Reviewerly/exHarmony huggingface dataset:
Fuzzy Logic-Refined Color Channel Transfer Synergism based Image Dehazing

This paper introduces a novel Refined Color Channel Transfer (RCCT) prior as an improved alternative of existing Color Channel Transfer (CCT) prior. Like CCT, RCCT also compensates the chromatic losses occurring in degraded hazy images by employing a global color transfer strategy but it performs color transfer using well-scaled reference images generated using our proposed Fuzzy logic based reference image generation technique in contrary to CCT which usually performs color transfer using reference images possessing over-enhanced glow (bright) regions and poorly enhanced lowlight regions. The presence of such over-enhanced /poorly enhanced regions in the references images used by CCT significantly affect the visibility of outputs obtained from the dehazing methods where CCT acts as a pre-processing step. To overcome these shortcomings, here we have proposed a novel Fuzzy logic based reference image generation technique which restricts the intensities of generated reference images within allowable ranges by introducing a control parameter `k'. A unique value of `k' used for controlling the intensity of each pixel is computed depending upon the properties of the super-pixel in which it belongs, using a novel set of Fuzzy Inference (FI) rules which facilitates the production of visually improved outputs and also enables RCCT to serve as an ideal preprocessing step of various daytime, nighttime and underwater dehazing methods which is experimentally proven in this work.
A Clustered Gaussian Process Model for Computer Experiments

A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability for large-scale dataset limit its availability in practice. In this article, we propose a clustered Gaussian process model which segments the input data into multiple clusters, in each of which a Gaussian process model is performed. The stochastic expectation-maximization is employed to efficiently fit the model. In our simulations as well as a real application to solar irradiance emulation, our proposed method had smaller mean square errors than its main competitors, with competitive computation time, and provides valuable insights from data by discovering the clusters. An R package for the proposed methodology is provided in an open repository.
Visual Breakpoint Debugging for Sum and Product Formulae

Software debugging is one of the most time consuming source code related tasks. Hence, we propose a novel approach to breakpoint debugging for formula code, i.e. source code implementing mathematical formulae. In this work, the focus is on source code which computes a numerical value via arithmetic operations as well as sum- and product formulae. We introduce and discuss breakpoints placed on an automatically inferred mathematical representation, i.e. in a common mathematical notation or by a mixed form of source code artifacts and maths symbols. Furthermore, we present visual debugging features aiming to facilitate the dynamic inspection of the formula code leveraging the mathematical representation. We briefly present a first prototype implementation of our formula debugging approach and indicate future directions of our work.
MOBILE HEALTH APPLICATION AND COVID-19: OPPORTUNITIES AND CHALLENGES

The Novel Coronavirus has been declared a pandemic by the World Health Organization (WHO), which as at April 27, 2020 COVID-19 infected more than 2,959,929 people worldwide and resulted in more than 202,733 deaths in 213 countries. In recent times, mobile technologies have expanded exponentially in both emerging and developed countries. Mobile technology adoption is now much greater than those of traditional internet and land-line telephones. Mobile Phone tracking approach is a powerful tool in the fight against the COVID- 19 pandemic. Since the outbreak of the pandemic, there has been a scramble to use mobile phone to fight the virus. A lot of measures have been taken to reduce the rate of community transmission and despite the various measures taken; the rate of the infection and mortality rate is still very high. Therefore, this paper discusses the model of mobile tracking surveillance and monitoring for prompt detection of infected patient. The paper also, made efforts to convey the benefits the government and public health authorities can derived in determining the best course of action to control the COVID-19 pandemic. The hope of using mobile tracking technology in COVID-19 will have a great impact on the quality of outbreak care that can be delivered to patients across socioeconomic and geographic boundaries.
The Lavic Lake Fault: A Long-Term Cumulative Slip Analysis via Combined Field Work and Thermal Infrared Hyperspectral Airborne Remote Sensing

The 1999 Hector Mine earthquake ruptured to the surface in eastern California, with &gt;5 m peak right-lateral slip on the Lavic Lake fault. The cumulative offset and geologic slip rate of this fault are not well defined, which inhibits tectonic reconstructions and risk assessment of the Eastern California Shear Zone (ECSZ). With thermal infrared hyperspectral airborne imagery, field data, and auxiliary information from legacy geologic maps, we created lithologic maps of the area using supervised and unsupervised classifications of the remote sensing imagery. We optimized a data processing sequence for supervised classifications, resulting in lithologic maps over a test area with an overall accuracy of 71 ± 1% with respect to ground-truth geologic mapping. Using all of the data and maps, we identified offset bedrock features that yield piercing points along the main Lavic Lake fault and indicate a 1036 +27/−26 m net slip, with 1008 +14/−17 m horizontal and 241 +51/−47 m vertical components. For the contribution from distributed shear, modern off-fault deformation values from another study imply a larger horizontal slip component of 1276 +18/−22 m. Within the constraints, we estimate a geologic slip rate of &lt;4 mm/yr, which does not increase the sum geologic Mojave ECSZ rate to current geodetic values. Our result supports previous suggestions that transient tectonic activity in this area may be responsible for the discrepancy between long-term geologic and present-day geodetic rates.
Improved Classification Based on Deep Belief Networks

For better classification, generative models are used to initialize the model and extract features before training a classifier. Typically, separate unsupervised and supervised learning problems are solved. Generative restricted Boltzmann machines and deep belief networks are widely used for unsupervised learning. We developed several supervised models based on deep belief networks in order to improve this two-phase strategy. Modifying the loss function to account for expectation with respect to the underlying generative model, introducing weight bounds, and multi-level programming are all applied in model development. The proposed models capture both unsupervised and supervised objectives effectively. The computational study verifies that our models perform better than the two-phase training approach. In addition, we conduct an ablation study to examine how a different part of our model and a different mix of training samples affect the performance of our models.
Cyber Security Risk is a Board-Level Issue

This paper will give an overview of the contents that should be presented to the Board, ways to gather and structure the information on cyber security risks exposure, and methods of presenting it in relatively short time given at the Board meetings, in a way that is understandable to the Board members, in order for them to make informed decisions in alignment with their Board responsibilities. It will also examine the prerequisites for having such a successful cyber risk management system that can deliver the requested outcome at any moment in time - a clear picture of the cyber security risks exposure and expected trends, for all the relevant stakeholders.
Development of future teachers ' competencies in the field of ensuring a safe professional and educational environment in the context of distance learning

The transformation processes taking place in education today actualize the need to introduce the concept of "safe educational environment", which should be considered comprehensively from the sides of each participant in the educational process - both students and teachers. The formation and maintenance of environmental conditions of this kind is the responsibility of teachers, and at the same time-a necessary condition for the full implementation of their own professional activities. The purpose of the study was to identify the competence of teachers to ensure the formation and maintenance of a safe professional and educational environment. The authors propose an approach to building a model of the professional and educational environment, describe its main components, and provide criteria for the competence of teachers for each component. The materials of the survey of first-year students of the pedagogical University who studied the module of health and safety of life (a set of disciplines and the same type of practice) are summarized. The main factors of threats to teachers ' professional activity are identified. The criteria and indicators of teachers ' competence in the direction of forming and maintaining a safe professional and educational environment are substantiated. The article describes the directions of development of these competencies in the context of distance learning.
Metaphor Design in Localising User Interface for Farmers in Malaysia Using User-Centered Approach

Metaphor is a representation of objects and situations that have meaning and comparable to the real thing to convey the message to people. This study reveals design metaphor issues for paddy farmer population in agriculture domain in Malaysia. The work is motivated by the ICT-Based Agriculture Flagship Blueprint of the Malaysian Government, an initiative to equip agriculture stakeholders in Malaysia including farmers with ICT knowledge on multiple platforms e.g. mobile phones. The metaphors design is deemed to have a huge potential to improve usability through localising user interface of related apps. The work is conducted involves three main studies in the fieldwork. The paddy farmers were involved in the entire development process to be able to obtain deeper understanding of their background, tasks, and environment. The study has led us to proposed nine elements incorporated as guidelines in metaphor design. A mobile paddy app is developed to demonstrate the practicality of the design metaphors. The app provides several main features such as diary, calendar, reminder, and pesticides and crop illness information. Then, this app was evaluated by conducting usability testing with the farmers. The results were gathered and being analysed using descriptive statistics which shown positive constructs. The participants (N=5) expressed agreement with the system usability (M=1.62, SD=0.43), strong agreement in system appearance (M=1.34, SD=0.22), and design metaphor (M=1.5, SD=0.27) and agreement for overall acceptance (M=1.6, SD=0.35).
Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing

The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.
Automatic Detection of Lexical Functions in Context

In this article, we explore the concept of lexical functions as a formalism to represent recurrent lexical, semantic, and syntactic relations among words. A lexical function takes a word as input and outputs a set of words related to the input in a certain way on the paradigmatic or syntagmatic level. For example, the syntagmatic lexical function termed Oper1 takes the noun decision as input and outputs the verb make with the semantics of ‘Agent realizes the action denoted by the noun’, so Oper1 captures the relation between the noun and the verb in the collocation make a decision. The numeric part of the Oper1 notation reflects two facts: first, the action of make is performed by the agent, which is viewed as the first semantic actant (or argument) in the Tesnière’s model, and second, the syntactic function of the word denoting the agent in utterances with make a decision is subject. In general, lexical functions represent common semantic and syntactic patterns typical for certain word classes and can aid in many tasks of natural language processing, lexical and syntactic disambiguation being the most fundamental one among them. In this article, we review various paradigmatic and syntagmatic lexical functions, their application and identification in natural language processing.
Learning Reddit User Reputation Using Graphical Attention Networks

In this work, we consider the problem of quantifying the effect, a social network agent is having on their community by learning associations between comment qualities and the reactions to that comment. We focus specifically on learning the responses to anti social behaviour, with the long term goal of better detecting all kinds of behaviour which disturb and disrupt a community. Extending previous work, we evaluate the performance of a recent graph based deep learning model and multiple text embeddings for this task. Our results show that a feature set built from a simple text embedding of comment text can exercise significant predictive power over a number of prediction tasks.
A Cyclical Post-Filtering Approach to Mismatch Refinement of Neural Vocoder for Text-to-Speech Systems

Recently, the effectiveness of text-to-speech (TTS) systems combined with neural vocoders to generate high-fidelity speech has been shown. However, collecting the required training data and building these advanced systems from scratch are time and resource consuming. An economical approach is to develop a neural vocoder to enhance the speech generated by existing or low-cost TTS systems. Nonetheless, this approach usually suffers from two issues: 1) temporal mismatches between TTS and natural waveforms and 2) acoustic mismatches between training and testing data. To address these issues, we adopt a cyclic voice conversion (VC) model to generate temporally matched pseudo-VC data for training and acoustically matched enhanced data for testing the neural vocoders. Because of the generality, this framework can be applied to arbitrary TTS systems and neural vocoders. In this paper, we apply the proposed method with a state-of-the-art WaveNet vocoder for two different basic TTS systems, and both objective and subjective experimental results confirm the effectiveness of the proposed framework.
CCSW'20: 2020 Cloud Computing Security Workshop

Clouds and massive-scale computing infrastructures are starting to dominate computing and will likely continue to do so for the foreseeable future. Major cloud operators are now comprising millions of cores hosting substantial fractions of corporate and government IT infrastructure.
SunSpec Cyberguardians Fall Recruitment Webinar 20200901

The webinar was used to promote the Cyberguardians program and to ensure ample news coverage to reach veterans, employers, and others that would benefit from this program. It covers the landscape for Distributed Energy employment, why we need the Cyberguardians program, opportunities for veteran students and job seekers, and employer benefits.
Twitter Sentiment Analysis on Big Data in Spark Framework

Abstract Earlier for communication between people is something which is delivered by hand from one person to another. In some words, or letter Social Media an important and also an integral part of everyone’s life now days. Twitter is a social media site and its primary purposes are to convert people and allow people to share their thoughts. Twitter an American Microbe Using twitter social media users post their views, opinions and to communicate with messages called as tweets. It is one of the social media that is gaining popularity now days. As of 2014 twitter social media has more than 284 million users are in active in monthly basis and above 500 million messengers sent messages on a daily base. Twitter is created by Jack Dorsey, Noah Class, Biz stone and Evan Willians in March 2006 and it was launched in the month of July in that year. Sentimental analysis means the analysis and classification of the emotions and feelings such as positive thought, negative thought and neutral thought using text analysis techniques within text data. The aim of the proposed analysis is to identify the public opinion using NLP (Natural Language Processing) with n-gram stemming algorithm in Spark framework.
Efficient Discrete Distribution Estimation Schemes Under Local Differential Privacy

Recently, local differential privacy (LDP) has increasingly been leveraged to cope with privacy issues in data collection. Discrete distribution estimation schemes under LDP are the fundamental tools in the LDP setting, which enable data collector to collect discrete distribution estimation information about a population while protecting each individual’s privacy, without relying on a trusted third party. Among these schemes, Ye-Barg mechanisms achieve the best utility in the medium privacy regime. Nevertheless, their communication cost between the user and data collector is O(k) which is too large to be deployed in practice when the domain size k is large (or even unbounded). In this paper, we propose a family of new efficient discrete distribution estimation schemes under LDP which reduce the communication cost to less than $$O(\mathrm {log}(2+e^\epsilon ))$$ and obtain almost the same expected estimation loss as Ye-Barg mechanisms under $$\ell _2^2$$ metric and $$\ell _1$$ metric. Additionally, we compare our schemes with Ye-Barg mechanisms theoretically and experimentally and confirm our conclusion.
Measuring and Modeling Human Probabilistic Segmentation Maps

Visual segmentation is a core function of biological vision, key to adaptive behavior in complex environments. Early models inspired by the feedforward processing in the visual system described texture-based human segmentation as a comparison of the summary statistics of low-level image features across space. Here we consider the alternative view that, due to image ambiguity and sensory noise, perceptual segmentation requires probabilistic inference. To test this hypothesis, we develop a novel paradigm to measure perceptual segmentation maps and their variability. We use composite textures: each segment is characterized by a different distribution of oriented features. Participants briefly view an image followed by two spatial cues and report whether the cued locations belong to the same segment. We repeat the sequence with different locations and reconstruct the full segmentation map from the binary choices, solving a system of equations. In a second set of experiments, we manipulate uncertainty by controlling the overlap between feature distributions and smoothing the texture boundary and measure texture discrimination performance. We find that segmentation maps are similar across observers but variable: perceptual variability correlates with intrinsic image uncertainty, and both are higher near segment boundaries. We then test the inference model that consists in assigning pixels to segments by evaluating which distribution explains best the observed features. Quantitative model comparison shows that perceptual variability reflects image uncertainty beyond sensory noise and that human segmentation is better explained by optimal probabilistic inference than by comparing summary statistics. Lastly, we find an interaction between the effects of contour uncertainty and feature distribution overlap. These results support the probabilistic inference hypothesis and suggest extending the model with contour specific components. Our work provides a normative explanation of human perceptual segmentation as probabilistic inference and demonstrates a novel framework to study perceptual segmentation, which could be extended to natural images.
Neuro-Fuzzy Cognitive Temporal Models For Predicting Multidimensional Time Series With Fuzzy Trends

A new type of Neuro-Fuzzy Cognitive Temporal Models (NFCTM) is proposed for predicting multidimensional time series (MTS) taking into account the fuzzy trends of all MTS components. NFCTMS allow predicting the MTS in conditions of non-stochastic uncertainty, non-linearity of mutual influence, partial inconsistency and significant interdependence of the MTS components, as well as in conditions of small samples. This takes into account the direct and indirect mutual influence of all the MTS components with different time lags relative to each other. To carry out temporal changes in specific MTS components the original neuro-fuzzy models of RecANFIS (Recurrent Adaptive Neuro-Fuzzy Inference System/Model) type are applied, that: firstly, allow to save the predicted values of the MTS components in the range of “liding-window” time series; secondly, identify fuzzy trends of the components of the MTS in the range of “sliding-window” time series; thirdly, adaptively take into account fuzzy trends of the MTS components based on the fuzzy mappings. An original way of a coherent learning NFCTM is described, which lies: firstly, in training RecANFISs for each concept NFCTM (MTS component) taking into account fuzzy trends; secondly, in coherence of all RecANFISs between each other to maximize the prediction accuracy of each of the MTS components without compromising the prediction accuracy of at least one of the other MTS components. Experimental studies have been carried out and the results of using the proposed NFCTM for multidimensional forecasting of the urban environment state in Moscow in conditions of a complex epidemiological situation have been obtained.