Reviewerly/exHarmony huggingface dataset:
Quantification of distortion in long-range video sequences: straightness metric and reference frame generation

In software-based turbulence mitigation, reliable no-reference image quality metrics are indispensable. In this research, a straight line detector (SLD)<sup>1</sup> is used to quantify warping due to turbulence. Our metric is based on the average length of detected straight lines, which constitutes a credible representation of the straightness in the image. The performance of the metric was tested on an anisoplanatic warping simulation. With increasing warping strength, the metric shows a decreasing average length of detected straight lines in the images. It is shown that the metric is able to differentiate between a set of simulated data with strong warping (C<sup>2</sup><sub>n</sub> = 10<sup>-14</sup> m<sup>-2/3</sup>) compared to weak warping (C<sup>2</sup><sub>n</sub> = 10<sup>-15</sup> m<sup>-2/3</sup>). Hereby the least and most warped frames found from the metric are considered and checked for credibility. A reference frame generation is developed by averaging the four least warped frames found from the metric. For evaluation of the reliability of the metric, it is tested on several data sets acquired at different distances. A correction of dewarping with optical ow is performed and the results with the reference frame generated based on the straightness metric and with a long exposure image as reference are compared.
Area Efficient VLSI Architecture for Reversible Radix-2 FFT Algorithm using Folding Technique and Reversible Gate

FFT is normally utilized in computerized flag preparing algorithms. 4G correspondence and different remote framework based correspondence are directly hotly debated issues of innovative work in the remote correspondence and organizing field. FFT is a calculation that speeds up the count of DFT. In the main stage, low multifaceted nature Radix-2 Multi-way Delay Commutator (R2MDC) FFT recurrence change method is created through Exceptionally Large Scale Integration System structure condition. Low power utilization, less zone and rapid are the VLSI primary parameters. Customary R2MDC FFT structure has more equipment multifaceted nature because of its escalated computational components. Two strategies are utilized to plan radix-2 FFT calculation. In firest strategy is plan radix-2 FFT with the help of reversible Peres gate and TR gate. Second method is design radix-2 FFT with the help of reversible DKG Gate. The all structure are usage vertex-4 gadget family Xilinx programming and looked at past calculation.
Database Design for Threat Modeling and Risk Assessment Tool of Automated Control Systems

The article describes the application being developed for threat modeling and risk assessment of the information security of an automated process control system (APCS) for a gas producing enterprise. The proposed solution considers the specific features of typical gas production enterprises belonging to the category of hazardous production facilities. One of the causes of emergency situations may be due to the lack of security of the automated process control system. In this regard, the urgent task is to ensure the correctness of the ongoing risk assessment of existing threats. The analysis of the most popular classes of information security solutions used for industrial networks was carried out, based on which solutions used in the market are considered in the class of threat modeling tools. The authors identified the specificity of the search for vulnerabilities and risk assessment in the industrial networks of the gas producing enterprise. A generalized mathematical model of information security for an automated process control system of a typical gas producing enterprise, developed by the authors, is described, as well as the application of this model for risk assessment. The basic formulas for calculating the risk assessment are given. The developed solution allows one to automate the process of risk assessment for a given topology of the process control system.
Multi-factor Influenced Integrated Driving Behavior Clustering Analysis Evaluation Model

For the discrete GPS driving behavior data, the traditional data clustering method cannot meet the rational clustering requirements. This paper analyzes and studies the commonly used clustering algorithms, as well as proposes an improved K-means++ algorithm for discrete data. In order to improve the accuracy of clustering, In order to improve the accuracy of clustering, the algorithm considers reducing the influence of non-principal components on data difference in the process of clustering difference metrics and gives the weighting process based on entropy weight method, so as to obtain a more reasonable clustering method. Finally, we implement the algorithm and apply it to the field of driving behavior analysis, and compare it with representative clustering algorithm. The experimental results show that the proposed algorithm has higher accuracy.
Blending Six Sigma and Software Development

While Six Sigma and other quality methodologies have been used extensively and quite effectively in the manufacturing sector for decades, the adoption rate in other industries such as the software development industry has been a bit slower. However, in recent years, research literature clearly suggests that the trend line is definitely on the upswing for software practitioners who see the value of applying Six Sigma and other proven quality methodologies in better managing software development projects, which have notoriously been prone to delays in migrating to production. We contemplate that the melding of these two methodologies will result in a sum that is greater than its two parts. In this paper, we elaborate on current issues/challenges of Six Sigma utilization in software development and management and explain problems that the software project managers encounter when dealing with them. We also investigate a broad collection of toolkits that are typically used in the Six Sigma framework and propose what tools, in each Six Sigma DMAIC (define, measure, analyze, improve, and control) phase are better candidates to consider and recuperate for software development and management.
Creating chatbots to talk with humans

More softwares are with the power of Artificial Intelligence (AI) making their functions, simultaneously more dynamic and capable of attending each users' personal needs. Chatfuel an application that allows users to create chatbots, an AI that can answer messages autonomously. However we question it: How easy chatbot creation is, especially when dealing the complex AI operating in them? In this sense, this exploratory research seeks to understand, through our volunteers' perspectives, with different levels of computational expertise, if this creation process is really as simple as divulged. To conduct the experiments, usability methods were used such as Cognitive Walkthrough, Communicability Evaluation and Attrakdiff. With the study results, among many others, users's noticed certain difficulty while using. the tool and we problematize certain aspects connected to AI, in the context of a human-computer interaction evaluation.
Development of the Lexicon of Argumentation Indicators

The paper presents the results of a preliminary analysis of the argumentation indicators observed in the corpus of popular science texts in Russian. Main pragmatic aspects of the argumentation signaled by discursive indicators are outlined. The classification of indicators takes into account pragmatic meaning and the type of language means used. Special attention is paid to insufficiently studied indicator constructions and classes of their core content words. We consider constructions with verbs and nouns of mental state, speech, inference, and mental impact. The process of creating a lexicon of argumentation indicators is described. Indicators are presented in the form of lexical units and lexical-grammatical patterns, which are automatically generated from annotated text fragments and can be manually corrected by the expert. The pattern description language allows to represent grammatical and semantic constraints, nested constructs, alternatives, and discontinuity. The lexicon of indicators will be used for automatic annotation of argument indicators in unannotated text, as well as for experiments in argument mining.
Quantum Recommendation System for Image Feature Matching and Pattern Recognition

The clustering and the classification techniques for pattern recognition are applied in variety of ways, but the problem of clustering binary vectors has not been thorough analyzed. This paper provides a novel approach towards the problem of pattern recognition through the ORB image descriptors. An advanced clustering method is provided in order to deal with the binary image feature descriptors, thus providing the opportunity of adding new classes of recognizable objects later-on: the k-majority algorithm over ORB descriptors is applied, where the Jaccard-Needham dissimilarity measure is used as a distance measure step of the algorithm. It is established, that the following methodology is well suited for a quantum interpretation of the system. A detailed analysis of such transformation is conducted and the Grover’s algorithm is proposed for providing the opportunity to search a specific feature in the available clusters, while reducing the number of iterations of the k-majority routine. In addition to the presentation of the system, described above, this paper provides also the main steps in constructing a similar recommendation system. To that the transformation from a classical to a quantum representation algorithm is described in detail. Such approach can be applied later-on in other applications. Both, the computational complexity and the verification correctness are also indicated below.
Transfer Learning based GDP Prediction from Uncertain Carbon Emission Data

This paper proposes the novel way to estimate the gross domestic product (GDP) of a country from its carbon emission (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) data. This alternative method to predict GDP is required for the war affected and non-accessible nations as the macroeconomic data available for those nations is highly unpredictable or insufficient. However, first we need to train and develop a reliable model for which we have used the Transfer learning (TL) approach. TL is applied in a way that a neural network (NN) or machine learning (ML) model is trained on the developed nation's data and used for testing with developing nation data. The NN models used in this paper are Extreme Learning Machine (ELM) and Generalized Regression Neural Network (GRNN), while ML model is Support Vector Regression (SVR). Since the data is measurement data collected from several devices, it contains noise and hence, uncertain. Thus, first we have modelled the dataset with type-1 fuzzy sets and then with the interval type-2 fuzzy sets. The results are then compared with the crisp input data values.
Comparision of Recommendation System Approaches

The usage of internet is increasing at a rapid speed. On internet data is available in a large amount, for accessing this data; there is requirement of personalization system is increasing permanently. This paper gives overview of approaches of recommendation system. Recommendation system is categorized into three categories: Content based Collaborative Filtering and hybrid approach. In this paper strength and weakness of these approaches has been discussed.
An Improvement of Compelling Graphical Confirmation Plan and Cryptography for Upgrading the Information Security and Preventing Shoulder Surfing Assault

Humans are continuously alluded as the weakest connect within the security chain stating that the issue does not lie with the security framework themselves but with clients who do not comply with the security conventions. The paper focuses on a proposed graphical authentication mechanism, since a human brain contains a way better capacity to memorize pictures or images, the proposed plot will serve the reason of better memorability, security as well shoulder surfing assault.
Adaptive lossless compression of skeleton sequences

The increasing importance of skeleton information in surveillance big data feature analysis demands significant storage space. The development of an effective and efficient solution for storage is still a challenging task. In this paper, we propose a new framework for the lossless compression of skeleton sequences by exploiting both spatial and temporal prediction and coding redundancies. Firstly, we propose a set of skeleton prediction modes, namely, spatial differential-based, motion vector-based, relative motion vector-based, and trajectory-based skeleton prediction mode. These modes can effectively handle both spatial and temporal redundancies present in the skeleton sequences. Secondly, we further enhance performance by introducing a novel approach to handle coding redundancy. Our proposed scheme is able to significantly reduce the size of skeleton data while maintaining exactly the same skeleton quality due to lossless compression approach. Experiments are conducted on standard surveillance and Posetrack action datasets containing challenging test skeleton sequences. Our method obviously outperforms the traditional direct coding methods by providing an average of 73% and 66% bit-savings on the two datasets.
How to Validate Ontologies with Themis

Validating ontologies regarding the requirements they need to satisfy is a crucial activity during ontology development in order to assure, both to domain experts and ontology developers, that the ontologies are complete regarding their needs. The aim of this work is to present Themis, a web-based tool for validating ontologies by means of test expressions, which represent the desired behaviour expected in an ontology if a requirement is satisfied. The purpose of these test expressions is to ease the formalization of the requirements into test cases and, therefore, the validation process.
Ensemble CNN and MLP with Nurse Notes for Intensive Care Unit Mortality

Nurse notes often contain subjective information of a patient's health status. However, these have not been widely used to predict clinical outcomes. Advances in natural language processing enable to extract information from unstructured clinical documents such as nurse notes. In this paper, we use TF-IDF representations of nurse notes to predict Intensive Care Unit (ICU) mortality while controlling for other candidate features such as gender, ICU type, age of patient at first admission, SAPS (Simplified Acute Physiology Score) II score, SAPS II probability, polarity and subjectivity scores of each nurse note. We introduce an ensemble of CNN and MLP model to predict 30-day ICU mortality. We apply our model to MIMIC III which is a medical benchmark dataset. We use TF-IDF representation of nurse notes as input to CNN and other ICU features to MLP network. Experimental results demonstrate that proposed ensembled model with nurse notes have higher performance than standalone models.
The Implementation of Context Clues Strategy in Infering the Meaning of Unknown Vocabulary to Improve Reading Skill

A key to reading comprehension is understanding the message conveyed and it depends heavily on the understanding of the meaning of words appeared in the text. For students, reading comprehension becomes one of the skills that they are more familiar with since they deal with reading texts every day even in examinations. This situation drives students to cope with many unfamiliar English words causing them stuck in comprehending the message conveyed. Therefore, assisting students to improve their comprehension by inferring word meaning from context is very important. Corresponding to the idea, the writer did an experimental study to implement context clues strategy which (is believed) can help students to infer words meaning in reading text. The study was implemented on 20 students of XII IPS 3 of SMAN 12 Banda Aceh to figure out how the strategy assists the students in inferring the meaning of unknown words. The result shows that the students’ comprehension improves well reflected on the mean score got by students from pretest and posttest increasing from 54.5 to 80.25. The development can be achieved since the strategy trains and guides the students to find out the clues provided in the text in the form of definition, synonym, antonym, example, or inference, that help them to guess the meaning of the word. In conclusion, context clues strategy does assist the students in inferring words meaning from context. Hence, it is suggested for English teacher to apply it in classroom in order to facilitate students’ reading comprehension.
Implementing a language with explicit assignment semantics

Anzen is a multi-paradigm programming language that aims to provide explicit and controllable assignment semantics. It is based on the observation that abstractions over memory management and data representation, as commonly adopted by contemporary programming languages, often transpire relics of the underlying memory model and lead to confusing assignment semantics in the presence of aliases. In response, Anzen’s goal is to offer a modern approach to programming, built on a sound and unambiguous semantics.This paper describes the implementation of a compiler for Anzen. Our implementation transpiles sources to an intermediate language inspired by the LLVM IR, designed to ease further analysis on Anzen’s statements. This intermediate representation is then consumed by a register-based virtual machine. We present the Anzen compiler’s architecture, introduce its intermediate language and describe the latter’s evaluation. Our work aims to set a reference implementation for future developments and extensions of the language.
A Framework for Queryable Video Analysis

Analysing video data requires the use of different models trained to retrieve or process data for a particular task. In this paper, we introduce an approach to represent the visual context within a video as queryable information. Through the use of computer vision techniques, we can detect and classify objects. Our system processes these classifications in order to construct a queryable data-set referred to as the real world model. The advantage of this approach is that through the formalisation of the information, we can create generic queries to retrieve information. This approach allows for processing to be done on edge devices such as embedded cameras while only transmitting detected information reducing the transmission bandwidth as well as infrastructural costs. The final recognition data is processed on the cloud. The analysed case study works on video traffic representation - an experiment around the transport domain. We evaluate and validate our approach by posing several queries to the system that generates information on the traffic situation, such as car counting and traffic flow. The results show that our approach can add context to classifications with a high degree of accuracy in some of the cases, achieving 95% car counting accuracy during the day. Fine tuning approaches are also discussed with reference to the video traffic representation case while keeping to the same proposed methodology.
Continuous learning mechanism of NLU-ML models boosted by human feedback

In this paper, we propose a novel framework that enables a machine learning model to constantly learn over a period of time and hence improve the performance with time and more data. We have compared the performance of different models which were trained only on the actual data against models trained with the data aided by the feedback collected by the automated framework.
Mapping translation technology and the multilingual needs of NGOs along the aid chain

Translation technology is now widely used almost in all areas where multilingual needs are identified, at least in commercial settings. From a market perspective, there is a wealth of tools that contribute to speeding up translation processes, reducing costs and improving quality. The multilingual needs of the third sector and more specifically those of NGOs in the aid chain, seem to have gone mostly unrecognized as far as translation technology is concerned. We see that there are no easily accessible/shareable large-scale resources in this area (translation memory databases, termbases or corpora, to name but a few) and stakeholders, working mostly on a volunteer basis, are ultimately forced to create their own ad hoc materials. With a few exceptions, no reference corpora, public sharing-data initiatives or collaborations can easily be found in the context of not-for-profit. It is my contention that the implementation of language tools and resources specifically designed for the needs of the third sector may see its impact on three key areas: Economic, social and technical. The ultimate purpose of the present contribution is to create momentum towards the association of translation technology and the humanitarian aid chain.
Hierarchical Control Architecture for a Learning Robot Based on Heterogenic Behaviors

The paper describes a hierarchical control architecture for robotic systems with learning that allows combining various goal-directed algorithms. A top-level control algorithm is proposed that switches control between base algorithms: Q-learning, random walk and a rule-based planning. The algorithm is implemented as a software module and is verified by the example of the task of finding a given door in a building of complex planning. The task is considered as a reinforcement learning problem in two distinct cases: with a goal fixed between the episodes and the goal changing from episode to episode. The simulation showed that the proposed method is more stable for different variants of the task than each of the basic ones separately, although it does not give the best result for each individual case.
Privacy Preserving Approach using t-closness and pailler encryption

Recently, clustering algorithms are used as a pre-processing model for privacy preserving methods in order to improve the diversity of anonymized data. In this paper we have compared various anonymization methods and encryption approaches. In that, T-closeness provides a better privacy when compared with k-anonymity, generalization and suppression. At the end we have used RSA and paillier comparison study to provide protection for the sensitive data.
Region-Focus Training: Boosting Accuracy for Deep-Learning Image Segmentation

At present, image segmentation is widely employed to identify areas of an object. Although most input images can be fit into the GPU memory, in some applications, such as lung histopathology images, each image has more than 1 gigapixels and it is impossible to fit the entire image into the GPU memory. In such a situation, we need to divide the image into sub-images and collectively segment each sub-region. In this work, we study behavior of the image segmentation in sub-images for wound segmentation. This work analyzes how changes in the training areas affect the accuracy of segmentation when results from sub-images are merged back to the original image size. In other words, we can tune the accuracy by sampling some regions more frequently to produce training data focusing on challenging regions. Our analysis showed that some regions of a target object could be harder to segment and providing sub-images from these regions tended to improve overall accuracy. We also propose to use accuracy heat map to visualize impacts of sampling subregions on the accuracy. The results of adding appropriate data to focus on challenging areas in which the machine has low accuracy problems improves performance in terms of intersection over union (IoU) as follows. When a machine was trained with 10 images, the IoU increased from 52.23% to 53.79%. When trained with 20 images and 115 images, IoU increased from 61.46% to 64.59%, and 77.97% to 78.16%. The proposed technique can be generalized and applied to other segmentation problems.