Reviewerly/exHarmony huggingface dataset:
Survival analysis for the inverse Gaussian distribution with the Gibbs sampler

This paper describes a comprehensive survival analysis for the inverse Gaussian distribution employing Bayesian and Fiducial approaches. It focuses on making inferences on the inverse Gaussian (IG) parameters μ and λ and the average remaining time of censored units. A flexible Gibbs sampling approach applicable in the presence of censoring is discussed and illustrations with Type II, progressive Type II, and random rightly censored observations are included. The analyses are performed using both simulated IG data and empirical data examples. Further, the bootstrap comparisons are made between the Bayesian and Fiducial estimates. It is concluded that the shape parameter (ϕ=λ/μ) of the inverse Gaussian distribution has the most impact on the two analyses, Bayesian vs. Fiducial, and so does the size of censoring in data to a lesser extent. Overall, both these approaches are effective in estimating IG parameters and the average remaining lifetime. The suggested Gibbs sampler allowed a great deal of flexibility in implementation for all types of censoring considered.
Reinforcement learning for the privacy preservation and manipulation of eye tracking data

In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation. It is based on two opposing agents, where one tries to classify the data correctly and the second agent looks for patterns in the data, which get manipulated to hide specific information. We show that our approach is successfully applicable to preserve the privacy of the subjects. For this purpose, we evaluate our approach iteratively to showcase the behavior of the reinforcement learning based approach. In addition, we evaluate the importance of temporal, as well as spatial, information of eye tracking data for specific classification goals. In the last part of our evaluation, we apply the procedure to further public data sets without re-training the autoencoder or the data manipulator. The results show that the learned manipulation is generalized and applicable to unseen data as well.
Autonomous Real-Time Gauge Reading in an Industrial Environment

In many industrial environments physical process values are still measured using analog gauges. An automatic reading of these instruments using wireless or wired data connections is often not feasible, due to cost- or technical issues. In such cases manual reading using human personnel is currently state-of-the-art. Especially in large plants (e.g. oil & gas refinery) these tasks are very error-prone, time-consuming and often dangerous due to exposed positions of the gauges. Recent research in the domain of autonomous robots and machine vision broadened the way of implementing either assistive or fully autonomous systems for reading analog gauges in hazardous environments. Therefore this work introduces a novel method for the detection of the gauge itself, the detection of the pointer needle and the extraction of information from the markings on the gauge. This allows the extraction of a physical measurement value without any prior knowledge about the gauge.
On the Steady State Performance of the Kalman Filter Applied to Acoustical Systems

The identification of transversal filters is important for numerous applications. In acoustical applications a first order Markov model is often used to describe the time-variant nature of transversal filters. The Kalman filter is the optimal unbiased estimator for such a Markov model. It inherently calculates the uncertainty of the current state estimate by its state error covariance matrix. In contrast to the broadband Kalman filter the covariance matrix of the exact Kalman filter depends on properties of the input signal. The single step covariance update of the exact Kalman filter is a discrete-time algebraic Riccati equation. We propose a solution for the steady state covariance matrix, which depends on the process parameters of the Markov model as well as properties of the input signal. It is derived based on the eigendecomposition of the covariance matrix and the autocorrelation matrix of the input signal. The proposed algorithm converges in few iterations and gives accurate results. We show how this result can be used to predict the steady state performance of the Kalman filter for system identification through numerical examples.
Digitalization of ecological education: trends and direction of development

Abstract The article presents the results of the analysis of the problem of digitalization of ed-ucation. Interrelations of digitalization of ecological education with noospheric essence of modern civilization and realization of the concept of sustainable development are established. Possibilities of use of developments of the Russian and foreign scientists and teachers are con-sidered. The importance and main directions of development of digitalization of environmental education are shown. Attention is drawn to the need for digitalization of environmental educa-tion in the preparation of not only environmental specialists, but also specialists in other areas of training. Digitalization of education should be aimed at the formation of eco-cultural compe-tencies. The article presents the results of an experimental study of the use of modern IT by various participants of the educational process. The studies were conducted by questionnaire method and allow to indicate the current directions of further development of digitalization of education, including environmental education.
Speaker profiling and forensic voice comparison

Speaker profiles are requested by the police for the purpose of finding a suspect. Information useful for that purpose includes age, sex, region, social status, foreign-language background, as well as individual linguistic characteristics that are noticeable by laypersons. If recorded material from a suspect is available, it is possible for an expert to conduct a forensic voice comparison and use it as evidence in court. This chapter provides an overview of these two tasks within the field of Forensic Phonetics and Acoustics. Emphasis is given to auditory-phonetic and linguistic methods and to acoustic-phonetic methods. One of the points argued for is that this duality of method types, which is captured in the subtitle ‘The auditory-acoustic approach’, should apply not only to voice comparison but also to speaker profiling. This point is strengthened by including information on acoustic features when discussing the profiling-relevant classes of age, sex/gender and body height. An overview is given to some of the most important speaker-specific characteristics (fundamental and formant frequencies). This is followed by a presentation of the leading principles behind voice comparison and is concluded with references to international discussions about the status of the likelihood ratio approach in forensic voice comparison.
A Deep Context-wise Method for Coreference Detection in Natural Language Requirements

Requirements are usually written by different stakeholders with diverse backgrounds and skills and evolve continuously. Therefore inconsistency caused by specialized jargons and different domains, is inevitable. In particular, entity coreference in Requirement Engineering (RE) is that different linguistic expressions refer to the same real-world entity. It leads to misconception about technical terminologies, and impacts the readability and understandability of requirements negatively. Manual detection entity coreference is labor-intensive and time-consuming. In this paper, we propose a DEEP context-wise semantic method named DeepCoref to entity COREFerence detection. It consists of one fine-tuning BERT model for context representation and a Word2Vec-based network for entity representation. We use a multi-layer perception in the end to fuse and make a trade-off between two representations for obtaining a better representation of entities. The input of the network is requirement contextual text and related entities, and the output is the predictive label to infer whether two entities are coreferent. The evaluation on industry data shows that our approach significantly outperforms three baselines with average precision and recall of 96.10% and 96.06% respectively. We also compare DeepCoref with three variants to demonstrate the performance enhancement from different components.
A Deep Q-Learning Network for Dynamic Constraint-Satisfied Service Composition

Traditional service composition methods usually address the constraint-satisfied service composition (CSSC) problem with static web services. Such solutions however are inapplicable to the dynamic scenarios where the services or their QoS values may change over time. Some recent studies are proposed to use reinforcement learning, especially, integrate the idea of Q-learning, to solve the dynamic CSSC problem. However, such Q-learning algorithm relies on Q-table to search for optimal candidate services. When the problem of CSSC becomes complex, the number of states in Q-table is very large and the cost of the Q-learning model will become extremely high. In this paper, the authors propose a novel solution to address this issue. By training a DQN network to replace the Q-table, this solution can effectively model the uncertainty of services with fine-grained QoS attributes and choose suitable candidate services to compose on the fly in the dynamic scenarios. Experimental results on both artificial and real datasets demonstrate the effectiveness of the method.
A Study on the Perception of Professors and Learners on the Remote Learning of University Education: Focused on the Cases of M University

The purpose of this study was to understand the perception of professors and students non-face-to face classes at the university. The subjects of this study were 194 professors and 1,543 students at the M University. In addition, it selected 23 mathematics students and 80 students of natural sciences and engineering to investigate the perception of using video contents method. It surveyed the evaluation from students after progressing online classes using video for one semester. The results of this study are as follows. Firstly the students preferred the types of distance education as the audio recording using lecture materials or class video contents. Secondly the learning satisfaction of autodidactic background students was ranked highly for the online class using video contents. Finally professors advanced preparation and careful consideration for the students enhanced the satisfaction of students.
Multimedia Food Logger

Logging what we eat is important for individuals and the aggregated information in these logs are important for businesses as well as public health. Food logging has received very little attention and has been mostly limited only to the recognition of food items ignoring context, situation, and health variable completely. In this demo we let the audience interact with our multimedia food logger system which is described in the following. We also describe how this system captures the major food-related information that could be used by all stakeholders in the food ecosystem. We will demonstrate the complete functionality of such a system in this demo.
Consecutive Context Perceive Generative Adversarial Networks for Serial Sections Inpainting

Image inpainting is a hot topic in computer vision research and has been successfully applied to both traditional and digital mediums, such as oil paintings or old photos mending, image or video denoising and super-resolution. With the introduction of artificial intelligence (AI), a series of algorithms, represented by semantic inpainting, have been developed and better results were achieved. Medical image inpainting, as one of the most demanding applications, needs to meet both the visual effects and strict content correctness. 3D reconstruction of microstructures, based on serial sections, could provide more spatial information and help us understand the physiology or pathophysiology mechanism in histology study, in which extremely high-quality continuous images without any defects are required. In this article, we proposed a novel Consecutive Context Perceive Generative Adversarial Networks (CCPGAN) for serial sections inpainting. Our method can learn semantic information from its neighboring image, and restore the damaged parts of serial sectioning images to maximum extent. Validated with 2 sets of serial sectioning images of mouse kidney, qualitative and quantitative results suggested that our method could robustly restore breakage of any size and location while achieving near realtime performance.
An overview of the digital forensic investigation infrastructure of Ghana

Cybercrime incidents continue to plague economic development in the African region. Prior research indicates that millions of dollars are lost annually due to this menace. The prospects of Digital Forensics Investigation (DFI) as evident in developed countries provide hope for defeating cybercriminals. The paper assesses the effectiveness of legal infrastructure, technical mechanisms, the availability of capacity building programs, organisational infrastructure as well as the existence of cooperation mechanisms among relevant institutions in Ghana. The paper contends to the existence of legislation and mandated institutions. It concludes that, though the sector has recorded advancements over the years, the progress has been very slow leaving DFI still at an infant stage. Existing legislations are scattered and cumbersome whereas mandated institutions lack the requisite capacity. There is a need to streamline existing laws into a comprehensively harmonized legal framework. Furthermore, heavy investments must be deployed to boost the capacities of relevant institutions.
Eyes on Code: A Study on Developers’ Code Navigation Strategies

What code navigation strategies do developers use and what mechanisms do they employ to find relevant information? Do their strategies evolve over the course of longer tasks? Answers to these questions can provide insight to educators and software tool designers to support a wide variety of programmers as they tackle increasingly-complex software systems. However, little research to date has measured developers’ code navigation strategies in ecologically-valid settings, or analyzed how strategies progressed throughout a maintenance task. We propose a novel experimental design that more accurately represents the software maintenance process in terms of software complexity and IDE interactions. Using this framework, we conduct an eye-tracking study (n=36) of realistic bug-fixing tasks, dynamically and empirically identifying relevant code areas. We introduce a three-phase model to characterize developers’ navigation behavior supported by statistical variations in eye movements over time. We also propose quantifiable notion of “thrashing” with the code as a navigation activity. We find that thrashing is associated with lower effectiveness. Our results confirm that the relevance of various code elements changes over time, and that our proposed three-phase model is capable of capturing these significant changes. We discuss our findings and their implications for tool designers, educators, and the research community.
A Framework of Cubature-<i>Hᵢ/H<sub>∞</sub> </i> -Fault Detection and Robust H-Infinity Kalman Filter of Ship SINS/GNSS Integrated System

This article deals with the problem of considering both robust fault detection and accurate state estimation for a strapdown inertial navigation system and global navigation satellite system (SINS/GNSS)-integrated navigation system for ships within a unified framework.Under the assumption that the process disturbance and measurement noise being l 2 -norm bounded, a cubature H i /H ∞ fault detection filter (FDF) considering both sensitivity and robustness to disturbance is proposed to detect faults with a postfilter and an observer gain.However, the estimated state value of the above algorithm is theoretically a suboptimal solution.To solve this limitation, an adaptive algorithm based on the measurement residual and a scaling factor is proposed to improve the accuracy of state estimation in the proposed cubature H i /H ∞ FDF.To guarantee stability when faults are extremely large, the framework uses a robust H-infinity cubature Kalman filter.Finally, a simulation is implemented to demonstrate the effectiveness of the proposed method, considering the threshold selection for different cases.
Visualizing AI Playtesting Data of 2D Side-scrolling Games

Human playtesting is a useful step in the game development process, but involves high economic costs and is time-consuming. While playtesting through artificial intelligence is gaining attention, it is challenging to analyze the collected data. We address the challenge by proposing visualizations to derive insights about level design in 2D side-scrolling games. To focus on the navigation behavior in the level design, we study the trajectories of computer agents trained using artificial intelligence. We demonstrate the effectiveness of our approach by implementing a web-based prototype and presenting the insights gained from the visualizations for the game Sonic the Hedgehog 2. We highlight lessons learned and directions to customize the approach for other analysis goals of playtesting.
Distributed SignSGD With Improved Accuracy and Network-Fault Tolerance

This paper proposes DropSignSGD, a communication-efficient and network-fault tolerant algorithm for training deep neural networks in a distributed and synchronous fashion. In DropSignSGD, all numerical elements communicated between machines are either 1 or -1, represented by only one bit. More importantly, DropSignSGD does not decline the benchmark accuracy on the ImageNet dataset when compared with the traditional distributed stochastic gradient descent algorithm, owing to a little trick in memorizing unused gradients. Experimental results are supported by a mathematical proof showing that DropSignSGD converges under standard assumptions.
A CNN-Based Approach for Lung 3D-CT Registration

Deep learning techniques have been applied to certain rigid or non-rigid medical image registration due to its potential advantages in meeting the clinical requirements of real-time and accuracy. Based on the deep learning model, this study aims to explore specific network models suitable for lung CT images. The proposed model took unlabeled 3D image pairs as input, and the convolutional neural network (CNN) was utilized and identified as a function with ability of sharing parameters to obtain displacement field. The image pair could be aligned by applying the acquired displacement field to the target image through spatial transformation. The similarity between the aligned image pair combined with the constraints on the displacement field was taken as the objective function to obtain the optimal parameters. Two models with different depths were designed and the consequent registration effects with different optimization methods and convolution kernel sizes were explored. The results proved that the designs with deeper level using Adam optimizer and smaller convolution kernels in obtaining displacement fields had higher accuracy and stronger robustness. The accuracy of the unsupervised model was comparable to state-of-the-art methods, while operating orders of magnitude faster. This study proposed a feasible registration method for lung 3D-CT, and its usefulness in aligning CT images has been demonstrated.
(Graph Database: A Survey)

The advantages of Relational Database Management System (RDBMS) model and design methodology are being utilized by industry/institutions for any software design and implementation. The future of RDBMS certainly will be the Graph Databases with NoSql methodologies, which is emerging as beyond of relational model. In this paper, there is a need to highlight all the databases evolved after RDBMS. They couldn't stay in market for so long period and survey has been made to highlight those databases after RDBMS. Relational Database Management System has certain advantages like (i) Storing in Tables, Column and Rows (ii) Data Storing in Normal Form (iii) Easy to use via SQL to retrieve information via complex join operators (iv) Maintainability via Reverse Engineering (v) Indexing and quick search. Due to these inherent features of RDBMS and SQL, it is necessary to explore and compare RDBMS with NoSQL methods to avoid complex join operation. Recently, numerous software industries and research institutions are trying their old RDBMS system to be re-engineered into some other architecture via nodes, edges and relationships where different type of information can be stored easily. So, it is a big challenges for any industry and institutions how quickly they can re-engineer their old RDBMS into Graph Databases which is also called now-a-days the future of databases. In this project, it is highlighted that the importance of the re-engineering work lies in three different directions such as (i) Comparison of RDBMS with GDBMS (Graph Database Management System) where face book, twitter, Amazon, Google are adopting (ii) Survey work of Graph Databases and (iii) Graph Database Models have increasingly become a topic of interest. The representation of data in the form of a graph lends itself well to structure a data with a schema. No standard system of query languages yet had been found to have been unique and stable for graph databases. Research and industry adoptions will determine the future direction of graph databases.(iv) Beyond RDBMS artifacts were established by industry and academics. It feeds a series of recycling collectives trying to eke out an existence of positive incentives and principles.
Efficient Composable Oblivious Transfer from CDH in the Global Random Oracle Model

Oblivious Transfer (OT) is a fundamental cryptographic protocol that finds a number of applications, in particular, as an essential building block for two-party and multi-party computation. We construct the first universally composable (UC) protocol for oblivious transfer secure against active static adversaries based on the Computational Diffie-Hellman (CDH) assumption. Our protocol is proven secure in the observable Global Random Oracle model. We start by constructing a protocol that realizes an OT functionality with a selective failure issue, but shown to be sufficient to instantiate efficient OT extension protocols. In terms of complexity, this protocol only requires the computation of 6 modular exponentiations and the communication of 5 group elements, five binary strings of security parameter length, and two binary strings of message length. Finally, we lift this weak construction to obtain a protocol that realizes the standard OT functionality (without any selective failures) at an additional cost of computing 9 modular exponentiations and communicating 4 group elements, four binary strings of security parameter length and two binary strings of message length. As an intermediate step before constructing our CDH based protocols, we design generic OT protocols from any OW-CPA secure public-key encryption scheme with certain properties, which could potentially be instantiated from more assumptions other than CDH.