Reviewerly/exHarmony huggingface dataset:
Prediction Accuracy With Electronic Medical Records Versus Administrative Claims

Objective: The objective of this study was to evaluate the incremental predictive power of electronic medical record (EMR) data, relative to the information available in more easily accessible and standardized insurance claims data. Data and Methods: Using both EMR and Claims data, we predicted outcomes for 118,510 patients with 144,966 hospitalizations in 8 hospitals, using widely used prediction models. We use cross-validation to prevent overfitting and tested predictive performance on separate data that were not used for model training. Main Outcomes: We predict 4 binary outcomes: length of stay (≥7 d), death during the index admission, 30-day readmission, and 1-year mortality. Results: We achieve nearly the same prediction accuracy using both EMR and claims data relative to using claims data alone in predicting 30-day readmissions [area under the receiver operating characteristic curve (AUC): 0.698 vs. 0.711; positive predictive value (PPV) at top 10% of predicted risk: 37.2% vs. 35.7%], and 1-year mortality (AUC: 0.902 vs. 0.912; PPV: 64.6% vs. 57.6%). EMR data, especially from the first 2 days of the index admission, substantially improved prediction of length of stay (AUC: 0.786 vs. 0.837; PPV: 58.9% vs. 55.5%) and inpatient mortality (AUC: 0.897 vs. 0.950; PPV: 24.3% vs. 14.0%). Results were similar for sensitivity, specificity, and negative predictive value across alternative cutoffs and for using alternative types of predictive models. Conclusion: EMR data are useful in predicting short-term outcomes. However, their incremental value for predicting longer-term outcomes is smaller. Therefore, for interventions that are based on long-term predictions, using more broadly available claims data is equally effective.
EDUCATED MAN AS A SUBJECT OF MANAGEMENT IN THE FORMING KNOWLEDGE-BASED SOCIETY

The article considers the increasing role of an educated man as a subject of management in the forming knowledge-based society. The author criticizes managerism, scientistic understanding of an educated man as a “company member” and concludes that an educated man of the knowledge-based society is a philosopher of management, professional and citizen. Development of an educated man as a subject of management occurs under the conditions of forming the industrial society, meritocracy, in the process of working in the “social sector”. The paper emphasizes that solidarity of an educated men, state and nation for the sake of public welfare is an instrument to govern the Russian society.
Risk assessment of heavy metal and trace elements contamination in groundwater in some parts of Ogun state

The present study was carried out to investigate the risk of heavy metal contamination in groundwater in Ota, Ogun state. Water samples were taken from seven (7) major groundwater sources popularly consumed by the population in the study area. The samples were analyzed for heavy metals and trace elements, which include K, Mn, Cr, and Cu at the ACME laboratory in Canada using ICP-MS. The average concentrations obtained for each metal are as follows: 0.30, 7.30, 0.85, 25.34 μg l−1. This could be represented in this order, Cu > Mn > Cr > K. Furthermore, the average daily dose was determined for the heavy metal and Trace Elements in each sample, samples 1 and 7 reported higher results in Cu for adult male and female while samples 1, 2, 3 and 4 reported 3.878, 1.653, 1.980 and 4.467 μg (kg.day)−1 for Cu in children. The concentration of these elements detected in the water samples could be as a result of the geology of the area of study or due to human actions. Further study revealed the values of hazard quotient to be less than the recommended safe limit of 1 for all the samples. The average hazard quotient also reported values lower than 1 for all the age group, but Cu was noticed to be prominent across all the estimation. Therefore, regular monitoring must be considered for groundwater samples in the study area in order to avoid possible health risk that may occur as a result of the increase in the concentration of these heavy metals and Trace Elements over a long period if their sources are not eliminated.
Custom silicone Face Masks: Vulnerability of Commercial Face Recognition Systems &amp; Presentation Attack Detection

The vulnerability of face recognition systems towards evolving presentation attacks has drawn significant interest in the last decade. In this paper, we present an empirical study on both vulnerability analysis and presentation attack detection for commercial face recognition systems (FRS) using custom 3D silicone face masks corresponding to real subjects. To this end, a new database is collected consisting of 8 custom 3D silicone masks together with bona fide presentations of the corresponding subjects using three different devices (smartphones). The vulnerability of FRS for 3D face silicone face masks is effectively evaluated using two well-known commercial-off-the-shelf (COTS) FRS (Verilook from Neurotechnology, and the Cognitec Face-VACS). Further, extensive experiments are carried out to evaluate the effectiveness of five state-of-the-art presentation attack detection (PAD) techniques for detecting such masks. Key insights on silicone mask PAD are provided along with a discussion on the accuracy achieved in our experiments.
Frowning

Evgeny Kim, Roman Klinger. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Expressive priors in Bayesian neural networks: Kernel combinations and periodic functions

A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e.g. summing a periodic and linear kernel can capture seasonal variation with a long term trend. Despite a well-studied link between GPs and Bayesian neural networks (BNNs), the BNN analogue of this has not yet been explored. This paper derives BNN architectures mirroring such kernel combinations. Furthermore, it shows how BNNs can produce periodic kernels, which are often useful in this context. These ideas provide a principled approach to designing BNNs that incorporate prior knowledge about a function. We showcase the practical value of these ideas with illustrative experiments in supervised and reinforcement learning settings.
Mining Blockchain Processes: Extracting Process Mining Data from Blockchain Applications

Blockchain technology has been gaining popularity as a platform for developing decentralized applications and executing cross-organisational processes. However, extracting data that allows analysing the process view from blockchains is surprisingly hard. Therefore, blockchain data are rarely used for process mining. In this paper, we propose a framework for alleviating that pain. The framework comprises three main parts: a manifest specifying how data is logged, an extractor for retrieving data (structured according to the XES standard), and a generator that produces logging code to support smart contract developers. Among others, we propose a convenient way to encode logging data in a compact form, to achieve relatively low cost and high throughput for on-chain logging. The proposal is evaluated with logs created from generated logging code, as well as with existing blockchain applications that do not make use of the proposed code generator.
Self-Attention Network for Session-Based Recommendation With Streaming Data Input

In the current era of the rapid development of big data, it has become increasingly critical and practical to study recommender systems with streaming data input. However, the recommender system is often faced with the challenge that the history records of new users or anonymous users are not available. Specifically, session-based recommendation, which aims to predict a user’s next actions, is a typical task to overcome the challenge. To capture a user’s long-term preference in session-based recommendations, recurrent neural networks (RNN)-based models have been widely applied with impressive results, but the inherent sequential nature of RNNs prevents parallelism within training examples, which is critical in long sessions because memory constraints limit batching across instances. In this paper, we propose a novel method, i.e., self-attention network for session-based recommendation (SANSR), which is based on only attention mechanisms, dispensing with recurrence, and supports parallelism in the session. The proposed model attempts to find items that are relevant based on previous time steps in the ongoing session and to assign them different weights to predict the next item. The extensive experiments are conducted on two real-world datasets, and the experimental results show that our proposed model is superior to the state-of-the-art methods.
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks

We study the dynamics of gradient descent on objective functions of the form $f(\prod_{i=1}^{k} w_i)$ (with respect to scalar parameters $w_1,\ldots,w_k$), which arise in the context of training depth-$k$ linear neural networks. We prove that for standard random initializations, and under mild assumptions on $f$, the number of iterations required for convergence scales exponentially with the depth $k$. We also show empirically that this phenomenon can occur in higher dimensions, where each $w_i$ is a matrix. This highlights a potential obstacle in understanding the convergence of gradient-based methods for deep linear neural networks, where $k$ is large.
Variational Graph Recurrent Neural Networks

Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dynamic graphs are still scant. In this paper, we develop a novel hierarchical variational model that introduces additional latent random variables to jointly model the hidden states of a graph recurrent neural network (GRNN) to capture both topology and node attribute changes in dynamic graphs. We argue that the use of high-level latent random variables in this variational GRNN (VGRNN) can better capture potential variability observed in dynamic graphs as well as the uncertainty of node latent representation. With semi-implicit variational inference developed for this new VGRNN architecture (SI-VGRNN), we show that flexible non-Gaussian latent representations can further help dynamic graph analytic tasks. Our experiments with multiple real-world dynamic graph datasets demonstrate that SI-VGRNN and VGRNN consistently outperform the existing baseline and state-of-the-art methods by a significant margin in dynamic link prediction.
Linear Stochastic Bandits Under Safety Constraints

Bandit algorithms have various application in safety-critical systems, where it is important to respect the system constraints that rely on the bandit's unknown parameters at every round. In this paper, we formulate a linear stochastic multi-armed bandit problem with safety constraints that depend (linearly) on an unknown parameter vector. As such, the learner is unable to identify all safe actions and must act conservatively in ensuring that her actions satisfy the safety constraint at all rounds (at least with high probability). For these bandits, we propose a new UCB-based algorithm called Safe-LUCB, which includes necessary modifications to respect safety constraints. The algorithm has two phases. During the pure exploration phase the learner chooses her actions at random from a restricted set of safe actions with the goal of learning a good approximation of the entire unknown safe set. Once this goal is achieved, the algorithm begins a safe exploration-exploitation phase where the learner gradually expands their estimate of the set of safe actions while controlling the growth of regret. We provide a general regret bound for the algorithm, as well as a problem dependent bound that is connected to the location of the optimal action within the safe set. We then propose a modified heuristic that exploits our problem dependent analysis to improve the regret.
A Genetic-Based Approach to Location-Aware Cloud Service Brokering in Multi-Cloud Environment

Various enterprises are increasingly gaining technical and economic benefits from global cloud marketplace. Multi-cloud environment makes it possible to coordinate access and utilize multiple cloud resources. Cloud service brokers can be regarded as the intermediation to discover, integrate, aggregate, customize, and optimize cloud services, e.g. Infrastructure as a Service (IaaS), from different providers. However, the existing cloud brokering schemes rarely consider the optimization techniques of deploying cloud services with respect to various criteria, such as cost and performance. In this paper, we propose a genetic-based approach to addressing the multi-cloud service brokering problem, i.e. selecting and leasing virtual machines (VMs) for cloud users with minimal cost and network latency. Experimental results show that our approach with problem-specific chromosome representation, fitness measurement, and population initialization strategy outperforms the existing approaches proposed in the literature.
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is a challenging class of vision and knowledge transfer problems in which both seen and unseen classes appear during testing. Existing GZSL approaches either suffer from semantic loss and discard discriminative information at the embedding stage, or cannot guarantee the visual-semantic interactions. To address these limitations, we propose a Dual Adversarial Semantics-Consistent Network (referred to as DASCN), which learns both primal and dual Generative Adversarial Networks (GANs) in a unified framework for GZSL. In DASCN, the primal GAN learns to synthesize inter-class discriminative and semantics-preserving visual features from both the semantic representations of seen/unseen classes and the ones reconstructed by the dual GAN. The dual GAN enforces the synthetic visual features to represent prior semantic knowledge well via semantics-consistent adversarial learning. To the best of our knowledge, this is the first work that employs a novel dual-GAN mechanism for GZSL. Extensive experiments show that our approach achieves significant improvements over the state-of-the-art approaches.
Model Compression with Adversarial Robustness: A Unified Optimization Framework

Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compression through a different lens: could we compress models without hurting their robustness to adversarial attacks, in addition to maintaining accuracy? Previous literature suggested that the goals of robustness and compactness might sometimes contradict. We propose a novel Adversarially Trained Model Compression (ATMC) framework. ATMC constructs a unified constrained optimization formulation, where existing compression means (pruning, factorization, quantization) are all integrated into the constraints. An efficient algorithm is then developed. An extensive group of experiments are presented, demonstrating that ATMC obtains remarkably more favorable trade-off among model size, accuracy and robustness, over currently available alternatives in various settings. The codes are publicly available at: https://github.com/shupenggui/ATMC.
General structure of Interval Type-2 fuzzy PI/PD controller of Takagi–Sugeno type

In this paper, a new configuration of Interval Type-2 (IT2) fuzzy Proportional–Integral (PI) or fuzzy Proportional–Derivative (PD) controller of Takagi–Sugeno (TS) type is presented. An attempt is made to generalize the IT2 fuzzy PI/PD controller structure using multiple fuzzy sets. Fuzzification of the inputs is done with three or more fuzzy sets having triangular/trapezoidal membership functions. The rule base consists of only three rules to reduce the number of tuneable parameters of the controller. Minimum (Min) triangular norm and Bounded Sum (BS) triangular co-norm are used as conjunction and disjunction operators to reduce the number of rules. Karnik–Mendel (KM) type reducer and Weighted Average (WA) defuzzifier are considered to derive the analytical structure of the fuzzy controller. Properties and gain variations of the fuzzy controller are investigated. Simulation study is carried out on nonlinear dynamical systems to verify the applicability of the fuzzy controllers.
Masked Gradient-Based Causal Structure Learning

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Masked Gradient-Based Causal Structure LearningIgnavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, and Jun WangIgnavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, and Jun Wangpp.424 - 432Chapter DOI:https://doi.org/10.1137/1.9781611977172.48PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract This paper studies the problem of learning causal structures from observational data. We reformulate the Structural Equation Model (SEM) with additive noises in a form parameterized by binary graph adjacency matrix and show that, if the original SEM is identifiable, then the binary adjacency matrix can be identified up to super-graphs of the true causal graph under mild conditions. We then utilize the reformulated SEM to develop a causal structure learning method that can be efficiently trained using gradient-based optimization, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix. It is found that the obtained entries are typically near zero or one and can be easily thresholded to identify the edges. We conduct experiments on synthetic and real datasets to validate the effectiveness of the proposed method, and show that it readily includes different smooth model functions and achieves a much improved performance on most datasets considered. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737Key words:Causal structure learning, gradient-based optimization, binary adjacency matrix, Gumbel-Softmax
A Security Framework for IoT Authentication and Authorization Based on Blockchain Technology

The decentralized and distributed nature of the Blockchain technology makes it a suitable solution to improve the device-to-device communication security challenges. This work explores the possible application of blockchain technology to address the IoT security problems under the 5G cellular system. We propose a multi-layer security network model for IoT network based on blockchain technology. The proposed model addresses the problems associated with the actual deployment of the blockchain technology by dividing the IoT network into a multi-layer decentralized system. In the proposed model, we divide the network into K-unknown clusters using Evolutionary Computation algorithms which are Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). A local authentication mechanism is chosen for authentication and authorization purpose within each cluster handed by each Cluster Head (CH). The high security and credibility assurance of the blockchain technology provides an authentication mechanism for CHs communication with each other and Base Stations (BS) through a local blockchain implementation without a central authority. We also propose a global blockchain implementation for BSs communications. Finally, our proposal implements an open source blockchain platform Hyperledger Fabric to verify the proposed system.
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, we propose a novel data-set which consists of over 1 million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world. We provide a first experimental study of these questions and our results indicate that learned models transfer poorly, but that model and hyperparameter selection is an effective means of transferring information to the real world.
Inductive Matrix Completion Based on Graph Neural Networks

We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (users) and columns (items), a majority of existing matrix completion methods are transductive, since the learned embeddings cannot generalize to unseen rows/columns or to new matrices. To make matrix completion inductive, content (side information), such as user's age or movie's genre, has to be used previously. However, high-quality content is not always available, and can be hard to extract. Under the extreme setting where not any side information is available other than the matrix to complete, can we still learn an inductive matrix completion model? In this paper, we investigate this seemingly impossible problem and propose an Inductive Graph-based Matrix Completion (IGMC) model without using any side information. It trains a graph neural network (GNN) based purely on local subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings. Our model achieves highly competitive performance with state-of-the-art transductive baselines. In addition, since our model is inductive, it can generalize to users/items unseen during the training (given that their ratings exist), and can even transfer to new tasks. Our transfer learning experiments show that a model trained out of the MovieLens dataset can be directly used to predict Douban movie ratings and works surprisingly well. Our work demonstrates that: 1) it is possible to train inductive matrix completion models without using any side information while achieving state-of-the-art performance; 2) local graph patterns around a (user, item) pair are effective predictors of the rating this user gives to the item; and 3) we can transfer models trained on existing recommendation tasks to new tasks without any retraining.