Reviewerly/exHarmony huggingface dataset:
Verb Focused Answering from CORD-19

At this time of a pandemic turning into an infodemic, it is significant to answer questions asked on the research related to that. This paper discusses a method of answering questions leveraging the syntactic structure of the sentences to find the verb of action in the context corresponding to the action in the question. This method generates correct answers for many factoid questions on descriptive context passages. The proposed method finds all the sentences in the passage, which has the same or synonymous verb as the verb in the question, processes the dependencies of the verbs obtained from the dependency parser and proceeds with further rule-based filtering for matching the other attributes of the answer span. We demonstrate this method on CORD-19 data [3] evaluated with free form natural language questions.
SPARKs: Succinct Parallelizable Arguments of Knowledge

We introduce the notion of a Succinct Parallelizable Argument of Knowledge (SPARK). This is an argument of knowledge with the following three efficiency properties for computing and proving a (non-deterministic, polynomial time) parallel RAM computation that can be computed in parallel time T with at most p processors: — The prover’s (parallel) running time is \( T + \mathrm{poly}\hspace{-2.0pt}\log (T \cdot p) \) . (In other words, the prover’s running time is essentially T for large computation times!) — The prover uses at most \( p \cdot \mathrm{poly}\hspace{-2.0pt}\log (T \cdot p) \) processors. — The communication and verifier complexity are both \( \mathrm{poly}\hspace{-2.0pt}\log (T \cdot p) \) . The combination of all three is desirable, as it gives a way to leverage a moderate increase in parallelism in favor of near-optimal running time. We emphasize that even a factor two overhead in the prover’s parallel running time is not allowed. Our main contribution is a generic construction of SPARKs from any succinct argument of knowledge where the prover’s parallel running time is \( T \cdot \mathrm{poly}\hspace{-2.0pt}\log (T \cdot p) \) when using p processors, assuming collision-resistant hash functions. When suitably instantiating our construction, we achieve a four-round SPARK for any parallel RAM computation assuming only collision resistance. Additionally assuming the existence of a succinct non-interactive argument of knowledge (SNARK), we construct a non-interactive SPARK that also preserves the space complexity of the underlying computation up to \( \mathrm{poly}\hspace{-2.0pt}\log (T\cdot p) \) factors. We also show the following applications of non-interactive SPARKs. First, they immediately imply delegation protocols with near optimal prover (parallel) running time. This, in turn, gives a way to construct verifiable delay functions (VDFs) from any sequential function. When the sequential function is also memory-hard, this yields the first construction of a memory-hard VDF.
Solving Nurikabe with Monte-Carlo Tree Serach

Puzzle solving with AI is becoming one of the hot topic fields in computer science. The algorithmic challenges that are laid behind this topic , make it more attractive. One of these NP-complete puzzles that is hard to solve for human being is Nurikabe Puzzle . few methods have been developed for solving this puzzle that have a poor performance in time and memory. Monte-Carlo Tree Search(MCTS) is a famous reinforcement algorithm that have been used in many Logical games .In this article we use Monte-Carlo Tree Search method for creating the efficient method that performs well on time that it takes for solving the puzzle .no one have ever used this method for solving this problem and also we test our algorithm with a wide range of test cases from easy to hardest ones.
Size Estimation of Puzzle Games

Software game development industry is increasing day by day. Games development is different and more complicated than other software development models. This leads toward difficulty in the accurate estimation of size, cost and effort required for successful development of computer games. Many projects fail due to poor size estimation. In this research, we developed a model to estimate the size of puzzle games. This work is an extension of the board-games model for size estimation. Dataset used for this research is collected from open source code repositories. We analyzed the predictors already defined for the board-based size estimation model and identified a new predictor for size estimation of puzzle games. Applying SLR and MLR on the data set of puzzle games gave us promising results. The value of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> obtained is 0.93. Results of validation of the model are MMRE 0.29, PRED (30) is 0.80. Using the K- fold cross validation, the model was validated using K=6. Results show that 4 out of 6 models have MMRE less than 0.30.
On Logical Literary Work Generation and More

In several papers, the author has discussed the possibility of the computational literary work generation. That was based on Julia Kristeva's the concept of the intertextuality. This very simple application was achieved by random combination of the selected words/phrases/letters. It is a simple technique such as the cutup method. The author showed even such a simple method could generate literary works that can be regarded as not so bad literary works. However, for more intelligent generation, the author introduced abduction. By abduction, it is possible to guarantee the logical consistency and coherency. However, it does not care the very cognitive aspects such as beautiful, sentimental, and valuable, which will be very important factor in the literature. In this chapter, the author will discuss to add such cognitive aspects to the logical generation of literary works.
Gradual Typing Using Union Typing With Records

Dynamic typed languages are characterized by their expressiveness and flexibility to develop prototypes, while static typed languages allow early detection of errors and the optimization of source code. Gradual typing languages allow programmers to make use of both approaches, static and dynamic typing, and thus, obtaining the advantages that both represent. The objective here is to revisit the static part of the approach to a gradual interpretation of union types based on the design of Gradual Union Types through an extension with the record data-structure. This contributes to understand the abstraction and reasoning behind Gradual Typing in order to have useful future extensions for other data-structures.
Facial Profiles Recognition Using Comparative Facial Soft Biometrics

This study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.
Application of Unscented Kalman Filter for Parameter Estimation of Nonlinear Systems

Sometimes the parameters of a system dynamics are not exactly known while these are required to set the control law and update the existing control scheme. This becomes much difficult when the dynamics of the system is nonlinear. Thus, this paper deals with the estimation of the parameters of a nonlinear system using unscented Kalman filter (UKF). The UKF handles the nonlinear dynamics without linearization and approximation during the estimation and hence estimates the parameters as well as states perfectly. A well-known example of nonlinear dynamics, Van Der Pol oscillator system, has been used to illustrate the parameter estimation. The simulation of the Van Der Pol oscillator has been done first to generate the measurements, and then, the state and measurement model of the system have been setup which further have been used during the estimation.
Implementation and Evaluation of Local Knowledge Inheritance System

In this study, we implemented and evaluated a local knowledge inheritance system using old maps and location-based augmented reality (AR) technology. This system is comprised of a mobile local knowledge inheritance application and a local knowledge inheritance data management system. This system provides local knowledge to users using a map axis and location-based AR on mobile terminals. The map axis clarifies the relevance associated with the times and themes in local knowledge. We conducted an evaluation experiment with 47 local residents. The results confirm the effectiveness of the system relative to improving understanding of local knowledge and the effectiveness of using museum materials.
Color Image Filtering in Bessel-Fourier Moments Domain

In this research, we have conducted a study on color image filtering in Bessel-Fourier moments domain. Bessel-Fourier moments of the two testing color images are computed independently from the three color channels (RGB), then lowpass and highpass filters are applied to the data in Bessel-Fourier moments domain for our investigation. For comparison, filters are applied in Fourier Frequency domain as well. The experimental results suggest that Bessel-Fourier moments of the lower orders contain mainly information of smooth varying components of images, while those of the higher orders are more related to details such as sharp transitions in intensity. It is also found that the Gaussian filters would reduce the ringing effect in Bessel-Fourier moments domain as they do in the Fourier Frequency domain.
Optimal averaging time for improving observer accuracy of stochastic dynamical systems

In the problem of remote estimation by a centralized observer, improvements to the accuracy of observer estimates come at a cost of higher communication bandwidth and energy consumption. In this article we improve observer estimation accuracy by reducing the measurement variance on the sensor node before its transmission to the centralized observer node. The main contribution is to show that measurement variance is a trade-off between dynamical system variance and sensor variance. As a result there is an optimal averaging time that minimizes measurement variance, providing more accurate measurement to the observer. The optimal averaging time is computable by solving a univariate optimization problem.
A survey of grammatical variability in Early Modern English drama

Grammar is one of the levels within the language system at which authorial choices of one mode of expression over others must be examined to characterise in full the style of the author. Such choices must however be assessed in the context of an understanding of the extent of variability that exists generally in the language. This study investigates a set of grammatical features to understand their variability in Early Modern English drama, and the extent to which Shakespeare’s grammatical style is distinct from or similar to that of his contemporaries in so far as these features are concerned. A review of prior works on Shakespeare’s grammar establishes that the quantitatively informed corpus linguistic approach utilised in this study is innovative to this topic. Using two of the grammatically annotated corpora created by the Encyclopedia of Shakespeare’s Language project, one made up of Shakespeare’s plays, one of plays by other playwrights of the period, we present a method which steers a course between the narrow focus of close reading and the naïvely quantitative metrics of authorship analysis. For a set of 15 grammatical features of stylistic interest, we retrieve all instances of each feature in each play via complex corpus search patterns and calculate its relative frequency. These results are then considered, in aggregate and at the text level, to assess the differences across plays, across dramatic genre, and between Shakespeare and the other dramatists, via both statistical summary and visual representation of variability. We find that Shakespeare’s grammatical style tends (especially in comedies and tragedies) to disprefer informationally dense noun phrases relative to the other playwrights; and, moreover, to prefer tense, aspect and pronoun features which suggest a greater degree of narrative focus in his style. Furthermore, we find Shakespeare to be highly distinct in his preferences regarding verb complement subordinate clause types. These findings point the way both to a novel methodology and to further as yet unconsidered questions on the subject of Shakespeare’s grammatical style.
Background Modeling and Elimination Using Differential Block-Based Quantized Histogram (D-BBQH)

The proposed work uses differential block-based quantized histogram (DBBQH) feature extraction technique to background modeling and elimination for foreground extraction. \(D\text {-}BBQH\) uses a statistical approach to estimate the temporal consistency of pixel intensity concerning location. Besides, the block-based technique is less noise prone than the same of pixel-based processing and includes local features to estimate the background more efficiently than that of global processing. The proposed approach used a low-dimensional feature set, which requires less amount of time for background modeling or foreground extraction. A benchmark SBMI dataset, which incorporates complex video data, is used to complete the experimentation of the proposed approach, and the results prove the effectiveness of the proposed approach.
An Efficient De-blocking Filter for Quality Improvement of Medical Image Analysis

High-Efficiency Video Coding (HEVC) is a video coding method which is used for the better accuracy among the other standards based on the compression of the video frames such as H.264/AVC. The HEVC is mainly used for the reduction of the bit rate. The proposed system works under the medical images, in image processing stages the de-blocking filter, is one of the filter used to reduce the noise by decoding the compressed video and Sample Adaptive Offset (SAO) filter decreases the distortion of the sample in the image. By using these two methods the video is obtained in better quality.
Statistical Granular Framework Towards Dealing Inconsistent Scenarios for Parkinson’s Disease Classification Big Data

While the medicinal and healthcare services sector is being changed by the competence to record gigantic measures of data about individual patients, the tremendous volume of information being gathered is outlandish for people to dissect/analyze. Over the past years, numerous techniques have been proposed so as to manage inconsistent data frameworks. Statistical applied ML facilitates an approach to consequently discover examples and reasoning about information. How one can transform raw data into valuable information that can empower healthcare professionals to make inventive automated clinical choices. The prior forecast and the location of disease cells can be profitable in curing the ailment in medical/healthcare appliances. This paper presents a novel statistical granular framework that deals with inconsistent instances, knowledge discovery, and further performs classification-based disease prediction. The experimental simulation is carried out on Parkinson’s disease classification dataset. The experimental results and comparative analysis with some significant existing approaches prove the novelty and optimality of our proposed prototype.
An Extension of the Stable Semantics via Lukasiewicz Logic

Logic Programming and fuzzy logic are active areas of research, and their scopes in terms of applications are growing fast. Fuzzy logic is a branch of many-valued logic based on the paradigm of inference under vagueness. In this work we recall some of the interplay between three 3-valued logics that are relevant in these areas: The Lukasiewicz logic, the intermediate logic G3 and the paraconsistent logic G3′, and we present a contribution to the area of answer sets that consists in extending a definition of stable model based on proof theory in logic G3, to a more general definition that can be based on any of the more expressive logics G3′ or Lukasiewicz. Finally we present and explore a new 4-valued logic that bears relation to G3 and to Lukasiewicz 4-valued logic.
OntoEskom: An Ontology for the Supply and Generation of Electricity by ESKOM

There has been a major concern with the electricity supply in South Africa with the problem of load shedding. Hence, an understanding of how the energy used for electricity is generated and supplied is essential to the general public as well as experts in the domain of energy-management. Therefore, this paper develops and ontology for the supply and generation of electricity by ESKOM, namely, OntoEskom. The aims are (1) to provide a formal, common and shared understanding of the domain of electricity generation and supply by Eskom to the general public and stakeholders as well as (2) developing a knowledge base system that may facilitate the integration of services at all ESKOM divisions for better efficiency in electricity supply and management. The proposed OntoEskom ontology is not intended to be a complete representation of the domain of electricity generation and supply by Eskom, but it constitutes a foundation for further development of a knowledge base system for Eskom.
Statistical Regression and Soft Computing in Estimating Permanent Deformation of HMA with the Inclusion of AIMS Coarse Aggregate Angularity Index

Estimation of the effects of aggregate angularity on permanent deformation of asphalt concrete is understudied because of its complexity. Existing permanent deformation models are based on linear and non-linear regression techniques and sometimes are not capable of dealing with a multiplicity of factors that contributes to hot mix asphalt (HMA) behaviour. This paper presents expressions that estimate the effects of aggregate angularity and other common factors on the permanent deformation of HMA. Laboratory repeated load creep test data of cylindrical samples of varying coarse aggregate angularity classifications (rounded, sub-rounded, sub-angular, angular), heights (100, 150 mm), temperature (25, 45°C) and loading (70, 200 kPa) were used. The study used non-linear regression analysis and soft computing technique to develop the mathematical expression. Statistically these two techniques provided acceptable prediction results. However, parametric and perturbation analysis of the proposed equations indicated that soft computing is the most accurate since it is capable of capturing the realistic dynamics of the physical process being modelled. The influence of a particular angularity classification maybe useful to road engineers when evaluating the mechanical behaviour, mix designs, field quality control, and assurance as well as the performance prediction of asphalt concrete.
Improving Universal Style Transfer using Sub-style Decomposition

Many universal Neural Style Transfer (NST) methods have been recently proposed. These methods are capable of transferring arbitrary styles in a style-agnostic manner by employing the appropriate feature transformations. Despite their apparent effectiveness when used to transfer a single style from relatively simple images, they are usually not capable of effectively handling more complex styles, producing artifacts, as well as reducing the quality of the synthesized textures in the stylized image. In this paper, we propose a novel universal style transfer method that separately models each sub-style that exists in a given style image, overcoming these limitations to a great extent. In this way, the proposed method is capable of detecting and semantically matching the sub-styles that exist both in the content and style images, improving the style transfer quality and reducing artifacts over state-of-the-art NST approaches, as demonstrated in this paper using both qualitative and quantitative experiments.
Content Based Image Retrieval Process for Speech Annotated Digital Images

In this paper, a new indexing and retrieval system for digital pictures has been presented with speech notes based on syllable-converted picture-like samples. The growth in reputation of digital camera spots in the direction of growing number of customers with huge album of digital images in their computers which includes gloss and retrieval, has become known as vital trouble in management of virtual photographs. Usually, customers must type such advanced content manually and repetitively to comment on their snap shots. Multidimensional scaling is used to identify n-best users to deal with recognition errors and is converted into an image-like sample. Though the availability of an integrated microphone in maximum virtual cameras, consumer can now articulate about their pictures onto the spot and records these observations into machine readable documents. Recently in automatic voice recognition technology, speech gloss and retrieval gives an option and predictable strategy for existing photograph ordering, recovery methods and supplanting the repetitive work of manual writing. In speech annotation and retrieval, a hybrid mechanism is utilized to assimilate picture-like styles, syllables, words and characters.
Zero‐variance minibatch Monte Carlo for pixel‐wise visual tracking

In this study, the authors present a novel visual tracking method using the pixel-wise posterior estimation and minibatch Monte Carlo sampling. To avoid background pixels in the noisy bounding box representation, they estimate the posteriors in a pixel-wise manner. To boost the pixel-wise posterior estimation, they adopt minibatch Monte Carlo sampling, where only a small portion of pixels are used for inference. Experimental results demonstrate that the proposed visual tracker produces accurate tracking results using a small portion of pixels for the posterior estimation and is comparable to state-of-the-art methods.
Companies Image Evaluation Using Social Media and Sentiment Analysis

While the literature contains many slightly different definitions for the image of a company, they all put great emphasis on its importance. Many of the messages posted on social media networks nowadays contain strong sentiment and emotion indications regarding almost any topic, therefore turning them into a rich and almost real-time data source for analyzing the public’s opinion on various subjects, including many of the factors that can influence the image of companies. Thus, in this chapter we propose a natural language processing (NLP) approach for monitoring and evaluating the companies’ image by extracting information from social media messages posted on Twitter. The messages are analyzed using a bag-of-words sentiment analysis approach. The results of the analysis are stored as semantically structured data, thus making it possible to fully exploit the possibilities offered by semantic web technologies, such as inference and accessing the vast amount of knowledge in Linked Open Data, for further analysis.
Blind recognition of binary BCH codes based on Euclidean algorithm

A novel method based on Euclidean algorithm is proposed to solve the problem of blind recognition of binary Bose–Chaudhuri–Hocquenghem (BCH) codes in non-cooperative applications. By carrying out iterative Euclidean divisions on the demodulator output bit-stream, the proposed method can determine the codeword length and generator polynomial of unknown BCH code. The computational complexity is derived as O (n 3 ). Simulation results show the efficiency of the proposed method.