Reviewerly/exHarmony huggingface dataset:
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints.

Learning optimal feedback control laws capable of executing optimal trajectories is essential for many robotic applications. Such policies can be learned using reinforcement learning or planned using optimal control. While reinforcement learning is sample inefficient, optimal control only plans an optimal trajectory from a specific starting configuration. In this paper we propose deep optimal feedback control to learn an optimal feedback policy rather than a single trajectory. By exploiting the inherent structure of the robot dynamics and strictly convex action cost, we can derive principled cost functions such that the optimal policy naturally obeys the action limits, is globally optimal and stable on the training domain given the optimal value function. The corresponding optimal value function is learned end-to-end by embedding a deep differential network in the Hamilton-Jacobi-Bellmann differential equation and minimizing the error of this equality while simultaneously decreasing the discounting from short- to far-sighted to enable the learning. Our proposed approach enables us to learn an optimal feedback control law in continuous time, that in contrast to existing approaches generates an optimal trajectory from any point in state-space without the need of replanning. The resulting approach is evaluated on non-linear systems and achieves optimal feedback control, where standard optimal control methods require frequent replanning.
From Checking to Inference: Actual Causality Computations as Optimization Problems

Actual causality is increasingly well understood. Recent formal approaches, proposed by Halpern and Pearl, have made this concept mature enough to be amenable to automated reasoning. Actual causality is especially vital for building accountable, explainable systems. Among other reasons, causality reasoning is computationally hard due to the requirements of counterfactuality and the minimality of causes. Previous approaches presented either inefficient or restricted, and domain-specific, solutions to the problem of automating causality reasoning. In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations. We contribute and compare two compact, non-trivial, and sound integer linear programming (ILP) and Maximum Satisfiability (MaxSAT) encodings to check causality. Given a candidate cause, both approaches identify what a minimal cause is. Also, we present an ILP encoding to infer causality without requiring a candidate cause. We show that both notions are efficiently automated. Using models with more than 8000 variables, checking is computed in a matter of seconds, with MaxSAT outperforming ILP in many cases. In contrast, inference is computed in a matter of minutes.
Improving Pattern Matching performance in Genome sequences using Run Length Encoding in Distributed Raspberry Pi Clustering Environment

Genomics and bioinformatics have grown as an independent field and are an area of active research currently. In this work, we first discuss the idea of the genome, its importance and wide-ranging applications in healthcare and medicine. We then give a brief overview of the various file formats used presently to store and manipulate genomic data along with their benefits and shortcomings. Moving on, we analyze and elaborate on how parallel and distributed computing can help in processing large files with genomic data and more importantly address the problem of pattern matching. In particular, we introduce the raspberry-pi and discuss in detail how a Raspberry Pi can accomplish the pattern matching task and how a Raspberry-Pi cluster can enable parallel computing to improvise the performance. This work also includes a brief account of various high-level application programming interfaces and libraries that can be used to parse genomic data like BioPython and Fuzzywuzzy. We conclude this work by summarizing some techniques that can be used for encoding and compressing files with genomic data like Run-Length encoding and genome differential compression respectively among other methods. The proposed work improvises the Genome pattern matching time by more than 50% in a distributed Raspberry Pi Computing environment. This system is useful to detect a particular genome-based abnormality where different pieces of Genome records are present on the different nodes in distributed computing and the input is given to find the disease. It can detect the abnormality within a short time. Also the different approach used here is to use Raspberry Pi based clustering to save cost and the energy.
Predicting Current Glycated Hemoglobin Levels in Adults From Electronic Health Records: Validation of Multiple Logistic Regression Algorithm

Electronic health record (EHR) systems generate large datasets that can significantly enrich the development of medical predictive models. Several attempts have been made to investigate the effect of glycated hemoglobin (HbA1c) elevation on the prediction of diabetes onset. However, there is still a need for validation of these models using EHR data collected from different populations.The aim of this study is to perform a replication study to validate, evaluate, and identify the strengths and weaknesses of replicating a predictive model that employed multiple logistic regression with EHR data to forecast the levels of HbA1c. The original study used data from a population in the United States and this differentiated replication used a population in Saudi Arabia.A total of 3 models were developed and compared with the model created in the original study. The models were trained and tested using a larger dataset from Saudi Arabia with 36,378 records. The 10-fold cross-validation approach was used for measuring the performance of the models.Applying the method employed in the original study achieved an accuracy of 74% to 75% when using the dataset collected from Saudi Arabia, compared with 77% obtained from using the population from the United States. The results also show a different ranking of importance for the predictors between the original study and the replication. The order of importance for the predictors with our population, from the most to the least importance, is age, random blood sugar, estimated glomerular filtration rate, total cholesterol, non-high-density lipoprotein, and body mass index.This replication study shows that direct use of the models (calculators) created using multiple logistic regression to predict the level of HbA1c may not be appropriate for all populations. This study reveals that the weighting of the predictors needs to be calibrated to the population used. However, the study does confirm that replicating the original study using a different population can help with predicting the levels of HbA1c by using the predictors that are routinely collected and stored in hospital EHR systems.
A Light Weight Convolutional Neural Network for Single Image Super-Resolution

Recently, many convolutional neural network based models obtain remarkable performance in single-image super-resolution task by stacking more number of convolution layers. However, those models require a huge amount of network parameters which increases the computational complexity of their single image super-resolution models. Due to this, they are no longer appropriate for many real-world applications. Hence, to design a network which can obtain better super-resolution performance with less number of network parameters is always an active area of research in the computer vision community. In this paper, we propose a light weight convolutional neural network based SR model called LWSRNet for the upscaling factor x4. In LWSRNet, we introduce a novel basic block which helps to extract complex features of the given low-resolution observation. Additionally, we use a weighted L2 loss function in order to train the network which is more effective than L1 and L2 loss functions. Various experiments have been carried out to validate the proposed method and observe that the super-resolution results obtained using the proposed LWSRNet method are better than that of the other existing single image super-resolution methods. Also, the proposed LWSRNet outperforms to the recently proposed state-of-the-art methods with approximately 20% to 60% less number of training parameters.
Hybrid Recommendation of Articles in Scientific Social Networks Using Optimization and Multiview Clustering

Several published articles are shared every day through scientific social network, which make it very difficult for researchers to find highly valuable and appropriate articles. To solve this issue, we propose in this paper a novel hybrid recommendation of articles combining an improved version of the content based filtering (CBF) and the collaborative filtering (CF) algorithms. First, the profiles of researchers and articles are built integrating the social tag information into the CBF algorithm. Then, the social friend information was integrated into the CF algorithm. Due to the problem sparsity of the CF, we have considered the singular matrix factorization (SVD) and unified probabilistic matrix factorization (PMF) algorithms. Finally, in order to further improve the performance of the CF, an optimized clustering has been applied using the Kmedoids algorithm and the BAT meta-heuristic. Different hybridization have been proposed: (1) a weighted hybrid algorithm which combines the two improved versions of the CBF and CF algorithms; and (2) the multiview clustering based hybrid algorithm. Experimental results conducted on the CiteULike dataset demonstrate that the proposed approach has significantly improved the recommendation accuracy and outperforms the baselines and exiting methods.
Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain

Exploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only in isolation and does not study the effects of de-identification on other tasks. In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction. In particular, we propose a stacked model with restricted access to privacy sensitive information and a multitask model. We set the new state of the art on benchmark datasets in English (96.1% F1 for de-identification and 88.9% F1 for concept extraction) and Spanish (91.4% F1 for concept extraction).
Representing Joint Hierarchies with Box Embeddings

Learning representations for hierarchical and multi-relational knowledge has emerged as an active area of research. Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph. In this work, we evaluate the capability of box embeddings to learn the transitive closure of a tree-like hierarchical relation graph with far fewer edges from the transitive closure. Box embeddings are not restricted to tree-like structures, however, and we demonstrate this by modeling the WordNet meronym graph, where nodes may have multiple parents. We further propose a method for modeling multiple relations jointly in a single embedding space using box embeddings. In all cases, our proposed method outperforms or is at par with all other embedding methods.
Optimal Timing Selection Approach to Moving Target Defense: A FlipIt Attack-Defense Game Model

The centralized control characteristics of software-defined networks (SDNs) make them susceptible to advanced persistent threats (APTs). Moving target defense, as an effective defense means, is constantly developing. It is difficult to effectively characterize an MTD attack and defense game with existing game models and effectively select the defense timing to balance SDN service quality and MTD decision-making benefits. From the hidden confrontation between the actual attack and defense sides, existing attack-defense scenarios are abstractly characterized and analyzed. Based on the APT attack process of the Cyber Kill Chain (CKC), a state transition model of the MTD attack surface based on the susceptible-infective-recuperative-malfunctioned (SIRM) infectious disease model is defined. An MTD attack-defense timing decision model based on the FlipIt game (FG-MTD) is constructed, which expands the static analysis in the traditional game to a dynamic continuous process. The Nash equilibrium of the proposed method is analyzed, and the optimal timing selection algorithm of the MTD is designed to provide decision support for the selection of MTD timing under moderate security. Finally, the application model is used to verify the model and method. Through numerical analysis, the timings of different types of attack-defense strategies are summarized.
Deep Low-Density Separation for Semi-supervised Classification

Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (ssl) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective ssl imposes structural assumptions on the data, e.g. that neighbors are more likely to share a classification or that the decision boundary lies in an area of low density. For complex and high-dimensional data, neural networks can learn feature embeddings to which traditional ssl methods can then be applied in what we call hybrid methods. Previously-developed hybrid methods iterate between refining a latent representation and performing graph-based ssl on this representation. In this paper, we introduce a novel hybrid method that instead applies low-density separation to the embedded features. We describe it in detail and discuss why low-density separation may better suited for ssl on neural network-based embeddings than graph-based algorithms. We validate our method using in-house customer survey data and compare it to other state-of-the-art learning methods. Our approach effectively classifies thousands of unlabeled users from a relatively small number of hand-classified examples.
Automated Steering Control System for Reverse Parking Maneuver of Semi-Trailer Vehicles Considering Motion Planning by Simulation of Feedback Control System

With the increase in the demand for road freight transportation, semi-trailers are being increasingly preferedowing to their large maximum load capacity. However, for such vehicles, excellent driving skills are required because unique steering is often necessary during reverse parking. In this paper, the concept of a parking assist system and path tracking controller is proposed. The control system consists of a pure pursuit motion planner for handling the reference path tracking and a feedback controller for stabilizing the hitch angles. We propose a control method to realize the ideal control performance of an actual vehicle subjected to unmeasured disturbance. An actual full-scale vehicle experiment is conducted and the effectiveness of the proposed approach is verified by evaluating the error from the target parking position.
Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint

Object tracking is widely utilized and becomes indispensable in automation technology. In environments containing many objects, however, occlusion and false recognition frequently occur. To alleviate these issues, in this paper, we propose a novel object tracking method based on moving horizon estimation incorporating probabilistic data association (MHE-PDA) through a probabilistic data association filter (PDAF). Since moving horizon estimation (MHE) is accomplished through numerical optimization, we can ensure that the estimation is consistent with physical constraints and robust to outliers. The robustness of the proposed method against occlusion and false recognition is verified by comparison with PDAF through simulations of a cluttered environment.
A Comprehensive Study and Evaluation of Recommender Systems

This paper presents a brief study within the field of recommender systems and describes the current generation of recommender system tools and evaluation metrics. Recommender system comprises of three methods, namely content-based filtering, collaborative filtering, and hybrid filtering algorithms. It addresses two common scenarios in collaborative filtering: rating prediction and item recommendation. There are many well-known accuracy metrics which replicate evaluation goals. This paper describes a few framework and libraries of recommender system that implements a state-of-the-art algorithmic rule furthermore as series of evaluation metric. We tend to find which recommender system tool performs quicker than different, whereas achieving competitive evaluating performance with steering for the comprehensive evaluation and choice of recommender algorithm.
Text Summarization: An Extractive Approach

TextSoni, Vishal summarizationKumar, Lokesh methodSingh, Aman Kumar producesKumar, Mukesh the shorter or abstract version of text after giving the large source text. It provides the meaningful information of the source text, i.e. the text’s meaning is intact and accurate. Text summarization tools have a powerful impact on today’s world due to the increasing information with a massive rate on the Internet. It is very difficult for a person to describe and ingest the whole content. The manual conversion or summarization is very difficult task, hence automation is need. The automation can be achieve using artificial intelligence techniques. Text summarization methods are classified into two categories: Extractive and abstractive. The extractive method, as its name suggests, consists of extracting important sentences or paragraph from some source of text and rejoining them to get the summarized form of the source content. The criteria for evaluating an importance of a sentence or paragraph is based on the statistical features parameter of the sentences, and the abstractive method is all about knowing the source text and re-writing the text in a few words that describes the whole source text. In addition, this method uses a linguistic approach to check and interpret the source text. In this article, extractive text summarization methods are applied to the job. The validation of the model is performed using the bench-marked source text. From the obtained result, it is evident that the summarization model performs well and do the summarization which is very precise and meaningful.
Determining success factors for improving rural livelihood strategies: how can participatory social network mapping help?

The integration of upgrading strategies (UPS) as part of the livelihood strategies of small farmers is vital for enhancing their food systems. The success of the UPS implementation largely relies on the interactions among key actors. Understanding the complex social realities of farmers is essential for adapting strategies to the local context. Therefore, the objective of this study is to determine the impact of UPS on the livelihood of participants in two case study areas in Tanzania. We used a mixed-method approach to understand the relations between, roles, and motivations of relevant actors in the participatory implementation of their UPS using the Net-Map tool for social network analysis and focus group discussions. Our results reveal why the implementation of UPS improved people´s livelihoods and allowed us to derive some success factors. Motivations for participating in a UPS group differed to some extent among all UPS groups and all types of actors. Based on our results, we found that UPS allow participants to enhance their social networks within their communities and develop skills that improve their livelihood strategies, resulting in more diverse livelihood outcomes. This research provides a knowledge base for UPS considerations and other participatory actions with local subsistence stakeholders.
Far-Right Extremism: Is it Legitimate Freedom of Expression, Hate Crime, or Terrorism?

Following the rise in far-right inspired terrorist attacks globally, social media and electronic communications companies have been criticized, mainly by politicians, for allowing far-right extremist content to be available. This article is a comparative legal study focusing on Australia, Canada, New Zealand, the U.K., and the U.S.’ legal provisions regarding the right to freedom of expression, hate crime, and proscription of terrorist organizations. This study found a disparity in the form of expression protected under this right. This disparity widens further when related to hate crime and proscribing groups as terrorist organizations. As such, social media and communications companies have difficulty setting at global level a baseline in determining whether content is legitimate commentary or is extremism promoting or inciting hatred and violence. The article concludes with a recommendation for how states can provide comparable legislation on hate crime as they have done in relation to Islamist inspired extremism. This will assist social media and communications companies in removing content and suspending accounts. These companies are not the guardians of freedom of expression, that is the role of states’ legislatures and judiciary.
ECCParaCorp: a cross-lingual parallel corpus towards cancer education, dissemination and application

The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians' healthcare practice, patients' health management and public health literacy. High-quality corpus containing cancer information is the necessary foundation of cancer education. Massive non-structural information resources exist in clinical narratives, electronic health records (EHR) etc. They can only be used for training AI models after being transformed into structured corpus. However, the scarcity of multilingual cancer corpus limits the intelligent processing, such as machine translation in medical scenarios. Thus, we created the cancer specific cross-lingual corpus and open it to the public for academic use.Aiming to build an English-Chinese cancer parallel corpus, we developed a workflow of seven steps including data retrieval, data parsing, data processing, corpus implementation, assessment verification, corpus release, and application. We applied the workflow to a cross-lingual, comprehensive and authoritative cancer information resource, PDQ (Physician Data Query). We constructed, validated and released the parallel corpus named as ECCParaCorp, made it openly accessible online.The proposed English-Chinese Cancer Parallel Corpus (ECCParaCorp) consists of 6685 aligned text pairs in Xml, Excel, Csv format, containing 5190 sentence pairs, 1083 phrase pairs and 412 word pairs, which involved information of 6 cancers including breast cancer, liver cancer, lung cancer, esophageal cancer, colorectal cancer, and stomach cancer, and 3 cancer themes containing cancer prevention, screening, and treatment. All data in the parallel corpus are online, available for users to browse and download ( http://www.phoc.org.cn/ECCParaCorp/ ).ECCParaCorp is a parallel corpus focused on cancer in a cross-lingual form, which is openly accessible. It would make up the imbalance of scarce multilingual corpus resources, bridge the gap between human readable information and machine understanding data resources, and would contribute to intelligent technology application as a preparatory data foundation e.g. cancer-related machine translation, cancer system development towards medical education, and disease-oriented knowledge extraction.