Reviewerly/exHarmony huggingface dataset:
Landmark Ordinal Embedding

In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form “item j is closer to item i than item k”. Existing approaches for this “ordinal embedding” problem require expensive optimization procedures, which cannot scale to handle increasingly larger datasets. To address this issue, we propose a landmark-based strategy, which we call Landmark Ordinal Embedding (LOE). Our approach trades off statistical efficiency for computational efficiency by exploiting the low-dimensionality of the latent embedding. We derive bounds establishing the statistical consistency of LOE under the popular Bradley- Terry-Luce noise model. Through a rigorous analysis of the computational complexity, we show that LOE is significantly more efficient than conventional ordinal embedding approaches as the number of items grows. We validate these characterizations empirically on both synthetic and real datasets. We also present a practical approach that achieves the “best of both worlds”, by using LOE to warm-start existing methods that are more statistically efficient but computationally expensive.
Depth Estimation with Occlusion Prediction in Light Field Images

This paper addresses the problem of depth estimation in light field images by handling occlusions in a robust way. Previous methods determine the occlusion maps either by using the edge information in the center view or by employing various cues based on disparity cost. Here we propose to determine the occlusions based on both disparity cost and edge information. The proposed method first gets a collective response from all the depth cues of the different views. Then it determines the occluded pixels by relying on the edge information in this collective response. Based on this predicted occlusion map, the resulting depth map is regularized by standard graph-cut optimization and filtered using weighted median filtering. Experimental results on synthetic light field images demonstrate the superiority of the proposed method compared to the state-of-the-art both in quantitatively and qualitatively.
An Adaptive Approach for Dealing with Flow Disruption in Virtualized Water-Cooled Data Centers

The recent availability of water cooling systems that can be easily retrofitted to stock servers by replacing the heatsinks with coldplates has made it possible to use such systems for non-HPC cloud/data center servers. These cooling systems use pumps to circulate water and the pumps are likely to fail in the long run. We present a technique to handle flow disruptions caused by the pump failures in a virtualized environment. The solution uses an estimation of the residual cooling capacity left in the failed cooling system to adaptively adjust the CPU clock frequency as virtual machines are migrated off the racks affected by the failure. This minimizes the degradation of the tail latencies of the served requests during the migration interval for all servers affected by the failure, as seen in the experimental results.
Feature Selection Facilitated Classification For Breast Cancer Prediction

Breast cancer is emerging as a torrid research area which attacks the women at an unprecedented rate. In this research we have concentrated on prediction of the breast cancer with few attributes. We have employed feature selection as the preprocessing step for the classification. We used three classifiers and two feature selection strategies for this paper. This work is mainly focused on using the minimal number of attributes for the prediction of cancer in order to reduce the data handling overhead. The preprocessing of data by feature selection creates a major impact in the outcome parameters of the classifier. The results are presented with the detailed discussion.
Yablo’s Paradox: Is the Infinite Liar Lying to Us?

In 1993, the American logic S. Yablo was proposed an original infinitive formulation of the classical ≪Liar≫ paradox. It questioned the traditional notion of self-reference as the basic structure for semantic paradoxes. The article considers the arguments underlying two different approaches to analysis of proposals of the ≪Infinite Liar≫ and understanding of the genuine sources for semantic paradoxes. The first approach (V. Valpola, G.-H. von Wright, T. Bolander, etc.) imposes responsibility for the emergence of semantic paradoxes on the negation of the truth predicate. It deprives the ≪Infinite Liar≫ sentences of consistent truth values. The second approach is based on a modified version of anaphoric prosententialism (D. Grover, R. Brandom, etc.). The concepts of truth and falsehood are treated as special anaphoric operators. Logical constructs similar to the ≪Infinite Liar≫ do not attribute any definite truth values to sentences from which they are composed, but only state certain types of relations between the semantic content of such sentences.
Paradigm-Based Morphological Analyzer for the Gujarati Language

A morphological analyzer is a tool, which makes a syntactic analysis of a word and obtains root form of an inflected word form. The elementary step to identify a given sentence is the morphological analysis. We portray Morph analyzer for the Gujarati language. For a superior understanding of a language, word level, sentence level, context level and discourse level analysis has to be done. One of the tasks is the morphological level analyzing for various word forms. In this paper, we have discussed a paradigm-based approach for morphological analysis for various POS tags. The algorithmic development gives an accuracy of analysis for noun 84.50%, 81.50% for verb and 80.50% for adjectives.
Domain Specific Grammar based Classification for Factoid Questions

The process of classifying questions in any question answering systems is the first step in retrieving accurate answers. Factoid questions are considered the most challenging type of question to classify. In this paper, a framework has been adapted for question categorization and classification. The framework consists of three main features which are, grammatical features, domain-specific features, and grammatical patterns. These features help in preserving and utilizing the structure of the questions. Machine learning algorithms were used for the classification process in which experimental results show that these features helped in achieving a good level of accuracy compared with the state-of-art approaches.
A unified method for augmented incremental recognition of online handwritten Japanese and English text

We present a unified method to augmented incremental recognition for online handwritten Japanese and English text, which is used for busy or on-the-fly recognition while writing, and lazy or delayed recognition after writing, without incurring long waiting times. It extends the local context for segmentation and recognition to a range of recent strokes called “segmentation scope” and “recognition scope,” respectively. The recognition scope is inside of the segmentation scope. The augmented incremental recognition triggers recognition at every several recent strokes, updates the segmentation and recognition candidate lattice, and searches over the lattice for the best result incrementally. It also incorporates three techniques. The first is to reuse the segmentation and recognition candidate lattice in the previous recognition scope for the current recognition scope. The second is to fix undecided segmentation points if they are stable between character/word patterns. The third is to skip recognition of partial candidate character/word patterns. The augmented incremental method includes the case of triggering recognition at every new stroke with the above-mentioned techniques. Experiments conducted on TUAT-Kondate and IAM online database show its superiority to batch recognition (recognizing text at one time) and pure incremental recognition (recognizing text at every input stroke) in processing time, waiting time, and recognition accuracy.
Application of Adam-BP neural network in leveling fitting

Abstract According to the accuracy of BP (Back Propagation) neural network in leveling fitting, an improved BP neural network model is proposed. In order to overcome the defect of the low convergence rate and local minimal in leveling fitting. The BP neural network is improved by using Relu (Rectified Linear Units) function as network activation function and Adam (Adaptive Moment Estimation) algorithm as network optimization function. The experimental results show that the improved BP neural network can effectively improve the accuracy.
A Novel method to safeguard patients details in IoT Healthcare sector using Image Steganography

Internet plays a major role in the lives of people these days. The word is linked through the internet. Any data which is being communicated or transferred can be done over the internet. Things are also connected together through internet which is termed as Internet of Things. With communication of data over the internet, comes its security which has to be addressed. Security is one aspect which cannot be compromised. Encryption to an extent helps in securing the data. Also, image steganography is useful when one needs to secretly send data in the form of an image. Associative pixel steganography is used as the steganographic technique. Combining encryption and steganography can ensure a secure transfer of data over internet in the Internet of Things (IoT) scenario.
Research on Performance Evaluation of Wargame System Based on Deep Reinforcement Learning

Abstract Deep reinforcement learning combines the advantages of deep learning and reinforcement learning to make end-to-end perception decisions in complex high-dimensional state action spaces. The paper proposes a deep reinforcement learning method for the deduction of the wargame system, which can better assist the combat commander in wartime decision-making. The simulation proves the effectiveness of the method.
Data Center Disaggregation Using NVMe

Big Data centers need efficient utilization of compute, network, and storage to provide these services, often for little or no price. Let us look at ways that modern data centers are achieving these efficiencies by better allocation of hardware using software. Much of the material below is based upon presentations at the 2019 Open Compute Project Summit in San Jose, CA.
Obtaining Robotic Object Models from the Equations of the Potential-Flow Method

Robotic systems almost always use for their work chemical current sources (CCS), the state of which directly affects the ability, duration and safety of their operation. To diagnose the technical condition of a CCS as a technical object (TO) and form laws governing them, it is necessary to know the relationship between their unobservable parameters, the observed output parameters and known external influences on them. The mentioned relations (laws of the technical object operation) can be learned from the equations of physicochemical processes in the objects under consideration, in particular, the equations of the potential-flow method [1]. In this paper a method for obtaining models of a technical object from the equations of the potential-flow method is presented.
Developing a Data Management Consultation Service for Faculty Researchers: a Case Study from a Large Midwestern Public University

To inform the development of data management services, a library research team at Kent State University conducted a survey of all tenured, tenure-track, and non-tenure track faculty about their data management practices and perceptions. The methodology and results will be presented in the article, as well as how this information was used to inform future work in the libraryâ€™s internal working group. Recommendations will be presented that other academic libraries could model in order to develop similar services at their institutions. Personal anecdotes are included that help ascertain current practices and sentiments around research data from the perspective of the researcher. The article addresses the particular needs of a large Midwestern U.S. academic campus, which are not currently reflected in literature on the topic.
6-DOF Industrial Manipulator Motion Planning Based on RRT-Connect Algorithm

The motion planning and obstacle avoidance of industrial manipulator are researched in the V-REP platform. Virtual motion control and motion simulation of PUMA-560 manipulator are realized in the simulation environment. The manipulator STL file drawn by Solidworks is imported into the V-REP platform. The embedded thread script of the LUA language uses OMPL as a plug-in to provide motion planning functions, enabling more flexible implementation of robot motion planning. The RRT-Connect algorithm called by the motion planning library in the embedded script form API is implemented in the simulation environment. The algorithm gets fast convergence in motion planning and maintains good accuracy and rapid response. But the simulation trajectory is random and blind. For the defects of simulation, this paper gives the direction of future work to realize trajectory optimization.
An Approach to Duality in Nonlinear Filtering

This paper revisits the question of duality between minimum variance estimation and optimal control first described for the linear Gaussian case in the celebrated paper of Kalman and Bucy. A duality result is established for nonlinear filtering, mirroring closely the original Kalman-Bucy duality of control and estimation for linear systems. The result for the finite state-space continuous time Markov chain is presented. It's solution is used to derive the classical Wonham filter.
On the Hybrid Kinematics of Tethered Mobile Robots

Tethered mobile robots have for a long time been utilized in search and rescue and operations in inaccessible or hazardous environments. From inspection and cleanup inside nuclear waste tanks to underwater inspection, tethers and umbilical cords have been a reliable means of uninterrupted power supply, high-bandwidth communication, and means of deployment and/or recovery in cases of system failure. While often tethered robots are teleoperated, this may not always be the case, and this paper is concerned with aspects of tether monitoring and management which are central to autonomous operations. Specifically, the paper reports on a method to approximate tether shape and configuration in cluttered workspaces for mobile robots equipped with spooling mechanisms capable of releasing or collecting a cable having its free end fixed at a point in the workspace.
Real-Time Detection of Analytical Outliers on the Roche Troponin T Generation 5 Assay

Abstract Introduction Analytical outliers occur with most troponin assays and can adversely affect patient management. We developed a system to detect analytical outliers for the Roche Troponin T (cTnT) Gen 5 assay using repeat analysis of samples manifesting changing values over 2 to 6 hours. Methods In our ED practice, troponins are collected at baseline, 2 hours, and at times at 6 hours in plasma separator tubes and processed on the Roche Cobas e411 (Indianapolis, IN). When troponin values change by ≥10 ng/L or ≤10 ng/L in the 2- or 6-hour sample compared to baseline, the sample that produced the result is repeated. If initial and repeat values differ by &gt; ±5 ng/L (cTnT &lt;100 ng/L) or ±5% (cTnT ≥100 ng/L), the initial value is deemed to be an analytical outlier. Outliers are confirmed by a third measurement. Results Since March 2018, 19 of 17,154 (0.11%) troponin samples analyzed have been labeled outliers. Outliers were falsely elevated in 14 cases and falsely decreased in 5. Mean (range) of initial cTnT values for outliers was 39 ng/L (18-621 ng/L), with only one initial value &gt;100 ng/L. All changes could affect analysis of change over time. Eleven of 19 outliers were outside gender-specific reference interval on both samples and thus did not cause a change from abnormal to normal. Repeat measurements on 8 of 19 outliers were within the URL, which may have greater clinical consequences. Conclusions Repeat analysis upon observation of changing values allows detection of analytical outliers in real time, prior to reporting results. These outliers are almost always clinically significant in terms of interpretation of changing values and at times change patients from above to below the URL. If we tested more than just changing samples, we might detect still more outliers.
Quantum-Based Modelling of Database States

Database design of real-world scenarios requires complex data structures in order to adequately model complex real-world objects. Complex data structures can be constructed by a recursive use of elementary data types and data type constructors. The mathematics behind quantum mechanics provides us an interesting theory combining concepts from linear algebra, probability calculus, and logic. In order to make the mathematics of quantum mechanics available for database structures and states we develop a mapping of concepts from type theory of databases to the mathematics of quantum mechanics.
Robust Velocity Control for Minimum Steady State Uncertainty in Persistent Monitoring Applications

We present a velocity controller for persistent monitoring applications that minimizes the maximum eigenvalue of the Kalman filter covariance for any initial sensing position and any initial covariance. A set of points of interest in the environment can be measured along a closed static path by an autonomous, mobile robotic sensing platform. We model the environmental phenomenon at the points of interest as a Wiener process that is estimated by a Kalman filter. We propose a Greedy Knockdown Algorithm to determine the optimal number of observations for each point of interest per cycle and formulate the problem as a linear program with a set of robustness constraints. In simulation, the proposed controller is compared to constant velocity and existing first-order velocity controllers in the literature. The proposed method outperforms existing methods across test cases with a range of different parameters: number of points of interest, noise level of the observation model, and maximum velocity.
Uncovering Information Flow Policy Violations in C Programs (Extended Abstract)

Programmers of cryptographic applications written in C need to avoid common mistakes such as sending private data over public channels or improperly ordering protocol steps. These secrecy, integrity, and sequencing policies can be cumbersome to check with existing general-purpose tools. We have developed a novel means of specifying and uncovering violations of these policies that allows for a much lighter-weight approach than previous tools. We embed the policy annotations in C’s type system via a source-to-source translation and leverage existing C compilers to check for policy violations, achieving high performance and scalability. We show through case studies of recent cryptographic libraries and applications that our work is able to express detailed policies for large bodies of C code and can find subtle policy violations. We show formal connections between the policy annotations and an information flow type system and prove a noninterference guarantee of our design.
Management of digital documents with encrypted signature, through the use of centralized PKI, and distributed using blockchain for a secure exchange

The project explores the use of digital documents as a response to the problems presented by physical documents, since they are at risk of partial or total loss.The solution is the digitalization that plays a very important role in society and the contemporary world. This helps sustainability and the preservation of natural resources.The security of the archives is a necessity that requires as solution to use the technology of public key infrastructure (PKI) to generate a digital document, besides registering the public and private keys of the personnel that has the legal power to sign them. These documents are stored on an official server and distributed among the registered hosts of the network. This certificatewill help to detect changes in an unauthorized way, when comparing the document with the original. In this phase of distributed verification, the Blockchain technology will be used.Then the proposal is to build a tool to generate digital documents, in addition to managing public keys, transaction logs and records. The use of Blockchain will allow to establish and configure a Peer to Peer (P2P) network for a secure exchange.
Analysis of Issues and Future Trends Impacting Drug Safety in South Korea

New drug safety issues are emerging that are beyond the existing medication safety management system. To pre-empt these problems, forecasting future drug safety trends and issues is a necessity. The objective of this study was to identify issues and future trends impacting drug safety using foresight methodologies. The study started by identifying global megatrends, trends in safety management of medicines, and key issues in drug safety. A total of 25 global megatrends were selected by extracting and clustering keywords from 26 reports concerning the future. Using the text-mining method, 10 trends in drug safety were identified from 3593 news articles. This study derived 60 issues which can arise from the trends, and finally, the 20 key issues with the highest urgency and impact scores were selected. Some examples of issues with high scores were as follows: illegal distribution of medicines, lack of technology for managing and utilizing big data, change in the pharmaceutical trade environment, lack of education and safety management for specific populations, lack of artificial intelligence-based technology for the safety management of medicines, and the prevalence of drug advertisements through social network services. The key issues could be used to establish plans for medication safety management.
Investigation of the evaluation of proficiency level in straddle-vault movements and teaching points according to the level for elementary school children

The purpose of this study was to identify the typical motor patterns of Basic Vaulting-Box movement (straddle-vault) for elementary school children based on observational evaluation, and to discuss the teaching points according to their proficiency levels. The subjects were 453 children (220 boys, 233 girls) from the 3rd-6th grade. They performed a straddle-vault and then motions were recorded from the left and front side. The motions were scored by the evaluation criterion made from descriptions in prior researches and movements of elementary school children and university students. Latent Class Analysis (LCA) was used to extract characteristic motions based on the response patterns to the criterion.The results were summarized as follows: 1 ．The observational evaluation criterion of straddle-vault comprised 30 items was created from consideration of the motion causality.2 ．Children's straddle-vault motions were categorized into five patterns: Failure vaulting, Arm dependent vaulting, Landing unstable vaulting, Stable vaulting and Strong push-off vaulting.3 ．From the viewpoint of success or failure of the vault, achievement rate rises as the grade increases.4 ．From the viewpoint of performance of the technique, it can not be said that high proficiency movements will increase with progress of grade.