Reviewerly/exHarmony huggingface dataset:
Classifier Ensembles for Credit Card Fraud Detection

There is a risk of payment card abuse in e-commerce, so it is important to note those transactions that are took place without the owner's knowledge. We use various predictive models to see how accurate they are in detecting whether a transaction is a normal payment or a fraud. In classification problem, Bagging, AdaBoost, Random forest and Gradient boosting classifier ensembles are used. We chose decision trees as the base classifiers in classifier ensembles because they are very accurate and sensitive to rotation of the feature axes. Consequences of employing different classifier ensembles are monitored. We present comparisons of classifier ensembles with different performance measures.
Human Age and Gender Prediction Using Deep Multi-Task Convolutional Neural Network

Gender and age prediction are the key areas of research in the biometric as well as human face recognition applications aimed at effective future prediction and the knowledge discovery about the specific person. The process makes use of assorted approaches and algorithms whereby the deep learning is also the prime in usage patterns. Our research presents a new idea based on modifying the deep network structure and using learning methods of the two other researchers. We made some modification on the structure of the convolutional neural network (CNN) that was used by the first researcher, then, we used two learning methods, which were adopted by the second researcher, Single-Task Learning (STL) and Deep Multi-Task Learning (DMTL) approach, and we present new structure of CNN according to the above two modifications, implemented and evaluated, and the results show the effective performance of our proposed structure. The proposed net presented in this work has the association of Adience-Face Benchmark face dataset, samples of a test and training set, the implementation is performed by Python software.
Decision making with uncertainty in immersive systems

Decision-making is defined as a process resulting in the selection of a course of action from a number of alternatives based on variables that represent key considerations to the task. This is a complex process where the goal is to generate the “best" course of action given the data and knowledge obtained. As the use of intelligent systems increases, so too does the amount of data to be considered by human analysts and commanders. As the military looks toward integration of intelligent system like smart devices or internet of things, the devices and the data from these devices are important for decision making in highly dynamic situations. Of critical importance is the uncertainty of information associated with the data produced from such systems. Any uncertainty must be captured and communicated to aid the decision-making process. Our work focuses on how this process can be investigated to understand and analyze the impact of uncertainty for decision-making in multi-domain operational environments. We conducted user studies and present our results to discuss the presentation of uncertainty within the decision-making cycle for our tasks .
A Quantum Expectation Value Based Language Model with Application to Question Answering

Quantum-inspired language models have been introduced to Information Retrieval due to their transparency and interpretability. While exciting progresses have been made, current studies mainly investigate the relationship between density matrices of difference sentence subspaces of a semantic Hilbert space. The Hilbert space as a whole which has a unique density matrix is lack of exploration. In this paper, we propose a novel Quantum Expectation Value based Language Model (QEV-LM). A unique shared density matrix is constructed for the Semantic Hilbert Space. Words and sentences are viewed as different observables in this quantum model. Under this background, a matching score describing the similarity between a question-answer pair is naturally explained as the quantum expectation value of a joint question-answer observable. In addition to the theoretical soundness, experiment results on the TREC-QA and WIKIQA datasets demonstrate the computational efficiency of our proposed model with excellent performance and low time consumption.
Reference Pose Generation for Visual Localization via Learned Features and View Synthesis

Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality. High quality datasets with accurate 6 Degree-of-Freedom (DoF) reference poses are the foundation for benchmarking and improving existing methods. Traditionally, reference poses have been obtained via Structure-from-Motion (SfM). However, SfM itself relies on local features which are prone to fail when images were taken under different conditions, e.g., day/night changes. At the same time, manually annotating feature correspondences is not scalable and potentially inaccurate. In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features. Given an initial pose estimate, our approach iteratively refines the pose based on feature matches against a rendering of the model from the current pose estimate. We significantly improve the nighttime reference poses of the popular Aachen Day-Night dataset, showing that state-of-the-art visual localization methods perform better (up to 47%) than predicted by the original reference poses. We extend the dataset with new nighttime test images, provide uncertainty estimates for our new reference poses, and introduce a new evaluation criterion. We will make our reference poses and our framework publicly available upon publication.
Dynamic and semantic-aware access-control model for privacy preservation in multiple data center environments

With the rapid development of intelligent perception and other data acquisition technologies in the Internet of things, large-scale scientific workflows have been widely used in geographically distributed multiple data centers to realize high performance in business model construction and computational processing. However, insider threats pose very significant privacy and security risks to systems. Traditional access-control models can no longer satisfy the reasonable authorization of resources in these new cross-domain environments. Therefore, a dynamic and semantic-aware access-control model is proposed for privacy preservation in multiple data center environments, which implements a semantic dynamic authorization strategy based on an anomaly assessment of users’ behavior sequences. The experimental results demonstrate that this dynamic and semantic-aware access-control model is highly dynamic and flexible and can improve the security of the application system.
Automatic breast segmentation in digital mammography using a convolutional neural network

Digital mammography (DM) has been considered as the primary modality for breast cancer screening. The relative amount of breast fibroglandular tissue, referred to as percent breast density (PD), has been considered as an important factor associated with breast cancer. We have developed and tested a robust method to accurately segment the pectoral muscle and the breast area using a deep learning approach. We use a U-Net architecture with a ResNet decoder to increase the depth of features. The architecture is trained using 555 DM images and tested and validated on an independent set of 555 images. The results show that our network achieves an average and standard deviation dice coefficient of 94.86% &plusmn; 1.93%, respectively, and sensitivity of 96.31% &plusmn; 1.87%. The method present here can be considered as the first step toward the automatic estimation of PD.
Research on power-law distribution of long-tail data and its application to tourism recommendation

Purpose One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data. Design/methodology/approach Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs. Findings The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation. Originality/value TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.
Promoting training of multi agent systems

The problem of incentive training of multi-agent systems in the game formulation for collective decision making under uncertainty is considered.Methods of incentive training do not require a mathematical model of the environment and enable decision making directly in the training process.Markov model of stochastic game is constructed and the criteria for its solution are formulated.An iterative Q-method for solving a stochastic game based on the numerical identification of a characteristic function of a dynamic system in space of state-action is described.Players' current gains are determined by the method of randomization of payment Q-matrix elements.Mixed player strategies are calculated using the Boltzmann method.Pure strategies are determined on the basis of discrete random distributions given by mixed player strategies.The algorithm for stochastic game solving is developed and results of computer implementation of game Q-method are analyzed.
Digital Citizenship and Education

The main objective of this paper is to show a state of the art of "digital citizenship". Furthermore, the aim is to promote the varied relationships between digital citizenship and education, from educational theory and policy to educational practice and research. Thus, we have extracted the documents from databases in Web of Science (the search results of "digital citizen*" were 411) and they were examined with the bibliographic analysis tool VOSviewer. The description of the documents claims to a dizzying rate in the last three years (half of the total) and to the importance of education as a category of scientific discipline (one third of the total). The bibliometric analysis provided co-occurrences by keywords and co-authorships by nationalities. The conclusions are the rise of the keywords "social media", "education" and "surveillance"; the increase of documents focused on youth and their digital engagement; and the relevance of American researchers followed by Spanish researchers.
On the Δ-equivalence of Boolean functions

Abstract A new equivalence relation on the set of Boolean functions is introduced: functions are declared to be Δ -equivalent if their autocorrelation functions are equal. It turns out that this classification agrees well with the cryptographic properties of Boolean functions: for functions belonging to the same Δ -equivalence class a number of their cryptographic characteristics do coincide. For example, all bent-functions (of a fixed number of variables) make up one class.
Learning To Classify Images Without Labels.

Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled. First, a self-supervised task from representation learning is employed to obtain semantically meaningful features. Second, we use the obtained features as a prior in a learnable clustering approach. In doing so, we remove the ability for cluster learning to depend on low-level features, which is present in current end-to-end learning approaches. Experimental evaluation shows that we outperform state-of-the-art methods by large margins, in particular +26.6% on CIFAR10, +25.0% on CIFAR100-20 and +21.3% on STL10 in terms of classification accuracy. Furthermore, our method is the first to perform well on a large-scale dataset for image classification. In particular, we obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime without the use of any ground-truth annotations. The code is made publicly available at this https URL.
Effect of Adherent Rain on Vision-Based Object Detection Algorithms

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Adverse weather conditions degrade the quality of images used in vision-based advanced driver assistance systems (ADAS) and autonomous driving algorithms. Adherent raindrops onto a vehicle’s windshield occlude parts of the input image and blur background texture in regions covered by them. Rain also changes image intensity and disturbs chromatic properties of color images. In this work, we collected a dataset using a camera mounted behind a windshield at different rain intensities. The data was processed to generate a set of distorted images by adherent raindrops along with ground truth data of clear images (just after a windshield wipe). We quantitatively evaluated the amount of distortion caused by the raindrops, using the Normalized Cross-Correlation and Structural Similarity methods. While most prior work in the field of rain detection and removal focuses on the image restoration aspects, they typically do not provide quantitative measures to the effect of degradation of input image quality on the performance of image-based algorithms. We quantitatively evaluated the effect of raindrop distortion on deep-learning-based object detection algorithms by comparing the detectors’ performance on the distorted images to the clear images. State-of-the-art detector algorithms were selected and used, namely, Faster Region-based Convolution Neural Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). For the overall performance of the object detection and classification algorithms, we used standard accuracy, precision, and recall measures.&lt;/div&gt;&lt;/div&gt;
Recurrences for the genus polynomials of linear sequences of graphs

Abstract Given a finite graph H , the n th member G n of an H - linear sequence is obtained recursively by attaching a disjoint copy of H to the last copy of H in G n −1 by adding edges or identifying vertices, always in the same way. The genus polynomial Γ G ( z ) of a graph G is the generating function enumerating all orientable embeddings of G by genus. Over the past 30 years, most calculations of genus polynomials Γ G n ( z ) for the graphs in a linear family have been obtained by partitioning the embeddings of G n into types 1, 2, …, k with polynomials <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable> <m:mtr> <m:mtd> <m:msubsup> <m:mi>Γ</m:mi> <m:mrow> <m:msub> <m:mi>G</m:mi> <m:mi>n</m:mi> </m:msub> </m:mrow> <m:mi>j</m:mi> </m:msubsup> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} \Gamma_{G_n}^j \end{array}$ ( z ), for j = 1, 2, …, k ; from these polynomials, we form a column vector <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mtable> <m:mtr> <m:mtd> <m:msub> <m:mi>V</m:mi> <m:mi>n</m:mi> </m:msub> <m:mo>(</m:mo> <m:mi>z</m:mi> <m:mo>)</m:mo> <m:mo>=</m:mo> <m:mo>[</m:mo> <m:msubsup> <m:mi>Γ</m:mi> <m:mrow> <m:msub> <m:mi>G</m:mi> <m:mi>n</m:mi> </m:msub> </m:mrow> <m:mn>1</m:mn> </m:msubsup> <m:mo>(</m:mo> <m:mi>z</m:mi> <m:mo>)</m:mo> <m:mo>,</m:mo> <m:msubsup> <m:mi>Γ</m:mi> <m:mrow> <m:msub> <m:mi>G</m:mi> <m:mi>n</m:mi> </m:msub> </m:mrow> <m:mn>2</m:mn> </m:msubsup> <m:mo>(</m:mo> <m:mi>z</m:mi> <m:mo>)</m:mo> <m:mo>,</m:mo> <m:mo>…</m:mo> <m:mo>,</m:mo> <m:msubsup> <m:mi>Γ</m:mi> <m:mrow> <m:msub> <m:mi>G</m:mi> <m:mi>n</m:mi> </m:msub> </m:mrow> <m:mi>k</m:mi> </m:msubsup> <m:mo>(</m:mo> <m:mi>z</m:mi> <m:mo>)</m:mo> <m:msup> <m:mo>]</m:mo> <m:mi>t</m:mi> </m:msup> </m:mtd> </m:mtr> </m:mtable> </m:math> $\begin{array}{} V_n(z) = [\Gamma_{G_n}^1(z), \Gamma_{G_n}^2(z), \ldots, \Gamma_{G_n}^k(z)]^t \end{array}$ that satisfies a recursion V n ( z ) = M ( z ) V n −1 ( z ), where M ( z ) is a k × k matrix of polynomials in z . In this paper, the Cayley-Hamilton theorem is used to derive a k th degree linear recursion for Γ n ( z ), allowing us to avoid the partitioning, thereby yielding a reduction from k 2 multiplications of polynomials to k such multiplications. Moreover, that linear recursion can facilitate proofs of real-rootedness and log-concavity of the polynomials. We illustrate with examples.
Ambivalences of touch

In this epilogue, we discuss the particularities of touch in comparison to other communication modalities that have been studied by interaction research. Drawing on examples of loving touch and violent touch from novels and memoirs, we identify two properties of tactile interaction that differ from modalities such as vocality, gesture, or gaze: tactile interaction is 1) essentially reciprocal – while we can be looked at without looking back and can be talked to without talking back, we cannot touch without being touched back – and 2) inherently private – it is invisible from the outside and based on its experiential qualities. The fundamental ambivalence of touch arises from these properties. Moreover, due to these properties, tactile interaction may require revisions in our research methodology and in the models of communication that we implicitly invoke and of the concepts and vocabulary with which we address it.
Causal Learning in Question Quality Improvement

To improve the quality of questions asked in Community-based questions answering forums, we create a new dataset from the website, Stack Overflow, which contains three components: (1) context: the text features of questions, (2) treatment: categories of revision suggestions and (3) outcome: the measure of question quality (e.g., the number of questions, upvotes or clicks). This dataset helps researchers develop causal inference models towards solving two problems: (i) estimating the causal effects of aforementioned treatments on the outcome and (ii) finding the optimal treatment for the questions. Empirically, we performed experiments with three state-of-the-art causal effect estimation methods on the contributed dataset. In particular, we evaluated the optimal treatments recommended by the these approaches by comparing them with the ground truth labels – treatments (suggestions) provided by experts.
FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

Wireless visual sensor networks are commonly employed on several applications contexts such as smart cities, intelligent transportation systems and industrial management, aiming at the use of visual data from cameras to provide enhanced information and to expand the networks utilities. In these scenarios, some applications may require high-definition images when performing more specialized tasks, for example in face and text recognition, adding an important monitoring requirement when using camera-based sensors. In fact, it is important to ensure that the network is able to gather visual data with the associated required quality to each task, and such perceived quality may be processed as a function of the Field of View (FoV) of the visual sensors. In order to address this issue, new quality metrics are proposed for wireless visual sensor networks that are deployed to perform area coverage, exploiting for that different perceptions of the FoV. Those metrics are proposed along with redeployment optimization methods for visual sensor nodes aiming at the improvement of the perceived monitoring quality, which are based on greedy and evolutionary-based approaches. The proposed metrics and algorithms are expected to be more realistic than previous solutions, allowing flexible processing of variables as cameras' positions, orientations and viewing angles, providing then high flexibility on the definition of parameters and significantly contributing to the development of sensor networks based on visual sensors.
A Comparative Study on the Effectiveness of Online Automatic Scoring of English Writing in Juku Correction Network and Mosoteach APP under Blended Teaching Mode

Abstract The rapid development of modern information technology has brought great changes to People’s Daily work and life, and the extensive application of network information technology in modern English teaching will bring about a series of innovations to the traditional English teaching mode. Based on the Blended English teaching model under the network environment, this paper attempts to clarify and present the advantages and disadvantages of two different online corrective software for college English writing teaching. Taking College English writing texts as the research object, this paper uses SPSS 19.0 data statistical software to compare and analyze the results of online automatic scoring of College English writing by Juku Correction Network and Mosoteach APP. Thus it is concluded that the Mosoteach APP is more convenient in assistant means of English writing teaching, while Juku Correction Network is more objective in the aspect of the effect of composition criticism, which will provide some reference for improving the quality of Chines college English teaching.
Efficient and Safe Motion Planning for Quadrotors Based on Unconstrained Quadratic Programming

SUMMARY An autonomous motion planning framework is proposed, consisting of path planning and trajectory generation. Primarily, a spacious preferred probabilistic roadmap algorithm is utilized to search a safe and short path, considering kinematics and threats from obstacles. Subsequently, a minimum-snap and position-clearance polynomial trajectory problem is transformed into an unconstrained quadratic programming and solved in a two-step optimization. Finally, comparisons with other methods based on statistical simulations are implemented. The results show that the proposed method achieves computational efficiency and a safe trajectory.