconfigurable computing core (RCC) for GANs is developed, which can support both convolutions and deconvolutions. Additionally, a reconfigurable architecture based on RCC is proposed to support both CONV and DeCONV layers. Moreover, a typical generative neural network, CycleGAN, is used to verify our design. The experimental results show that our design, when performing deconvolutions and convolutions in CycleGAN, can reach 352.50 GOPS under 200MHz working frequency on Intel Arria 10SX. In brief, the proposed design outperforms existing works significantly, particularly in terms of hardware efficiency.
Non-commutative Ring Learning with Errors from Cyclic Algebras

Abstract The Learning with Errors (LWE) problem is the fundamental backbone of modern lattice-based cryptography, allowing one to establish cryptography on the hardness of well-studied computational problems. However, schemes based on LWE are often impractical, so Ring LWE was introduced as a form of ‘structured’ LWE, trading off a hard to quantify loss of security for an increase in efficiency by working over a well-chosen ring. Another popular variant, Module LWE, generalizes this exchange by implementing a module structure over a ring. In this work, we introduce a novel variant of LWE over cyclic algebras (CLWE) to replicate the addition of the ring structure taking LWE to Ring LWE by adding cyclic structure to Module LWE. We show that the security reductions expected for an LWE problem hold, namely a reduction from certain structured lattice problems to the hardness of the decision variant of the CLWE problem (under the condition of constant rank d ). As a contribution of theoretic interest, we view CLWE as the first variant of Ring LWE which supports non-commutative multiplication operations. This ring structure compares favorably with Module LWE, and naturally allows a larger message space for error correction coding.
A Novel Modular Multiplier for Isogeny-Based Post-Quantum Cryptography

Supersingular isogeny key encapsulation (SIKE) protocol is a promising candidate for the standard of post quantum cryptography (PQC), but it suffers from high computational complexity. Since the modular multiplication takes up a large proportion of the computations in SIKE protocol, accelerating this operation can efficiently speed up the entire protocol. In this paper, we propose a new modular multiplication algorithm, which can achieve lower complexity than prior arts. The SIKE-friendly prime with form of p = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nx</sup> l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ny</sup> +1 = R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> +1 is considered. The modulo-p operation is mainly replaced by n modulo-R operations, for which a general Barrett reduction( GBR) algorithm is presented and applied. Moreover, an efficient architecture is designed for the proposed algorithm, where the pipelining and interleaved techniques are applied. For the multiply-accumulate (MAC) part, various optimization techniques are introduced to reduce the data path and the complexity. The FPGA implementation results show that for a level-5 quantum-security parameter, our design achieves the fastest clock speed with middle number of clock cycles and small resources consumption among the state-of-the-art works.
Vιsual question answering models Evaluation

Visual question answering (VQA), visual dialogs, visual chat bot are multi-discipline exploration problems, which is a blend of Natural Language Processing (NLP), Image feature extraction and Knowledge Reasoning (KR). Rather than captioning, which is naïve approach of computer vision, VQA problems enhances the perspective by providing interactivity to ask domain specific as well as open ended questions to images and give us the insights based on image features or characteristics. Our research is the evaluate the performance of VQA on counting problems. Given an image, VQA model is expected to answer "how many" question type. We have used few pre-trained models for VQA and visual dialog and tabulated the findings of accuracy of predicted answer with the pre-defined ground truth.