Reviewerly/exHarmony huggingface dataset:
Adaptive Chunk-Based Dynamic Weighted Majority for Imbalanced Data Streams With Concept Drift

One of the most challenging problems in the field of online learning is concept drift, which deeply influences the classification stability of streaming data. If the data stream is imbalanced, it is even more difficult to detect concept drifts and make an online learner adapt to them. Ensemble algorithms have been found effective for the classification of streaming data with concept drift, whereby an individual classifier is built for each incoming data chunk and its associated weight is adjusted to manage the drift. However, it is difficult to adjust the weights to achieve a balance between the stability and adaptability of the ensemble classifiers. In addition, when the data stream is imbalanced, the use of a size-fixed chunk to build a single classifier can create further problems; the data chunk may contain too few or even no minority class samples (i.e., only majority class samples). A classifier built on such a chunk is unstable in the ensemble. In this article, we propose a chunk-based incremental learning method called adaptive chunk-based dynamic weighted majority (ACDWM) to deal with imbalanced streaming data containing concept drift. ACDWM utilizes an ensemble framework by dynamically weighting the individual classifiers according to their classification performance on the current data chunk. The chunk size is adaptively selected by statistical hypothesis tests to access whether the classifier built on the current data chunk is sufficiently stable. ACDWM has four advantages compared with the existing methods as follows: 1) it can maintain stability when processing nondrifted streams and rapidly adapt to the new concept; 2) it is entirely incremental, i.e., no previous data need to be stored; 3) it stores a limited number of classifiers to ensure high efficiency; and 4) it adaptively selects the chunk size in the concept drift environment. Experiments on both synthetic and real data sets containing concept drift show that ACDWM outperforms both state-of-the-art chunk-based and online methods.
Who can benefit from augmented reality in chemistry? Sex differences in solving stereochemistry problems using augmented reality

Abstract This paper is about augmented reality (AR) and its potentials to support students in handling scientific representations. For this purpose, first representations are examined from a science educational and instructional psychology perspective. After giving a short overview of AR in general and how it can be delineated from virtual reality (VR), potential advantages of an educational use of AR are outlined considering typical difficulties of students when learning with scientific representations. Since literature frequently reports sex differences in spatial abilities, this study focuses on potential differential effects in the use of AR depending on sex. Against this background, AR might be a tool which can help to compensate for disadvantages in spatial abilities. In order to investigate this question, chemistry students had to answer 20 items related to stereochemistry, a concept that places high demands on mental spatial rotation skills. While one half of the items had to be solved using two‐dimensional (2D) ball‐and‐stick figures of molecules, the other half had to be answered with the help of AR representations. Due to the aforementioned sex differences regarding visuo‐spatial abilities between males and females found in the literature, it is hypothesized that AR representations will support all students but females in particular by reducing cognitive load. If this assumption is correct, the AR items would have to be correctly solved by the students more often than the 2D items. The results of analyses of variance indeed reveal a significant effect of the sex variable dependent on the type of representation. In addition, a questionnaire was administered to survey the students’ attitudes towards learning with the AR app used.
Blockchain Technology as Economic Infrastructure: Revisiting the Electronic Markets Hypothesis

In the late 1980s and early 1990s the electronic markets hypothesis offered a prediction about effect of information technology on industrial organisation, and many business writers forecast significant changes to the shape and nature of the firm. However, these changes did not come to pass. This paper provides an economic analysis of why, using the transaction cost economic framework of Ronald Coase and Oliver Williamson. Non-hierarchical corporate organisation struggled against contracting problems in the presence of possible opportunistic behaviour. Technologies of trust offer an institutional mechanism that acts on the margin of trust, suppressing opportunism. The paper concludes that blockchain technology provides an economic infrastructure for the coordination of economic activity and the possible realisation of the electronic markets hypothesis.
Dynamic Hand Gesture Recognition Using 3DCNN and LSTM with FSM Context-Aware Model

With the recent growth of Smart TV technology, the demand for unique and beneficial applications motivates the study of a unique gesture-based system for a smart TV-like environment. Combining movie recommendation, social media platform, call a friend application, weather updates, chatting app, and tourism platform into a single system regulated by natural-like gesture controller is proposed to allow the ease of use and natural interaction. Gesture recognition problem solving was designed through 24 gestures of 13 static and 11 dynamic gestures that suit to the environment. Dataset of a sequence of RGB and depth images were collected, preprocessed, and trained in the proposed deep learning architecture. Combination of three-dimensional Convolutional Neural Network (3DCNN) followed by Long Short-Term Memory (LSTM) model was used to extract the spatio-temporal features. At the end of the classification, Finite State Machine (FSM) communicates the model to control the class decision results based on application context. The result suggested the combination data of depth and RGB to hold 97.8% of accuracy rate on eight selected gestures, while the FSM has improved the recognition rate from 89% to 91% in a real-time performance.
Investigating Users' Preferences and Expectations for Always-Listening Voice Assistants

Many consumers now rely on different forms of voice assistants, both stand-alone devices and those built into smartphones. Currently, these systems react to specific wake-words, such as "Alexa," "Siri," or "Ok Google." However, with advancements in natural language processing, the next generation of voice assistants could instead always listen to the acoustic environment and proactively provide services and recommendations based on conversations without being explicitly invoked. We refer to such devices as "always listening voice assistants" and explore expectations around their potential use. In this paper, we report on a 178-participant survey investigating the potential services people anticipate from such a device and how they feel about sharing their data for these purposes. Our findings reveal that participants can anticipate a wide range of services pertaining to a conversation; however, most of the services are very similar to those that existing voice assistants currently provide with explicit commands. Participants are more likely to consent to share a conversation when they do not find it sensitive, they are comfortable with the service and find it beneficial, and when they already own a stand-alone voice assistant. Based on our findings we discuss the privacy challenges in designing an always-listening voice assistant.
Deep feature selection using a teacher-student network

High-dimensional data in many machine learning applications leads to computational and analytical complexities. Feature selection provides an effective way for solving these problems by removing irrelevant and redundant features, thus reducing model complexity and improving accuracy and generalization capability of the model. In this paper, we present a novel teacher-student feature selection (TSFS) method in which a ‘teacher’ (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension. Then a ‘student’ network (a simple neural network) is used to perform feature selection by minimizing the reconstruction error of low dimensional representation. Although the teacher-student scheme is not new, to the best of our knowledge, it is the first time that this scheme is employed for feature selection. The proposed TSFS can be used for both supervised and unsupervised feature selection. This method is evaluated on different datasets and is compared with state-of-the-art existing feature selection methods. The results show that TSFS performs better in terms of classification and clustering accuracies and reconstruction error. Moreover, experimental evaluations demonstrate a low degree of sensitivity to parameter selection in the proposed method.
Active Fences against Voltage-based Side Channels in Multi-Tenant FPGAs

Dynamic and partial reconfiguration together with hardware parallelism make FPGAs attractive as virtualized accelerators. However, recently it has been shown that multi-tenant FPGAs are vulnerable to remote side-channel attacks (SCA) from malicious users, allowing them to extract secret keys without a logical connection to the victim core. Typical mitigations against such attacks are hiding and masking schemes, to increase attackers' efforts in terms of side-channel measurements. However, they require significant efforts and tailoring for a specific algorithm, hardware implementation and mapping. In this paper, we show a hiding countermeasure against voltage-based SCA that can be integrated into any implementation, without requiring modifications or tailoring to the protected module. We place a properly mapped Active Fence of ring oscillators between victim and attacker circuit, enabled as a feedback of an FPGA-based sensor, leading to reduced side-channel leakage. Our experimental results based on a Lattice ECP5 FPGA and an AES-128 module show that two orders of magnitude more traces are needed for a successful key recovery, while no modifications to the underlying cryptographic module are necessary.
Deep Non-Negative Matrix Factorization Architecture Based on Underlying Basis Images Learning

The non-negative matrix factorization (NMF) algorithm represents the original image as a linear combination of a set of basis images. This image representation method is in line with the idea of “parts constitute a whole” in human thinking. The existing deep NMF performs deep factorization on the coefficient matrix. In these methods, the basis images used to represent the original image is essentially obtained by factorizing the original images once. To extract features reflecting the deep localization characteristics of images, a novel deep NMF architecture based on underlying basis images learning is proposed for the first time. The architecture learns the underlying basis images by deep factorization on the basis images matrix. The deep factorization architecture proposed in this paper has strong interpretability. To implement this architecture, this paper proposes a deep non-negative basis matrix factorization algorithm to obtain the underlying basis images. Then, the objective function is established with an added regularization term, which directly constrains the basis images matrix to obtain the basis images with good local characteristics, and a regularized deep non-negative basis matrix factorization algorithm is proposed. The regularized deep nonlinear non-negative basis matrix factorization algorithm is also proposed to handle pattern recognition tasks with complex data. This paper also theoretically proves the convergence of the algorithm. Finally, the experimental results show that the deep NMF architecture based on the underlying basis images learning proposed in this paper can obtain better recognition performance than the other state-of-the-art methods.
Meta-RCNN: Meta Learning for Few-Shot Object Detection

Despite significant advances in deep learning based object detection in recent years, training effective detectors in a small data regime remains an open challenge. This is very important since labelling training data for object detection is often very expensive and time-consuming. In this paper, we investigate the problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the meta-learning principle, we propose a new meta-learning framework for object detection named "Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data. This learning scheme helps acquire a prior which enables Meta-RCNN to do few-shot detection on novel tasks. Built on top of the popular Faster RCNN detector, in Meta-RCNN, both the Region Proposal Network (RPN) and the object classification branch are meta-learned. The meta-trained RPN learns to provide class-specific proposals, while the object classifier learns to do few-shot classification. The novel loss objectives and learning strategy of Meta-RCNN can be trained in an end-to-end manner. We demonstrate the effectiveness of Meta-RCNN in few-shot detection on three datasets (Pascal-VOC, ImageNet-LOC and MSCOCO) with promising results.
How Participatory Design Works

We argue that the distinguishing features of Participatory Design are not the participatory activities as such but the mechanisms used, the effects produced and the way in which these are sustained. We use program theory to illuminate how participatory design works and how it may be understood as more than a collection of methods or a matter of configuring user participation. Program theory operates by formulating the causal relations between the planned inputs, the process and the effects in terms of output, outcome and impact. While participatory design might appear similar to co-design or user-centered design on the level of design activities, PD differs in terms of the mechanisms employed and how effects and their sustainment are conceived. Looking at participatory design through the lens of program theory highlights how particular mechanisms work towards not only the designed product but also towards generating gains and lasting effects for participants.
Large Scale Learning of General Visual Representations for Transfer.

Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT). By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes -- from 1 example per class to 1M total examples. BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. We conduct detailed analysis of the main components that lead to high transfer performance.
ArtChain: Blockchain-Enabled Platform for Art Marketplace

Blockchain is an emerging technology that has the potential to revolutionize the global industry and create a trusted relationship in a multi-party business network. There are a number of practical use cases where blockchain has been applied. One specific area is the Art industry, where it is a natural fit in the way that art forensics and transactions are conducted, tracked and recorded. This motivates us to develop the ArtChain platform to assist the Art Industry. In this paper, we present ArtChain, which is an integrated trading system based on blockchain. It includes the front end, the back end, the services, the smart contract, the chain connection and the deployment scripts from the bottom to the top. To the best of our knowledge, this is the first deployed blockchain-enabled art trading platform in Australia. It provides a transparent yet privacy-preserving, and tamper-proof transaction history for registration, provenance, and traceability of art assets. Our objective analysis and evaluation show that the ArtChain platform is applicable and practical. For the interest of other researchers, our system implementation related resources are open-sourced on Github.
Audio Steganography Based on Iterative Adversarial Attacks Against Convolutional Neural Networks

Recently, convolutional neural networks (CNNs) have demonstrated superior performance on digital multimedia steganalysis. However, some studies have noted that most CNN-based classifiers can be easily fooled by adversarial examples, which form slightly perturbed inputs to a target network according to the gradients. Inspired by this phenomenon, we first introduce a novel steganography method based on adversarial examples for digital audio in the time domain. Unlike related methods for image steganography, such as [1]-[4], which are highly dependent on some existing embedding costs, the proposed method can start from a flat or even a random embedding cost and then iteratively update the initial costs by exploiting the adversarial attacks until satisfactory security performances are obtained. The extensive experimental results show that our method significantly outperforms the existing nonadaptive and adaptive steganography methods and achieves state-of-the-art results. Moreover, we also provide experimental results to investigate why the proposed embedding modifications seem evenly located at all audio segments despite their different content complexities, which is contrary to the content adaptive principle widely employed in modern steganography methods.
Machine Learning Techniques and A Case Study for Intelligent Wireless Networks

With the widespread deployment of wireless technologies and IoT, 5G wireless networks will support various communication connectivity and services for the huge number of wireless smart/ intelligent devices and machines. The challenge lies in assisting wireless networks to intelligently learn experience, autonomously optimize network configurations and smartly make decisions to support massive wireless smart devices with minimum human intervention, so the diverse and colorful service requirements can be satisfied with the optimum performance. Machine learning, as one of the powerful artificial intelligence tools, is capable of efficiently supporting wireless smart devices by assisting them to smartly observe the environment, analyze data and make decisions with the intelligence. Hence, in this article, we briefly review the major concepts of common machine learning techniques and present their potential applications in intelligent wireless networks, including spectrum sensing, channel estimation, device clustering, behavior prediction, position tracking, data demission reduction, adaptive routing, energy harvesting/efficiency, resource management, and so on. Furthermore, we propose deep reinforcement learning for intelligent resource management in intelligent wireless networks in an exemplary case study. Simulation results demonstrate the effectiveness and advance of machine learning in intelligent wireless networks.
How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

Abstract Although numerous Earthquake Early Warning (EEW) algorithms have been developed to date, we lack a detailed understanding of how often and under what circumstances useful ground motion alerts can be provided to end users. In particular, it is unclear how often EEW systems can successfully alert sites with high ground motion intensities. These are the sites that arguably need EEW alerts the most, but they are also the most challenging ones to alert because they tend to be located close to the epicenter where the seismic waves arrive first. Here we analyze the alerting performance of the Propagation of Local Undamped Motion (PLUM), Earthquake Point‐Source Integrated Code (EPIC), and Finite‐Fault Rupture Detector (FinDer) algorithms by running them retrospectively on the seismic strong‐motion data of the 219 earthquakes in Japan since 1996 that exceeded Modified Mercalli Intensity (MMI) of 4.5 on at least 10 sites ( M w 4.5–9.1). Our analysis suggests that, irrespective of the algorithm, EEW end users should expect that EEW can often but not always provide useful alerts. Using a conservative warning time ( t w ) definition, we find that 40–60% of sites with strong to extreme shaking levels receive alerts with t w &gt; 5 s. If high‐intensity shaking is caused by shallow crustal events, around 50% of sites with strong (MMI~6) and &lt;20% of sites with severe and violent (MMI ≥ 8) shaking receive alerts with t w &gt; 5 s. Our results provide detailed quantitative insight into the expected alerting performance for EEW algorithms under realistic conditions. We also discuss how operational systems can achieve longer warning times with more precautionary alerting strategies.
Understanding the creation of trust in cryptocurrencies: the case of Bitcoin

Abstract Contemporary cryptocurrencies lack legal, monetary, and institutional backing that traditional financial services employ. Instead, cryptocurrencies provide trust through technology. Despite the plethora of research in both trust and cryptocurrencies, the underlying attributes of the technologies that drive trust in cryptocurrencies are not well understood. To uncover these attributes, we analyze the corpus of 1.97 million discussion posts related to Bitcoin, the oldest and most widely used cryptocurrency. Based on earlier research, we identified functionality, reliability, and helpfulness as the focal constructs with which to evaluate users’ trust in technology. In our analysis, we discovered 11 different attributes related to three technology constructs that are significant in creating and maintaining users’ trust in Bitcoin. The findings are discussed in detail in the article.