Reviewerly/exHarmony huggingface dataset:
Explaining Missing Data in Graphs: A Constraint-based Approach

This paper introduces a constraint-based approach to clarify missing values in graphs. Our method capitalizes on a set Σ of graph data constraints. An explanation is a sequence of operational enforcement of Σ towards the recovery of interested yet missing data (e.g., attribute values, edges). We show that constraint-based approach helps us to understand not only why a value is missing, but also how to recover the missing value. We study Σ-explanation problem, which is to compute the optimal explanations with guarantees on the informativeness and conciseness. We show the problem is in Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</sup> for established graph data constraints such as graph keys and graph association rules. We develop an efficient bidirectional algorithm to compute optimal explanations, without enforcing Σ on the entire graph. We also show our algorithm can be easily extended to support graph refinement within limited time, and to explain missing answers. Using real-world graphs, we experimentally verify the effectiveness and efficiency of our algorithms.
Metrics and continuity in reinforcement learning

In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limited set of samples. The notion of state similarity used, and the neighbourhoods and topologies they induce, is thus of crucial importance, as it will directly affect the performance of the algorithms. Indeed, a number of recent works introduce algorithms assuming the existence of well-behaved neighbourhoods, but leave the full specification of such topologies for future work. In this paper we introduce a unified formalism for defining these topologies through the lens of metrics. We establish a hierarchy amongst these metrics and demonstrate their theoretical implications on the Markov Decision Process specifying the reinforcement learning problem. We complement our theoretical results with empirical evaluations showcasing the differences between the metrics considered.
Membership Privacy for Machine Learning Models Through Knowledge Transfer.

Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e.g., differential privacy and adversarial regularization. Unfortunately, these defenses produce ML models with unacceptably low classification performances.Our work proposes a new defense, called distillation for membership privacy (DMP), against MIAs that preserves the utility of the resulting models significantly better than prior defenses. DMP leverages knowledge distillation to train ML models with membership privacy. We provide a novel criterion to tune the data used for knowledge transfer in order to amplify the membership privacy of DMP.Our extensive evaluation shows that DMP provides significantly better tradeoffs between membership privacy and classification accuracies compared to state-of-the-art MIA defenses. For instance, DMP achieves ~100% accuracy improvement over adversarial regularization for DenseNet trained on CIFAR100, for similar membership privacy (measured using MIA risk): when the MIA risk is 53.7%, adversarially regularized DenseNet is 33.6% accurate, while DMP-trained DenseNet is 65.3% accurate. We have released our code at github.com/vrt1shjwlkr/AAAI21-MIA-Defense.
Misogynistic and Xenophobic Hate Language Online: A Matter of Anonymity

In this paper, we quantify hateful content in online civic discussions of politics and es- timate the causal link between hateful content and writer anonymity. To measure hate, we first develop a supervised machine-learning model that predicts hate against foreign residents and hate against women on a dominant Swedish Internet discussion forum. We find that an exogenous decrease in writer anonymity leads to less hate against foreign residents but an increase in hate against women. We conjecture that the mechanisms behind the changes comprise a combination of users decreasing the amount of their hate- ful writing and a substitution of hate against foreign residents for hate against women. The discussion of the results highlights the role of social repercussions in discouraging antisocial and criminal activities.
Regularization is all you Need: Simple Neural Nets can Excel on Tabular Data

Tabular datasets are the last unconquered castle for deep learning, with traditional ML methods like Gradient-Boosted Decision Trees still performing strongly even against recent specialized neural architectures. In this paper, we hypothesize that the key to boosting the performance of neural networks lies in rethinking the joint and simultaneous application of a large set of modern regularization techniques. As a result, we propose regularizing plain Multilayer Perceptron (MLP) networks by searching for the optimal combination/cocktail of 13 regularization techniques for each dataset using a joint optimization over the decision on which regularizers to apply and their subsidiary hyperparameters. We empirically assess the impact of these regularization cocktails for MLPs on a large-scale empirical study comprising 40 tabular datasets and demonstrate that (i) well-regularized plain MLPs significantly outperform recent state-of-the-art specialized neural network architectures, and (ii) they even outperform strong traditional ML methods, such as XGBoost.
The Educational Technology of Monological Speaking Skills Formation of Future Lawyers

In modern higher education, there was a change in the educational paradigm, when the emphasis shifted to the formation of competencies. The goal of the competence approach is to improve the quality of education, which is impossible without the use of educational technologies that guarantee a steady effective result. The priority of teaching foreign languages to future lawyers is the foreign speaking skills formation in general and the monological ones in particular. The introduction of educational technology in the process of monological speaking skills formation, including a set of methods and techniques such as peer review, discussion, working with the text, etc., took place over the course of the pedagogical experiment. Qualitative and quantitative data analysis allowed us to determine the difference in the level of the monological speaking skills formation in experimental and control groups at all stages of the experiment conducted. The effectiveness of the developed technology is determined by the significance of the adaptation stage, the use of the peer review method, which implements the potential of students’ metacognitive skills, and the possibility to change the role while evaluating students’ answers.
A study of the relationship between learning flow and learning burnout in college online classes

This study aims to investigate the relationship among the learning flow, perceived effectiveness of online learning and learning burnout in college online classes. A total of 149 college students participated in the study and completed the survey including learning flow, learning burnout and demographic information. The results showed that participants with higher learning flow showed less learning burnout. In addition, the perceived effectiveness of online learning partially mediated the relationship between cognitive learning flow and learning burnout, but did not mediate the relationship between defined learning flow and learning burnout. Since learners with higher cognitive learning flow perceived online learning as more effective, it is necessary to find out how to build learners’ cognitive learning flow to decrease learning burnout. The results imply that universities should provide students with administrative support and an appropriate online learning environment.
Dissipative encoding of quantum information

We formalize the problem of dissipative quantum encoding, and explore the advantages of using Markovian evolution to prepare a quantum code in the desired logical space, with emphasis on discrete-time dynamics and the possibility of exact finite-time convergence. In particular, we investigate robustness of the encoding dynamics and their ability to tolerate initialization errors, thanks to the existence of non-trivial basins of attraction. As a key application, we show that for stabilizer quantum codes on qubits, a finite-time dissipative encoder may always be constructed, by using at most a number of quantum maps determined by the number of stabilizer generators. We find that even in situations where the target code lacks gauge degrees of freedom in its subsystem form, dissipative encoders afford nontrivial robustness against initialization errors, thus overcoming a limitation of purely unitary encoding procedures. Our general results are illustrated in a number of relevant examples, including Kitaev's toric code.
Node Augmentation Methods for Graph Neural Network based Object Classification

Graph neural networks (GNNs) are powerful models for learning representations of relational data, and have achieved impressive results both academically and industrially. To further enhance the performance of GNNs on the most studied node classification problem, we present NodeAug, a novel augmentation method that operates on graph-structured data, yielding virtual nodes by mixing pairs of nodes and corresponding graph structures. We first generate isolated virtual nodes with features and labels being convex combinations of existing nodes, without considering edges, which is termed as NodeAug-I and can enhance many GNN variants, demonstrating the effectiveness of node augmentation. Still, it does not exploit the graph structure, which is essential in GNNs while incorporating the structure in augmentation is a key challenge. Regarding this, we further propose two novel algorithms that can mix information of neighbors as well, taking graph structures into account. We conduct experiments on a wide range of benchmarks, including Cora, CiteSeer, Pubmed, CoraFull, Amazon and CLUSTER, and obtain considerable enhancement for well-known models, including GCN, GraphSAGE, GIN and GAT.
Improved Gastrointestinal Screening: Deep Features using Stacked Generalization

Gastric malignancy - one of the five most deadliest types of cancer - exceeds annual cases by a million worldwide since 2017. Automated screening tools may help speed up the screening and clinical procedures. In this paper, we propose a binary classification approach to classify gastrointestinal cancer tissues, namely Microsatellite Instable (MSI) and Microsatellite Stable (MSS) through stacked generalization based ensemble Deep Neural Network (DNN). Using a dataset of size 192, 315 images, we achieve an overall accuracy of 94.91% and sensitivity of 95.95%. Our results outperform previous works.
Africans' Durable Demand for Democracy

The "supply" of democracy in Africa is under threat not only from the covid-19 pandemic, but also from rising insurgencies in parts of the continent, the growing influence of China with its indifference to democratic governance, and the internal challenges facing some Western democracies. Much of Africa remains caught up in a struggle between leaders who would circumscribe democracy and retreat from democratic openings and individuals who lay themselves on the line to defend and extend them. Data from recent Afrobarometer surveys confirm that most ordinary Africans remain unflinching in their preference for democracy and core democratic norms and institutions.
Digital Twin Registration Technique of Spatial Augmented Reality for Tangible Interaction

<p indent="0mm">In spatial augmented reality (SAR) systems supporting tangible interaction, user interactions can cause rapid changes in orientation and position of interaction objects. To ensure the efficiency and accuracy of registration during objects movements, a digital twin registration technique for SAR is proposed. Models are made in the digital space and their geometric parameters are entirely consistent with the objects in the physical space. The orientation and position parameters of the physical objects are tracked in real-time during user interaction. Then the digital objects are adjusted according the parameters. Moreover, intrinsic and extrinsic parameters of the projector, which are calibrated in advance, are used to set the virtual camera in the digital space. The projection patterns are rendered in that virtual camera and then projected onto the interaction objects. The efficiency and accuracy are evaluated in the middle school physical experiment learning magnetic induction line based on the projection interaction tabletop, which meet the requirements of tangible interaction in SAR systems.
Gold-and silver-bearing assemblages in the Ann-Mason copper porphyry deposit, Yerington, Nevada

Porphyry deposits are an important source of copper and their value may be upgraded by minor contents of precious metals, if they are extractable. The mechanisms for enrichment of precious and base metals in these systems are not well understood. This study investigates the deportment of trace metals in the Ann-Mason porphyry deposit, including gold, silver, palladium, mercury, lead and bismuth. Petrographic and geochemical data are integrated to characterise the size and habit of minerals, the host mineral assemblages, and their association with alteration assemblages. Ann-Mason is one of several ore deposits hosted by the Yerington batholith in Nevada, and is being assessed for copper extraction from bornite and chalcopyrite ore assemblages, associated with potassic alteration. Mineralisation assemblages at Ann-Mason have been categorised as chalcopyrite-bornite, chalcopyrite-pyrite and pyrite > chalcopyrite zones. Limited two-metre assay data indicates that gold is most closely associated with bornite, although it is found in all three mineralisation zones. Analysis by SEM-EDS shows that trace elements are mostly present as microparticles hosted by sulfide minerals. Of the 438 grains measured, 89% are hosted by a sulfide mineral, with 68% hosted by bornite. Silver is most commonly present as hessite and gold as electrum, hosted by bornite and associated with potassic alteration. Palladium, mercury and bismuth are most commonly observed as telluride and selenide minerals. Palladium minerals are associated with potassic alteration, while mercury and bismuth minerals are mostly found with sericitic alteration. High fineness native gold is observed as three round blebs hosted by pyrite or silicates. Observations suggest that the trace metals in the chalcopyrite-bornite zone may have been incorporated by bornite and chalcopyrite, and exsolved upon cooling. In the chalcopyrite-pyrite and pyrite > chalcopyrite zones, the trace metals may have been deposited directly from a mineralising hydrothermal fluid and incorporated into the growing sulphide minerals. These findings have implications for the paragenesis of porphyry deposits, as well as for the milling processes that would be needed to extract precious metals at the Ann-Mason porphyry deposit.
THE EPISTEMOLOGY OF SMART TECHNOLOGIES: IS SMART EPISTEMOLOGY DERIVED FROM SMART EDUCATION?

The paper deals with the impact of smart technologies on cognitive and educational activities and assesses the role of smart education in education and cognition from semiotics and epistemology. The authors of the article consider smart-technologies as modern information technologies of various profiles, developed mainly for the performance of the semiotic and epistemological functions of the person with its maximum possible replacement in different areas of life. The article notes that when evaluating smart technologies, some criteria are often overlooked, while the importance of others is exaggerated. In general, quantitative scenarios for the use of smart technologies prevail over qualitative ones. This situation leads to the fact that the main characteristics of smart technologies are replaced by secondary ones, causing overestimated expectations. For example, the authors examined the misconception that a student who studies a subject as part of online learning using smart technology begins to participate in an epistemological situation from a semiotic perspective. It is because online learning makes students “discover” knowledge independently, without the necessary methodology and teacher support. An overwhelming amount of research sees this situation as an achievement, and the authors consider it to be a negative factor. However, according to the assessment of the consequences of smart learning, the best results are shown by students who already possess some methodological knowledge. At the same time, the vast majority of students show a decline in their performance in online education. The authors of the article note that from an epistemological point of view, such a property of smart technologies as a functional substitution of the subject is very consonant with some constructivist trends in epistemology and cognitive sciences, admitting “cognition without a subject.” These smart technologies’ parameters in education and epistemology allow some studies to voice ideas about the possibility of forming smart education and smart epistemology as non-subject ways of knowledge and cognition. The article demonstrated that this situation is permissible if one does not distinguish between the concepts of “information” and “knowledge” and the processes of cognition and informing. It is shown that if this condition is ignored, then the concepts of “knowledge” and “cognition” lose their meaning since the process of cognition is a way of relating knowledge and information, and it is impossible without a subject. The authors conclude that smart technologies should be considered an additional tool used for similar, but not heuristic, creative and primary actions prioritizing the subject in education and epistemology.
Deep learning-based real-world object detection and improved anomaly detection for surveillance videos

In this fast processing world, we need fast processing programs with maximum accuracy. This can be achieved when computer vision is connected with optimized deep learning models and neural networks. The goal of this project is to build an Artificial Intelligent system that will take live CCTV camera feed as input and detect what is happening in the video and do further analysis. Concerning current technology, there are a lot many models which use computer vision, machine learning for image and video processing. All models are different from each other, use various libraries, and are difficult to integrate or need high-end systems to process. This paper aims to use a convolutional neural network model for video processing and solve most of the important video processing features like detection of the liveliness of objects, estimating counts, and anomaly detection. And also further deploy it in such a way that it'll be easy to integrate and easy to use with API calls.
Quantum Algorithms for Matrix Scaling and Matrix Balancing

Matrix scaling and matrix balancing are two basic linear-algebraic problems with a wide variety of applications, such as approximating the permanent, and pre-conditioning linear systems to make them more numerically stable. We study the power and limitations of quantum algorithms for these problems. We provide quantum implementations of two classical (in both senses of the word) methods: Sinkhorn's algorithm for matrix scaling and Osborne's algorithm for matrix balancing. Using amplitude estimation as our main tool, our quantum implementations both run in time O(√mn/∈4) for scaling or balancing an n×n matrix (given by an oracle) with m non-zero entries to within l1-error ∈. Their classical analogs use time O(m/∈2), and every classical algorithm for scaling or balancing with small constant ∈ requires Ω(m) queries to the entries of the input matrix. We thus achieve a polynomial speed-up in terms of n, at the expense of a worse polynomial dependence on the obtained l1-error ∈. Even for constant ∈ these problems are already non-trivial (and relevant in applications). Along the way, we extend the classical analysis of Sinkhorn's and Osborne's algorithm to allow for errors in the computation of marginals. We also adapt an improved analysis of Sinkhorn's algorithm for entrywise-positive matrices to the l1-setting, obtaining an O(n1.5/∈3)-time quantum algorithm for ∈-l1-scaling. We also prove a lower bound, showing our quantum algorithm for matrix scaling is essentially optimal for constant ∈: every quantum algorithm for matrix scaling that achieves a constant l1-error w.r.t. uniform marginals needs Ω(√ mn) queries.
Analytic energy gradient for state-averaged orbital-optimized variational quantum eigensolvers and its application to a photochemical reaction

Elucidating photochemical reactions is vital to understand various biochemical phenomena and develop functional materials such as artificial photosynthesis and organic solar cells, albeit its notorious difficulty by both experiments and theories. The best theoretical way so far to analyze photochemical reactions at the level of ab initio electronic structure is the state-averaged multi-configurational self-consistent field (SA-MCSCF) method. However, the exponential computational cost of classical computers with the increasing number of molecular orbitals hinders applications of SA-MCSCF for large systems we are interested in. Utilizing quantum computers was recently proposed as a promising approach to overcome such computational cost, dubbed as SA orbital-optimized variational quantum eigensolver (SA-OO-VQE). Here we extend a theory of SA-OO-VQE so that analytical gradients of energy can be evaluated by standard techniques that are feasible with near-term quantum computers. The analytical gradients, known only for the state-specific OO-VQE in previous studies, allow us to determine various characteristics of photochemical reactions such as the minimal energy (ME) points and the conical intersection (CI) points. We perform a proof-of-principle calculation of our methods by applying it to the photochemical {\it cis-trans} isomerization of 1,3,3,3-tetrafluoropropene. Numerical simulations of quantum circuits and measurements can correctly capture the photochemical reaction pathway of this model system, including the ME and CI points. Our results illustrate the possibility of leveraging quantum computers for studying photochemical reactions.
Optimal Trajectories for Autonomous Human-Following Carts with Gesture-Based Contactless Positioning Suggestions

Human-following autonomous robots can help human-oriented tasks in many fields. In this paper, we focus on replacing traditional shopping carts with human-following robots as a way to aid public health measures under events such as a global pandemic. The framework mirrors efforts made in other domains to introduce human-following robots into human-oriented tasks, and consists of two major building blocks: human pose estimation from 3D data, and collision-free navigation with gesture-based positioning suggestions. RGB-D data is used to estimate human poses and extract pointing gestures. The pose and pointing direction are used as positioning suggestions. The cart then either selects to move as close to the positioning suggestion as possible, or follows the human if they move too far away. The multi-cart system uses a model predictive control (MPC) based optimization method to generate multiple collision-free paths that satisfy as many positioning suggestions as possible. We validate our pathing method with both large-scale multi-cart simulations in ROS/Gazebo. We further demonstrate the human-following block with in-lab tests using a group of omni-directional robots.