Reviewerly/exHarmony huggingface dataset:
Modelling hierarchical clustered censored data with the hierarchical Kendall copula

Abstract This article proposes a new model for right‐censored survival data with multi‐level clustering based on the hierarchical Kendall copula model of Brechmann (2014) with Archimedean clusters. This model accommodates clusters of unequal size and multiple clustering levels, without imposing any structural conditions on the parameters or on the copulas used at various levels of the hierarchy. A step‐wise estimation procedure is proposed and shown to yield consistent and asymptotically Gaussian estimates under mild regularity conditions. The model fitting is based on multiple imputation, given that the censoring rate increases with the level of the hierarchy. To check the model assumption of Archimedean dependence, a goodness‐of test is developed. The finite‐sample performance of the proposed estimators and of the goodness‐of‐fit test is investigated through simulations. The new model is applied to data from the study of chronic granulomatous disease. The Canadian Journal of Statistics 47: 182–203; 2019 © 2019 Statistical Society of Canada
Space-Efficient Algorithms for Longest Increasing Subsequence

Given a sequence of integers, we want to find a longest increasing subsequence of the sequence. It is known that this problem can be solved in $O\left (n \log n\right )$ time and space. Our goal in this paper is to reduce the space consumption while keeping the time complexity small. For $\sqrt {n} \le s \le n$, we present algorithms that use $O\left (s \log n\right )$ bits and $O\left (\frac {1}{s} \cdot n^{2} \cdot \log n\right )$ time for computing the length of a longest increasing subsequence, and $O\left (\frac {1}{s} \cdot n^{2} \cdot \log ^{2} n\right )$ time for finding an actual subsequence. We also show that the time complexity of our algorithms is optimal up to polylogarithmic factors in the framework of sequential access algorithms with the prescribed amount of space.
Multiple Hypothesis Tracking with Kinematics and Appearance Models on Traffic Flow for Wide Area Traffic Surveillance

This paper presents a study of multiple hypothesis tracking (MHT) of vehicles recorded in wide area motion imagery (WAMI) that has persistent coverage. To take advantage of visual information contained in such aerial imagery, the authors propose a novel MHT-KAM method that combines multiple hypothesis tracking (MHT) with a kinematics and appearance model (KAM). Experiments were designed and implemented to test MHT-KAM on synthetic data sets with various frame rates, traffic configurations, and detection error rates. The experimental results indicate that this method can achieve promising performance for tracking individual vehicles, even in saturated traffic flow. The experimental findings indicate that the combination of applying high appearance weights in MHT-KAM and using large Mahalanobis distance-based gating solves the longstanding “closely-spaced targets” problem. The results also reveal satisfactory performance on existing aerial imagery data sets with limited quality and frame rates. This novel MHT-KAM method combined with previous computer vision-based approach has the potential to achieve a reliable and robust traffic surveillance system for extracting accurate microscopic data from persistent WAMI for diverse applications.
Categorizing Software Feedback in Current Language Software

Through the integration of the linguistic and computer science perspective of corrective feedback, this paper seeks to examine the forms of feedback present in current CALL software. This paper also expounds on the similarities and differences in the identification of feedback from these perspectives.
Agile on a Large Scale

Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing.A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations).These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target.In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented.The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2).Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies.RTs and reach trajectories of the movements to cued and uncued targets were analysed.RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets.The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.
Comparative Study of Batch and Stream Learning for Online Smartphone-based Human Activity Recognition

The availability of diverse embedded sensors in modern smartphones has created exciting opportunities for developing context-aware services and applications, such as Human activity recognition (HAR) in healthcare and smart buildings. However, recognizing human activities using smartphones remains a challenging task and requires efficient data mining approaches due to the limited resources of the device. For example, the training process is usually performed offline but rarely online on the mobile device itself, because traditional batch learning usually needs a large dataset of many users. Therefore, building models using complex multiclass algorithms is generally very time-consuming. In this paper, we present a comparison study using two approaches in order to reduce training time and memory usage while maintaining significant performance. In the first approach, we conducted experiments using batch learning on a GPU platform. Results showed that High Performance Extreme Learning Machine (HPELM) offers the best compromise: accuracy, memory usage and training time. Moreover, it achieved better performance on two dynamic activities, outperforming the best SVM model obtained in our previous study. In the second approach, we conducted experiments using online stream learning on the MOA platform. Unlike the first approach, experiments were performed using accelerometer data only. We also studied the effects of user/device dependency and feature engineering on the classification performance and memory usage by comparing five constructed real data streams. Simulation results showed that Hoeffding Adaptive Tree has comparable performance to batch learning, especially for user and device dependent data streams.
An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health.
Token-based spelling variant detection in Middle Low German texts

In this paper we present a pipeline for the detection of spelling variants, i.e., different spellings that represent the same word, in non-standard texts. For example, in Middle Low German texts in and ihn (among others) are potential spellings of a single word, the personal pronoun ‘him’. Spelling variation is usually addressed by normalization, in which non-standard variants are mapped to a corresponding standard variant, e.g. the Modern German word ihn in the case of in. However, the approach to spelling variant detection presented here does not need such a reference to a standard variant and can therefore be applied to data for which a standard variant is missing. The pipeline we present first generates spelling variants for a given word using rewrite rules and surface similarity. Afterwards, the generated types are filtered. We present a new filter that works on the token level, i.e., taking the context of a word into account. Through this mechanism ambiguities on the type level can be resolved. For instance, the Middle Low German word in can not only be the personal pronoun ‘him’, but also the preposition ‘in’, and each of these has different variants. The detected spelling variants can be used in two settings for Digital Humanities research: On the one hand, they can be used to facilitate searching in non-standard texts. On the other hand, they can be used to improve the performance of natural language processing tools on the data by reducing the number of unknown words. To evaluate the utility of the pipeline in both applications, we present two evaluation settings and evaluate the pipeline on Middle Low German texts. We were able to improve the F1 score compared with previous work from $$0.39$$ to $$0.52$$ for the search setting and from $$0.23$$ to $$0.30$$ when detecting spelling variants of unknown words.
Trend Mining: A Visualization Technique to Discover Variable Trends in the Objective Space

Practical multi-objective optimization problems often involve several decision variables that influence the objective space in different ways. All variables may not be equally important in determining the trade-offs of the problem. Decision makers, who are usually only concerned with the objective space, have a hard time identifying such important variables and understanding how the variables impact their decisions and vice versa. Several graphical methods exist in the MCDM literature that can aid decision makers in visualizing and navigating high-dimensional objective spaces. However, visualization methods that can specifically reveal the relationship between decision and objective space have not been developed so far. We address this issue through a novel visualization technique called trend mining that enables a decision maker to quickly comprehend the effect of variables on the structure of the objective space and easily discover interesting variable trends. The method uses moving averages with different windows to calculate an interestingness score for each variable along predefined reference directions. These scores are presented to the user in the form of an interactive heatmap. We demonstrate the working of the method and its usefulness through a benchmark and two engineering problems.
Bootstrapping privacy services in today's internet

Internet users today have few solutions to cover a large space of diverse privacy requirements. We introduce the concept of privacy domains, which provide flexibility in expressing users' privacy requirements. Then, we propose three privacy services that construct meaningful privacy domains and can be offered by ISPs. Furthermore, we illustrate that these services introduce little overhead for communication sessions and that they come with a low deployment barrier for ISPs.
Parallel, asynchronous, fuzzy logic systems realized in CMOS technology

Fuzzy systems play an important role in many industrial applications. Depending on the application, they can be implemented using different techniques and technologies. Software implementations are the most popular, which results from the ease of such implementations. This approach facilitates modifications and testing. On the other hand, such realizations are usually not convenient when high data rate, low cost per unit, and large miniaturization are required. For this reason, we propose efficient, fully digital, parallel, and asynchronous (clock-less) fuzzy logic (FL) systems suitable for the implementation as ultra low-power-specific integrated circuits (ASICs). On the basis of our former work, in which single FL operators were proposed, here we demonstrate how to build larger structures, composed of many operators of this type. As an example, we consider Lukasiewicz neural networks (LNN) that are fully composed of selected FL operators. In this work, we propose FL OR, and AND Lukasiewicz neurons, which are based on bounded sum and bounded product FL operators. In the comparison with former analog implementations of such LNNs, digital realization, presented in this work, offers important advantages. The neurons have been designed in the CMOS 130nm technology and thoroughly verified by means of the corner analysis in the HSpice environment. The only observed influence of particular combinations on the process, voltage, and temperature parameters was on delays and power dissipation, while from the logical point of view, the system always worked properly. This shows that even larger FL systems may be implemented in this way.
ActEV18: Human Activity Detection Evaluation for Extended Videos

Video analytic technologies that are able to detect and classify activity are crucial for applications in many domains, such as transportation and public safety. In spite of many data collection efforts and benchmark studies in the computer vision community, there has been a lack of system development that meets practical needs for such specific domain applications. In this paper, we introduce the Activities in Extended Video (ActEV) challenge to facilitate development of video analytic technologies that can automatically detect target activities, and identify and track objects associated with each activity. To benchmark the performance of currently available algorithms, we initiated the ActEV’18 activity-level evaluation along with reference segmentation and leaderboard evaluations. In this paper, we present a summary of results and findings from these evaluations. Fifteen teams from academia and industry participated in the ActEV18 evaluations using 19 activities from the VIRAT V1 dataset.
Deep Learning for Trilingual Character Recognition

As India is a multilingual country, in which the national language is Hindi, regional languages still exist in each of the corresponding states. In government offices, for the purpose of communication and maintenance of files and ledgers, the languages preferred are the regional languages and Hindi. As corporate offices and private organizations also exist in the country, these bodies mainly prefer the English language with the regional language in recording documents and ledgers. So, in this regard, in India a document contains multilingual texts, and there is a need of a multilingual OCR system. In this article, a trilingual OCR system is developed using deep learning for supporting English, Hindi and Kannada languages, the regional language of the state Karnataka.
Early Detection of Possible Undergraduate Drop Out Using a New Method Based on Probabilistic Rough Set Theory

For any educational project, it is important and challenging to know, at the moment of enrollment, whether a given student is likely to successfully pass the academic year. This task is not simple at all because many factors contribute to college failure. Being able to infer how likely is an enrolled student to present promotions problems, is undoubtedly an interesting challenge for the areas of data mining and education. In this paper, we propose the use of data mining techniques in order to predict how likely a student is to succeed in the academic year. Normally, there are more students that success than fail, resulting in an imbalanced data representation. To cope with imbalanced data, we introduce a new algorithm based on probabilistic Rough Set Theory (RST). Two ideas are introduced. The first one is the use of two different threshold values for the similarity between objects when dealing with minority or majority examples. The second idea combines the original distribution of the data with the probabilities predicted by the RST method. Our experimental analysis shows that we obtain better results than a range of state-of-the-art algorithms.
Research on Student Management Model of Higher Vocational Colleges Based on Cloud Platform

This paper analyzes the current situation of higher vocational students and the problems existing in the management of higher vocational students, and puts forward the idea of making use of cloud computing technology to innovate the management of higher vocational students, that is, to construct an interactive system based on the educational cloud platform. To realize the interaction between teachers and students. The construction of student management model based on cloud platform is the requirement of information construction of student management and the reform of talent training mode in higher vocational colleges.
Optimal Distribution of Privacy Budget in Differential Privacy

Privacy budget management plays an important role when applying differential privacy, as it sets an upper limit in the ability to utilise the private database. In this paper, we explore the possibility of extending the total allocated privacy budget, taking into consideration the data consumer characteristics and the data utilisation context. To this end, we first study the problem of privacy budget distribution in adaptive multi-data consumer differential privacy use cases. Then, we present an extension of the classic differential privacy formal model that allows taking into consideration data consumers’ information disclosure risk when distributing the privacy budget among them. Finally, we define a method that allows to optimally distribute a given privacy budget among a private database’s data consumers.
Cyber-Physical Systems for Environment and People Monitoring in Large Facilities: A Study Case in Public Health

Traditionally, public health policies have been based on sociological studies where data are collected by experts, and citizens are informed through media such as television. Nevertheless, new digital society and innovative technological paradigms enable the realization of these activities in a much more efficient and automatic manner, allowing (besides) a personalized and ubiquitous interaction with people. Although different technological solutions could be employed for this purpose, Cyber-Physical Systems (CPS) is the most promising one. In fact, in the last ten years, many different cyber-physical applications for environment and people monitoring have been reported, which (we argue) could be employed to support and promote public health policies. Therefore, in this paper it is described a new Cyber-Physical System to promote healthy behaviors to reduce cardiovascular risks. The system monitors people’s behavior (through a laser barrier and WiFi access points), and incentives positive and healthy activities by providing specific digital services. The proposed solution was simulated and implemented, deployed and validated using commercial components and statistical procedures, comparing the obtained result to which obtained from traditional mechanisms.
PID and Fuzzy Logic Controllers for DC Motor Speed Control

Proportional, integral and derivative (PID) controllers are commonly applied in industrial environments because of their performance and simplicity application in linear systems. On the other hand, Fuzzy logic controllers (FLC) imitate the human knowledge applying a linguistic ideology instead of mathematical calculations. These features make FLC suitable for nonlinear systems by providing an affordable response in terms of speed control. This research proposes a comparative study between PID controller and FLC for separately excited DC motor. Both controllers are tested under different conditions such as overshoot percentage, rise time, torque load disturbance and multiple steps input. Furthermore, this study determines the benefits and drawbacks of each controller when they are evaluated to obtain an appropriated output for DC motor speed control.
Partial and Full Goal Satisfaction in the MUSA Middleware

Classical goal-based reasoning frameworks for agents suppose goals are either achieved fully or not achieved at all: unless achieved completely, the agents have failed to address them. This behavior is different from how people do and therefore is far from real-world scenarios: in every moment a goal has reached a certain level of satisfaction. This work proposes to extend the classical boolean definition of goal achievement by adopting a novel approach, the Distance to Goal Satisfaction, a metric to measure the distance to the full satisfaction of a logic formula. In this paper we defined and implemented this metric; subsequently, we extended MUSA, a self-adaptive middleware used to engineer a heterogeneous range of applications. This extension allows solving real situations in which the full achievement represented a limitation.
An Investigation of Cyber Autonomy on Government Websites

From a national security viewpoint, a higher degree of cyber autonomy is crucial to reduce the reliance on external, oftentimes untrustworthy entities, in order to achieve better resilience against adversaries. To probe into the concept of government cyber autonomy, this study examines the external dependency of public-facing government websites across the world's major industrialized, Group of Seven (G7) countries. Over a two-year period, we measured HTTPS adoption rates, the autonomy status of CAs, and the autonomy status of CPs on G7 government websites. We find that approximately 85% of web resources loaded by G7 government sites originate from the United States. By reviewing policy documents and surveying technicians who maintain government websites, we identify four significant forces that can influence the degree of a government's autonomy, including government mandates on HTTPS adoption, website development outsourcing, the citizens' fear of large-scale surveillance, and user confusion. Because a government websites are considered critical information infrastructures, we expect this study to raise awareness of their complex dependency, thereby reducing the risk of blindly trusting external entities when using critical government services.