Reviewerly/exHarmony huggingface dataset:
Disease Risk Prediction from Clinical Texts

Huge amounts of healthcare data collected can be leveraged to predict the prognosis of a particular patient. Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. As of 2015, cardiovascular diseases contribute to 32% of global deaths. A number of models like Framingham, SCORE, and QRISK are used for risk prediction of these diseases. In this proposed method we are focusing on predicting the risk of having coronary artery disease. Using unstructured data such as clinical texts allows the inclusion of a variety of information like patient history and lifestyle. These unstructured data if used along with structured clinical reports can contribute to a better prediction.
Simultaneous Learning of Objective Function and Policy from Interactive Teaching with Corrective Feedback

Some imitation learning approaches rely on Inverse Reinforcement Learning (IRL) methods, to decode and generalize implicit goals given by expert demonstrations. The study of IRL normally has the assumption of available expert demonstrations, which is not always possible. There are Machine Learning methods that allow non-expert teachers to guide robots to learn complex policies, which eventually fills the expert dependencies of IRL. This work introduces an approach for simultaneously teaching robot policies and objective functions from vague human corrective feedback. The main goal is to generalize the insights that a non-expert human teacher provides to the robot, to unseen conditions, without further need for human effort in the complementary training process. We present an experimental validation of the introduced approach for transfer learning of knowledge to scenarios not considered while the non-expert was teaching. Experimental results show that the learned reward functions obtain similar performance in RL processes compared to engineered reward functions used as baseline, both in simulated and real environments.
Demonstration of Device-Independent Certification of a 398 M Link for Future Quantum Networks

Certification of complex devices is an important requirement for their application, especially in security-related scenarios. One of the strongest ways to test components is the so-called "device-independent" one, where no knowledge about or trust in their internal workings is required. Self-testing is a method for device-independent certification of entanglement using the result of a Bell test.
Clustering-Based Hybrid Approach for Multiclass Classification Using SVM

To develop a decision support system, various soft computing techniques are used like evolutionary computation, machine learning, deep learning, etc. To develop effective decision support system, classification and clustering is used in various applications. SVM is a binary classifier technique as it is based on separating hyperplane. Over time to enhance the applicability of SVM in multiclass classification, some methods have been discovered, but results in multiclass classification through SVM are not impressive like binary classification. This paper presents a clustering-based multiclass SVM classification. In this approach, to obtain better accuracy in multiclass problem, k-means clustering is used to partition the datasets based on properties of conditional attributes and then the SVM multiclass classifiers are developed for each partition. To make decision about testing instance, nearest cluster in each instance is searched and then the corresponding trained SVM model is used to assign the class value of instance. The simulation results of proposed approach show superior accuracy rate for both one-versus-one and one-versus-all SVM methods in comparison to normal SVM-based multiclass classification.
Construction of Automatic Fusion Database with Anchor Modeling and Description Logic

Anchor Modeling enables databases to record the time dimension of data and replay the history of information. This paper, aimming at building an automatic fusion database which can record meta-data other than time and semantic information of data, extends Anchor Modeling and introduces description logic. First, the tie of Anchor Modeling or the relation of Entity Relation Model is decomposed into a First Class Object association and its roles, so that a more flexible data description format is found for ABox. Second, the ALCF(D) is extended to cover the FCO association, and information in TBox is rewritten as assertions in ABox with the new format. Third, the data items in both ABox and TBox are translated into clauses of Prolog, as a demonstration, to support the querying and reasoning. These works make it possible for the automatic fusion database to provide distributed storage and automatic data fusion, semantic querying and reasoning, and visual modeling tools.
A Covariance Matching based Adaptive MCKF Algorithm

For the problem that the accuracy of the traditional Kalman filtering algorithm is decreased when the system model contains unknown noise parameters, an adaptive Monte Carlo Kalman filtering algorithm is proposed to estimate the unknown noise parameters online and in real time. The core idea of this algorithm is to approximate the theoretical value with the average of the innovation covariance sequence in time, and then obtain the estimation of unknown noise parameters. In the algorithm, we first estimate the covariance of measurement noise by using covariance matching method, and linearize the measurement function. Then we estimate the process noise covariance by the orthogonal property of the residual sequence and the innovation sequence. At last, the adaptive MCKF method is applied to track a random sinusoidal signal, the result shows that this algorithm with unknown noise parameters in system model, can better estimate the unknown parameters, and the adaptive filtering algorithm has higher accuracy and stronger robustness.
Planar Optical Antennas as Efficient Single-Photon Sources for Free-Space and Fiber-Based Operation in Quantum Optics and Metrology

Practical implementations of quantum technologies, ranging from optical quantum computing to metrological measurements, suffer from the lack of high-rate, on-demand sources of indistinguishable single photons.
Evaluating the Impact of Point Marking Precision on Situated Modeling Performance

Three-dimensional modeling in augmented reality allows the user to create or modify the geometry of virtual content registered to the real world. One way of correctly placing the model is by creating points over real-world features and designing the model derived from those points. We investigate the impact of using point marking techniques with different levels of precision on the performance of situated modeling, considering accuracy, and ease of use. Results from a formal user study indicate that high-precision point marking techniques are needed to ensure the accuracy of the model, while ease of use is affected primarily by perceptual issues. In domains where correctness of the model is critical for user understanding and judgment, higher precision is needed to ensure the usefulness of the application.
Hardware-Independent Authentication Scheme Using Intelligent Captcha Technique

This paper provides hardware-independent authentication named as Intelligent Authentication Scheme, which rectifies the design weaknesses that may be exploited by various security attacks. The Intelligent Authentication Scheme protects against various types of security attacks such as password-guessing attack, replay attack, streaming bots attack (denial of service), keylogger, screenlogger and phishing attack. Besides reducing the overall cost, it also balances both security and usability. It is a unique authentication scheme.
Determination of surface profiles of transparent plates by means of frequency estimation with wavelength tuning interferometry

The information about the profiles of both surfaces of the transparent plate are contained in an interferogram. This information can be extracted by processing fringe patterns measured at different wavelengths. The conventional Fourier analysis applied to solve such problems with a set of a restricted number of the fringe patterns, otherwise this analysis is quite sensitive to the error of frequency drift and suffers from fringe patterns interference noise. This study proposes a method of frequency estimation to obtain profiles of surfaces of transparent plate. A series of fringe patterns obtained at different phase shift caused by wavelength changing are regarded as a set of overlapped sinusoidal signal. Using Total Least Squares method to find the frequency of different signal to attain the separation of interferogram. The simulation shows that the proposed method has the immunity from noise interference.
A Density Estimation Approach for Detecting and Explaining Exceptional Values in Categorical Data

In this work we deal with the problem of detecting and explaining exceptional behaving values in categorical datasets. As a first main contribution we provide the notion of frequency occurrence which can be thought as a form of Kernel Density Estimation applied to the domain of frequency values. As a second contribution, we define an outlierness measure for categorical values that, leveraging the cdf of the density described above, decides if the frequency of a certain value is rare if compared to the frequencies associated with the other values. This measure is able to simultaneously identify two kinds of anomalies called lower outliers and upper outliers, namely exceptionally low or high frequent values. The experiments highlight that the method is scalable and able to identify anomalies of different nature from traditional techniques.
Reimagining technical services

Much like the electronic resources librarian position itself, electronic resources management (ERM) began as a specialized type of public service, later transitioning to a siloed form of technical services with its own staff, workflows, and separate systems. The level of separate development for ERM and the rest of technical services in the late 1990 and 2000s varied across libraries, but the requirement to provide online services in real time and the use of distinctive tools such as knowledge bases, link resolvers, and ERM systems to support these efforts tended to make integration impractical. However, siloed development did not last for long. Given the overall decline in the usage of print materials in the 21st century, the emphasis on distance education, the ongoing growth of electronic resources, the emergence of discovery, and overall improvements in technology and automation, many librarians began to realize that ERM had become the core function of technical services. Efforts to create technical integration by removing the silo between ERM functions and more traditional integrated library system (ILS) functions into a single library services platform (LSP) coexisted with efforts to reorganize staff into new technical services departments redesigned and reorganized to meet new requirements.
Enemy Attack Management Algorithm for Action Role-Playing Games

Enemy management in the game is one of the research hotspots in the field of game AI. Enemy attack management algorithms are especially important in action role-playing games, of which the main body is combat interaction. The paper proposes an enemy attack management algorithm suitable for action role-playing games. Traditional enemy attack management algorithms have many limitations in the process of game implementation with poor adaptability, which affects the player experience. The algorithm has high adaptability, which can avoid the problems above effectively. The goal of the algorithm is to provide an efficient and complete solution for the game designers to implement the enemy attack management system, shorten the development cycle, and then complete the game with appropriate game mechanism, art and music design. Finally, the extension, implementation and application effects of the algorithm are prospected.
Phase-shifting ESPI micro-deformation testing algorithm and experimental research

Electronic Speckle Interferometry (ESPI) is a common method for deformation measurements by introducing a phase shift technique. The traditional method needs to subtract the distribution of the phase before and after deformation to get the deformation information. During this process, the noise of the object wavefront will partially remain in the deformation signal. In order to solve this problem, this paper proposes a 5+5 phase-shifting algorithm to suppress the influence of the error from the calculation. In this algorithm, the deformed phase map is directly obtained by combining the interferograms before and after the deformation. Several experiments have been carried out to verify this method. Finally, the results show a better characteristic to quantify deformation than the traditional method. In addition, the algorithm also has better redundancy
Self-Tuning Fusion Filter for Multi-Sensor Systems with Unknown Packet Receiving Rates

This paper studies a self-tuning estimation problem for a multi-sensor networked linear stochastic discrete-time system with unknown packet receiving rates and unknown model parameters. Packet dropouts may occur when measurements of a sensor are transmitted to a local processor. Phenomena of packet dropouts are described by a group of Bernoulli distributed random variables. By model transformation, the identification problem of packet receiving rates is transformed into that of unknown model parameters for a new augmented system. The Recursive Extended Least Squares (RELS) algorithm is used to simultaneously identify packet receiving rates and model parameters of original systems. Using an optimal matrix-weighted fusion estimation algorithm in linear unbiased minimum variance sense, a fusion identifier of model parameters for original systems is presented. Further, a self-tuning state fusion filter is obtained by substituting identified fusion model parameters and packet receiving rates into the corresponding optimal estimation algorithms. A simulation example verifies the effectiveness of the proposed algorithms.
Computers Learn to Speak

Against the background of knowledge-based systems, Turing’s famous question, which moved early AI researchers, can be taken up again: Can these systems “think”? Are they “intelligent”? The analysis shows that knowledge-based expert systems as well as conventional computer programs are based on algorithms. Even the separation of knowledge base and problem solving strategy does not change this, because both components of an expert system must be represented in algorithmic data structures in order to finally become programmable on a computer.This also applies to the realization of natural language through computers. One example is J. Weizenbaum’s ELIZA language program. As a human expert, ELIZA will simulate a psychiatrist talking to a patient. These are rules on how to react to certain sentence patterns of the patient with certain sentence patterns of the “psychiatrist”. In general, it is about the recognition or classification of rules with regard to their applicability in situations. In the simplest case, the equality of two symbol structures must be determined, as determined by the EQUAL function in the LISP programming language for symbol lists. An extension exists if terms and variables are included in the symbolic expressions, e.g.(x B C)(A B y)
Research for Tibetan-Chinese Name Transliteration Based on Multi-granularity

In order to solve the problem of data sparseness caused by less training corpus in Tibetan-Chinese transliteration, this paper analyzes the alignment granularity of Tibetan-Chinese names as the research object and uses the pronunciation feature to reduce the corresponding relationships. The method of transliteration of Tibetan and Chinese names and the design of related experiments is comparable with traditional methods and improve the top-1 accuracy of transliteration of Tibetan and Chinese names to 65.72%. The experimental results show that the method can improve the accuracy of Tibetan-Chinese name transliteration.
Optimized observation arrangement based on multi-agent

In the configuration that a formation is chased by a group of chasing vehicles, it is a common strategy for the formation to observe the chasing vehicles coordinately. Collaborative observation assignment need to be optimized to maximum the ability and effectiveness of observation with minimum switching cost. Master-Slave scheme relies on the center node, so the reliability and robust of formation is affected. In this paper, a multi-agent algorithm based observation arrangement optimizing strategy is proposed. In the proposed strategy, each member of formation acts as agent separately and collaboratively. In each agent, from the guidance recognition results of chasing vehicles, the chased target member is identified and overall threat level of chasing group is evaluated. Rearrangement decision or the switch of observation target, is made individually on each agent's local observation and shared information. Simulation results proved that with distributed strategy deployed on individual members of a formation, the overall threat level of chasing group can be minimized with optimized switching cost. Even in extreme conditions that some members failed, the observation ability can be maintained at acceptable level.
Utterance Alignment in Custom Service by Integer Programming

In customer service (CS), customers pose questions that will be answered by customer service staff, and the communication in CS is a typical multi-round conversation. However, there are no explicit correspondences among conversational utterances, and obtaining the explicit alignments of those utterances not only contributes to dialogue analysis but also provides valuable data for learning intelligent dialogue systems. In this paper, we first present a study on utterance alignment (UA) in CS. We divide the alignment of utterances into four types: None, One-to-One, One-to-Many and Jump. The direct design models such as rule-based and matching-based methods are often only good at solving part of types, and the major reason is that they ignore the interactions of different utterances. Therefore, to model the mutual influence of different utterances as well as their alignments, we propose a joint model which models the UA as a task of joint disambiguation and resolved by integer programming. We conduct experiments on a dataset of an in-house online CS. And the results indicate that it performs better than baseline models, especially for One-to-Many and Jump alignments.
Research on Fusion Filtering Algorithm for Lost Observation System

Aiming at the difficulty of information fusion estimation for multi-sensor linear discrete stochastic systems with unknown loss observation rate. In this paper, a novel information fusion estimation method based on matrix weighted optimal fusion filtering algorithm with observation loss rate is proposed. Firstly, using a group of random variables which satisfies the Bernoulli distribution to describe the phenomenon of sensor observation loss. Secondly, the correlation function is applied to identify the unknown loss rate online under the condition of the sensor observation loss rate is unknown. Finally, the identified observation loss rate is introduced into the proposed matrix weighted optimal fusion filtering algorithm. Compared with traditional multi-sensor matrix weighted Kalman filtering algorithm, the matrix weighted optimal fusion algorithm with missing observers has taken into account the missing observation data, this method uses the statistical principles to compensate for these missing observations. Therefore, it has obvious advantages, the simulation examples verify the effectiveness of the proposed algorithm.
The Next AMD Innovation Is Here and Now: Welcome to Interactive Data Visualization

Academy of Management DiscoveriesVol. 5, No. 3 The Next AMD Innovation Is Here and Now: Welcome to Interactive Data VisualizationPeter Bamberger and Marlys ChristiansonPeter BambergerTel Aviv University and Marlys ChristiansonUniversity of TorontoPublished Online:14 Oct 2019https://doi.org/10.5465/amd.2019.0209AboutSectionsView articleView Full TextPDF/EPUB ToolsDownload CitationsAdd to favoritesTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail View article"The Next AMD Innovation Is Here and Now: Welcome to Interactive Data Visualization." Academy of Management Discoveries, 5(3), pp. 207–208REFERENCESErtug, G., Gruber M., Nyberg A., & Steensma H. K. 2018. From the editors—A brief primer on data visualization opportunities in management research. Academy of Management Journal, 61(5): 1613–1625.Link , Google ScholarFraher, A. L., Branicki L. J., & Grint K. 2017. Mindfulness in action: Discovering how U.S. Navy Seals build capacity for mindfulness in high-reliability organizations (HROs). Academy of Management Discoveries, 3(3): 239–261.Link , Google ScholarGreve, H. R. 2017. From the editor. Administrative Science Quarterly, 62(2): v–vi. Google ScholarHatch, M. J., Schultz M., & Skov A. M. 2015. Organizational identity and culture in the context of managed change: Transformation in the Carlsberg Group, 2009–2013. Academy of Management Discoveries, 1(1): 58–90.Link , Google ScholarLeBaron, C. 2017. Leveraging new technologies to enrich and enable management research: AMD’s media strategy. Academy of Management Discoveries, 3(1): 1–2.Link , Google ScholarLima, M., 2011. Visual Complexity: Mapping Patterns of Information. New York: Princeton Architectural Press. Google ScholarLyneis, J., & Sterman, J. 2015. How to save a leaky ship: Capability traps and the failure of win-win investments in sustainability and social responsibility. Academy of Management Discoveries, 2(1): 7–32. Google ScholarMeirelles, I. 2013. Design for information: An introduction to the histories, theories, and best practices behind effective information visualizations. Beverly, MA: Rockport Publishers. Google ScholarRiza, S.D., & Higgins, M. 2019. The dynamics of developmental networks. Academy of Management Discoveries, 5(3): 221–250. Google ScholarVan de Ven, A. H., Ang, S., Arino, A., Bamberger, P., LeBaron, C., Miller, C., & Milliken, F. 2015. Welcome to the Academy of Management Discoveries (AMD). Academy of Management Discoveries, 1(1): 1–4.Link , Google ScholarFiguresReferencesRelatedDetails Vol. 5, No. 3 Permissions Metrics in the past 12 months History Published online 14 October 2019 Published in print 1 September 2019 Information© Academy of Management DiscoveriesDownload PDF
An arbitrated quantum signature protocol using rotation and permutation operators

Abstract The existing improved arbitrated quantum signature (AQS) protocols still have some security problems. First, the security of one AQS protocol is analysed, including existential forgery attack and the signer’s disavowal attack. To resist these attacks, a novel AQS protocol using rotation and permutation operators is proposed. And the analysis shows that the protocol can resist all the known forgery attacks and the signer’s disavowal attacks.
Pre-screen

Video editing is a difficult task for both professionals and amateur editors. One of the biggest reasons is that screening useful clips from raw material in the early stage of editing is too much time-consuming and laborious. To better understand current difficulties faced by users in editing task, we first conduct a pilot study involving a survey and an interview among 20 participants. Based on the results, we then design Pre-screen, a novel tool to provide users with both global-view and detailed-view video analysis as well as material screening features based on intelligent video processing, analysis and visualization methods. User study shows that Pre-screen can not only effectively help users screen and arrange raw video material to save much more time than a widely used video editing tool in video editing tasks, but also inspire and satisfy users.