Reviewerly/exHarmony huggingface dataset:
Parallel Hierarchical Subspace Clustering of Categorical Data

Parallel clustering is an important research area of big data analysis. The conventional Hierarchical Agglomerative Clustering (HAC) techniques are inadequate to handle big-scale categorical datasets due to two drawbacks. First, HAC consumes excessive CPU time and memory resources; and second, it is non-trivial to decompose clustering tasks into independent sub-tasks executed in parallel. We solve these two problems by a MapReduce-based hierarchical subspace-clustering algorithm - called PAPU - using LSH-based data partitioning. PAPU is conducive to partitioning a large-scale dataset into multiple independent sub-datasets, into which similar data objects are mapped. Advocating parallel computing, PAPU obtains sub-clusters corresponding to respective attribute subspaces from independent chunks in the local clustering phase. To improve the accuracy of approximated clustering results, PAPU measures various scale clusters by applying the hierarchical clustering scheme to iteratively merge sub-clusters during the global clustering phase. We implement PAPU on a 24-node Hadoop computing platform. The experimental results reveal that hierarchical subspace-clustering coupled with the data-partitioning strategy achieves high clustering efficiency on both synthetic and real-world large-scale datasets. The experiments also demonstrate that PAPU delivers superior performance in terms of extensibility and scalability (e.g., a nearly linear speedup).
On the Asymptotics of Solving the LWE Problem Using Coded-BKW With Sieving

The learning with errors problem (LWE) has become a central topic in recent cryptographic research. In this paper, we present a new solving algorithm combining important ideas from previous work on improving the Blum-Kalai- Wasserman (BKW) algorithm and ideas from sieving in lattices. The new algorithm is analyzed and demonstrates an improved asymptotic performance. For the Regev parameters q = n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and √ noise level σ = n1.5/( 2π log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 2 n), the asymptotic complexity is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.893n</sup> in the standard setting, improving the previously best known complexity of roughly 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.930n</sup> . The newly proposed algorithm also provides asymptotic improvements when a quantum computer is assumed or when the number of samples is limited.
A robust loss function for classification with imbalanced datasets

Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a robust SVM framework is presented which results in a Bayes optimal classifier. However, nonconvexity makes the model difficult to optimize. We develop an alternative iterative algorithm to solve the proposed model. What's more, we analyze the robustness of the proposed model theoretically from a re-weighted SVM viewpoint and the obtained optimal solution is consistent with Bayesian optimal decision rule. Furthermore, numerical experiments are carried out on databases that are drawn from UCI Machine Learning Repository and a practical application. With two different types of noise environments, one with label noise and one with feature noise, experiment results show that on these two databases the proposed method achieves better generalization results compared to other SVM methods.
Can Bitcoin become money? The monetary rule problem

Abstract The growing literature on Bitcoin can be divided into two groups. One performs an economic analysis of Bitcoin focusing on its monetary characteristics. The other one takes a financial look at the price of Bitcoin. Interestingly, both of these groups have not given much more than passing comments to the problem of whether or not Bitcoin has the right monetary rule in order to become a well‐established currency. This paper argues that Bitcoin in particular, and cryptocurrencies in general, do not have a good monetary rule and that this shortcoming seriously limits its prospect of becoming widely used money.
Software meta-language engineering and CBS

The SLE conference series is devoted to the engineering principles of software languages: their design, their implementation, and their evolution. This paper is about the role of language specification in SLE. A precise specification of a software language needs to be written in a formal meta-language, and it needs to co-evolve with the specified language. Moreover, different software languages often have features in common, which should provide opportunities for reuse of parts of language specifications. Support for co-evolution and reuse in a meta-language requires careful engineering of its design. The author has been involved in the development of several meta-languages for semantic specification, including action semantics and modular variants of structural operational semantics (MSOS, I-MSOS). This led to the PLanCompS project, and to the design of its meta-language, CBS, for component-based semantics. CBS comes together with an extensible library of reusable components called ‘funcons’, corresponding to fundamental programming constructs. The main aim of CBS is to optimise co-evolution and reuse of specifications during language development, and to make specification of language semantics almost as straightforward as context-free syntax specification. The paper discusses the engineering of a selection of previous meta-languages, assessing how well they support co-evolution and reuse. It then gives an introduction to CBS, and illustrates significant features. It also considers whether other current meta-languages might also be used to define an extensible library of funcons for use in component-based semantics.
A Genetic Algorithm Based Feature Selection for Handwritten Digit Recognition

Background: The data proliferation has been resulted in large-scale, high dimensional data and brings new challenges for feature selection in handwriting recognition problems. The practical challenges like the large variability and ambiguities present in the individual’s handwriting style demand an optimal feature selection algorithm that would be capable to enhance the recognition accuracy of handwriting recognition system with reduced training efforts and computational cost. Objective: This paper gives emphasis on the feature selection process and proposed a genetic algorithm based feature selection technique for handwritten digit recognition. Methods: A hybrid feature set of statistical and geometrical features is developed in order to get the effective feature set consist of local and global characteristics of sample digits. The method utilizes a genetic algorithm based feature selection for selecting best distinguishable features and k-nearest neighbour for evaluating the fitness of features of handwritten digit dataset. Results: The experiments are carried out on standard The Chars74K handwritten digit dataset and reported a 66% reduction in the original feature set without sacrificing the recognition accuracy. Conclusion: The experiment results show the effectiveness of the proposed approach.
Crowd behavior analysis from fixed and moving cameras

Current crowd behavior analyses based on 'live' visual or audiovisual camera streams require numerous personnel and is error prone due to human mistakes. Hence, automatic crowd behavior detection is urgently needed, in particular for crowded public spaces that need 24/7 monitoring. However, the complex nature of crowd behaviors and real-life constraints debase the advantages in sophisticated computer vision and analytics algorithms when they are applied in crowed monitoring systems. It is evident that crowd behavior analysis and recognition are critical to many important applications including surveillance, robotics, information retrieval, psychology, entertainment (movies, CGI, games, etc.) and market research. Automated crowd behavior analysis has been a topic of great interest in computer vision and cognitive sciences. Recently, with the growth of crowd phenomena in the real world, this area of research has attracted much attention. Therefore, over the past few years the number of works on crowd behavior analysis increased covering macroscopic (holistic) and microscopic (object-based) approaches, including techniques based on motion patterns, tracking, activity analysis and modeling, anomaly detection, and density estimation. In this chapter state-of-the-art techniques on this topic are outlined. An overview of the related challenging tasks is provided and existing and popular solutions as well as evaluation metrics and datasets are discussed.
Integrating Knowledge Management into Information Security

Repeated information security (InfoSec) incidents have harmed the confidence of people on enterprises' InfoSec capability. While most organisations adopt control frameworks such as ISO27001 and COBIT, the role and contribution of knowledge management on InfoSec was inadequately considered. The authors integrated the concepts of knowledge-centric information security and IT Governance (ITG) into an ITG-driven knowledge framework (ITGKF) for reinforcing InfoSec maturity and auditability of enterprises. The authors also tried to assess whether ITG can embrace proper knowledge circulation within the InfoSec community. The authors confirmed the positive influence of IT governance on knowledge-centric information security (KCIS) and information security maturity and audit result (ISMAR), the positive influence of KCIS on ISMAR, and the mediating role of KCIS between ITG and ISMAR. These indicated the significance of KM in InfoSec area. Based on the findings, they proposed possible changes of integrating KM in different InfoSec practices and audit standard.
Exploring touch feedback display of virtual keyboards for reduced eye movements

When typing on smartphones or palm tablets, users generally make an effort to type correctly while simultaneously checking the small keyboard and the text display. Unlike physical keyboards that allow users to perform typing based on long-term muscle memory, virtual keyboards typically require more frequent eye movements between the keyboard and the text display areas. This study proposes a new way of designing a virtual keyboard display to reduce the effort associated with frequent eye-movements. For this study, we developed virtual keyboard display systems featuring both static and dynamic word-by-word (WBW) feedback displays. The two display systems were examined in comparison with a more conventional method known as character-by-character (CBC) feedback display. We investigated user satisfaction, typing performance and the user's eye gaze shifts. Eye gaze shifts were measured between the keyboard and the text display areas across the three conditions using self-report, log, and eye-tracking measures. In the static WBW condition, the words being typed displayed in a fixed area at the top of the virtual keyboard; in the dynamic WBW display, the words displayed in a small popup window at the tip of the selected key. Using a repeated measure experiment for the three display conditions, participants were asked to type fifteen phrases using a palm tablet while wearing eye-tracking glasses for each condition. We conducted a mixed-model design ANOVA with group (SLOW vs. FAST typing; men vs. women) as between-subject factors and display condition (CBC vs. WBW). We found a significant (11%) improvement in typing speed with the dynamic WBW over the CBC display for less experienced keyboard users. In addition, participants reported higher satisfaction with the two WBW conditions than the CBC condition. Eye fixations, dwell times, and heat map data also supported that WBW displays are advantageous for less experienced, slower typists by helping them stay focused more on the keyboard, thus reducing eye transitions to the text display. Our study systematically demonstrates how and to what extent the virtual keyboard display strategy influences typing performance and subjective experience based on self-reports and eye-tracking measures. The approach and findings of this study should provide useful information and practical guidance to mobile application developers and designers who are interested in improving virtual keyboard functionalities and user satisfaction.
Gaze Estimation Using Residual Neural Network

Eye gaze tracking has become an prominent research topic in human-computer interaction and computer vision. It is due to its application in numerous fields, such as the market research, medical, neuroscience and psychology. Eye gaze tracking is implemented by estimating gaze (gaze estimation) for each individual frame in offline or real-time video captured. Therefore, in order to produce the secure the accurate tracking, especially in the emerging use in medical and community, innovation on the gaze estimation posts a challenge in research field. In this paper, we explored the use of the deep learning model, Residual Neural Network (ResNet-18), to predict the eye gaze on mobile device. The model is trained using the large-scale eye tracking public dataset called GazeCapture. We aim to innovate by incorporating methods/techniques of removing the blinking data, applying image histogram normalisation, head pose, and face grid features. As a result, we achieved 3.05cm average error, which is better performance than iTracker (4.11cm average error), the recent gaze tracking deep-learning model using AlexNet architecture. Upon observation, adaptive normalisation of the images was found to produce better results compared to histogram normalisation. Additionally, we found that head pose information was useful contribution to the proposed deep-learning network, while face grid information does not help to reduce test error.
An online support vector machine for the open-ended environment

Support Vector Machine (SVM) has been widely applied in expert systems. Traditional SVM works as a closed system, which means the parameters of the SVM freeze after training is finished. This strategy leads to the SVM cannot adapt to the online learning environment and also hinders the application of the SVM in expert systems which need to be updated in an online way. Many online SVMs retrain SVM with the current support vectors and new incoming data. However, the retained system usually shows a declined performance due to losing much information about the data. To handle this problem, we propose an Online Support Vector Machine (OSVM). The OSVM includes a Representative Prototype Area (RPA), which retains the representative data of all historical data. In the RPA, each class is maintained by an Online Incremental Feature Map (OIM) which learns a suitable representative set from the stream data automatically, e.g., we do not need to predefine a compression ratio, the OIM will dynamically adapt to the current input data. Retraining is periodically conducted on the updated OIM, which can avoid a declined performance and significantly reduce the computational cost. Variety data sets from different fields are used to test our method and the experimental results demonstrate that the accuracy of OSVM is comparable to that of the state-of-the-art SVM and the speed of OSVM is much faster. Moreover, OSVM is more stable than the other methods in the open-ended learning environment, where stream data from novel classes occur. This implies the OSVM has a good application prospect in online learning expert systems.
Fingerprinting multiple gold mineralization events at the Dome mine in Timmins, Ontario, Canada: Trace element and gold content of pyrite

The Dome mine is host to over 16 Moz of orogenic gold deposited during a period of up to 25 Ma through multi-stage enrichment. The earliest economic stage of veining is an extensive set of ankerite veins which were historically considered barren except where overprinted by subsequent veining events. Gold in these veins is intimately associated with pyrite mineralization, and three stages of pyrite growth have been identified (Py1,2,3). The trace element content of each stage of pyrite growth determined by dynamic-secondary ion mass spectrometry and synchrotron radiation x-ray fluorescence reveals that there were three auriferous fluid events, each with a unique gold and trace element fingerprint which can be related to deposits wide mineralization events. Early ankerite vein forming fluids were auriferous, lower temperature (200–250 °C) and deposited Py1 enriched in gold, metals and metalloids (As, Cu, Ni, Zn). Py2 growth is related to deposit wide quartz-tourmaline and quartz-fuchsite veining based on trace element geochemistry, Au:Ag, and associated mineral assemblages. This deposited Ni enriched pyrite, tellurides, galena, and gold with anomalously high Au:Ag ratios. There is also evidence for remobilization of early gold and trace elements from Py1 pyrite during the Py2 forming event. The final stage of gold endowment in the ankerite veins is related to a deposit (and camp) wide quartz veining event. Py3 growth is trace element (As, Ni, Cu) and Au poor with respect to Py1 and Py2, however, this veining event contributed the bulk of the ankerite vein gold endowment. The remobilization of gold and trace elements from sieve textured Py1 in addition to overprinting Py2 and Py3 and their associated gold endowments helps explain the difficulty in applying bulk rock trace element vectors to gold at the Dome mine. The work also provides conclusive evidence for the auriferous nature of early ankerite veining at the Dome mine, and reveals that the system was fertile for a period of over 7 to 25 Ma, a key component for the formation of a world-class deposit.
Deep learning approach to generate offline handwritten signatures based on online samples

Reduced training sets are major problems typically found on the task of offline signature verification. To increase the number of samples, the use of synthetic signatures can be taken into account. In this work, a new method for the generation of synthetic offline signatures by using dynamic and static (real) ones is presented. The synthesis is here faced under the perspective of supervised training: the learning model is trained to perform the task of online-to-offline signature conversion. The approach is based on a deep convolutional neural network. The main goal is to enlarge offline training dataset in order to improve performance of the offline signature verification systems. For this purpose, a machine-oriented evaluation on the BiosecurID signature dataset is carried out. The use of synthetic samples (in the training phase) generated with the proposed method on a state-of-the-art classification system exhibits performance similar to those obtained using real signatures; moreover, the combination of real and synthetic signatures in the training set is also able to show improvements of the equal error rate.
Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges

Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in several languages. We report on our experience in the organization of SENTIment POLarity Classification Task (SENTIPOLC), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets-which include an enriched annotation scheme for dealing with the impact of figurative language on polarity-the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions.
Automatic first-arrival picking method based on an image connectivity algorithm and multiple time windows

The use of a computer to automatically pick the first-arrival of a seismic signal is an operation that involves picking and screening the first arrival of the wave according to the criteria established in the manual picking process. To increase the picking accuracy for data with low-to-moderate signal-to-noise ratio (SNR), we propose a new single-trace boundary detection algorithm. This algorithm includes three steps: (1) calculate the first-arrival characteristic values through multi time windows; (2) take the times corresponding to the maximum characteristic values given by different time windows as intermediate results; (3) compare the intermediate results: if the difference is too large, it is marked the time is abnormal, otherwise the average time of the intermediate results is taken as the first-arrival time. Using this energy boundary detection method, the characteristic values obtained are bi-directionally expanded to allow the use of the trace connectivity algorithm which is improved from the region growing method. Determining the connectivity between the first-arrival characteristic values is a way to simulate how the human eye discriminates true first arrivals. This method significantly improves the elimination of false or abnormal first-arrivals. Next, a small-step fitting algorithm is applied to the remaining first-arrival characteristic values to complete the calculation of the final characteristic values. Based on the retained first-arrival characteristic values, the missing values are assigned by interpolation. The characteristic values are mapped on the original record and finally the first-arrival picking is completed using a small time window. Theoretical results as well as the results obtained from real data demonstrate that the proposed automatic first-arrival picking method effectively improves the accuracy of the first-arrival picking. Finally, the new picking algorithm is presented more efficient than the energy ratio method, as well as cross-correlation method.
Extreme Photovoltaic Power Analytics for Electric Utilities

Obtaining high-fidelity information on extreme photovoltaic (PV) power is critical for electric utility system planning and operations. However, a scarcity of extreme data has previously made achieving an accurate estimate of extreme PV power an intractable challenge. In response to this challenge, this paper presents Extreme PV Power Analytics (EPVA). It utilizes k-means clustering to determine which PV systems have similar behaviors in their extreme capacity factors (ECFs) in order to incorporate more extreme data in an extreme value analysis. This extreme value analysis is subsequently applied to obtain the distribution of ECFs. Zone partitioning results and ECF distribution results for The United Illuminating Company service territory are presented to validate the effectiveness and efficacy of EPVA.