Reviewerly/exHarmony huggingface dataset:
Saliency detection via integrating deep learning architecture and low-level features

Deep learning methods, with their good performance in semantic representation of different images, have been widely used for saliency detection. Recent saliency detection methods have applied deep learning to obtain high-level features and combined them with hand-crafted low-level features to estimate saliency in the images. However, it is difficult to find the relationship between high-level and low-level features, resulting in incomplete integration framework for saliency detection. In this paper, we novely propose a saliency detection model by integrating high-level and low-level features with joint probability estimation. Firstly, the high-level features from FCN-8S network are used to estimate the probability of each superpixel as foreground or background region. Secondly, low-level features are extracted from each superpixels and clustered via affinity propagation (AP) clustering. The distributions of vectors from different clusters are consequently utilized to calculate the conditional probability of each superpixel as salient object under different assumptions. Thirdly, the joint probability of each superpixel as salient object in foreground or background is computed to compose the saliency map of the whole image. To further improve the uniformity of saliency in the same object region, the structured random forest (SRF) method is used to detect the contour of the image and the saliency of superpixels in homogeneous regions are uniformly merged. The advantage of high-level features in representing semantic regions and that of low-level features in differentiating local details in the image are unified and restrained by the joint probability estimation in the proposed model. Experimental results demonstrate that the proposed method provide better saliency detection performance than the state-of-the-art methods on 5 public databases.
Uncovering Algebraic Structures in the MPC Landscape

A fundamental problem in the theory of secure multi-party computation (MPC) is to characterize functions with more than 2 parties which admit MPC protocols with information-theoretic security against passive corruption. This question has seen little progress since the work of Chor and Ishai (1996), which demonstrated difficulties in resolving it. In this work, we make significant progress towards resolving this question in the important case of aggregating functionalities, in which m parties $$P_1,\dots ,P_m$$ hold inputs $$x_1,\dots ,x_m$$ and an aggregating party $$P_0$$ must learn $$f(x_1,\dots ,x_m)$$ . We uncover a rich class of algebraic structures that are closely related to secure computability, namely, "Commuting Permutations Systems" (CPS) and its variants. We present an extensive set of results relating these algebraic structures among themselves and to MPC, including new protocols, impossibility results and separations. Our results include a necessary algebraic condition and slightly stronger sufficient algebraic condition for a function to admit information-theoretically secure MPC protocols. We also introduce and study new models of minimally interactive MPC (called UNIMPC and ), which not only help in understanding our positive and negative results better, but also open up new avenues for studying the cryptographic complexity landscape of multi-party functionalities. Our positive results include novel protocols in these models, which may be of independent practical interest. Finally, we extend our results to a definition that requires UC security as well as semi-honest security (which we term strong security). In this model we are able to carry out the characterization of all computable functions, except for a gap in the case of aggregating functionalities.
A Comparison of Notification Techniques for Out-of-View Objects in Full-Coverage Displays

Full-coverage displays can place visual content anywhere on the interior surfaces of a room (e.g., a weather display near the coat stand). In these settings, digital artefacts can be located behind the user and out of their field of view - meaning that it can be difficult to notify the user when these artefacts need attention. Although much research has been carried out on notification, little is known about how best to direct people to the necessary location in room environments. We designed five diverse attention-guiding techniques for full-coverage display rooms, and evaluated them in a study where participants completed search tasks guided by the different techniques. Our study provides new results about notification in full-coverage displays: we showed benefits of persistent visualisations that could be followed all the way to the target and that indicate distance-to-target. Our findings provide useful information for improving the usability of interactive full-coverage environments.
A Model Driven Approach Towards Improving the Performance of Apache Spark Applications

Apache Spark applications often execute in multiple stages where each stage consists of multiple tasks running in parallel. However, prior efforts noted that the execution time of different tasks within a stage can vary significantly for various reasons (e.g., inefficient partition of input data), and tasks can be distributed unevenly across worker nodes for different reasons (e.g., data co-locality). While these problems are well-known, it is nontrivial to predict and address them effectively. In this paper we present an analytical model driven approach that can predict the possibility of such problems by executing an application with a limited amount of input data and recommend ways to address the identified problems by repartitioning input data (in case of task straggler problem) and/or changing the locality configuration setting (in case of skewed task distribution problem). The novelty of our approach lies in automatically predicting the potential problems a priori based on limited execution data and recommending the locality setting and partition number. Our experimental result using 9 Apache Spark applications on two different clusters shows that our model driven approach can predict these problems with high accuracy and improve the performance by up to 71%.
Accurate Segmentation of Heart Volume in CTA With Landmark-Based Registration and Fully Convolutional Network

Accurate delineation of cardiac structures in CTA images remains challenging, due to the similar appearance of different components, the highly variable shape and intensity among individuals, and especially the vicinity of heart volumes and backgrounds (including fat tissues, great vessels, and livers). Therefore, we proposed an accurate heart volume segmentation method with landmark-based registration (LMR) and 3D fully convolutional network (3D-FCN). First, we defined uniformity constrained landmarks in the training images and then trained a regression forest model (RFM) to detect these landmarks in the testing image. Second, the registration between the landmarks in each training image and the test image was performed by a shallow neural network, which guided the label propagation from atlases to the test image. After the label fusion with majority voting, we finally constructed a 3D-FCN to further refine the boundary voxels with low voting values. In 22 cardiac CTA images, we compared our method with multi-atlas segmentation, active shape model, DeepMedic, LMR + DeepMedic, and the separately implemented LMR and 3D-FCN. The results demonstrated the superiority of our method for the segmentation of heart volumes, with the average accuracy, dice coefficient, and mean Hausdorff distance as 96.25%, 93.98%, and 2.12 voxels, respectively. Furthermore, the training efficiency of the constructed 3D-FCN was higher than that of the DeepMedic through the comparison of training time and convergence of loss. The proposed heart segmentation could provide an accurate region of interest (ROI) for not only the four heart chambers but also the major trunks of vessels and coronary arteries.
Anomaly Detection Based on Mining Six Local Data Features and BP Neural Network

Key performance indicators (KPIs) are time series with the format of (timestamp, value). The accuracy of KPIs anomaly detection is far beyond our initial expectations sometimes. The reasons include the unbalanced distribution between the normal data and the anomalies as well as the existence of many different types of the KPIs data curves. In this paper, we propose a new anomaly detection model based on mining six local data features as the input of back-propagation (BP) neural network. By means of vectorization description on a normalized dataset innovatively, the local geometric characteristics of one time series curve could be well described in a precise mathematical way. Differing from some traditional statistics data characteristics describing the entire variation situation of one sequence, the six mined local data features give a subtle insight of local dynamics by describing the local monotonicity, the local convexity/concavity, the local inflection property and peaks distribution of one KPI time series. In order to demonstrate the validity of the proposed model, we applied our method on 14 classical KPIs time series datasets. Numerical results show that the new given scheme achieves an average F1-score over 90%. Comparison results show that the proposed model detects the anomaly more precisely.
Optimizing Programming Language Learning Through Student Modeling in an Adaptive Web-Based Educational Environment

This chapter describes ELaCv2, which is the 2nd improved version of ELaC that is described in a previous work [1]. ELaCv2 is a novel integrated adaptive educational environment that provides e-training in programming and the language 'C'. It adapts the learning material and process to the learner's background, knowledge level, needs and ability. The adaptivity is achieved due to the incorporation of a 4-parameter student model that was developed taking into consideration the data and results that have been gathered by the student model of ELaC. The particular student model is responsible for identifying and updating the student's knowledge level and needs each time from the beginning to the end of the learning process, allowing the learner to complete the e-training course at her/his own pace and according to her/his ability. The system can identify, each time and for each individual learner, which domain concepts are partially or completely known, which domain concepts are unknown, which domain concepts have been assimilated and which domain concepts need revision. Thus, the system schedules dynamically the learning material for each individual learner on the fly, minimizing the time that is required for her/his to complete the e-training course, and improving, simultaneously, the learning results.
Combining Quality Metrics for Improved HDR Image Quality Assessment

We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework and non-linearly combine the scores from different quality metrics using support vector machine regression. To improve performance and reduce complexity, we use the Sequential Forward Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.
Using tool integration for improving traceability management testing processes: An automotive industrial experience

Abstract Despite the high relevance of traceability in software processes, the activities of traceability creation and management are not always adequately supported in practice. The lack of integration between the tools adopted in the development processes is one of the main causes of such an ineffective management, where traceability relationships are still manually generated and maintained. In this paper we present an industrial experience we performed for improving the traceability management in a testing process performed in the Fiat Chrysler Automobiles company. In this context, we carried out a process for analyzing and identifying the main issues due to the ineffective traceability management and proposed a solution for addressing them. We designed and implemented a software architecture for integrating the existing application lifecycle management platform with the tools used in the process with the aim of automating the process execution and the traceability links management. The new architecture was validated by a case study that showed how the integration solution produced beneficial effects on quality attributes of the testing process.
Online video-based abnormal detection using highly motion techniques and statistical measures

At the essence of video surveillance, there are abnormal detection approaches, which have been proven to be substantially effective in detecting abnormal incidents without prior knowledge about these incidents. Based on the state-of-the-art research, it is evident that there is a trade-off between frame processing time and detection accuracy in abnormal detection approaches. Therefore, the primary challenge is to balance this trade-off suitably by utilizing few, but very descriptive features to fulfill online performance while maintaining a high accuracy rate. In this study, we propose a new framework, which achieves the balancing between detection accuracy and video processing time by employing two efficient motion techniques, specifically, foreground and optical flow energy. Moreover, we use different statistical analysis measures of motion features to get robust inference method to distinguish abnormal behavior incident from normal ones. The performance of this framework has been extensively evaluated in terms of the detection accuracy, the area under the curve (AUC) and frame processing time. Simulation results and comparisons with ten relevant online and non-online frameworks demonstrate that our framework efficiently achieves superior performance to those frameworks, in which it presents high values for he accuracy while attaining simultaneously low values for the processing time.
An Efficient Method for Euler’s Elastica Based Image Deconvolution

Variational models involving Euler's elastica energy have a wide range of applications in digital image processing.Recently, fast methods, such as the proximal-augmented Lagrangian method (PALM), have been successfully used to solve nonlinear higher order models for image restoration.In this paper, we extend fast method PALM to Euler's elastica deconvolution models with quadratic and nonquadratic fidelity terms.The proposed variational model can eliminate blur and noise and preserve edges while reducing the blocky and staircase artifacts during image restoration.We present an efficient and effective solution to the proposed minimization problems by a proximal-based numerical scheme.Our numerical experiments demonstrate several results on image deblurring and denoising, which shows a clear improvement of the proposed model over standard variational models such as total variation and Hessian-based model.
More than the Sum of Makers

Human Computer Interaction has developed great interest in the Maker Movement. Previous work has explored it from various perspectives, focusing either on its potentials or issues. As these are however only fragmented portrayals, this paper aims to take a broader perspective and interconnect some of the fragments. We conducted a qualitative study in the context of two Maker Faires to gain a better understanding of the complex dynamics that makers operate in. We captured the voices of different stakeholders and explored how their respective agendas relate to each other. The findings illustrate how the event is co-created at the nexus of different technological, social and economic interests while leaving space for diverse practices. The paper contributes a first focused analysis of Maker Faire, probes it as a site for research and discusses how holistic perspectives on the Maker Movement could create new research opportunities.
Some Prior(s) Experience Necessary

Bayesian statistical analysis has gained attention in recent years, including in HCI. The Bayesian approach has several advantages over traditional statistics, including producing results with more intuitive interpretations. Despite growing interest, few papers in CHI use Bayesian analysis. Existing tools to learn Bayesian statistics require significant time investment, making it difficult to casually explore Bayesian methods. Here, we present a tool that lowers the barrier to exploration: a set of R code templates that guide Bayesian novices through their first analysis. The templates are tailored to CHI, supporting analyses found to be most common in recent CHI papers. In a user study, we found that the templates were easy to understand and use. However, we found that participants without a statistical background were not confident in their use. Together our contributions provide a concise analysis tool and empirical results for understanding and addressing barriers to using Bayesian analysis in HCI.
A Field Study of Computer-Security Perceptions Using Anti-Virus Customer-Support Chats

Understanding users' perceptions of suspected computer-security problems can help us tailor technology to better protect users. To this end, we conducted a field study of users' perceptions using 189,272 problem descriptions sent to the customer-support desk of a large anti-virus vendor from 2015 to 2018. Using qualitative methods, we analyzed 650 problem descriptions to study the security issues users faced and the symptoms that led users to their own diagnoses. Subsequently, we investigated to what extent and for what types of issues user diagnoses matched those of experts. We found, for example, that users and experts were likely to agree for most issues, but not for attacks (e.g., malware infections), for which they agreed only in 44% of the cases. Our findings inform several user-security improvements, including how to automate interactions with users to resolve issues and to better communicate issues to users.
Enhance PATE on Complex Tasks With Knowledge Transferred From Non-Private Data

Privacy protection is considered as an important problem in learning-based systems. Recently, various works based on differential privacy have been proposed to protect an individual's privacy in the machine learning and deep learning contexts. One of the state-of-the-art approaches is Private Aggregation of Teacher Ensembles (PATE), a generic framework which can be successfully applied to many different learning algorithms. In PATE, we need to split the private dataset into many disjoint subsets and train an ensemble of teachers on these subsets. Then, we transfer noisy predictions from the ensemble of teachers to a student model. In this paper, we show that for complex datasets and tasks, such as nature image classification, the training set allocated for one teacher may be too small with respect to the corresponding task to achieve an ideal performance. To alleviate this problem, we propose the TrPATE framework which extends PATE with transfer learning. Based on PATE, we transfer and share the knowledge extracted from a publicly available non-private dataset to the teachers. The extensive experiments are conducted on various datasets, and the empirical results demonstrate the effectiveness of our method.
Thinking Too Classically

Quantum computing is a fundamentally different way of performing computation than classical computing. Many problems that are considered hard for classical computers may have efficient solutions using quantum computers. Recently, technology companies including IBM, Microsoft, and Google have invested in developing both quantum computing hardware and software to explore the potential of quantum computing. Because of the radical shift in computing paradigms that quantum represents, we see an opportunity to study the unique needs people have when interacting with quantum systems, what we call Quantum HCI (QHCI). Based on interviews with experts in quantum computing, we identify four areas in which HCI researchers can contribute to the field of quantum computing. These areas include understanding current and future quantum users, tools for programming and debugging quantum algorithms, visualizations of quantum states, and educational materials to train the first generation of "quantum native" programmers.
Mechanism analysis and feature extraction algorithm of communication emitter fingerprint

A fingerprint extraction method based on the fingerprint mechanism (FM) of a communication emitter is proposed to solve the shortcomings of existing emitter identification technology. First, the Taylor polynomial model of the communication emitter fingerprint is established by analyzing the mechanism of the emitter fingerprint. Second, the polynomial coefficients containing the emitter fingerprints are defined with focus on single-frequency and narrow-band signal. Identifiable features are extracted through parameter estimation. Theoretical analysis shows that the FM algorithm focuses on the fingerprint essence of communication emitter, and the effects of modulations are overcome. Experimental results show that the identification performance of the FM algorithm is better than that of three other algorithms due to the high anti-noise performance, inner-class clustering and inter-class scattering of extracted features.
CNR: Cross-network Recommendation Embedding User’s Personality

With the explosive growth of available data, recommender systems have become an essential tool to ease users with their decision-making procedure. One of the most challenging problems in these systems is the data sparsity problem, i.e., lack of sufficient amount of available users’ interactions data. Recently, cross-network recommender systems with the idea of integrating users’ activities from multiple domain were presented as a successful solution to address this problem. However, most of the existing approaches utilize users’ past behaviour to discover users’ preferences on items’ patterns and then suggest similar items to them in the future. Hence, their performance may be limited due to ignore recommending divers items. Users are more willing to be recommended with a variety set of items not similar to those they preferred before. Therefore, diversity plays a crucial role to evaluate the recommendation quality. For instance, users who used to watch comedy movie, may be less likely to receive thriller movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-independent factor which has a strong correlation with user’s preferences. We present a novel technique and an algorithm to capture users’ personality type implicitly without getting users’ feedback (e.g., filling questionnaires). We integrate this factor into matrix factorization model and demonstrate the effectiveness of our approach, using a real-world dataset.