Reviewerly/exHarmony huggingface dataset:
The OnHW Dataset

This paper presents a handwriting recognition (HWR) system that deals with online character recognition in real-time. Our sensor-enhanced ballpoint pen delivers sensor data streams from triaxial acceleration, gyroscope, magnetometer and force signals at 100 Hz. As most existing datasets do not meet the requirements of online handwriting recognition and as they have been collected using specific equipment under constrained conditions, we propose a novel online handwriting dataset acquired from 119 writers consisting of 31,275 uppercase and lowercase English alphabet character recordings (52 classes) as part of the UbiComp 2020 Time Series Classification Challenge. Our novel OnHW-chars dataset allows for the evaluations of uppercase, lowercase and combined classification tasks, on both writer-dependent (WD) and writer-independent (WI) classes and we show that properly tuned machine learning pipelines as well as deep learning classifiers (such as CNNs, LSTMs, and BiLSTMs) yield accuracies up to 90 % for the WD task and 83 % for the WI task for uppercase characters. Our baseline implementations together with the rich and publicly available OnHW dataset serve as a baseline for future research in that area.
Faster SVM training via conjugate SMO

We propose an improved version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure. This new approach only involves a modest increase on the computational cost of each iteration but, in turn, usually results in a substantial decrease in the number of iterations required to converge to a given precision. Besides, we prove convergence of the iterates of this new Conjugate SMO as well as a linear rate when the kernel matrix is positive definite. We have implemented Conjugate SMO within the LIBSVM library and show experimentally that it is faster for many hyper-parameter configurations, being often a better option than second order SMO when performing a grid-search for SVM tuning.
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies

Abstract Objective Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning. Materials and Methods We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks: the risk prediction of heart failure in diabetes patients and the risk prediction of pancreatic cancer. Two popular models were evaluated: logistic regression and a recurrent neural network. Results For logistic regression, using UMLS delivered the optimal area under the receiver operating characteristics (AUROC) results in both dengue hemorrhagic fever (81.15%) and pancreatic cancer (80.53%) tasks. For recurrent neural network, UMLS worked best for pancreatic cancer prediction (AUROC 82.24%), second only (AUROC 85.55%) to PheWAS (AUROC 85.87%) for dengue hemorrhagic fever prediction. Discussion/Conclusion In our experiments, terminologies with larger vocabularies and finer-grained representations were associated with better prediction performances. In particular, UMLS is consistently 1 of the best-performing ones. We believe that our work may help to inform better designs of predictive models, although further investigation is warranted.
Touching holograms with windows mixed reality: Renovating the consumer retailing services

Recent technological advances in wearable technologies, such as mixed-reality devices, have enabled consumers to interact with artificial three-dimensional visual environments. This presents an incredible opportunity for service retailers to present alternative ways of interacting with their services. This study empirically investigates the potential applications of Windows Mixed Reality devices, while specifically examining various forms of consumer perceptions and behavioural intentions. This research is among the first to empirically examine the effect of windows mixed reality experiences, enabled by the latest wearable devices, on intentions of users in a services retailing context. The results of this study help guide retailers who are looking to integrate Windows Mixed Reality devices in their practice to increase user satisfaction, trust, and utilitarian needs. The paper recommends specific theoretical and managerial implications.
Improved Blockchain Consensus Mechanism Based on PBFT Algorithm

Practical Byzantine Fault Tolerance (PBFT) is a blockchain consensus mechanism that is widely used at present, but the confidence of blockchain node in PBFT cannot be guaranteed, and a large amount of communication resources will be consumed in the process of reaching consensus. The paper proposes a new consensus mechanism, namely the Dynamic Grouping Byzantine Fault Tolerance Mechanism (DGBFT) based on confidence. The principles of DGBFT are as follows: 1) By extending the node's attributes with the confidence, and designing a mechanism to evaluate the node's confidence, therefore, the confidence adjustment and grouping adjustment can be performed on the nodes in the system. By grouping the confidence nodes by the confidence group, the communication complexity is greatly reduced, and the malicious nodes can be effectively excluded. Finally, the experimental results show that the blockchain applying the improved mechanism can significantly improve the communication efficiency of the system and the overall confidence of the system.
Why is phishing still successful?

In recent times, there has been a dramatic shift from bulk spam emails to targeted email phishing campaigns. Such attacks have started to cause huge brand, financial and operational damage to organisations globally. Phishing attacks involve simple, straightforward, masquerading methodology. 1 The aim is to lure and trick an unsuspecting victim in order to elicit as much information as possible, using SMS, email, WhatsApp and other messaging services, or phone calls that have been spoofed to appear is if they are from known, reliable friends or colleagues. 2
A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

Abstract In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.
Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation

Modern recommenders usually consider both collaborative features from user behavior data (e.g., clicks) and content information about the users and items (e.g., user ages or item images) for improved recommendations. While encouraging, the uncovered user preference representations derived from these collaborative and content-based perspectives can be entangled by intermixing the influence from each other, leading to sub-optimal performance and unstable recommendations. Hence, we propose to disentangle representations learned from user behavior data and content information. Specifically, we propose a novel two-level disentanglement generative recommendation model (DICER) that supports both content-collaborative disentanglement and feature disentanglement: for the content-collaborative disentanglement, DICER decomposes the features by their marginal distributions based on content and user-item interactions, to ensure the learned features from each type are statistically independent. For feature disentanglement, by decomposing the Kullback-Leibler divergence, we theoretically show that extracted features within each type are disentangled at a granular level. Furthermore, DICER utilizes a co-decoder that simultaneously decodes the content and user-item interactions to ensure the high-quality of learned features. Through extensive experiments on three real-world datasets, results show that DICER significantly outperforms other state-of-the-art methods by 13.5% in NDCG and 14.4% in hit ratio on average.
Personality Bias of Music Recommendation Algorithms

Recommender systems, like other tools that make use of machine learning, are known to create or increase certain biases. Earlier work has already unveiled different performance of recommender systems for different user groups, depending on gender, age, country, and consumption behavior. In this work, we study user bias in terms of another aspect, i.e., users’ personality. We investigate to which extent state-of-the-art recommendation algorithms yield different accuracy scores depending on the users’ personality traits. We focus on the music domain and create a dataset of Twitter users’ music consumption behavior and personality traits, measuring the latter in terms of the OCEAN model. Investigating [email protected] and [email protected] of the recommendation algorithms SLIM, embarrassingly shallow autoencoders for sparse data (EASE), and variational autoencoders for collaborative filtering (Mult-VAE) on this dataset, we find several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits.
Continuous-variable quantum key distribution under strong channel polarization disturbance

In a commercial fiber-based quantum key distribution (QKD) system, the state of polarization (SOP) of the optical fields is inevitably disturbed by random birefringence of the standard single-mode fiber due to an external complex environment. We analyze theoretically the effect of SOP fluctuations on the continuous-variable (CV) QKD system and experimentally verify its validity. To overcome the influence of the polarization variations, a self-adaptive gradient algorithm is proposed to achieve high-speed polarization controlling under pulsed light with field programmable gate array hardware. For single random polarization scrambling, the polarization extinction ratio achieved is over 30 dB and the average time of polarization control is 827 $\ensuremath{\mu}\mathrm{s}$. In order to eliminate further the effect of the occasional failures of polarization control, we properly filter the raw keys in terms of the relative phase fluctuations between the signal and the local oscillator. By combining this approach with the high-speed polarization control, we demonstrate a CV QKD under a continuous polarization scrambling of 314 rad/s. Our results provide useful references for practical application of fiber-based CV QKD in a field environment.
LOOP

Wearables, mobile devices and Internet-of-Things (IoT) sensors are enabling us to monitor our environment, understand our social connections, and track our personal health. However, most of these systems communicate data through information visualizations that are often ‘hidden’ inside devices, such as mobile phones and tablets, requiring users to undertake explicit actions to reveal them. Novel interfaces and devices embedded in people's everyday life have the potential to help users visualize, use, and appropriate their collected personal data. To this end, we designed the physical artifact LOOP, which provides an abstract visualization of the user's activity data by changing its shape. In this paper, we elaborate on the design and present a one-week field study in which LOOP was deployed in the homes of five end-users. We found that the physical presence of LOOP facilitated reflection and the layered visualization supported various personal tracking.
Coherent superconducting qubits from a subtractive junction fabrication process

Josephson tunnel junctions are the centerpiece of almost any superconducting electronic circuit, including qubits. Typically, the junctions for qubits are fabricated using shadow evaporation techniques to reduce dielectric loss contributions from the superconducting film interfaces. In recent years, however, sub-micron scale overlap junctions have started to attract attention. Compared to shadow mask techniques, neither an angle dependent deposition nor free-standing bridges or overlaps are needed, which are significant limitations for wafer-scale processing. This comes at the cost of breaking the vacuum during fabrication, but simplifies integration in multi-layered circuits, implementation of vastly different junction sizes, and enables fabrication on a larger scale in an industrially-standardized process. In this work, we demonstrate the feasibility of a subtractive process for fabrication of overlap junctions. In an array of test contacts, we find low aging of the average normal state resistance of only 1.6\% over 6 months. We evaluate the coherence properties of the junctions by employing them in superconducting transmon qubits. In time domain experiments, we find that both, the qubit life- and coherence time of our best device, are on average greater than $20\,\si{\micro\second}$. Finally, we discuss potential improvements to our technique. This work paves the way towards a more standardized process flow with advanced materials and growth processes, and constitutes an important step for large scale fabrication of superconducting quantum circuits.
Reconciling solar forecasts: Probabilistic forecasting with homoscedastic Gaussian errors on a geographical hierarchy

Hierarchical forecasting and reconciliation are new to the field of solar engineering. Previous papers in this series, namely, Yang et al. (2017a, 2017b), and Yagli et al. (2019b), discussed various reconciliation techniques for deterministic solar forecasts obtained across spatio-temporal hierarchies. This paper extends the discussion into probability space, and studies how reconciliation can affect the performance of probabilistic forecasting. More specifically, qualities of the parametric predictive distributions before and after reconciliation are compared. Four minimum-trace-based reconciliation techniques are used to reconcile day-ahead and hour-ahead forecasts generated using two datasets: (1) distributed solar power generation for 318 simulated PV systems in California, and (2) satellite-derived irradiance over Arizona. The empirical result shows that reconciliation not only improves the accuracy of point forecasts, but also leads to high-quality predictive distributions in terms of sharpness, calibration, and skill score. Moreover, such improvement is quite general, and does not seem to depend on data, hierarchy structure, nor the underlying forecasting model.
A Blockchain and Edge-Computing-Based Secure Framework for Government Tender Allocation

Governments and public sector entities around the world are actively exploring new ways to keep up with technological advancements to achieve smart governance, work efficiency, and cost optimization. Blockchain technology is an example of such technology that has been attracting the attention of Governments across the globe in recent years. Enhanced security, improved traceability, and lowest cost infrastructure empower the blockchain to penetrate various domains. Generally, governments release tenders to some third-party organizations for different projects. During this process, different competitors try to eavesdrop the tender values of others to win the tender. The corrupt government officials also charge high bribe to pass the tender in favor of some particular third party. In this article, we presented a secure and transparent framework for government tenders using blockchain. Blockchain is used as a secure and immutable data structure to store the government records that are highly susceptible to tampering. This work aims to create a transparent and secure edge computing infrastructure for the workflow in government tenders to implement government schemes and policies by limiting human supervision to the minimal.
Towards Improved Deep Contextual Embedding for the identification of Irony and Sarcasm

Humans use tonal stress and gestural cues to reveal negative feelings that are expressed ironically using positive or intensified positive words when communicating vocally. However, in textual data, like posts on social media, cues on sentiment valence are absent, thus making it challenging to identify the true meaning of utterances, even for the human reader. For a given post, an intelligent natural language processing system should be able to identify whether a post is ironic/sarcastic or not. Recent work confirms the difficulty of detecting sarcastic/ironic posts. To overcome challenges involved in the identification of sentiment valence, this paper presents the identification of irony and sarcasm in social media posts through transformer-based deep, intelligent contextual embedding - T-DICE - which improves noise within contexts. It solves the language ambiguities such as polysemy, semantics, syntax, and words sentiments by integrating embeddings. T-DICE is then forwarded to attention-based Bidirectional Long Short Term Memory (BiLSTM) to find out the sentiment of a post. We report the classification performance of the proposed network on benchmark datasets for #irony & #sarcasm. Results demonstrate that our approach outperforms existing state-of-the-art methods.
Deep Active Contour Network for Medical Image Segmentation

Image segmentation is vital to medical image analysis and clinical diagnosis. Recently, convolutional neural networks (CNNs) have achieved tremendous success in this task, however, it performs poorly at recognizing precise object boundary due to the information loss in the successive downsampling layers. To overcome this problem, we integrate an active contour model (convexified Chan-Vese model) into the CNN structure (DenseUNet), forming a new framework called deep active contour network (DACN). Instead of manual setting, DACN applies a CNN backbone to learn the initialization and parameters of active contour model (ACM) automatically. The proposed DACN leverages the advantage of ACM to detect object boundaries accurately, which can be trained in an end-to-end differential manner. The experimental results on two public datasets demonstrate the effectiveness of DACN, and the trimap experiment confirms the superior ability of DACN to obtain precise boundary delineation.
Enabling On-Device CNN Training by Self-Supervised Instance Filtering and Error Map Pruning

This work aims to enable on-device training of convolutional neural networks (CNNs) by reducing the computation cost at training time. CNN models are usually trained on high-performance computers and only the trained models are deployed to edge devices. But the statically trained model cannot adapt dynamically in a real environment and may result in low accuracy for new inputs. On-device training by learning from the real-world data after deployment can greatly improve accuracy. However, the high computation cost makes training prohibitive for resource-constrained devices. To tackle this problem, we explore the computational redundancies in training and reduce the computation cost by two complementary approaches: 1) self-supervised early instance filtering on data level and 2) error map pruning (EMP) on the algorithm level. The early instance filter selects important instances from the input stream to train the network and drops trivial ones. The EMP further prunes out insignificant computations when training with the selected instances. Extensive experiments show that the computation and energy cost is substantially reduced without any or with marginal accuracy loss. For example, when training ResNet-110 on CIFAR-10, we achieve 67.8% computation saving while preserving full accuracy and 75.1% computation saving with a marginal accuracy loss of 1.3%. When training LeNet on MNIST, we save 79% computation while boosting accuracy by 0.2%. Besides, practical energy saving is measured on edge platforms. We achieve 67.6% energy saving when training ResNet-110 on mobile GPU and 74.1% energy saving when training LeNet on MCU without any accuracy loss.
Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data

Abstract Many next-generation sequencing datasets contain only relative information because of biological and technical factors that limit the total number of transcripts observed for a given sample. It is not possible to interpret any one component in isolation. The field of compositional data analysis has emerged with alternative methods for relative data based on log-ratio transforms. However, these data often contain many more features than samples, and thus require creative new ways to reduce the dimensionality of the data. The summation of parts, called amalgamation, is a practical way of reducing dimensionality, but can introduce a non-linear distortion to the data. We exploit this non-linearity to propose a powerful yet interpretable dimension method called data-driven amalgamation. Our new method, implemented in the user-friendly R package amalgam, can reduce the dimensionality of compositional data by finding amalgamations that optimally (i) preserve the distance between samples, or (ii) classify samples as diseased or not. Our benchmark on 13 real datasets confirm that these amalgamations compete with state-of-the-art methods in terms of performance, but result in new features that are easily understood: they are groups of parts added together.