Reviewerly/exHarmony huggingface dataset:
Pedagogical Data Federation toward Education 4.0

Pedagogical data analysis has been recognized as one of the most important issues in pursuing Education 4.0. The recent rapid development of IT technologies benefits pedagogical data analysis via the provisioning of many advanced technologies such as big data analysis and machine learning. Meanwhile, the privacy of the students become another concern and this makes the educational institutions reluctant to share their students' data, forming isolated data islands and hindering the realization of big pedagogical data analysis. To tackle such challenge, in this paper, we propose a federated learning based education data analysis framework FEEDAN, via which pedagogical data federations can be formed by a number of institutions. None of them needs to direct exchange the their pedagogical data with each other and they always keep the data in their own place to guarantee their students' privacy. We apply our framework to analyze real pedagogical data. The experiment results show that it not only guarantees the students' privacy but also indeed breaks the borders of data island by achieving a higher analysis quality. Our framework can much approach the performance of centralized analysis which needs to collect the data in a common place with the risk of privacy exposure.
Indoor space compositions based on genetic algorithms to optimize neural networks

Constitutional design is a new grammatical relationship and thinking mode of modern morphological design. It reflects the basic characteristics of modern design that engages in the essence of things and uses analysis and comprehensive scientific methods to create shapes. Genetic algorithms are bionic algorithms in a macro sense and a branch of evolutionary algorithms. The structure of neural networks considerably affects their performance. If there are too many neurons in a network, it may fit the training data well, and the network may have few training errors, but it can be prone to problems such as “overfitting” and poor network generalization. Based on the this background, in this article, the research content is based on genetic algorithms to optimize neural networks’ indoor space composition. This paper starts with genetic algorithms and proposes an improved genetic algorithm based on the neural network structure optimization algorithm (IGA). Based on the proposed algorithm, MATLAB is used as an experimental platform to approximate the non-linear function y=e−(x−1)2+e−(x+1)2. The experiment proves that the IGA has a certain optimization effect on the composition of the neural network structure in indoor space. The best individual fitness value is 0.786, which improves the neural network’s adaptability and generalization, providing good global rapid convergence performance.
Don't Believe The Hype!

Following the initial hype and high expectations of conversational user interfaces (CUIs), a number of creation tools have emerged to simplify development of these complex systems. These have the potential to democratise and expand application development to those without programming skills. However, while such tools allow end-user developers to build language understanding and dialog management capability into a CUI application, actually fulfilling or executing an action still requires programmatic API integration. In this paper, we look at how CUI builders that claim to be "no code required" struggle to yield more than toy examples, with an aim to provoke the community to develop better tools for CUI creation.
Stable Autonomous Robotic Wheelchair Navigation in the Environment With Slope Way

In this article, we present a path planning approach that is capable of generating a feasible trajectory for stable robotic wheelchair navigation in the environment with slope way. Firstly, the environment is modeled by a lightweight navigation map, with which the proposed sampling-based path planning scheme with a modified extension function can generate a feasible path. Then, the path is further optimized by the proposed utility function involving the human comfort and the path cost. To improve the searching efficiency of an optimal trajectory, we present an adaptive weighting Gaussian Mixture Model (GMM) based sampling strategy. Particularly, the weights of the components in GMM are adjusted adaptively in the planning process. It is also worth noting that the proposed sampling-based planning paradigm can indicate the unsafe regions in the navigation map, which forms a traversable map and further guarantees the safety of the wheelchair robot navigation. Furthermore, the effectiveness and the efficiency of the proposed path planning method are verified in both simulation and real-world experiments.
Wideband spectral monitoring using deep learning

We present a system to perform spectral monitoring of a wide band of 666.5 MHz, located within a range of 6 GHz of Radio Frequency (RF) bandwidth, using state-of-the-art deep learning approaches. The system detects, labels, and localizes in time and frequency signals of interest (SOIs) against a background of wideband RF activity. We apply a hierarchical approach. At the lower level we use a sweeping window to analyze a wideband spectrogram, which is input to a deep convolutional network that estimates local probabilities for the presence of SOIs for each position of the window. In a subsequent, higher-level processing step, these local frame probability estimates are integrated over larger two-dimensional regions that are hypothesized by a second neural network, a region proposal network, adapted from object localization in image processing. The integrated segmental probability scores are used to detect SOIs in the hypothesized spectro-temporal regions.
Examining the controls on gold deposit distribution in the Swayze greenstone belt, Ontario, Canada, using multi-scale methods of spatial data analysis

Understanding the spatial controls on mineral deposits is crucial in mineral exploration targeting. This paper uses a combination of spatial data analysis techniques, such as the distance distribution method, Fry, nearest neighbor index, Ripley’s K-function multi-distance, fractal, and kernel density analysis, to better define controls on the distribution of gold deposits in the Swayze greenstone belt, Ontario, Canada. According to results obtained with the distance distribution method, D2 structures were critical in controlling the spatial distribution of the gold mineralization, with 97% of the gold deposits occurring within 7 km of these structures (and 65% within 4 km). Fry analysis confirmed this spatial relationship with a close agreement between the orientations of the D2 structures and gold deposit alignment. The outcome of Ripley’s K-function cluster analysis suggests that gold deposits occur within 4 km distances of other gold deposits. These findings are consistent with the results from the fractal analysis, showing that gold deposit distribution is bifractal and that different geological features occurred at local (<8 km) versus regional (>8 km) scales. We conclude that higher-order D2 structures and lower-order faults connected to the D2 structures acted as pathways to focus gold-bearing hydrothermal fluids, thus leading to the observed concentration and distribution of gold deposits at the regional to local scales.
Effective Image Segmentation using Modified K-Means Technique

In general, images are segmented based on some similarity characteristics. This technique is very useful in medical, satellite, multi-focus, image processing applications. Once images are segmented, it is easy to process the important regions in the images. Clustering can be implemented in many ways. The most popular unsupervised clustering algorithm is a K-means clustering algorithm. This is used to make several clusters. In this work, to begin with [11] K-means clustering algorithm is applied to the original image. In the second step edge detection is used to segment the regions effectively. Experiments are performed on five images. Experimental results are indicating that the modified K-Means algorithm is giving better results. To examine the performance of the present algorithm, the proposed work have analyzed the performance metrics like accuracy, precision, recall, F1 score, and sensitivity.
Deep learning-based explainable target classification for synthetic aperture radar images

Deep learning has been extensively useful for its ability to mimic the human brain to make decisions. It is able to extract features automatically and train the model for classification and regression problems involved with complex images databases. This paper presents the image classification using Convolutional Neural Network (CNN) for target recognition using Synthetic-aperture Radar (SAR) database along with Explainable Artificial Intelligence (XAI) to justify the obtained results. In this work, we experimented with various CNN architectures on the MSTAR dataset, which is a special type of SAR images. Accuracy of target classification is almost 98.78% for the underlying preprocessed MSTAR database with given parameter options in CNN. XAI has been incorporated to explain the justification of test images by marking the decision boundary to reason the region of interest. Thus XAI based image classification is a robust prototype for automatic and transparent learning system while reducing the semantic gap between soft-computing and humans way of perception.
On stochastic mirror descent with interacting particles: Convergence properties and variance reduction

An open problem in optimization with noisy information is the computation of an exact minimizer that is independent of the amount of noise. A standard practice in stochastic approximation algorithms is to use a decreasing step-size. This however leads to a slower convergence. A second alternative is to use a fixed step-size and run independent replicas of the algorithm and average these. A third option is to run replicas of the algorithm and allow them to interact. It is unclear which of these options works best. To address this question, we reduce the problem of the computation of an exact minimizer with noisy gradient information to the study of stochastic mirror descent with interacting particles. We study the convergence of stochastic mirror descent and make explicit the tradeoffs between communication and variance reduction. We provide theoretical and numerical evidence to suggest that interaction helps to improve convergence and reduce the variance of the estimate.
Exploring the Need for Transparency in Educational Recommender Systems

Educational Recommender Systems (EdRecSys) are different in nature from conventional Recommender Systems (RecSys) --mostly related to e-commerce-- as the main goal of EdRecSys is supporting students learning' instead of maximizing users' satisfaction from consuming the recommended items. Thus, research on transparency for traditional RecSys is hard to transfer from e-commerce contexts to educational scenarios, as the level of knowledge of the end-user (i.e. the student) is crucial for generating and evaluating the impact of the recommendations on students' learning. In this paper I present the main idea of my thesis proposal, which aims to fill this gap by taking a user-centered approach that combines design and evaluation of personalized recommender algorithms and explanatory interfaces with students in real learning contexts.
A Design Theory-Based Gamification Approach for Information Security Training

This study reviews previous information security (InfoSec) training studies and identifies three significant gaps. They are (1) lacking pedagogical theories developed specifically in IS training context, therefore, lacking appropriate pedagogical theory-based training approaches; (2) ineffectiveness of InfoSec training delivery methods due to unengaging, non-authentic security risks and training activities; and (3) and lacking an effective way of measuring effectiveness of InfoSec training. The paper proposes employing design theory as the theoretical basis for InfoSec training, and gamification as the main training and testing method to overcome these gaps. We argue that the design theory for InfoSec training associated with gamification can improve learning results for training and effectiveness testing through providing a joyful, realistic and interactive training. An action research is proposed to further evaluate the effectiveness of the approach.
Improved Intermediate Data Management for MapReduce Frameworks

MapReduce is a popular distributed framework for big data analysis. However, the current MapReduce framework is insufficiently efficient in handling intermediate data, which may cause bottlenecks in I/O operations, computation, and network bandwidth. Previous work addresses the I/O problem by aggregating map task outputs (i.e. intermediate data) for each single reduce task on one machine. Unfortunately, when there are a large number of reduce tasks, their concurrent requests for intermediate data generate a large amount of I/O operations. In this paper, we present APA (Aggregation, Partition, and Allocation), a new intermediate data management system for the MapReduce framework. APA aggregates the intermediate data from the map tasks in each rack to one file, and the file host pushes the needed intermediate data to each reduce task. Thus, it reduces the number of disk seeks involved in handling intermediate data within one job. Rather than evenly distributing the intermediate data among reduce tasks based on the keys as in current MapReduce, APA partitions the intermediate data to balance the execution latency of different reduce tasks. APA further decides where to allocate each reduce task to minimize the intermediate data transmission time between map tasks and reduce tasks. Through experiments on a real MapReduce Hadoop cluster using the HiBench benchmark suite, we show that APA improves the performance of the current Hadoop by 40%-50%.
A network security classifier defense

The discovery of practical adversarial machine learning (AML) attacks against machine learning-based wired and wireless network security detectors has driven the necessity of a defense. Without a defense mechanism against AML, attacks in wired and wireless networks will go unnoticed by network security classifiers resulting in their ineffectiveness. Therefore, it is essential to motivate a defense against AML attacks for network security classifiers. Existing AML defenses are generally within the context of image recognition. However, these AML defenses have limited transferability to a network security context. Unlike image recognition, a subject matter expert generally derives the features of a network security classifier. Therefore, a network security classifier requires a distinctive strategy for defense. We propose a novel defense-in-depth approach for network security classifiers using a hierarchical ensemble of classifiers, each using a disparate feature set. Subsequently we show the effective use of our hierarchical ensemble to defend an existing network security classifier against an AML attack. Additionally, we discover a novel set of features to detect network scanning activity. Lastly, we propose to enhance our AML defense approach in future work. A shortcoming of our approach is the increased cost to the defender for implementation of each independent classifier. Therefore, we propose combining our AML defense with a moving target defense approach. Additionally, we propose to evaluate our AML defense with a variety of datasets and classifiers and evaluate the effectiveness of decomposing a classifier with many features into multiple classifiers, each with a small subset of the features.
Experimental use of CIS model

Abstract The development of CIS model is significant both for the development of CIS from scratch and for the introduction of new functionality. It is also useful to have such a model for projects that replace one CIS with another. The lack of a correct CIS model can lead to an incorrect functional model of integrated solution, incorrect data model and the choice of incorrect integration method. It results in unsuccessful project.
Augmented reality: capabilities and challenges in machining industry aligned with industry 4.0

Augmented Reality (AR) currently plays an important role to undertake the challenges in integrating technologies to expedite the march towards Industrial Revolution 4.0 (IR 4.0). Therefore, the machining industry relies on new design concepts and methods of an innovative human–machine interaction application that overlays virtual components on a real-world environment. Nowadays, many potential applications being developed in different fields, from gaming activities to everyday life, education and industrial sectors. This paper reviews and investigates the capabilities of AR as new emerging technologies that will improvise machining operation to embrace Industry 4.0 (I4.0) for product precision, cost and maintenance. Due to the massive implementation, there are still several challenges that lie ahead include resources, facilities and etc. The I4.0 has been considered a new industrial stage in which several emerging technologies are converging to provide digital solutions. In future, AR will change the perception of people viewing the world and its objects and to help us see, hear and feel our environments in various fields. As a result, AR will support and enhance the psychomotor phase in human skills development as well as a tool of cybergogy concept for engaging learners online.
Simulation Environment for Guiding the Design of Contextual Personalization Systems in the Context of Hearing Aids

Adjusting the settings of hearing aids in a clinic is challenging as the measured thresholds of audibility do not reflect many aspects of cognitive perception or the resulting differences in auditory preferences across different contexts. Online personalization systems have a potential to solve this problem, yet the lack of contextual user preference data constitutes a major obstacle in designing and implementing them. To address this challenge, we propose a simulation-based framework to inform and accelerate the development process of online contextual personalization systems in the context of hearing aids. We discuss how to model hearing aid users and context allowing partial observability, and propose how to generate plausible preference models using Gaussian Processes incorporating assumptions about the environment in a controlled way. Finally, on a simple example we demonstrate how an uncertainty-driven agent can efficiently learn from noisy user responses within the proposed framework. We believe that such simulated environments are vital for successful development of complex context-aware online recommender systems.
Cross‐modal retrieval with dual multi‐angle self‐attention

Abstract In recent years, cross‐modal retrieval has been a popular research topic in both fields of computer vision and natural language processing. There is a huge semantic gap between different modalities on account of heterogeneous properties. How to establish the correlation among different modality data faces enormous challenges. In this work, we propose a novel end‐to‐end framework named Dual Multi‐Angle Self‐Attention (DMASA) for cross‐modal retrieval. Multiple self‐attention mechanisms are applied to extract fine‐grained features for both images and texts from different angles. We then integrate coarse‐grained and fine‐grained features into a multimodal embedding space, in which the similarity degrees between images and texts can be directly compared. Moreover, we propose a special multistage training strategy, in which the preceding stage can provide a good initial value for the succeeding stage and make our framework work better. Very promising experimental results over the state‐of‐the‐art methods can be achieved on three benchmark datasets of Flickr8k , Flickr30k , and MSCOCO .
When Are Cyber Blackouts in Modern Service Networks Likely?

Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout? , where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-risk managers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.
Bidirectional GRU with Multi-Head Attention for Chinese NER

Named entity recognition (NER) is a basic task of natural language processing (nlp), which purpose is to locate the named entities in natural language text, and classify them into predefined categories such as persons (PER), locations (LOC), and organizations (ORG). Nowadays, deep learning methods have been widely used in this task and have achieved advanced results. However, most of the NER is conducted in English not in Chinese, and because of the complex language characteristics of Chinese itself, the current effect of named entity recognition in Chinese is not very satisfactory. In order to cope with the above problems, this paper proposes a new network structure, which is named BERT-BGRU-MHA-CRF. Experiments show that the model can achieve an F1 value of 94.14% on the MSRA corpus, which is better than the current mainstream model Bi-LSTM-CRF, and achieves a good accuracy on the Chinese named entity recognition task.
Partial local entropy and anisotropy in deep weight spaces

We refine a recently proposed class of local entropic loss functions by restricting the smoothening regularization to only a subset of weights. The new loss functions are referred to as partial local entropies. They can adapt to the weight-space anisotropy, thus outperforming their isotropic counterparts. We support the theoretical analysis with experiments on image classification tasks performed with multilayer, fully connected, and convolutional neural networks. The present study suggests how to better exploit the anisotropic nature of deep landscapes, and it provides direct probes of the shape of the minima encountered by stochastic gradient descent algorithms. As a byproduct, we observe an asymptotic dynamical regime at late training times where the temperature of all the layers obeys a common cooling behavior.