Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Segmentation and Classification In HDR Satellite Imagery Using Neural Network

Satellite-based image analysis becomes a study subject in a variety of applications such as geological survey, agricultural applications for soil analysis, environmental impact analysis, natural disaster to find out the change, urban planning to find appropriate locations for further urban development, etc. In this work image classification plays most important role in these applications. In this work HDR satellite image dataset used for pre-processing purpose. Normally the sky's dynamic range is as large as 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> :1, while the LDR (Low Dynamic Range) display has a dynamic range of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> :1. Therefore, HDR satellite image needs to compress this dynamic range to get LDR image. TMO (Tone Mapping Operator) is used for excellent visualization purposes to solve this issue and has served excellent image quality. Satellite captured images are very large in size, with ordinary display facing difficulties in viewing and understanding the information available in an image. Therefore, the whole image needs to be divided into tiny components with big data, so it helps to comprehend and visualize. To overcome this problem segmentation method is to be used. There are various image segmentation methods like Edge based, Clustering based, watershed based, etc. In this work compared clustering based image segmentation like K-means clustering, Mean shift clustering and SLIC (Simple Linear Iterative Clustering) by extracting various features like processing time, Mean, Entropy, variance, etc,. In this work HDR satellite image classified using Artificial Neural Network (ANN) with feed forward algorithm based on supervised learning which delivered 99.53% accuracy.
A Myhill-Nerode theorem for register automata and symbolic trace languages

We propose a new symbolic trace semantics for register automata (extended finite state machines) which records both the sequence of input symbols that occur during a run as well as the constraints on input parameters that are imposed by this run. Our main result is a generalization of the classical Myhill-Nerode theorem to this symbolic setting. Our generalization requires the use of three relations to capture the additional structure of register automata. Location equivalence ≡l captures that symbolic traces end in the same location, transition equivalence ≡t captures that they share the same final transition, and a partial equivalence relation ≡r captures that symbolic values v and v′ are stored in the same register after symbolic traces w and w′, respectively. A symbolic language is defined to be regular if relations ≡l, ≡t and ≡r exist that satisfy certain conditions, in particular, they all have finite index. We show that the symbolic language associated to a register automaton is regular, and we construct, for each regular symbolic language, a register automaton that accepts this language. Our result provides a foundation for grey-box learning algorithms in settings where the constraints on data parameters can be extracted from code using e.g. tools for symbolic/concolic execution or tainting. Moving to a grey-box setting may overcome the scalability problems of state-of-the-art black-box learning algorithms.
Burrows‐Wheeler post‐transformation with effective clustering and interpolative coding

Summary Lossless compression methods based on the Burrows‐Wheeler transform (BWT) are regarded as an excellent compromise between speed and compression efficiency: they provide compression rates close to the PPM algorithms, with the speed of dictionary‐based methods. Instead of the laborious statistics‐gathering process used in PPM, the BWT reversibly sorts the input symbols, using as the sort key as many following characters as necessary to make the sort unique. Characters occurring in similar contexts are sorted close together, resulting in a clustered symbol sequence. Run‐length encoding and Move‐to‐Front (MTF) recoding, combined with a statistical Huffman or arithmetic coder, is then typically used to exploit the clustering. A drawback of the MTF recoding is that knowledge of the character that produced the MTF number is lost. In this paper, we present a new, competitive Burrows‐Wheeler posttransform stage that takes advantage of interpolative coding—a fast binary encoding method for integer sequences, being able to exploit clusters without requiring explicit statistics. We introduce a fast and simple way to retain knowledge of the run characters during the MTF recoding and use this to improve the clustering of MTF numbers and run‐lengths by applying reversible, stable sorting, with the run characters as sort keys, achieving significant improvement in the compression rate, as shown here by experiments on common text corpora.
Justice Reframed? A Comparative Critical Discourse Analysis of Twitter Campaigns and Print Media Discourse on Two High-Profile Sexual Assault Verdicts in Ireland and Spain

Analyses of media discourses on judicial verdicts in sexual violence cases offer critical insight into how this topic is mediated. This study explores post-verdict mainstream and social media reaction to two high-profile verdicts in sexual assault cases in Ireland and Spain: #IBelieveHer, launched in March 2018 following the acquittal of four men accused of rape in Belfast, and #YoTeCreo which coalesced online after five men were given a lesser sentence for sexual abuse in Pamplona in April 2018. This study first identifies the stance taken by mainstream media where verdicts were contrary to “popular” opinion. Secondly, it analyses dominant hashtags that emerged on Twitter following both verdicts. Finally, it traces similarities and differences in discourse patterns identified on mainstream and social media platforms across both countries. For analysis, we employed a Critical Discourse Analysis-based theoretical framework (e.g.,KhosraviNik 2017 KhosraviNik, M. 2017. “Social Media Critical Discourse Studies (SM-CDS).” In Handbook of Critical Discourse Analysis, edited by J. Flowerdew and J.E. Richardson, 583–596. London: Routledge.[Crossref] , [Google Scholar], “Social Media Critical Discourse Studies (SM-CDS).” In Handbook of Critical Discourse Analysis, 582–596) with resources from Framing Analysis (e.g.,Goffman 1974 Goffman, E. 1974. Frame Analysis: An Essay on the Organization of Experience. Vancouver: Harvard University Press. [Google Scholar], Frame Analysis: An Essay on the Organization of Experience. Vancouver: Harvard University Press) for methodological purposes. Findings suggest Spanish print media contained greater debate around legal understandings of sexual violence while the Spanish Twitter campaign was outward-oriented and explicitly feminist. #IBelieveHer displayed a narrower focus, with the “celebrity” dimension to this case contributing to a personalised, less nuanced, discourse on social and print media and more polarised discussion.
DeepDiseaseInsight: A Deep Learning &amp; NLP based Novel Framework for generating useful Insights from Disease News Articles

We live in an era of information explosion where media for information communication and information sharing have proliferated due to the onset of a digital age. One such important source and medium of consolidated, comprehensive and reliable information, which has persisted through centuries, is news articles. News articles have been considered as a primary source of information, ranging from local to global scope, and encompassing a wide array of domains, by most of the human population. However, as the technology for dissemination of information attracts innovations, it becomes equally important that the consumption and utilization of this information becomes smart as well. If processed properly and treated in an intelligent manner, the information contained in news articles can reveal useful insights and hidden patterns that might prove to be of critical importance in certain decision making pertaining to the topics and domains in question. The paper puts forward a novel framework created by combining Natural Language Processing methodologies like Sentiment Analysis and Information Retrieval, which are performed on top of a Deep Learning based classification with aptly defined classes for generating useful insights from a news article dataset. The paper contains the details regarding the implementation of this system and also a presentation of results on a particular data set of news articles - articles on HIV-AIDS, which have been categorized into meaningful classes and have been processed using aforementioned NLP tasks. Through this paper, a curated dataset of 3,000 news articles related to HIV-AIDS has also been introduced, which will be open sourced so as to inspire further research and experimentation in this specific domain.
An Efficient Clustering Approach utilizing an Advanced Particle Swarm Optimization Variant

Clustering plays an important role in almost every field of real time applications and whenever optimization is the core concern, good quality clustering is crucial. Different optimization techniques are proposed with respect to clustering and find its applications in areas like Image Processing and Pattern Recognition, Finance, Communication Networks, Biological Sequences, etc. This paper uses a superior variant of Particle Swarm Optimization (PSO) algorithm based on one of the density based clustering methodologies i.e. Subtractive Clustering (SC). The implementation of the algorithm proved its excellence in numerical and text data clustering by addressing the several issues those were came across in the literature survey of PSO based clustering techniques. The algorithm is intended to cluster image datasets for fine classification of images. The performance of the algorithm is evaluated against K-means, K-PSO, Subtractive-PSO over synthesized and MRI image datasets with respect to quantization error, intra and inter cluster distances. The obtained results showed a better or comparable performance.
Advancement on Substitution Cipher using Multiple Substitution Table

Cryptography is one of the ways to secure data while transmitting from one location to another. There are number of techniques to secure a data by using cryptography. This paper focuses on Substitution Cipher by using Multiple Substitution Table. In ESC (Extended Substitution Cipher), the key has two parts. The first part is used as a seed value and the second part is `n' which is used to generate n different substitution cipher which will be used for encrypting the plain text. After generating cipher text two attacks are applied one is Brute Force attack and another one is frequency analysis attack. Results are generated. This is fastest and improved method for generating cipher text.
Efficiency Analysis of Neural Networks Ensembles Using Synthetic Data

Ensembles of neural networks are used to improve the performance of the model and show themselves well in the case of using deep recurrent and convolutional networks on serial [1] and graphic data [2]. The article discusses the advisability of using a snapshot ensemble of not deep feedforward neural networks on a synthetic dataset.
Classification of SOA-Based Cloud Services Using Data Mining Technique

Cloud computing model fulfills storage and computation requirements of users in the form of services that are based on Service-Oriented Architecture (SOA). Services in cloud computing are offered with different quality attributes such as flexibility, mobility, adaptation, migration, etc. In recent years, cloud computing has grown at a very fast pace offering a huge number of cloud services with various quality attributes but it still faces some issues. One of them is that there is a lack of approach which can classify a cloud service based on its quality attributes for the cloud users. In this paper, a new approach is presented to classify cloud services using data mining “Bayesian classification” technique. The proposed approach is evaluated empirically via experiments that are performed on cloud-based data set containing data for 20 services, where each service having ten quality attributes. Results show that the proposed approach successfully classifies the cloud service in one of three classes (best, good, and satisfactory). Further, the proposed research will help the users in selecting the services as per their desired class.
Object tracking scheme using part-based correlation filters

Object tracking remains a challenging problem in computer vision. Recently, methods based on correlation filters have achieved good performance in benchmarks. Usually, most of these visual trackers rely on tracking the object as a whole, not being able to handle the object variations. This paper proposes a scheme using several local correlation filters combined with a global correlation filter for improving the performance of object tracking methods based on correlation filters. We integrated this scheme into the traditional Kernelized Correlation Filter (KCF) method to evaluate our proposed approach. Experiments show that the proposed scheme is consistent and achieves better results compared to the baseline.
An Improved Adaptive Transfer Function for Explosion Spark Generation in Fireworks Algorithm

Si, Tapas Mukhopadhyay, AmitFireworks algorithm (FWA) is a swarm intelligence algorithm simulating the explosion process of fireworks in the sky at night. An improved FWA with an adaptive transfer function (ATF) termed as (FWA-ATF) is recently developed. This ATF is used to compute the number of "explosion sparks" and the amplitudes of explosion. This paper devises a modified FWA-ATF algorithm with deterministically controlled ATF and the best selection strategy. The improved ATF controls the exploration and exploitation of the fireworks, whereas the best selection strategy is used to keep diversity in the search space. The devised algorithm is applied on fifteen CEC2015 benchmark optimization problems and a comparative study has been made with state-of-the-art variants of FWA such as FWA-ATF, dynamic FWA with covariance mutation (DynFWA-CM) and cooperative framework in FWA (CoFFWA). The experimental results demonstrate that the devised algorithm statistically outperforms others in function optimization.
Vision-Based Automated Traffic Signaling

Traffic management has become one of the most complicated issues of recent times in metropolitan cities. Conventional traffic signaling systems are pre-programmed and alternate between red and green lights without any estimation of traffic. This signaling methodology leads to problems during peak hours at the intersections, where traffic ratio in a few lanes are dense when compared to others. Therefore, an efficient model is needed, which can manage the traffic flow at a certain point. The proposed model offers a solution using the CCTV footage from signal cameras to decongest traffic, based on a live estimate of traffic density. A state-of-the-art Deep Neural Network algorithm determines the number of vehicles and their type at a particular signal for object detection called You Only Look Once (YOLO), as it provided speed and accuracy in real-time. Based on vehicle count and road associated parameters, traffic density is computed to provide a dynamic extension of signaling time for a particular lane. Therefore, time saved from empty lanes is used to clear traffic on other busy lanes.
A Raspberry Pi-Based Identity Verification Through Face Recognition Using Constrained Images

In this paper, we proposed a novel, cost-effective and energy-efficient framework by introducing a Raspberry Pi-based identity verification through face recognition in an offline mode. A Raspberry Pi device and mobile phone is wirelessly connected to standard pocket WIFI to process the face detection and face verification. The experimental tests were done using 3000 public constrained images. The proposed method is implemented on Raspberry Pi 3 run in Python 3.7 where the datasets and trained datasets were experimented using LBP algorithm for face detection and face verification in five split testing. The result was interpreted using the confusion matrix and Area Under the Curve (AUC) and Receiver Operating Characteristics (ROC). To sum up, the proposed method showed an average result of 0.98135% accuracy, with 98% recall score and an F1-score of 0.9881. During the offline mode testing, the face detection and verification average timing is 1.4 s.
An Empirical Study of Non-Cryptographically Secure Pseudorandom Number Generators

This paper gives an empirical study of various pseudorandom number generators (PRNG), along with their brief history, from the traditional ones such as Middle Square Method to the most advanced ones like Xoroshiro128+, along with various modifications that were added to them. Comparing them on the basis of their computational times, code complexities and the algorithms used by them, it can be deduced that which of the PRNG is the fastest, or which gives the most equidistant random numbers and which generator is most useful for a particular purpose. This paper doesn’t cover any cryptographically secure pseudorandom number generator (CSPRNG) and all the generators mentioned henceforth are cryptographically insecure.
Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes

In the Master of Science program Business Information Systems at a Swiss university, the authors have been teaching artificial intelligence (AI) methods, in particularly computational intelligence (CI) methods, for about ten years. AI and CI require the ability and readiness of a deeper understanding of algorithms, which can hardly be achieved with classical didactic concepts. Therefore, the focus is on assignments that lead the students to develop new algorithms or modify existing ones, or make them suitable for new areas of applications. This article discusses certain teaching concepts, their changes over time and experiences that have been made with a focus on improving students’ learning outcomes in understanding and applying special AI/CI methods such as neural networks and evolutionary algorithms.
Semi-automatic Causal Graph Construction System

As a kind of special relation, causality demonstrates the direct impact for causes on effects. Especially in the medical field, causal graph plays an essential role, which can help in the diagnosis and analysis of diseases. Some researchers have tried to design methods to extract causalities from literature, however, the causality knowledge is limited in literature. A causal graph construction system is needed to integrate causality knowledge from various sources. Hereby, this paper aims to design a semiautomatic causal graph construction system to integrate causalities from the literature and human experiences. The system consists of three modules, including causal graph illustration, causal graph modification, causality extraction from free texts. Researchers can use this system to build their own causal graphs by extracting causalities from literature and modifying them according to their own knowledge.
Browser Based Digital Sticky Notes for Design Thinking

The Design thinking process is a common process across a range of industries. The process, often utilizing sticky notes, has people coming together to collaboratively come up with ideas, aimed at deciding on features, functionality, sale-ability, etc. As part of the process, in its co-located set-up, users are moving around, creating temporary ad-hoc groupings, which help to drive new insights. In this demo, we show a web browser based sticky note application that caters to these co-located ideals, while utilizing ubiquitous interfaces, smartphones and regular TV screens. Users interact with the system through their phone, which gives them options to create notes and then utilize their camera to place or pick-up notes. The large shared screen utilizes a grid-based QR code system that allows for many simultaneous users onto the same screen, detecting where a user's camera is pointing, without blocking any other users using the system simultaneously.
Pictorial Unleashed

In this pictorial, we present two different tangible interaction designs that enable managing of wireless connections between devices in a smart home environment. One design (Interaction Tile) utilizes a centralized approach based on a high level of semantic abstraction. The other design (Nodes) employs a distributed and localized approached building on laws of grouping from Gestalt psychology. These works were previously used for a comparative study to develop insights into the different mental models users developed, while using these designs. In this pictorial, we focus on the interactive qualities of the two designs and we propose a way to invite the reader to actively and physically engage with the pages of this publication. The reader is asked to print the pages of this pictorial and to engage in folding sequences on particular pages in order to actively engage with the different ways of interacting between the two different designs.
Ontological Model of an Electronic Archive Document

The task of semantic structuring and extracting the necessary knowledge from large electronic archives when making management decisions is very urgent. This work presents an ontological model of a project document and an algorithm for classifying a large amount of unstructured information in an electronic archive. The work also presents the results of experiments on data from the electronic archive of the Federal Scientific and Practical Center MARS. The experimental results show the effectiveness of the developed models and algorithms.
Structured (De)composable Representations Trained with Neural Networks

This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to-end deep learning to learn structured and composable representations from input images and discrete labels. The obtained representations are based on distance estimates between the distributions given by the class label and those given by contextual information, which are modeled as environments. We prove that the representations have a clear structure allowing decomposing the representation into factors that represent classes and environments. We evaluate our novel technique on classification and retrieval tasks involving different modalities (visual and language data). In various experiments, we show how the representations can be compressed and how different hyperparameters impact performance.