Reviewerly/exHarmony huggingface dataset:
Prospects of using the augmented reality application in STEM-based Mathematics teaching

The purpose of the study is improving the methodology of teaching Mathematics using cloud technologies and augmented reality, analyzing the peculiarities of the augmented reality technology implementing in the educational process. Attention is paid to the study of adaptation of Augmented Reality technology implementing in teaching mathematical disciplines for students. The task of the study is to identify the problems requiring theoretical and experimental solutions. The object of the study is the process of teaching Mathematics in higher and secondary education institutions. The subject of the study is augmented reality technology in STEM-based Mathematics learning. In the result of the study an overview of modern augmented reality tools and their application practices was carried out. The peculiarities of the mobile application 3D Calculator with Augmented reality of Dynamic Mathematics GeoGebra system usage in Mathematics teaching are revealed.
Lipi Gnani

A Kannada OCR, called Lipi Gnani , has been designed and developed from scratch, with the motivation of it being able to convert printed text or poetry in Kannada script, without any restriction on vocabulary. The training and test sets have been collected from more than 35 books published from 1970 to 2002, and this includes books written in Halegannada and pages containing Sanskrit slokas written in Kannada script. The coverage of the OCR is nearly complete in the sense that it recognizes all punctuation marks, special symbols, and Indo-Arabic and Kannada numerals. Several minor and major original contributions have been done in developing this OCR at different processing stages, such as binarization, character segmentation, recognition, and Unicode mapping. This has created a Kannada OCR that performs as good as, and in some cases better than, Google’s Tesseract OCR, as shown by the results. To the best of our knowledge, this is the maiden report of a complete Kannada OCR, handling all issues involved. Currently, there is no dictionary-based postprocessing, and the obtained results are due solely to the recognition process. Four benchmark test databases containing scanned pages from books in Kannada, Sanskrit, Konkani, and Tulu languages, but all of them printed in Kannada script, have been created. The word-level recognition accuracy of Lipi Gnani is 5.3% higher on the Kannada dataset than that of Google’s Tesseract OCR, 8.5% higher on the Sanskrit dataset, and 23.4% higher on the datasets of Konkani and Tulu.
Capturing the Silences in Digital Archaeological Knowledge

The availability and accessibility of digital data are increasingly significant in the creation of archaeological knowledge with, for example, multiple datasets being brought together to perform extensive analyses that would not otherwise be possible. However, this makes capturing the silences in those data—what is absent as well as present, what is unknown as well as what is known—a critical challenge for archaeology in terms of the suitability and appropriateness of data for subsequent reuse. This paper reverses the usual focus on knowledge and considers the role of ignorance—the lack of knowledge, or nonknowledge—in archaeological data and knowledge creation. Examining aspects of archaeological practice in the light of different dimensions of ignorance, it proposes ways in which the silences, the range of unknowns, can be addressed within a digital environment and the benefits which may accrue.
Perspectives on the tensions in teaching with technology in Norwegian teacher education analysed using Argyris and Schön’s theory of action

Abstract Teachers’ professional digital competence (PDC) is of increasing importance in classrooms in the majority of EU countries. Norway is one of the countries that has been exposed to a strong top-down implementation of information and communication technology in education. However, despite national efforts, practitioners in the education system do not seem to work in line with the given policy. There is therefore a gap between the micro and macro levels, necessitating the need for a closer exploration of the issue. We have conducted a quantitative study of teacher educators and their students in Northern Norway ( N = 112). Amongst the staff, professional attitudes have a stronger impact than PDC regarding the extent of the educational use of digital tools, whereas amongst students, PDC has a stronger influence. These results are interpreted using Argyris and Schön’s theory of action in learning organisations.
Weakly-Supervised Deep Stain Decomposition for Multiplex IHC Images

Multiplex immunohistochemistry (mIHC) is an innovative and cost-effective method that simultaneously labels multiple biomarkers in the same tissue section. Current platforms support labeling six or more cell types with different colored stains that can be visualized with brightfield light microscopy. However, analyzing and interpreting multi-colored images comprised of thousands of cells is a challenging task for both pathologists and current image analysis methods. We propose a novel deep learning based method that predicts the concentration of different stains at every pixel of a whole slide image (WSI). Our method incorporates weak annotations as training data: manually placed dots labelling different cell types based on color. We compare our method with other approaches and observe favorable performance on mIHC images.
Interaction in Augmented Reality

Augmented reality (AR) is an emerging technology that has yet to be a mature consumer product. Given that user experience plays a significant role in the success of new technologies, the development of appropriate AR user interfaces needed. As such, defining the challenges to current AR user interfaces is a steppingstone to enhancing user experience. There are three principal components of interaction in AR systems: the user, the user interface and the virtual content. Examples from the literature are provided to identify the challenges to each interaction component. Both the layout of the AR interaction component and the identified challenges aim to help and inform researchers and AR software developers to choose the AR interaction aspects that best serves their purpose, while also contributing to the enhancement of user experience.
Security Enhancement on a Lightweight Authentication Scheme With Anonymity Fog Computing Architecture

The multi-level, heterogeneous and third-party attributes of fog computing (FC) have caused great concern in the communication security of next-generation IoT systems.This paper proposes a secure authentication scheme for dynamic key generation capability, which can enhance the security of the entire heterogeneous network without the constraints on device types, attributes and communication protocols.The communication devices exchange IDs and random numbers for registration, then generate anonymous information.During the authentication process, the cloud device (e.g., the server) uses the pseudonym information to match the recorded random numbers.Both FC devices utilize the protocol with such random numbers to generate session keys and deliver updated random numbers.Comprehensive security and performance analysis shows that the scheme can meet the security requirements of the FC architecture with lower overhead.
BLESeis: Low-Cost IoT Sensor for Smart Earthquake Detection and Notification

The Internet of Things (IoT) has been implemented to provide solutions for certain event detection because of ease of installation, computing and communication capability, and cost-effectiveness. Seismic event detection, however, is still a challenge due to a lack of high-fidelity sensing and classification efficiency. This study proposes BLESeis, an IoT sensor for smart earthquake detection. BLESeis comprises three main parts: (1) high-fidelity vibration sensing using a MEMS accelerometer and digital filtering; (2) an embedded earthquake detection algorithm; (3) BLE (Bluetooth low energy) beacon for earthquake notification. For high-fidelity vibration sensing, a triggering algorithm and embedded finite impulse response (FIR) low-pass filter are developed. The acquired vibration is then classified by the earthquake detection algorithm developed to identify the earthquake signal from other vibration sources using time and frequency domain analysis. Upon detection of an earthquake, the BLE beacon broadcasts using the proposed data packet for efficient notification and visualization. The performance of the proposed system is evaluated through numerical simulations and a set of experiments using shaking table tests. The experiments show the feasibility of the low-cost earthquake detection and notification system.
Queuing Without Patience: A Novel Transaction Selection Mechanism in Blockchain for IoT Enhancement

There is evidence that blockchain plays a crucial role in the Internet of Things (IoT)-based implementation due to its transparency, traceability, and immutability, in which the participants are incentivized to behave authentically and precisely for rewards. Despite the domination of subsidy in reward, the decrease of the mining rate and the imperativeness of fees make the fee market become a pivotal role to motivate miners in the blockchain. However, the current mechanism for selecting transactions into a block poses a risk to the stability of the system, which stems from the vicious competition of users and the insufficient incentives of miners. In this article, we propose a novel transaction selection mechanism by leveraging the Lyapunov optimization and large deviation theory. This article is: 1) fair because the proposed mechanism is not single-factor dominated, both personal utility of the miner and overall utility of the system are taken into account; 2) sustainable since miners are incentivized greatly to guarantee the mining behavior; and 3) robust. The analysis based on the large deviation theory enhances the robustness of the blockchain. To the best of our knowledge, we are the first to consider both miner's benefit as well as system benefit to establish a better fee market in the blockchain for IoT enhancement. Our theoretical analyses and simulation results demonstrate the effectiveness of the proposed mechanism.
Semi-supervised elastic manifold embedding with deep learning architecture

Graph-based embedding aims to reduce the dimension of high dimensional data and to extract relevant features for learning tasks. In this letter, we propose an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. We introduce a flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models. The proposed deep architecture incorporates the geometrical manifold structure of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently. We apply the algorithm on five public image datasets including scene, face and object datasets. These experiments demonstrate the effectiveness of the proposed embedding method, and also show that the proposed method compares favorably with many competing state-of-the-art graph-based methods.
Exploring Eye-Gaze Wheelchair Control

Eye-gaze may potentially be used for steering wheelchairs or robots and thereby support independence in choosing where to move. This paper investigates the feasibility of gaze-controlled interfaces. We present an experiment with wheelchair control in a simulated, virtual reality (VR) driving experiment and a field study with five people using wheelchairs. In the VR experiment, three control interfaces were tested by 18 able-bodied subjects: (i) dwell buttons for direction commands on an overlay display, (ii) steering by continuous gaze point assessment on the ground plane in front of the driver, and (iii) waypoint navigation to targets placed on the ground plane. Results indicate that the waypoint method had superior performance, and it was also most preferred by the users, closely followed by the continuous-control interface. However, the field study revealed that our wheelchair users felt uncomfortable and excluded when they had to look down at the floor to steer a vehicle. Hence, our VR testing had a simplified representation of the steering task and ignored an important part of the use-context. In the discussion, we suggest potential improvements of simulation-based design of wheelchair gaze control interfaces.
A Formative Study on Designing Accurate and Natural Figure Captioning Systems

Automatic figure captioning is widely useful for improving the readability and accessibility of figures. Despite recent advances in figure question answering and parsing figure elements that enable machines to accurately read information from figures, the machine learning community still lacks sufficient understanding of this problem, on what contents are important to include in a caption and how to make it sound natural. In this work, we crawled, annotated, and analyzed a corpus of real-world human-written figure captions. Our study results show that real-world captions usually consist of a finite set of caption units and that automatic figure captioning should be formulated as a multi-stage task. The first stage is to generate caption units with high accuracy and the second is to stitch together the units with diverse stitching patterns, to form a natural caption.
Adversarial open set domain adaptation via progressive selection of transferable target samples

In recent years, many Unsupervised Domain Adaptation (UDA) methods have been proposed to tackle the domain shift problem. Most existing UDA methods are derived for Close Set Domain Adaptation (CSDA) in which source and target domains are assumed to share the same label space. However, target domain may contain unknown class different from the known ones in the source domain in practice, i.e., Open Set Domain Adaptation (OSDA). Due to the presence of unknown class, aligning the whole distribution of the source and target domain for OSDA as in the previous methods will lead to negative transfer. Existing methods developed for OSDA attempt to assign smaller weights to target samples of unknown class. Despite promising performance achieved by existing methods, the samples of the unknown class are still used for distribution alignment, which makes the model suffer from the risk of negative transfer. Instead of reweighting, this paper presents a novel method namely Thresholded Domain Adversarial Network (ThDAN), which progressively selects transferable target samples for distribution alignment. Based on the fact that samples from the known classes must be more transferable than target samples of the unknown one, we derive a criterion to quantify the transferability by constructing classifiers to categorize known classes and to discriminate unknown class. In ThDAN, an adaptive threshold is calculated by averaging transferability scores of source domain samples to select target samples for training. The threshold is tweaked progressively during the training process so that more and more target samples from the known classes can be correctly selected for adversarial training. Extensive experiments show that the proposed method outperforms state-of-the-art domain adaptation and open set recognition approaches on benchmarks.
Online random forests regression with memories

In recent years, the online schema of the conventional Random Forests(RFs) have attracted much attention because of its ability to handle sequential data or data whose distribution changes during the prediction process. However, most research on online RFs focuses on structural modification during the training stage, overlooking critical aspects of the sequential dataset, such as autocorrelation. In this paper, we demonstrate how to improve the predictive accuracy of the regression model by exploiting data correlation. Instead of modifying the structure of the off-line trained RFs, we endow RFs with memory during regression prediction through an online weight learning approach, which is called Online Weight Learning Random Forest Regression(OWL-RFR). Specifically, the weights of leaves are updated based on a novel adaptive stochastic gradient descent method, in which the adaptive learning rate considers the current and historical prediction bias ratios, compared with the static learning rate. Thus, leaf-level weight stores the learned information from the past data points for future correlated prediction. Compared with tree-level weight which only has immediate memory for current prediction, the leaf-level weight can provide long-term memory. Numerical experiments with OWL-RFR show remarkable improvements in predictive accuracy across several common machine learning datasets, compared to traditional RFs and other online approaches. Moreover, our results verify that the weight approach using the long-term memory of leaf-level weight is more effective than immediate dependency on tree-level weight. We show the improved effectiveness of the proposed adaptive learning rate in comparison to the static rate for most datasets, we also show the convergence and stability of our method.
An embedding strategy on fusing multiple image features for data hiding in multiple images

Data hiding in multiple images has been a significant research direction in information security. How to reasonably design the embedding strategy to spread the payload among multiple images is still an open issue. In this paper, we propose an embedding strategy on fusing multiple features. We utilize the typical characteristic parameters of gray level co-occurrence matrix, the image entropy and the shape parameter to describe image complexity. Furthermore, we combine with the number of cover images, the number of cover images assigned to steganographer and the size of cover image to estimate the steganographic capacity of each image. The strategy is implemented together with some state-of-the-art single image steganographic algorithms. Experimental results demonstrate that the security performance of the proposed strategy is higher than that of the state-of-the-art embedding strategy against the blind universal pooled steganalysis.
Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring

Locality-Sensitive Hashing (LSH) is one of the most popular methods for c-Approximate Nearest Neighbor Search (c-ANNS) in high-dimensional spaces. In this paper, we propose a novel LSH scheme based on the Longest Circular Co-Substring (LCCS) search framework (LCCS-LSH) with a theoretical guarantee. We introduce a novel concept of LCCS and a new data structure named Circular Shift Array (CSA) for k-LCCS search. The insight of LCCS search framework is that close data objects will have a longer LCCS than the far-apart ones with high probability. LCCS-LSH is LSH-family-independent, and it supports c-ANNS with different kinds of distance metrics. We also introduce a multi-probe version of LCCS-LSH and conduct extensive experiments over five real-life datasets. The experimental results demonstrate that LCCS-LSH outperforms state-of-the-art LSH schemes.
Improving the robustness of machine reading comprehension model with hierarchical knowledge and auxiliary unanswerability prediction

Machine Reading Comprehension (MRC) aims to understand a passage and answer a series of related questions. With the development of deep learning and the release of large-scale MRC datasets, many end-to-end MRC neural networks have achieved remarkable success. However, these models are fragile and lack of robustness when there are some imperceptible adversarial perturbations in the input. In this paper, we propose an MRC model which has two main components to improve the robustness. On the one hand, we enhance the representation of the model by leveraging hierarchical knowledge from external knowledge bases. On the other hand, we introduce an auxiliary unanswerability prediction module and perform supervised multi-task learning along with a span prediction task. Experimental results on benchmark datasets show that our model can achieve consistent improvement compared with other strong baselines.
The Impact of Block Parameters on the Throughput and Security of Blockchains

It has been well recognized that traditional blockchains have limited throughput. It is intuitive to achieve higher throughput by increasing the block size and shortening the block interval. In this paper, we study the security implications on doing so, and define the boundary for acceptable block sizes and block intervals. We define the security of the blockchain in terms of the stale block rate in the network and carry out an empirical study using a blockchain simulator to find the optimal block parameters (i.e., size and interval). We show that it is possible to achieve sufficiently high throughput for a blockchain platform to be used for activities beyond cryptocurrency, such as state-level electronic voting.
GazeConduits: Calibration-Free Cross-Device Collaboration through Gaze and Touch

We present GazeConduits, a calibration-free ad-hoc mobile interaction concept that enables users to collaboratively interact with tablets, other users, and content in a cross-device setting using gaze and touch input. GazeConduits leverages recently introduced smartphone capabilities to detect facial features and estimate users' gaze directions. To join a collaborative setting, users place one or more tablets onto a shared table and position their phone in the center, which then tracks users present as well as their gaze direction to determine the tablets they look at. We present a series of techniques using GazeConduits for collaborative interaction across mobile devices for content selection and manipulation. Our evaluation with 20 simultaneous tablets on a table shows that GazeConduits can reliably identify which tablet or collaborator a user is looking at.
Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach

With the growth of online shopping for fashion products, accurate fashion recommendation has become a critical problem. Meanwhile, social networks provide an open and new data source for personalized fashion analysis. In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To this end, we present an item-to-set metric learning framework that learns to compute the similarity between a set of historical fashion items of a user to a new fashion item. To extract features from multi-modal street-view fashion items, we propose an embedding module that performs multi-modality feature extraction and cross-modality gated fusion. To validate the effectiveness of our approach, we collect a real-world social media dataset. Extensive experiments on the collected dataset show the superior performance of our proposed approach.
Three Stream 3D CNN with SE Block for Micro-Expression Recognition

Micro-expression is a kind of short-term and imperceptible facial expression. In this paper, we propose a method of micro-expression recognition using SEnet and three-stream 3D CNN (SETFNet). Three different facial regions are used as the input of three streams. Adding SE block to the network can learn the weights of each feature channel adaptively. Experimental results show that this method can effectively improve the recognition performance of micro-expression.