Reviewerly/exHarmony huggingface dataset:
Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping

• Coarse code granularity, erroneous data entry and poor generalizability may influence the performance of phenotyping algorithms. • Vocabulary-driven methods for concept sets creation shows advantages in improving the accuracy for phenotyping. • Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model facilitate phenotype generalizability and consistency. • More data is not necessarily better: performance of a diagnosis-based chronic kidney failure algorithm is not improved by adding other codes indirectly related to chronic kidney disorder. Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9–48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.
Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1–5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study six strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20–1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.
Multi-Task Learning with Generative Adversarial Training for Multi-Passage Machine Reading Comprehension

Multi-passage machine reading comprehension (MRC) aims to answer a question by multiple passages. Existing multi-passage MRC approaches have shown that employing passages with and without golden answers (i.e. labeled and unlabeled passages) for model training can improve prediction accuracy. In this paper, we present MG-MRC, a novel approach for multi-passage MRC via multi-task learning with generative adversarial training. MG-MRC adopts the extract-then-select framework, where an extractor is first used to predict answer candidates, then a selector is used to choose the final answer. In MG-MRC, we adopt multi-task learning to train the extractor by using both labeled and unlabeled passages. In particular, we use labeled passages to train the extractor by supervised learning, while using unlabeled passages to train the extractor by generative adversarial training, where the extractor is regarded as the generator and a discriminator is introduced to evaluate the generated answer candidates. Moreover, to train the extractor by backpropagation in the generative adversarial training process, we propose a hybrid method which combines boundary-based and content-based extracting methods to produce the answer candidate set and its representation. The experimental results on three open-domain QA datasets confirm the effectiveness of our approach.
How the Duration of the Learning Period Affects the Performance of Random Gradient Selection Hyper-Heuristics

Recent analyses have shown that a random gradient hyper-heuristic (HH) using randomised local search (RLSk) low-level heuristics with different neighbourhood sizes k can optimise the unimodal benchmark function LeadingOnes in the best expected time achievable with the available heuristics, if sufficiently long learning periods τ are employed. In this paper, we examine the impact of the learning period on the performance of the hyper-heuristic for standard unimodal benchmark functions with different characteristics: Ridge, where the HH has to learn that RLS1 is always the best low-level heuristic, and OneMax, where different low-level heuristics are preferable in different areas of the search space. We rigorously prove that super-linear learning periods τ are required for the HH to achieve optimal expected runtime for Ridge. Conversely, a sub-logarithmic learning period is the best static choice for OneMax, while using super-linear values for τ increases the expected runtime above the asymptotic unary unbiased black box complexity of the problem. We prove that a random gradient HH which automatically adapts the learning period throughout the run has optimal asymptotic expected runtime for both OneMax and Ridge. Additionally, we show experimentally that it outperforms any static learning period for realistic problem sizes.
Deep Neural Network Approximated Dynamic Programming for Combinatorial Optimization

In this paper, we propose a general framework for combining deep neural networks (DNNs) with dynamic programming to solve combinatorial optimization problems. For problems that can be broken into smaller subproblems and solved by dynamic programming, we train a set of neural networks to replace value or policy functions at each decision step. Two variants of the neural network approximated dynamic programming (NDP) methods are proposed; in the value-based NDP method, the networks learn to estimate the value of each choice at the corresponding step, while in the policy-based NDP method the DNNs only estimate the best decision at each step. The training procedure of the NDP starts from the smallest problem size and a new DNN for the next size is trained to cooperate with previous DNNs. After all the DNNs are trained, the networks are fine-tuned together to further improve overall performance. We test NDP on the linear sum assignment problem, the traveling salesman problem and the talent scheduling problem. Experimental results show that NDP can achieve considerable computation time reduction on hard problems with reasonable performance loss. In general, NDP can be applied to reducible combinatorial optimization problems for the purpose of computation time reduction.
pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization

We propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term pbSGD. The proposed pbSGD method simply raises the stochastic gradient to a certain power elementwise during iterations and introduces only one additional parameter, namely, the power exponent (when it equals to 1, pbSGD reduces to SGD). We further propose pbSGD with momentum, which we term pbSGDM. The main results of this paper present comprehensive experiments on popular deep learning models and benchmark datasets. Empirical results show that the proposed pbSGD and pbSGDM obtain faster initial training speed than adaptive gradient methods, comparable generalization ability with SGD, and improved robustness to hyper-parameter selection and vanishing gradients. pbSGD is essentially a gradient modifier via a nonlinear transformation. As such, it is orthogonal and complementary to other techniques for accelerating gradient-based optimization such as learning rate schedules. Finally, we show convergence rate analysis for both pbSGD and pbSGDM methods. The theoretical rates of convergence match the best known theoretical rates of convergence for SGD and SGDM methods on nonconvex functions.
Attentive User-Engaged Adversarial Neural Network for Community Question Answering

We study the community question answering (CQA) problem that emerges with the advent of numerous community forums in the recent past. The task of finding appropriate answers to questions from informative but noisy crowdsourced answers is important yet challenging in practice. We present an Attentive User-engaged Adversarial Neural Network (AUANN), which interactively learns the context information of questions and answers, and enhances user engagement with the CQA task. A novel attentive mechanism is incorporated to model the semantic internal and external relations among questions, answers and user contexts. To handle the noise issue caused by introducing user context, we design a two-step denoise mechanism, including a coarse-grained selection process by similarity measurement, and a fine-grained selection process by applying an adversarial training module. We evaluate the proposed method on large-scale real-world datasets SemEval-2016 and SemEval-2017. Experimental results verify the benefits of incorporating user information, and show that our proposed model significantly outperforms the state-of-the-art methods.
An Efficient Document Skew Detection Method Using Probability Model and Q Test

Document skew detection is one of the key technologies in most of the document analysis systems. However, existing skew detection methods either have low accuracy or require a large amount of computation. To achieve a good tradeoff between efficiency and performance, we propose a novel skew detection approach based on bounding boxes, probability model, and Dixon’s Q test. Firstly, bounding boxes are used to pick out the eligible connected components (ECC). Then, we calculate the slopes of the skew document with the probability model. Finally, we find the optimal result with Dixon’s Q test and projection profile method. Moreover, the proposed method can detect the skew angle in a wider range. The experimental results show that our skew detection algorithm can achieve high speed and accuracy simultaneously compared with existing algorithms.
Lifelong Learning with a Changing Action Set

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the size of the action set changes remains unaddressed. In this paper, we present first steps towards developing an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.
Citation Recommendation with a Content-Sensitive DeepWalk Based Approach

Systems for recommending scientific papers mainly help researchers to find a list of references that related to the researcher's interest effectively and automatically. Many state-of-the-art technique have been used for recommendation system, however, the traditional approaches has the issues of data scarcities and cold start, and existing recommended approaches with network representation only focus on one aspect of node information and cannot leverage content information. In this paper, we proposed a Citation Recommendation method with a Content-Aware bibliographic network representation, called CR-CA, whose recommended process contains two levels: (1) At the node content level, the proposed approach calculates similarities between the target and candidate papers, selecting an initial seed set of papers; (2) At the citation network structure level, this approach exploits citation relationship between papers to study latent representation of the scientific papers based on a deep natural language method-DeepWalk. The proposed approach was tested on the AAN dataset demonstrate that this approach outperforms baseline algorithms, in the true positive rate (Recall) and normalized discounted cumulative gain (NDCG).
BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding.

For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task. Our novel BERTgrid, which is based on Chargrid by Katti et al. (2018), represents a document as a grid of contextualized word piece embedding vectors, thereby making its spatial structure and semantics accessible to the processing neural network. The contextualized embedding vectors are retrieved from a BERT language model. We use BERTgrid in combination with a fully convolutional network on a semantic instance segmentation task for extracting fields from invoices. We demonstrate its performance on tabulated line item and document header field extraction.
DepthMove: Leveraging Head Motions in the Depth Dimension to Interact with Virtual Reality Head-Worn Displays

Head-based interactions are very handy for virtual reality (VR) head-worn display (HWD) systems. A useful head-based interaction technique could help users to interact with VR environments in a hands-free manner (i.e., without the need of a hand-held de-vice). Moreover, it can sometimes be seamlessly integrated with other input modalities to provide richer interaction possibilities. This paper explores the potential of a new approach that we call DepthMove to allow interactions that are based on head motions along the depth dimension. With DepthMove, a user can interact with a VR system proactively by moving the head perpendicular to the VR HWD forward or backward. We use two user studies to investigate, model, and optimize DepthMove by taking into con-sideration user performance, subjective response, and social ac-ceptability. The results allow us to determine the optimal and comfortable DepthMove range. We also distill recommendations that can be used to guide the design interfaces that use DepthMove for efficient and accurate interaction in VR HWD systems. A third study is conducted to demonstrate the usefulness of DepthMove relative to other techniques in four application scenarios.
Performance analysis of CLEFIA and PRESENT lightweight block ciphers

The demand and fulfillment of various smart-needs is endangering day to day life posing security challenges. Deploying new security methods has become critical for resource-limited smart devices. NIST (National Institute of Standards and Technology) has validated CLEFIA and PRESENT among many lightweight security methods. In this paper, we have implemented lightweight CLEFIA and PRESENT encryption methods in python and evaluated them in terms of security strength, throughput and resource utilization. Our results have shown that PRESENT outperforms CLEFIA in terms of memory usage and security but CLEFIA gives better throughput than PRESENT.
An Advanced Taxonomy for Social Engineering Attacks

Rapid technological advancement has not only resulted in a change in the pace of economic development, but also led to increase in cyber-threats.A social engineering attack is one such threat where an attacker not only accesses critical information about a user through technology, but also through manipulation.Although the types of attacks are different i.e. social, physical, technical or socio-technical, the process is the same.This study creates an advanced taxonomy of social engineering attacks with the aim of facilitating the development and implementation of better prevention measures, stressing the importance of organizational awareness.
Spatially Situated End-User Robot Programming in Augmented Reality

Nowadays, industrial robots are being programmed using proprietary tools developed by robot manufacturer. A skilled robot programmer is needed to create even as simple task as pick a well-known object and put it somewhere else. Contrary, in every-day life people are using end-user programming to make different electronic devices work in expected manner, without even noticing they are actually programming. We propose augmented reality-enabled end-user programming system allowing regular shop-floor workers to program industrial robotic tasks. The user interface prototype for this system was evaluated in the user study with 7 participants with respect to usability, mental workload and user experience.
Implementation Of Data Mining For Determining Majors Using K-Means Algorithm In Students Of SMA Negeri 1 Pangkalan Kerinci

SMA Negeri 1 Pangkalan kerinci is one of the middle schools located at Jalan Lintas Timur Kerinci Pelalawan Indonesia which currently has 2 majors namely Science and IPS. This student majors can lead learners to focus more on developing their own abilities and interests. Selection of inappropriate majors can be very detrimental to students of their interests and careers in the future. With the majors are expected to maximize the potential, talent or individual talents, so as to maximize academic value. Based on the background, then by applying data mining techniques are expected to help students to determine the correct majors in accordance with the criteria set. Data mining techniques used in the determination of this department using K-Means Algorithm.
Error-Ellipse-Resampling-Based Particle Filtering Algorithm for Target Tracking

In this paper, an error-ellipse-resampling-based particle filter (EER-PF) algorithm is proposed for target tracking in wireless sensor networks. In order to improve the effectiveness of the particles, in the process of resampling, the error ellipse of different confidence levels is established according to the error covariance matrix of particles. The particles are divided into different levels based on the geometrical position, and then the particles are screened and optimized. The effectiveness of the proposed method in a cumulative error optimization was verified by comparing with the performance of posterior Cramér-Rao lower bound (PCRLB). Experimental results show that the proposed algorithm can effectively solve the problem of sample degeneracy and impoverishment, and has higher positioning accuracy.
Are Classification Deep Neural Networks Good for Blind Image Watermarking?

Image watermarking is usually decomposed into three steps: (i) a feature vector is extracted from an image; (ii) it is modified to embed the watermark; (iii) and it is projected back into the image space while avoiding the creation of visual artefacts. This feature extraction is usually based on a classical image representation given by the Discrete Wavelet Transform or the Discrete Cosine Transform for instance. These transformations require very accurate synchronisation between the embedding and the detection and usually rely on various registration mechanisms for that purpose. This paper investigates a new family of transformation based on Deep Neural Networks trained with supervision for a classification task. Motivations come from the Computer Vision literature, which has demonstrated the robustness of these features against light geometric distortions. Also, adversarial sample literature provides means to implement the inverse transform needed in the third step above mentioned. As far as zero-bit watermarking is concerned, this paper shows that this approach is feasible as it yields a good quality of the watermarked images and an intrinsic robustness. We also tests more advanced tools from Computer Vision such as aggregation schemes with weak geometry and retraining with a dataset augmented with classical image processing attacks.
Second-order moiré method for accurate deformation measurement with a large field of view

In this study, we propose a second-order moiré method by performing digital sampling at two stages to realize high-accuracy deformation measurement in a wide field of view, where a grid image is recorded at a low magnification. Simulations have verified that this method has high strain measurement accuracy when the grid pitch is close to or even smaller than two pixels for both parallel and oblique grids with random noise. As an application, the two-dimensional microscale strain distributions of a carbon fiber reinforced plastic specimen when the grid pitch was about 2.1 pixels were presented. Shear strain concentration was detected before an interlaminar crack emerged, and tensile strain concentration was found prior to the occurrence of a transverse crack. The proposed method makes the two-step phase-shifting technique achieved indirectly, not only enlarging the field of view, but also maintaining the measurement accuracy.
High Throughput Implementation of the Keccak Hash Function Using the Nios-II Processor

Presently, cryptographic hash functions play a critical role in many applications, such as digital signature systems, security communications, protocols, and network security infrastructures. The new standard cryptographic hash function is Secure Hash Algorithm 3 (SHA-3), which is not vulnerable to attacks. The Keccak algorithm is the winner of the NIST competition for the adoption of the new standard SHA-3 hash algorithm. In this work, we present hardware throughput optimization techniques for the SHA-3 algorithm using the Very High Speed Integrated Circuit Hardware Description Language (VHDL) programming language for all output lengths in the Keccak hash function (224, 256, 384 and 512). Our experiments were performed with the Nios II processor on the FPGA Arria 10 GX (10AX115N2P45E1SG). We applied two architectures, one without custom instruction and one with floating point hardware 2. Finally, we compare the results with other existing similar designs and found that the proposed design with floating point 2 optimizes throughput (Gbps) compared to existing FPGA implementations.