Reviewerly/exHarmony huggingface dataset:
Using a Multiplatform Chatbot as an Online Tutor in a University Course

Allowing students to ask questions in a university course is a crucial aspect of learning, which leads to increased learning effectiveness but also increased workload of the teaching staff. To reduce their workload, this paper presents the design of a chatbot for instantly answering students' questions on multiple common social platforms including Telegram, Facebook Messenger and Line. The chatbot can answer questions in natural language and commands. Once the teachers upload the necessary course-related information to an online database, the chatbot can answer questions on the course materials and course logistics (e.g., class schedule). The chatbot also supports a login system so as to provide answers according to different student profiles (e.g., schedule of their enrolled class and score dissemination). A survey on ten undergraduate computer science students showed that our chatbot can effectively act as an online tutor to reduce teachers' workload and will be a useful tool to be integrated into other e-learning platforms.
Robust energy preserving embedding for multi-view subspace clustering

With the increasing popularity of multi-view data, multi-view subspace clustering (MVSC) has attracted intensive attention. However, the curse of dimensionality of the high-dimensional multi-view data constantly obsesses the development of multi-view method. Together with the curse of dimensionality, the existence of noise and outliers further obstructs the ability of MVSC to exploit the underlying subspace structure. To solve these challenging problems, in this paper, we propose a novel MVSC method, namely robust energy preserving embedding (REPE), which aims to exploit the underlying subspace structure of multi-view data through energy preserving embedding projection and low-rank constraint. In a different manner than before, we use dictionary learning to integrate self-expressiveness learning and embedded subspace learning together, ensure the preservation of energy by restoring the data in embedding space to the original space. Moreover, Schatten p-norm is introduced to better approximate the low-rank constraint. So that we can reduce the dimension of the data while maintain the major energy of the original view data. Thereby, a high quality affinity graph can be learned, such that the promising clustering results can subsequently be obtained. Extensive experiments on five benchmarks have validated the effectiveness of the proposed method, by comparing it with the state of arts.
A Comparison of Weight Initializers in Deep Learning-Based Side-Channel Analysis

The usage of deep learning in profiled side-channel analysis requires a careful selection of neural network hyperparameters. In recent publications, different network architectures have been presented as efficient profiled methods against protected AES implementations. Indeed, completely different convolutional neural network models have presented similar performance against public side-channel traces databases. In this work, we analyze how weight initializers' choice influences deep neural networks' performance in the profiled side-channel analysis. Our results show that different weight initializers provide radically different behavior. We observe that even high-performing initializers can reach significantly different performance when conducting multiple training phases. Finally, we found that this hyperparameter is more dependent on the choice of dataset than other, commonly examined, hyperparameters. When evaluating the connections with other hyperparameters, the biggest connection is observed with activation functions.
VR industrial applications―A singapore perspective

Virtual Reality (VR) has been around for a long time but has come into the spotlight only recently. From an industrial perspective, this article serves as a proverbial scalpel to dissect the different use cases and commercial applications of VR in Singapore. Before researching the Singapore market, we examine how VR has evolved. At the moment, the global annual budget for VR (and augmented reality) is at an upward trend with a leading growth in market value for the training sector. VR in Singapore has also seen a rapid development in recent years. We discuss some of the Singapore government's initiatives to promote the commercial adoption of VR for the digital economy of the nation. To address the mass adoption of VR, we present VRcollab's business solutions for the construction and building industry. 2020 is one of the most important years for VR in history.
Big Data analytics in Agile software development: A systematic mapping study

Over the last decade, Agile methods have changed the software development process in an unparalleled way and with the increasing popularity of Big Data, optimizing development cycles through data analytics is becoming a commodity. Although a myriad of research exists on software analytics as well as on Agile software development (ASD) practice on itself, there exists no systematic overview of the research done on ASD from a data analytics perspective. Therefore, the objective of this work is to make progress by linking ASD with Big Data analytics (BDA). As the primary method to find relevant literature on the topic, we performed manual search and snowballing on papers published between 2011 and 2019. In total, 88 primary studies were selected and analyzed. Our results show that BDA is employed throughout the whole ASD lifecycle. The results reveal that data-driven software development is focused on the following areas: code repository analytics, defects/bug fixing, testing, project management analytics, and application usage analytics. As BDA and ASD are fast-developing areas, improving the productivity of software development teams is one of the most important objectives BDA is facing in the industry. This study provides scholars with information about the state of software analytics research and the current trends as well as applications in the business environment. Whereas, thanks to this literature review, practitioners should be able to understand better how to obtain actionable insights from their software artifacts and on which aspects of data analytics to focus when investing in such initiatives.
Enabling security for the Industrial Internet of Things using deep learning, blockchain, and coalitions

Abstract In a wireless Industrial Internet of Things (IIoT) network, enforcing security is a challenge due to the large number of devices forming the network and their limited computation capabilities. Furthermore, different security attacks require specifically tailored security protocols to prevent their occurrence. As an alternative to these conventional centralized security protocols, the application of Blockchain (BC) and Deep learning (DL) for securing IIoT networks hold great potential. BC facilitates security by being an immutable record of the changes happening in a network. Coalition Formation theory aids decentralization and promotes energy efficiency. And to enforce a state‐of‐the‐art attack detection technique, Deep learning provides an adaptive and reliable platform. Thus, in this paper, a security framework that facilitates generalized security for the IIoT network using BC and Coalition Formation theory is proposed. Additionally, we promote a sophisticated deep learning‐based classification algorithm to efficiently classify malicious and benign devices in IIoT scenarios. In the proposed model, connection links can only be established if the details of the connection are mined on the BC by the “sender” device. Therefore, we propose a Proof of Reliance algorithm that dynamically increases the computational difficulty to prevent malicious devices from attacking the network. Through simulations, it is experimentally proven that malicious devices can never attack the network when the proposed framework is employed for IIoT security.
DeepBatch: A hybrid deep learning model for interpretable diagnosis of breast cancer in whole-slide images

The gold standard for breast cancer diagnosis, treatment, and management is the histological analysis of a suspected section. Histopathology consists in analyzing the characteristics of the lesions using tissue sections stained with hematoxylin and eosin. However, pathologists are currently subjected to high workloads, mainly due to the fundamental role of histological analysis in the patient’s treatment. In this context, methods able to reduce histological analysis time, provide a second opinion, or even point out suspicious locations as a screening tool become increasingly important for pathologists. This article proposes a model based on Convolutional Neural Networks (CNN) to provide a refined and multiclass segmentation of Whole Slide Imaging (WSI) for breast cancer. The methodology is divided into four modules: pre-processing, ROI detection, ROI sampling, and region segmentation. These modules are organized to decode the information learned using CNNs in interpretable predictions for pathologists. The preprocessing module is responsible for removing background and noise from WSI. At ROI detection, we use the U-Net convolutional architecture to identify suspicious regions in low magnification WSI. The sampling module maps the identified suspected areas from low magnifications to 40× magnifications. region segmentation module segments high-magnification areas using a ResNet50/U-Net. To validate the methodology, we use data sets from different sources that can be used together or separately in each module, depending on its purpose. We used 205 breast cancer WSI for training, validation, and testing. For the detection of suspicious regions by ROI detection, we obtained an IoU of 93.43%, accuracy of 91.27%, sensitivity of 90.77%, specificity of 94.03%, F1 score of 84.17%, and an AUC of 0.93. For the refined segmentation of WSI by the region segmentation module, we obtained an IoU of 88.23%, accuracy of 96.10%, sensitivity of 71.83%, specificity of 96.19%, F1 score of 82.94%, and an AUC of 0.88. In short, the model provides refined segmentation of breast cancer WSIs using a cascade of CNNs. This segmentation can assist pathologists in interpreting the diagnosis by accurately presenting the regions considered during the inference of WSI. Our results indicate the possibility of using the model as a second screening system.
Optimizing quantum cloning circuit parameters based on adaptive guided differential evolution algorithm

Quantum cloning operation, started with no-go theorem which proved that there is no capability to perform a cloning operation on an unknown quantum state, however, a number of trials proved that we can make approximate quantum state cloning that is still with some errors. To the best of our knowledge, this paper is the first of its kind to attempt using meta-heuristic algorithm such as Adaptive Guided Differential Evolution (AGDE), to tackle the problem of quantum cloning circuit parameters to enhance the cloning fidelity. To investigate the effectiveness of the AGDE, the extensive experiments have demonstrated that the AGDE can achieve outstanding performance compared to other well-known meta-heuristics including; Enhanced LSHADE-SPACMA Algorithm (ELSHADE-SPACMA), Enhanced Differential Evolution algorithm with novel control parameter adaptation (PaDE), Improved Multi-operator Differential Evolution Algorithm (IMODE), Parameters with adaptive learning mechanism (PALM), QUasi-Affine TRansformation Evolutionary algorithm (QUATRE), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Cuckoo Search (CS), Bat-inspired Algorithm (BA), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). In the present study, AGDE is applied to improve the fidelity of quantum cloning problem and the obtained parameter values minimize the cloning difference error value down to 10-8. Accordingly, the qualitative and quantitative measurements including average, standard deviation, convergence curves of the competitive algorithms over 30 independent runs, proved the superiority of AGDE to enhance the cloning fidelity.
Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems

The dynamical behaviour of complex quantum systems can be harnessed for information processing. With this aim, quantum reservoir computing (QRC) with Ising spin networks was recently introduced as a quantum version of classical reservoir computing. In turn, reservoir computing is a neuro-inspired machine learning technique that consists in exploiting dynamical systems to solve nonlinear and temporal tasks. We characterize the performance of the spin-based QRC model with the Information Processing Capacity (IPC), which allows to quantify the computational capabilities of a dynamical system beyond specific tasks. The influence on the IPC of the input injection frequency, time multiplexing, and different measured observables encompassing local spin measurements as well as correlations, is addressed. We find conditions for an optimum input driving and provide different alternatives for the choice of the output variables used for the readout. This work establishes a clear picture of the computational capabilities of a quantum network of spins for reservoir computing. Our results pave the way to future research on QRC both from the theoretical and experimental points of view.
Deep Interaction Machine

Click-Through Rate (CTR) prediction is a crucial task for various online applications, such as recommendation and online advertising. The task of CTR prediction is to predict the probability of users' clicking behaviors, with high-dimensional input features. To avoid heavy handcrafted feature engineering, the core topic of CTR prediction is the automatic interactions of the input features. Factorization Machine (FM) is an effective approach for modeling second-order feature interactions. Recently, FM has been extended for modeling higher-order feature interactions, such as xDeepFM and Higher-Order Factorization Machine (HOFM). However, these approaches are with either high complexity or iterative computation consuming much time and space. To overcome above problems, we express arbitrary-order FM in the form of power sums according to Newton's identities. Accordingly, we propose a novel Interaction Machine (IM) model. IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields. Via IM, we can conduct arbitrary-order feature interactions in a very simple way. Moreover, we perform IM together with deep neural networks, and the resulted DeepIM model is more efficient than xDeepFM with comparable or even better performance. We conduct experiments on two real-world datasets, in which effectiveness and efficiency of both IM and DeepIM are strongly verified.
HMD Light: Sharing In-VR Experience via Head-Mounted Projector for Asymmetric Interaction

We present HMD Light, a proof-of-concept Head-Mounted Display (HMD) implementation that reveals the Virtual Reality (VR) user's experience in the physical environment to facilitate communication between VR and external users in a mobile VR context. While previous work externalized the VR user's experience through an on-HMD display, HMD Light places the display into the physical environment to enable larger display and interaction area. This work explores the interaction design space of HMD Light and presents four applications to demonstrate its versatility. Our exploratory user study observed participant pairs experience applications with HMD Light and evaluated usability, accessibility and social presence between users. From the results, we distill design insights for HMD Light and asymmetric VR collaboration.
Raising the Responsible Child

Commercial activity trackers are increasingly being designed for children as young as 3 years old. However, we have limited understanding of family use practices around these trackers. To provide an overall view of how families naturally use activity trackers towards collaborative management of family health, we systematically identified 9 trackers designed for children available on 4 consumer electronics retailers. Our data is composed of 2,628 user reviews both from the consumer retailers (for the wearables) and mobile application stores (for the associated apps). Our findings indicate children's and parents' collaborative use of these technologies beyond health and wellness. Parents state that their children enjoy practicing independence and rewards while contributing to family health management and daily life requirements. Parents expect these devices to ease their life and to teach their children to become more responsible for their health, daily tasks, and schedule. However, the current designs give limited agency on child's side and require parents' active participation for wearable-app coordination. For these reasons, they do not fully address parents' expectations in decreasing their workload. On the other hand, they have the potential to facilitate family interaction with challenges structured around the data reported through trackers.
Cycle-consistent GAN-based stain translation of renal pathology images with glomerulus detection application

Motivation: Renal biopsy is an irreplaceable diagnostic tool for kidney diseases. Glomeruli provide important information for an accurate disease diagnosis. This paper applies deep learning techniques to automate translation of renal pathology images and glomerulus detection to improve the efficiency and accuracy on pathological diagnoses. Methods: This paper first proposes a new method for automatic translation of different renal pathology staining styles using the cycle-consistent Generative Adversarial Network (GAN). This paper then proposes the combination of faster region-based convolutional neural network (R-CNN) with an aspect ratio filter to detect glomeruli in light microscopy images processed with four different stains at various optical magnifications. Finally, this paper improves glomerulus detection at different stains by using translated image stains from the CycleGAN. Results: To show the effectiveness of the translation and detection methods, in addition to quantitative analysis of the results, the involvement of assessment from four physicians is also performed. Experimental results show that the physicians fail to differentiate real and translated stains and the automatic glomerulus detection method outperforms that manually labeled by the physicians. Conclusion: The proposed method works well and improves the efficiency of renal pathological diagnosis. This work contributes in the area of automated medical diagnosis.
Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems

Recently, knowledge-grounded conversations in the open domain gain great attention from researchers. Existing works on retrieval-based dialogue systems have paid tremendous efforts to utilize neural networks to build a matching model, where all of the context and knowledge contents are used to match the response candidate with various representation methods.
Foreign aid and entrepreneurship in Africa: the role of remittances and institutional quality

This study investigates the impact of foreign aid and remittance inflows on entrepreneurship progress in Africa. The role of institutional quality in the relationship between foreign aid and entrepreneurship is also investigated. We explore data of 19 African countries for a period of 2006–2017, and panel data regression techniques are employed. The study finds that: (1) Foreign aid impact on entrepreneurship is negative. (2) The remittances mediate the negative impact of foreign aid on entrepreneurship. (3) Institutional quality mediates the negative impact of foreign aid on entrepreneurial progress. (4) The threshold level of remittance at which foreign aid would meaningfully enhance entrepreneurship is 10.59 as a percentage of GDP while that of institutional quality is 5.04 on a scale of 10 point. (5) The role of remittances and foreign aids is complementary in firm start-up activities. (6) Institutional quality plays important roles in moderating impact of foreign aid on firm start-up activities. In addition, our results show that concentration in banking industry does not benefit entrepreneurial activities. The study concludes that foreign aids and remittances perform complementary roles in improving the level of entrepreneurial development in Africa. The strong institutional environment is very important in promoting entrepreneurial success. These findings are robust to alternative estimations.
Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data

Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete pathology develops through a number of stereotypical routes, characterized by `points of no return' and `final states' (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow up) observations. Here we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations. The methodology allows positioning a patient on a particular clinical trajectory (pathological scenario) and characterizing the degree of progression along it with a qualitative estimate of the uncertainty of the prognosis. Overall, our pseudo-time quantification-based approach gives a possibility to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data. We developed a tool $ClinTrajan$ for clinical trajectory analysis implemented in Python programming language. We test the methodology in two large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data.