Reviewerly/exHarmony huggingface dataset:
Structural Image De-Identification for Privacy-Preserving Deep Learning

Due to the risk of data leakage while training deep learning models in a shared environment, we propose a new privacy-preserving deep learning (PPDL) method using a structural image de-identification approach for object classification. The proposed structural image de-identification approach is designed based on the fact that the degree of structural distortion of an image object has the greatest impact on human's perceptual system. Thus, by modifying only the structural parts of the original one using order preserving encryption(OPE), the proposed structural image de-identification approach decreases only the recognition rate by human. From the experimental results using different standard datasets, we show that the object classification accuracy of the proposed structural image de-identification method is almost the same as the deep learning performance for non-encrypted images, without revealing the original image contents including sensitive information. Also, by handling the trade-off between object classification accuracy and privacy protection for the de-identified image, we experimentally find the optimal size of input image for the proposed structural image de-identification approach.
Discovering Density-Based Clustering Structures Using Neighborhood Distance Entropy Consistency

Traditional clustering algorithms model the clustering problem as an optimization task, in which the objective is defined based on minimizing specific metrics. These algorithms are limited to find clusters with convex polytopes. In contrast, density-based clustering algorithms aim at overcoming this limitation and try to partition data objects into meaningful groups that have relatively high density separated by low-density regions. This work describes and evaluates a new density-based clustering algorithm, called neighborhood distance entropy consistency (NDEC), which is able to not only detect clusters of arbitrary size, shape, and density, but also identify outliers. To this end, both local and global densities are considered simultaneously to accurately discover the intrinsic clustering structure. In addition, the consistency of neighborhood distance entropy is used as an important criterion to merge potential subclusters. Experiments on synthetic and real benchmark clustering data sets have demonstrated the efficiency and effectiveness of the NDEC method. Comparisons with k-means, DBSCAN, OPTICS, and density peaks clustering algorithms further show that NDEC can successfully discover natural clusters. Additionally, the utility of NDEC is demonstrated with its application on two real-world problems including brain white matter tracts segmentation using diffusion tensor imaging and characterizing motor unit potential trains extracted from electromyographic signals.
Average Top-k Aggregate Loss for Supervised Learning

In this work, we introduce the average top- k ( ATk) loss, which is the average over the k largest individual losses over a training data, as a new aggregate loss for supervised learning. We show that the ATk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss. Yet, the ATk loss can better adapt to different data distributions because of the extra flexibility provided by the different choices of k. Furthermore, it remains a convex function over all individual losses and can be combined with different types of individual loss without significant increase in computation. We then provide interpretations of the ATk loss from the perspective of the modification of individual loss and robustness to training data distributions. We further study the classification calibration of the ATk loss and the error bounds of ATk-SVM model. We demonstrate the applicability of minimum average top- k learning for supervised learning problems including binary/multi-class classification and regression, using experiments on both synthetic and real datasets.
MakeAware

People new to making and makerspaces often struggle with identifying what tools are available and where they are, understanding how to operate the tools, and predicting how their decisions will affect their final product. From literature on novices and our interviews with expert makers, we identified situation awareness support as one possible way to address some of the challenges faced by novices. We present a set of design goals intended to scaffold situation awareness in a makerspace, and MakeAware, a prototype system we implemented based on those design goals. MakeAware provides a combination of environmental cues, information about the project process, and background knowledge. In a preliminary evaluation, we found MakeAware can help novices make conscious choices during a project and put more emphasis on planning, thereby exhibiting traits associated with having situation awareness while making.
WiSM: Windowing Surrogate Model for Evaluation of Curvature-Constrained Tours With Dubins Vehicle

Dubins tours represent a solution of the Dubins traveling salesman problem (DTSP) that is a variant of the optimization routing problem to determine a curvature-constrained shortest path to visit a set of locations such that the path is feasible for Dubins vehicle, which moves only forward and has a limited turning radius. The DTSP combines the NP-hard combinatorial optimization to determine the optimal sequence of visits to the locations, as in the regular TSP, with the continuous optimization of the heading angles at the locations, where the optimal heading values depend on the sequence of visits and vice versa. We address the computationally challenging DTSP by fast evaluation of the sequence of visits by the proposed windowing surrogate model (WiSM), which estimates the length of the optimal Dubins path connecting a sequence of locations in a Dubins tour. The estimation is sped up by a regression model trained using close to optimum solutions of small Dubins tours that are generalized for large-scale instances of the addressed DTSP utilizing the sliding-window technique and a cache for already computed results. The reported results support that the proposed WiSM enables fast convergence of a relatively simple evolutionary algorithm to high-quality solutions of the DTSP. We show that with an increasing number of locations, our algorithm scales significantly better than other state-of-the-art DTSP solvers.
Active shape from projection defocus profilometry

Fringe projection profilometry (FPP) using a vertical scanning setup has the merit of avoiding occlusions and shadows over triangulation based approaches. However, very few methods with a vertical scanning setup has revealed the supporting physical model to explicate the relationship between depth and the level of defocus of fringe patterns. In this paper, we propose a novel active shape from projection defocus profilometry (ASPDP) method that reconstructs the 3D shape by directly quantifying the physical depth-defocus relationship of squared binary structures. To the best of our knowledge, this is the first ever method that explicitly reveals the physical model relating the shape and the level of defocus of active fringe projection. Experiments show that the proposed ASPDP method can achieve an accuracy up to 14 µm.
Strategies for Deploying Unreliable AI Graders in High-Transparency High-Stakes Exams

We describe the deployment of an imperfect NLP-based automatic short answer grading system on an exam in a large-enrollment introductory college course. We characterize this deployment as both high stakes (the questions were on an mid-term exam worth 10% of students’ final grade) and high transparency (the question was graded interactively during the computer-based exam and correct solutions were shown to students that could be compared to their answer). We study two techniques designed to mitigate the potential student dissatisfaction resulting from students incorrectly not granted credit by the imperfect AI grader. We find (1) that providing multiple attempts can eliminate first-attempt false negatives at the cost of additional false positives, and (2) that students not granted credit from the algorithm cannot reliably determine if their answer was mis-scored.
Towards a Smart Interface-Based Automated Learning Environment Through Social Media for Disaster Management and Smart Disaster Education

This article discusses the problem of integrating artificial intelligence technologies, social networks, disaster management, neural learning and smart disaster education. It consists of an automated learning environment that is based on an extension of the Real-Time Alert Model used for natural and anthropogenic disaster management that is integrating encapsulations from multiple sources and retrieve information by combining multiple search results. This experience forms a background for our automated learning environment and provides some good ideas for that. Finally, we summarize the main features of this approach, provide some references to related work, and discuss some issues with artificial intelligence, social media and disaster management.
Predicting technical debt from commit contents: reproduction and extension with automated feature selection

Abstract Self-admitted technical debt refers to sub-optimal development solutions that are expressed in written code comments or commits. We reproduce and improve on a prior work by Yan et al. (2018) on detecting commits that introduce self-admitted technical debt. We use multiple natural language processing methods: Bag-of-Words, topic modeling, and word embedding vectors. We study 5 open-source projects. Our NLP approach uses logistic Lasso regression from Glmnet to automatically select best predictor words. A manually labeled dataset from prior work that identified self-admitted technical debt from code level commits serves as ground truth. Our approach achieves + 0.15 better area under the ROC curve performance than a prior work, when comparing only commit message features, and + 0.03 better result overall when replacing manually selected features with automatically selected words. In both cases, the improvement was statistically significant ( p &lt; 0.0001). Our work has four main contributions, which are comparing different NLP techniques for SATD detection, improved results over previous work, showing how to generate generalizable predictor words when using multiple repositories, and producing a list of words correlating with SATD. As a concrete result, we release a list of the predictor words that correlate positively with SATD, as well as our used datasets and scripts to enable replication studies and to aid in the creation of future classifiers.
EBTree

The emergence of smart contract promotes the popularity of blockchain applications, leading the dramatically growth of Ethereum blockchain data size. The analysis on blockchain data is urgently needed for users, e.g., collecting statistics of tokens, monitoring the status of Ethereum blockchain. However, Ethereum could only support simple searches on blockchain data on account of its storage model. This paper proposes the EBTree, an index for Ethereum blockchain Data, and implements it based on Ethereum client (Geth1.8). With the properties of B+ tree, EBTree could support real-time top-k, range, equivalent search on Ethereum blockchain data. Besides, EBTree takes up relatively small s-torage space because it only stores the identifiers of blockchain data. Meanwhile, considering of the time intervals of mining block and synchronizing data from Ethereum network, the time of insertion in EBTree has little influence on the performance of Ethereum client. We conduct experiments to evaluate the performance of EBTree. According to the result of experiments, EBTree shows great performance on searches and insertion at low cost of storage.
A Survey Paper on Object Detection Methods in Image Processing

In the last decades, the increasing potential of Information technology revolutionized data and information management, in particular, the data acquisition, data processing, and predictions. The effort has been truly interdisciplinary, where, image processing techniques, and AI based model implementation have played their roles. The latest technology innovations have enabled the researchers to execute computational experiments which would had never been possible if would have tried using the conventional methods. This survey paper provides study of various methodologies for object detection. This paper provides systematic analysis of various existing object detection techniques with precise and arranged representation. We have backed up the study with the merits and demerits of existing methods and the future scope in this area.
CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.
Artificial Intelligence and Risk in Design

As artificial intelligence (AI) technologies are more and more integrated into everyday lives, both scholarly and popular discourses on AI's often revolve around charting the various risks that may be associated with them. The manner and magnitude of risk that various researchers identify and foresee varies; however, what is common between them is, undoubtedly, the concept of risk itself. This concept, we argue, has been largely taken for granted by the fields involved in the research on AI's; in other words, "risk" has been employed with an everyday sensibility without due critical examination. In this paper, we address risk as a concept directly, by examining interdisciplinary theories and literatures on risk to discuss examples of AI technologies. Through this work, we aim to begin a critical discussion of the importance of theorising risk within design research and practice, and within the development of emerging technologies.
Estimating runtime of a job in Hadoop MapReduce

Abstract Hadoop MapReduce is a framework to process vast amounts of data in the cluster of machines in a reliable and fault-tolerant manner. Since being aware of the runtime of a job is crucial to subsequent decisions of this platform and being better management, in this paper we propose a new method to estimate the runtime of a job. For this purpose, after analysis the anatomy of processing a job in Hadoop MapReduce precisely, we consider two cases: when a job runs for the first time or a job has run previously. In the first case, by considering essential and efficient parameters that higher impact on runtime we formulate each phase of the Hadoop execution pipeline and state them by mathematical expressions to calculate runtime of a job. In the second case, by referring to the profile or history of a job in the database and use a weighting system the runtime is estimated. The results show the average error rate is less than 12% in the estimation of runtime for the first run and less than 8.5% when the profile or history of the job has existed.
General rational approximation of Gaussian wavelet series and continuous‐time <i>g</i><sub><i>m</i></sub>‐C filter implementation

Summary A general method of rational approximation for Gaussian wavelet series and Gaussian wavelet filter circuit design with simple g m ‐C integrators is presented in this work. Firstly, the multiorder derivatives of Gaussian function are analyzed and proved as wavelet base functions. Then a high‐accuracy general approximation model of Gaussian wavelet series is constructed, and the transfer function of first‐order derivative of Gaussian wavelet filter is obtained using quantum differential evolution (QDE) algorithm. Thirdly, as an example, a fifth‐order continuous‐time analog first‐order derivative of Gaussian wavelet filter circuit is designed based on multiple loop feedback structure with a simple g m ‐C integrator as the basic blocks. Finally, simulation results demonstrate that the proposed method is an excellent way for the wavelet transform implementation. The designed first‐order derivative of Gaussian wavelet filter circuit operates from a 0.53‐V supply voltage and a bias current 2.5 nA. The power dissipation of the wavelet filter circuit at the basic scale is 41.1 nW. Moreover, the high‐accuracy QRS detection based on the designed wavelet filter has been validated in application analysis.
Exploring eye movement data with image-based clustering

In this article, we describe a new feature for exploring eye movement data based on image-based clustering. To reach this goal, visual attention is taken into account to compute a list of thumbnail images from the presented stimulus. These thumbnails carry information about visual scanning strategies, but showing them just in a space-filling and unordered fashion does not support the detection of patterns over space, time, or study participants. In this article, we present an enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended. To also indicate the temporal sequence of the thumbnails, we add color-coded links and further visual features to dig deeper in the visual attention data. The usefulness of the technique is illustrated by applying it to eye movement data from a formerly conducted eye tracking experiment investigating route finding tasks in public transport maps. Finally, we discuss limitations and scalability issues of the approach.
Self-generated encryption model of acoustics

True Random Number Generator is a popular research area as a result of its encryption usage. Encrypting any data with a random source ensures security of information. The aim of this paper is to develop a self-generated True Random Number for audio encryption. To this end, an audio encryption system was created. Digital audio data is converted into binary form. XOR operation is performed to design Random Number Generator. NIST 800-22 Test Suite and TestU01 are applied on bit stream. Original data and encryption key are evaluated in case of randomness. Encryption of audio is made by generated bits. This paper proves that, a self-generated audio encryption system can be implemented. Statistical analyses and data distributions shows that encryption process with self-generation is successful. In this work, an audio encryption model of self-generated True Random Number is proposed.
wiseCIO: Web-Based Intelligent Services Engaging Cloud Intelligence Outlet

A web service usually serves web documents (HTML, JSON, XML, Images) in interactive (request) and responsive (reply) ways for specific domain problem solving over the web (WWW, Internet, HTTP). However, as more and more websites are shifted onto the cloud computing environment, customers tend to lose interest in “dry”, but in favor of “live” informative content. Furthermore, customers may pursue anything from the Cloud that is disclosed as intelligence for business, education, or entertainment (iBEE) for the sake of decision-making. WiseCIO was created to provide web-based intelligent services (WISE) via human-centered computing technology throughout computational thinking. As part of the intelligent service, a timely born “iBee” runs on individual devices as a Just-in-time Agent System (JAS, an imaginary “intelligent bee” helping with iBEE) to access, assemble and synthesize in order to propagate iBEE over the big database. Imaginatively, tens of thousands of concurrent and distributed “iBee” of JAS run on devices with universal interface (UnI), user-centered (UcX) experience via ubiquitous web-intensive sections (UwS). This is how wiseCIO works as a wise CIO: it involves Cloud computing over multidimensional databases, Intelligence synthesized via context-aware pervasive service, and Outlet that enables sentimental presentation out of information analytical synthesis. In particular, wiseCIO eliminates organizational and experiential issues that may be seen with traditional websites across a variety of devices. As a result, wiseCIO has achieved UnI without being programmed in HTML/CSS, UcX of not driving users like “a chasing after webpages” (a saying borrowed from Ecclesiastes 1:14), and UwS of analytical synthesis via failover and load-balancing in a feasible, automated, scalable, and testable (FAST) approach toward novel networking operations via logical organization of web content and relational information groupings that are vital steps in the ability of an archivist or librarian to recommend and retrieve information for a researcher. More important, wiseCIO also plays a key role as a delivery system and platform in web content management and web-based learning with capacity of hosting 10,000+ traditional webpages with great ease.
Clustering with missing and left‐censored data: A simulation study comparing multiple‐imputation‐based procedures

Cluster analysis, commonly used to explore large biomedical datasets, can be challenging, notably due to missing data or left-censored data induced by the sensitivity limits of the biochemical measurement method. Usually, complete-case analysis, simple imputation, or stochastic simple imputation are applied before clustering. More recently, consensus methods following multiple imputation have been proposed. However, they ignore left-censoring and do not allow the number of clusters to vary across the partitions of each imputed dataset. Here, we developed a consensus-based clustering algorithm in which left-censored data are taken into account using a modified multiple imputation method and the number of clusters is estimated for each imputed dataset. A simulation study was conducted to assess the performance in terms of the number of clusters, the percentage of unclassified observations, and the adjusted Rand index. The simulation results showed that the investigated method works well compared to several alternative approaches. A real-world application in breast cancer patients showed that the proposed method may reveal novel clusters of patients.