Reviewerly/exHarmony huggingface dataset:
A Fuzzy Model for Noise Estimation in Magnetic Resonance Images

Noise is a critical factor which destroys the visual clarity of Magnetic Resonance (MR) images. In many of the denoising schemes used on MR images, the depth of denoising is decided based on the strength of noise and their operational parameters are tuned in proportion to the Standard Deviation (SD) of noise. Most of the state-of-the-art noise models estimate noise statistics indirectly from the standard probability density function of choice, fitted on the image histogram. A mathematical model to estimate the noise statistics in Magnetic Resonance (MR) images, from the image fuzziness is proposed in this work. Noise significantly affects the randomness of gray levels, gradient and fuzziness of the image. Based on this principle, a direct method for computing the noise variance is proposed in this paper. Noise variance of the image is directly estimated from the fuzziness of the noisy image by using the polynomial model. The fuzzy membership values at each pixel are set in proportion to the normalized local gradient. On phantom studies, quadratic index of fuzziness is observed to be well-correlated with standard deviation of noise with a correlation of 0.9532±0.3315. The proposed polynomial model exhibited a goodness of fit, r = 0.9996. The model is found to be superior to the existing models with regard to Root Mean Squared Error (RMSE). On simulation studies on MR phantom, noise variance is found to be well-correlated with image fuzziness. The proposed model exhibited high goodness of fit. The model is found to be superior to the noise models available in literature in terms of Root Mean Square Error (RMSE). The principle of the proposed fuzziness based noise model is straightforward compared to indirect noise models which make use of image histogram.
Detecting F-formations &amp; Roles in Crowded Social Scenes with Wearables: Combining Proxemics &amp; Dynamics using LSTMs

In this paper, we investigate the use of proxemics and dynamics for automatically identifying conversing groups, or so-called F-formations. More formally we aim to automatically identify whether wearable sensor data coming from 2 people is indicative of F-formation membership. We also explore the problem of jointly detecting membership and more descriptive information about the pair relating to the role they take in the conversation (i.e. speaker or listener). We jointly model the concepts of proxemics and dynamics using binary proximity and acceleration obtained through a single wearable sensor per person. We test our approaches on the publicly available MatchNMingle dataset which was collected during real-life mingling events. We find out that fusion of these two modalities performs significantly better than them independently, providing an AUC of 0.975 when data from 30-second windows are used. Furthermore, our investigation into roles detection shows that each role pair requires a different time resolution for accurate detection.
Exploring How Component Factors and Their Uncertainty Affect Judgements of Risk in Cyber-Security

Subjective judgements from experts provide essential information when assessing and modelling threats in respect to cyber-physical systems. For example, the vulnerability of individual system components can be described using multiple factors, such as complexity, technological maturity, and the availability of tools to aid an attack. Such information is useful for determining attack risk, but much of it is challenging to acquire automatically and instead must be collected through expert assessments. However, most experts inherently carry some degree of uncertainty in their assessments. For example, it is impossible to be certain precisely how many tools are available to aid an attack. Traditional methods of capturing subjective judgements through choices such as high, medium or low do not enable experts to quantify their uncertainty. However, it is important to measure the range of uncertainty surrounding responses in order to appropriately inform system vulnerability analysis. We use a recently introduced interval-valued response-format to capture uncertainty in experts’ judgements and employ inferential statistical approaches to analyse the data. We identify key attributes that contribute to hop vulnerability in cyber-systems and demonstrate the value of capturing the uncertainty around these attributes. We find that this uncertainty is not only predictive of uncertainty in the overall vulnerability of a given system component, but also significantly informs ratings of overall component vulnerability itself. We propose that these methods and associated insights can be employed in real world situations, including vulnerability assessments of cyber-physical systems, which are becoming increasingly complex and integrated into society, making them particularly susceptible to uncertainty in assessment.
A Denotational Semantics for Low-Level Probabilistic Programs with Nondeterminism

Probabilistic programming is an increasingly popular formalism for modeling randomness and uncertainty. Designing semantic models for probabilistic programs has been extensively studied, but is technically challenging. Particular complications arise when trying to account for (i) unstructured control-flow, a natural feature in low-level imperative programs; (ii) general recursion, an extensively used programming paradigm; and (iii) nondeterminism, which is often used to represent adversarial actions in probabilistic models, and to support refinement-based development. This paper presents a denotational-semantics framework that supports the three features mentioned above, while allowing nondeterminism to be handled in different ways. To support both probabilistic choice and nondeterministic choice, the semantics is given over control-flow hyper-graphs. The semantics follows an algebraic approach: it can be instantiated in different ways as long as certain algebraic properties hold. In particular, the semantics can be instantiated to support nondeterminism among either program states or state transformers. We develop a new formalization of nondeterminism based on powerdomains over sub-probability kernels. Semantic objects in the powerdomain enjoy a notion we call generalized convexity, which is a generalization of convexity. As an application, the paper sketches an algebraic framework for static analysis of probabilistic programs, which has been proposed in a companion paper.
Mental workload characteristics of manipulator teleoperators with different spatial cognitive abilities

The main research on manipulator teleoperation includes robust of high-degree of freedom manipulators, sensor measurement accuracy, time delay, and mechanical structure design. Increased mental capacity requirements for complex assignments result in an increased mental workload. Spatial cognitive ability was considered to be the key factor affecting teleoperation performance. To accomplish this, we had 50 participants performed teleoperation while recorded their electroencephalogram. Electroencephalogram data of each task were divided into two periods, which correspond to the observation and large-scale transfer stages of teleoperation, respectively (period 1) and adjust the attitude of the manipulator to approach and align with the target stage (period 2). Brain topographic maps of period 1 (period 1 wavelet packet energy minus resting state wavelet packet energy) and period 2 (period 2 wavelet packet energy minus resting state wavelet packet energy) show that the frontal, central, and occipital regions are the main working areas of low spatial cognitive operators in period 1, while the frontal, central, and occipital regions are the main working areas of high spatial cognitive operators in period 1. The main changes in period 2 were frontal, central, parietal, and occipital regions. This study has implication for the analysis of electroencephalogram signal characteristics of mental workload in different populations to improve operators’ well-being and safety at teleoperation work.
Diagnosing Linguistic Problems in English Academic Writing of University Students: An Item Bank Approach

This article describes the steps we went through in designing and validating an item bank to diagnose linguistic problems in the English academic writing of university students in Hong Kong. Test items adopt traditional item formats (e.g., MCQ, grammatical judgment tasks, and error correction) but are based on authentic language materials extracted from a manually error-tagged corpus of target students’ essays. A total of 257 items were developed to assess 25 high-frequency and grave linguistic errors. To validate test items and calibrate their psychometric qualities, four parallel tests were assembled and given to 338 students. Rasch modeling was conducted to examine item dimensionality, DIF sizes, fit indices, reliability, and difficulty. The results supported the validity and reliability of the remaining 219 items in the bank. Moreover, we investigated the effects of item formats and target errors on item difficulty and item-measure correlations and the relations amongst error difficulty error frequency and prevalence. The item bank approach to developing diagnostic tests was found useful insofar as it provided more precise information about knowledge gaps than the corpus-based textual analysis and had the potential to pinpoint high priority areas for remedial instruction.
Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification

Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate classifier under the different conditions. The simulation study illustrates the proposed approach is more successful than the other classifiers in classification in most scenarios generated under the different conditions. Our proposed method is compared to the other classifiers on Cleveland heart disease data. An implementation of the proposed approach is demonstrated on urine data. Moreover, the proposed algorithm is released under R package GMDH2 under the name of “dceGMDH” for implementation.
Earthquake probability prediction in Sumatra Island using Poisson Hidden Markov Model (HMM)

In this paper, the probability of an earthquake occurring in Sumatra Island will be estimated. From the earthquake data that is obtained from USGS for the period of 1 December 2008 until 1 December 2018 in Sumatera Island, it is found that the observed variance is larger than the observed mean, which is called overdispersion. By checking the Autocorrelation Function (ACF) of earthquake data in Sumatera, there is a strong indication that earthquake data in Sumatera is serially-dependent. For these reasons, the Poisson Hidden Markov Model (HMM) is a good model to estimate the probability of earthquake in Sumatra.After the starting values for parameters have been determined, the next step is inputting the starting values to the Expectation-Maximization Algorithm to yield the best model. Then, after getting the best model of earthquake events, the model obtained is used to estimate the probability of earthquake events occurred. The result of this study can be used as one of the steps in disaster mitigation for the people in Sumatra
Enhanced Probabilistic Inference Algorithm Using Probabilistic Neural Networks for Learning Control

In model-based methods of reinforcement learning (RL), the probabilistic inference for learning control (PILCO) algorithm, which relies on Gaussian process (GP) for building probabilistic dynamics models, is getting attention for its advantages of requiring only a small amount of data, and being able to learn from scratch in a few trials by explicitly incorporating model uncertainty into long-term planning. However, the time complexity of the GP, which is cubic with respect to the number of trials, makes it challenging to scale the framework to high-dimensional observation spaces. Moreover, the cost function of a task is limited as a locally quadratic function for calculating the policy gradient analytically. To solve these problems, we proposed a probabilistic neural network (PNN) to replace the GP in building probabilistic dynamics models and develop a deterministic control policy by using long-term predictions. In particular, we determine model uncertainty through basic prior knowledge and calculate cumulative cost by sampling from state distributions. This approach can help reduce the influence of model error and time consumption. Compared with the state-of-the-art model-based RL, the proposed approach can reconcile data efficiency and speed of learning even in high-dimensional observation spaces.
A Deep Introduction to AI Based Software Defect Prediction (SDP) and its Current Challenges

Software systems are driving the quality of human life. Annual investment in software systems development is exceeding 4 trillion USD. Given the increasing complexity of software products and cognitive nature of software development process, the software defects are on the rise resulting in poor quality software that needs about 2.8 trillion USD to fix. To help improve software quality, the field of Software Defect Prediction (SDP) emerged. Its novel attempt to isolate defective software units enables defect removal as well as better utilization of resources in software development and maintenance activities. Having begun with a humble set of statistical models, SDP now employs sophisticated machine learning techniques delivering good results. SDP is now considering use of highly accurate search-based techniques for defects prediction. Also SDP is emerging as a formal methodology having its own process, model and evaluation criteria. Still, the SDP is troubled by a variety of problems such as diversity of datasets, difficulty in selecting ideal set of features to predict software defects, lack of robust methodology to build SDP models and non-availability of feature rich, cross-project industry (commercial) datasets to build ideal SDP models for use across multiple projects etc. There is a plenty of scope to strengthen SDP and also take measures to promote its practical use within the industry by simplifying SDP models and quantifying their outputs and benefits.
Methodology for Data Processing in Modular IoT System

The Internet of Things (IoT) is a technological paradigm in the sphere of networks that has the potential to influence how we live and how we work. This technology allows communication between all types of physical objects over the Internet that includes data sharing and also allows data to be collected and actions taken based on the information received. “Things” in the “Internet of Things” consist of a variety of hardware specifications, communication capabilities and service qualities, making IoT heterogeneous in its nature. The lack of Reference Modelling IoT Architecture prevents a common approach to processing the generated data. The need to retrieve and analyze this data from the Internet based complex systems in real-time requires applying of statistical data analysis and machine learning (ML) techniques as well as a sufficient amount of computational resources. In the paper a methodology to deal with a variety of data is proposed. A modular IoT system is considered as an instance for implementation of several methods for processing of heterogeneous data. The approaches for resolving problems that can affect the creation of predictive models are outlined.
Simulation of artefact detection in Viber and Telegram instant messengers in Windows operating systems

Messengers are popular today on mobile devices and traditional computers.Starting as a small text messaging service, they have turned into effective communication channels for both private and corporate users, becoming more than just an SMS replacement.Users entrust to them a huge amount of information, such as a time-based map of activity, photos and other personal data.Messengers changed the way communication is done; they reduce the distance to the user and along with social networks become tools for fraud, spam or blackmail and terrorism.In this regard, it is vital to study instant messengers from a forensic point of view.This research explores and compares two popular messengers: Viber and Telegram, which is rapidly gaining popularity in the criminal world and the darknet as secure message tools.The main purpose of the research is to investigate and analyze potential artefacts remaining during the installation and use of instant messengers, as well as after their uninstallation.The authors have done several experiments to investigate the artefacts in different environments and provide clear explanation of the results.The experiments showed that even though Telegram is considered to be one of the most secure instant messengers, important and useful material on a hard drive and registry remain after complete uninstallation of the application.Exploring Viber artefacts showed up information that helps to restore the whole history of a communication.Moreover, the study confirmed that artefacts are still accessible in Windows after removal of the application.
IT Application for Organizing of Research Competitions

The article is devoted to a IT system “Contest SSR” for management of organizing scientific and technological research competitions. The authors used web programming languages and frameworks HTML5, Yii2, Materialize, JQuery, AJAX, MySQL for development. The IT system is used for registration and authorization of participants, publication of scientific projects, arranging contests, experts' evaluation, winners' determination, production of diplomas, administration of users, technical support. The article stresses the system design, the principles of data placement. In the article, a contest “Digital Surf 2019” that was hosted at Sholom-Aleichem Priamursky State University is described. It found the benefits of using the “Contest SSR” system, especially in the development of commercial services and adjusting to a mobile application.
Analysis of Frequent Itemsets Mining Algorithm Againts Models of Different Datasets

Abstract Data mining is a study that uses statistical knowledge, mathematical calculations, artificial intelligence methods, machine learning by extracting and identifying useful information and related knowledge from various large databases.. One of them is looking for itemsets combination from the data stack, the search process can be done using the Apriori Association Rules algorithm, the FPGrowth Association Rule and Closed Association rule. The three algorithms are some of the implementations of frequent itemsets search methods. Two datasets will be tested, namely retail datasets and accidents datasets derived from fimi datasets, with the aim of knowing the behavior of the three algorithms against the dataset model we tested. Each algorithm will test both datasets, with minimum support values for retail datasets ranging from 0.01-0.05 and min-conf 0.01-0.05. Likewise on the accident min-sup and min-conf 0.6-1 datasets. For retail algorithm data with the fastest processing time, the FP-Growth AR algorithm is more than 85% compared to Apriori-AR, followed by AR-Apriori and Apriori-Close-AR. For detailed memory usage results the Apriori-AR algorithm is lighter and outperforms. Accident data the experimental results show that FP-Growth-AR is a little more memory efficient and the fastest process is Apriori-Close, while the Apriori-AR with the longest processing time.
On the convergence rate of the elitist genetic algorithm based on mutation probability

The Markov chain approach to the elitist genetic algorithm enables not only to prove its convergence to an equilibrium distribution, but also to establish its convergence rate. These convergence rates are based on the transition matrix of the Markov chain which models the algorithm. This paper improves existing estimates of the convergence rates of the elitist genetic algorithm and presents new ones based only on the mutation probability. Experimental results illustrate that, for a fixed mutation probability, the algorithm's mean convergence time tends to remain unchanged as the crossover probability varies. On the other hand, the reciprocal is not observed.
Mechanical arm teleoperation control system by dynamic hand gesture recognition based on kinect device

The research achieved to control the mechanical arm by using real-time dynamic gesture recognition based on Kinect. It uses the unmarked gesture segmentation algorithm based on the palm neighbourhood and the threshold detection algorithm based on the palmar contour to identify the operator's gestures and moving trajectories, and to convert it into the specific action of the mechanical arm. Through wireless network, this system sends control instructions to the mechanical arm to realise teleoperation control. The system also provides video feedback of mechanical arm operation site. It improves the teleoperation telepresence and interactivity and avoids operation error such as grasp nothing or fall. The results of the experiment indicate that the operation of the gesture control system is simple and easy, the response of mechanical arm is quick and accurate and the human–machine interaction is intuitive and friendly.
UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations. In this paper, we present a novel frame-work for jointly predicting facial landmark locations and the associated uncertainties, modeled as 2D Gaussian distributions, using Gaussian log-likelihood loss. Not only does our joint estimation of uncertainty and landmark locations yield state-of-the-art estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations (face alignment). Our method's estimates of the uncertainty of landmarks' predicted locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
Towards Constructing the SSA form using Reaching Definitions Over Dominance Frontiers

The Static Single Assignment (SSA) form is an intermediate representation used for the analysis and optimization of programs in modern compilers. The φ-function placement is the most computationally expensive part of converting any program into its SSA form. The most widely-used φ-function placement algorithms are based on computing dominance frontiers. However, this kind of algorithms works under the limiting assumption that all variables are defined at the beginning of the program, which is not the case for local variables. In this paper, we introduce an innovative algorithm based on computing reaching definitions, only assuming that global variables and formal parameters are defined at the beginning of the program. We implemented our algorithm and compared it to a well-known dominance frontiers-based algorithm in the Clang/LLVM compiler framework by performing experiments on a benchmarking suite for Perl. The results of our experiments show that, besides a few computationally expensive cases, our algorithm is fairly efficient, and most notably it produces up to 169% and on an average 74% fewer φ-functions than the reference dominance frontiers-based algorithm.