Reviewerly/exHarmony huggingface dataset:
Robust Face Recognition

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès.
Supplemental Material: Plagioclase archives of depleted melts in the oceanic crust

Details of experimental, analytical, and regression methods, as well as full lists of data sources and new experimental data.&lt;br&gt;
Intersecting families of graphs of functions over a finite field

Let $U$ be a set of polynomials of degree at most $k$ over $\mathbb{F}_q$, the finite field of $q$ elements. Assume that $U$ is an intersecting family, that is, the graphs of any two of the polynomials in $U$ share a common point. Adriaensen proved that the size of $U$ is at most $q^k$ with equality if and only if $U$ is the set of all polynomials of degree at most $k$ passing through a common point. In this manuscript, using a different, polynomial approach, we prove a stability version of this result, that is, the same conclusion holds if $|U|>q^k-q^{k-1}$. We prove a stronger result when $k=2$. For our purposes, we also prove the following results. If the set of directions determined by the graph of $f$ is contained in an additive subgroup of $\mathbb{F}_q$, then the graph of $f$ is a line. If the set of directions determined by at least $q-\sqrt{q}/2$ affine points is contained in the set of squares/non-squares plus the common point of either the vertical or the horizontal lines, then up to an affinity the point set is contained in the graph of some polynomial of the form $\alpha x^{p^k}$.
Book Review

Despite service robots' importance in the tourism industry, few studies have investigated human–robot interaction from a reciprocal exchange perspective. This paper applies social exchange theory to the context of human–robot interaction along with relational and psychological states based on two empirical studies with two service robots (i.e., Pepper and Connie) in hotel settings. Findings reveal significant positive effects of perceived intelligence, perceived social presence, and perceived social interactivity on trust, leading to usage intentions. Similarly, perceived social presence and perceived social interactivity are positively related to rapport, driving usage intentions. Perceived intelligence and perceived social interactivity negatively lead to uniqueness neglect to contribute to usage intentions. We additionally identify mediating roles of rapport, trust, and uniqueness neglect in the relationships between human–robot attributes and usage intentions. Our results deliver key insight into how human–robot interaction attributes influence users’ relational and psychological states and subsequent usage intentions.
Toward realization of remote controlled telecytopathology—a validation study from a large academic medical center from the southeast United States

With increase in the number and types of biopsies requiring rapid on-site evaluation for adequacy, telecytopathology is one of the solutions. Using a microscope camera with MS Surface Pro, a live telecytopathology audio video feed for the adequacy of 55 study set validation cases was sent over Zoom from the satellite hospital over 10 miles away with cytopathologists at the main hospital. The study set cases included Diff-Quik–stained smears and core imprints. The overall percent of positive agreement (accuracy) for adequacy during rapid on-site evaluation via telecytopathology was 96%. Core imprint percentage for positive agreement was slightly higher (96.2%), than fine-needle aspiration smears (95.8%). Use of telecytopathology is the best solution for optimizing the cytopathologist’s time for evaluating biopsy adequacy from distant sites.
Reputation Gaming in Stack Overflow

Stack Overflow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the incentive system prone to manipulation. This paper offers, for the first time, a comprehensive study of the reported types of reputation manipulation scenarios that might be exercised in Stack Overflow and the prevalence of such reputation gamers by qualitative study of 1,697 posts from meta Stack Exchange sites. We found six different types of reputation fraud scenarios, such as voting rings where communities form to upvote each other repeatedly on similar posts. We sought to develop algorithms to allow platform managers to automatically identify these suspicious reputation gaming scenarios, for review. The first algorithm identifies isolated/semi-isolated communities where probable reputation frauds may occur mostly by collaborating with each other. The second algorithm looks for sudden unusual big jumps in the reputation scores of users. We evaluated the performance of our algorithms by examining the reputation history dashboard of Stack Overflow users from the Stack Overflow website. We observed that around 60-80% of users that are considered to be suspicious by our algorithms got their reputation scores removed by Stack Overflow.
A Two-Dimensional Intrinsic Gaussian Markov Random Field for Blood  Pressure Data

Many real-world phenomena are naturally bivariate. This includes blood pressure, which comprises systolic and diastolic levels. Here, we develop a Bayesian hierarchical model that estimates these values and their interactions simultaneously, using sparse data that vary substantially between countries and over time. A key element of the model is a two-dimensional second-order Intrinsic Gaussian Markov Random Field, which captures non-linear trends in the variables and their interactions. The model is fitted using Markov chain Monte Carlo methods, with a block Metropolis-Hastings algorithm providing efficient updates. Performance is demonstrated using simulated and real data.
Fast and Real-time End to End Control in Autonomous Racing Cars Through  Representation Learning

The challenges presented in an autonomous racing situation are distinct from those faced in regular autonomous driving and require faster end-to-end algorithms and consideration of a longer horizon in determining optimal current actions keeping in mind upcoming maneuvers and situations. In this paper, we propose an end-to-end method for autonomous racing that takes in as inputs video information from an onboard camera and determines final steering and throttle control actions. We use the following split to construct such a method (1) learning a low dimensional representation of the scene, (2) pre-generating the optimal trajectory for the given scene, and (3) tracking the predicted trajectory using a classical control method. In learning a low-dimensional representation of the scene, we use intermediate representations with a novel unsupervised trajectory planner to generate expert trajectories, and hence utilize them to directly predict race lines from a given front-facing input image. Thus, the proposed algorithm employs the best of two worlds - the robustness of learning-based approaches to perception and the accuracy of optimization-based approaches for trajectory generation in an end-to-end learning-based framework. We deploy and demonstrate our framework on CARLA, a photorealistic simulator for testing self-driving cars in realistic environments.
Generative Adversarial Network Algorithms in Art: Data Video

The recent development of machine learning to synthesize the dataset and manipulate images into new works of art, bringing essential changes in visual art and the method of art creation. The paper aims at applying the Generative Adversarial Network (GAN) to the new media art in particular the image generation and video synthesis through latent space interpolation, through the indirect training in GAN to process a series of still images as the dataset, the generated work presents the ability of machine algorithms in learning and processing the image creation, as well as the next stage of machine-made art. The generated images through latent space interpolation are the artificial imitation among the images by the machine, indicating a new form of image interpretation and representation where human’s intervention in art creation is restricted in the pre-data selection and post-data appreciation.
Preparing a Data Archive or Repository for Changing Research Data and Materials Retention Policies

Archival expectations and requirements for researchers’ data and code are changing rapidly, both among publishers and institutions, in response to what has been referred to as a “reproducibility crisis.” In an effort to address this crisis, a number of publishers have added requirements or recommendations to increase the availability of supporting information behind the research, and academic institutions have followed. Librarians should focus on ways to make it easier for researchers to effectively share their data and code with reproducibility in mind. At the Cornell Center for Social Sciences, we have instituted a Results Reproduction Service (R-Squared) for Cornell researchers. Part of this service includes archiving the R-Squared package in our CoreTrustSeal certified Data and Reproduction Archive, which has been rebuilt to accommodate both the unique requirements of those packages and the traditional role of our data archive. Librarians need to consider roles that archives and institutional repositories can play in supporting researchers with reproducibility initiatives. Our commentary closes with some suggestions for more information and training.
TaskSum: Task-Driven Extractive Text Summarization for Long News Documents Based on Reinforcement Learning

A popular and state-of-the-art family of extractive summarization is to explore pre-trained language models through reinforcement learning (RL). Despite gaining promising results, existing RL-based methods suffer from three drawbacks. First, they often adopt sparse reward signal schemes, which only give rewards to some of the extracted sentences, and result in neglecting salient sentences. Second, they often deem summarization as an independent task and neglect the latent connections existing between summarization and other downstream tasks, that could provide insightful hints to guide the upstream summarization task in return. Third, the length of input sequences in most summarization methods is restricted by the utilized pre-trained language models. To address these problems, we propose a novel RL-based Seq2Seq extractive summarization model, namely TaskSum, which combines extractive text summarization with multiple associated tasks via a dense reward signal scheme. Moreover, we implement a BERT-based hierarchical encoder to effectively encode documents of arbitrary length. Empirical results demonstrate that TaskSum can overcome the above-mentioned drawbacks of existing RL-based summarization methods and achieve significantly better results for long documents.
A Hybrid Approach Entropy - TOPSIS for the Selection of Machine Learning Classifiers for Software Defect Prediction

The software is developed with much more complex functionalities to meet user requirements. Therefore, it is more vulnerable and subject to defects during the software development life cycle (SDLC). There are many efforts to avoid and reduce the number of defects, ensure good performance, and achieve defect-free software before releasing it to the market. To minimize the effort and optimize testing using Machine Learning, classifier models can be created to classify and predict which modules of the developed software may or may not be more prone to defects. There is a wide variety of classifier models; however, there is no classifier model that performs better than another in general terms. Various performance metrics can be used using multiple historical data sets to compare and evaluate classifier models. To select the best classifier model, a hybrid approach combining two multi-criteria decision making (MCDM) methods, Entropy and TOPSIS, is proposed. Entropy is used to calculate the criteria weights, and TOPSIS compares and ranks the alternatives. The results show that the proposed hybrid method can make the distribution of weights more reasonable and the selection of other options more efficient.
SPDCinv: Inverse Quantum-Optical Design of High-Dimensional Qudits

Spontaneous parametric down-conversion in quantum optics is an invaluable resource for the realization of high-dimensional qudits with spatial modes of light. One of the main open challenges is how to directly generate a desirable qudit state in the SPDC process. This problem can be addressed through advanced computational learning methods; however, due to difficulties in modeling the SPDC process by a fully differentiable algorithm that takes into account all interaction effects, progress has been limited. Here, we overcome these limitations and introduce a physically-constrained and differentiable model, validated against experimental results for shaped pump beams and structured crystals, capable of learning every interaction parameter in the process. We avoid any restrictions induced by the stochastic nature of our physical model and integrate the dynamic equations governing the evolution under the SPDC Hamiltonian. We solve the inverse problem of designing a nonlinear quantum optical system that achieves the desired quantum state of down-converted photon pairs. The desired states are defined using either the second-order correlations between different spatial modes or by specifying the required density matrix. By learning nonlinear volume holograms as well as different pump shapes, we successfully show how to generate maximally entangled states. Furthermore, we simulate all-optical coherent control over the generated quantum state by actively changing the profile of the pump beam. Our work can be useful for applications such as novel designs of high-dimensional quantum key distribution and quantum information processing protocols. In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
Seeking Salient Facial Regions for Cross-Database Micro-Expression  Recognition

This paper focuses on the research of cross-database micro-expression recognition, in which the training and test micro-expression samples belong to different microexpression databases. Mismatched feature distributions between the training and testing micro-expression feature degrade the performance of most well-performing micro-expression methods. To deal with cross-database micro-expression recognition, we propose a novel domain adaption method called Transfer Group Sparse Regression (TGSR). TGSR learns a sparse regression matrix for selecting salient facial local regions and the corresponding relationship of the training set and test set. We evaluate our TGSR model in CASME II and SMIC databases. Experimental results show that the proposed TGSR achieves satisfactory performance and outperforms most state-of-the-art subspace learning-based domain adaption methods.
Film Trailer Generation via Task Decomposition

Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These diverse functions make automatic trailer generation a challenging endeavor. We decompose it into two subtasks: narrative structure identification and sentiment prediction. We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information (e.g., characters, actions, situations) from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches.
Blockchain for the Industrial Internet of Things

The Internet of Things (IoT), in particular the Industrial Internet of Things (IIoT), has developed rapidly and has received consideration in science and profession; however, due to the lack of basic security solutions, privacy issues, and IoT protection mechanisms have emerged. The block chain concept is provided as a distributed system method to ensure safety precautions and promote the formation of the IoT and IIoT thanks to its decentralization and dissemination of information. To upgrade the functionality and gain of blockchain methods on the IoT and the IIoT, this chapter describes the growing interest paid blockchain-based IoT demand. We provide the latest research trends in every key industry area, as well as prosperous trade blockchain implementations in these sectors. Industrial and production applications such as production automation, remote machines diagnosis, predictive condition monitoring of industrial machinery, and supply chain control use the IoT. Introducing a blockchain platform for the IIoT (BPIIoT), the BPIIoT guidelines make use of the blockchain era’s permitting factors in a broadcast and rely on the type of approval a community to speak with different nodes without the need for a relied-on middleman. In this setting, we can examine how blockchain is implemented in the economic IoT and the utility of the blockchain of the IoT. This chapter especially summarizes the maximum critical current studies effects on the demanding situations of the IoT, the technical traits of the blockchain, the benefits of the usage of the blockchain era in the IoT, the safety-demanding situations (key control, intrusion detection, gaining entry for control, privacy protection), and coping with them in this era. The authors provide recommendations and give references for expected inquiry trends based on the findings of this study.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural  Networks

Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They are popular for their simple principles and strong performance on unstructured, feature-based data that is commonplace in many practical applications. However, they cannot learn to adapt for a particular set of data due to their lack of trainable parameters. In this paper, we begin by unifying local outlier methods by showing that they are particular cases of the more general message passing framework used in graph neural networks. This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method. LUNAR learns to use information from the nearest neighbours of each node in a trainable way to find anomalies. We show that our method performs significantly better than existing local outlier methods, as well as state-of-the-art deep baselines. We also show that the performance of our method is much more robust to different settings of the local neighbourhood size.
Improving Predictions of Tail-end Labels using Concatenated  BioMed-Transformers for Long Medical Documents

Multi-label learning predicts a subset of labels from a given label set for an unseen instance while considering label correlations. A known challenge with multi-label classification is the long-tailed distribution of labels. Many studies focus on improving the overall predictions of the model and thus do not prioritise tail-end labels. Improving the tail-end label predictions in multi-label classifications of medical text enables the potential to understand patients better and improve care. The knowledge gained by one or more infrequent labels can impact the cause of medical decisions and treatment plans. This research presents variations of concatenated domain-specific language models, including multi-BioMed-Transformers, to achieve two primary goals. First, to improve F1 scores of infrequent labels across multi-label problems, especially with long-tail labels; second, to handle long medical text and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experiments are performed on the Medical Information Mart for Intensive Care (MIMIC-III) database. Results show that concatenated BioMed-Transformers outperform standard transformers in terms of overall micro and macro F1 scores and individual F1 scores of tail-end labels, while incurring lower training times than existing transformer-based solutions for long input sequences.
Development and Validation of Potential Creativity Scale For Korean Preschool Children (PCS-K)

This study aims to develop a potential creativity scale for Korean preschool children (PCS-K) and examine the scale’s reliability and validity. Participants were 307 mothers with preschool children from U city. Data were analyzed using exploratory factor analysis, reliability (Cronbach’s α), correlation, t-test, and analysis of variance. The data analysis revealed the following main themes: (1) The potential creativity scale of PCS-K consisted of 7 factors including flow, courage/risk-taking, originality, sensitivity, curiosity/interest, humor, and imagination, with 30 items. (2) There was a significant relationship between PCS-K and RSCCP (Lee et al.). (3) Cronbach’s α ranged from .71 to .83. (4) The potential creativity was not significantly influenced by the preschool children’s age. However, the subfactors of creativity, including flow, sensitivity, and imagination, were significantly influenced by the preschool children s sex. All these results indicate that the potential creativity scale of PCS-K is quite reliable and valid.
SCI-Tree: An Incremental Algorithm for Computing Support Counts of all Closed Intervals from an Interval Dataset

Interval data mining is used to extract unknown patterns, hidden rules, associations etc. associated in interval based data. The extraction of closed interval is important because by mining the set of closed intervals and their support counts, the support counts of any interval can be computed easily. In this work an incremental algorithm for computing closed intervals together with their support counts from interval dataset is proposed. Many methods for mining closed intervals are available. Most of these methods assume a static data set as input and hence the algorithms are non-incremental. Real life data sets are however dynamic by nature. An efficient incremental algorithm called CI-Tree has been already proposed for computing closed intervals present in dynamic interval data. However this method could not compute the support values of the closed intervals. The proposed algorithm called SCI-Tree extracts all closed intervals together with their support values incrementally from the given interval data. Also, all the frequent closed intervals can be computed for any user defined minimum support with a single scan of SCI-Tree without revisiting the dataset. The proposed method has been tested with real life and synthetic datasets and results have been reported.