Reviewerly/exHarmony huggingface dataset:
Universal Sinhala Library: Language Specific Encryption Platform for Sinhala Language

Security has become a significant challenge in the modern world. Number science and mathematics opened up a vast path to work with programming models to create innovative mechanisms which improve text encryption. Universal Sinhala Library is a Sinhala language-specific text encryption platform. Platform architecture has been designed to demonstrate every possible combination of the Sinhala alphabet, including comma, space and period. The hypothetical architecture includes every book that ever has been written in Sinhala, and every book that ever could be, including every poem, every scientific paper and every piece of document in Sinhala. The main goal of the research is to create an encryption mechanism for the Sinhala language. Linear congruential generator and extended euclidean algorithm have been used along with the Hull–Dobell Theorem to outline the backbone of the encryption platform. At present, it contains all possible combinations of Sinhala characters virtually. Sinhala text to be encrypted should be searched in the platform, and it will return the location of that particular text in the virtual library architecture, which is the encrypted text string for the searched Sinhala text. It is helpful to the people who are using or working with the Sinhala language, moreover for sharing and transferring Sinhala context securely.
High-speed tracking for overlapped vehicles using Instance Segmentation and contour deformation

In this paper, we propose a method for tracking with identification and separation using high-speed vision. This method is based on template matching and updating of template images by instance segmentation. We used an image inside the contour of the tracking object as the template image for template matching, and reduced the tracking noise. To validate the proposed method, experiments were conducted on images acquired by a camera mounted on an actual vehicle. Tracking based on contour information realized more robust high-speed tracking than that achieved in recent research in case of overlapping of vehicles.
Extended framework for Sindhi numerals OCR using gradient orientation histograms

The accuracy on MINST dataset for roman numerals is already 99.65%. However, same models showed low accuracy on Sindhi numerals. It is because Sindhi numerals have high correlation between the shapes of the numerals. In this paper, correlation based template matching is used to analyze the shape ambiguity by identifying the dominant false positives (FP) and false negatives (FN) for every numeral. Furthermore, the Gradients Histogram Orientation (GOH) features are used to improve the accuracy of existing classifiers by image-to-image matching. The classical OCR using simple binary features are not sufficient to address the problems of shape ambiguity in Sindhi numerals, i.e., the shape of digits 2, , and 3, , are very similar. The raw pixel values are used as features for the classification in the first stage. In second stage, the input image is matched with the dominant FP and FN of the predicted class, and the final decision is made by the image-to-image matching based on GOH features. Decision based on image to image matching with dominant FP and FN increase the accuracy of the classifier. Support vector machine (SVM), K-nearest neighbor, and template based matching classifiers are used. The proposed extension substantially improves the accuracy of all mentioned classifiers.
Sunshine based models for estimation of monthly mean daily global solar radiation (Case studies of north eastern location (Patna) in India)

In learning of solar energy utilization, one should know about global solar radiation data precisely and it is compulsory on behalf of optimal design and evaluation of the performance of any solar energy conversion system. In this topic, a north-eastern location in India (Patna) as case studies, six models based on empirical formula are like quadratic, cubic, logarithmic, exponential and exponent i.e. derivative of Angstrom- Prescott obtained linear regression model; relating the global solar radiation data with the sunshine data. Estimate the coefficients (constants) of different models by the curve estimation technique with the help of curve fitting in MATLAB. Utilizing seven statistical indicators, correlation among estimated and determined global solar radiation has been performed. As a result, the quadratic model is obtained as the most appropriate for the expectation of solar radiation in Patna. Also, this model may then be utilized for areas in India with comparative meteorological and geological attributes at which sun-based information is not available.
An Ontology of Medical Wearables

Nowadays, wearable devices are rapidly conquering the market, and there are wearables for a lot of types of activities. In the medical field, a lot of devices are invading the market, day by day. Our work focuses on the creation of an ontology that will help in the description and classification of the main wearable medical devices. In our ontology, medical wearables are classified following characteristics such as battery life, diseases and symptoms monitored, the presence of an integrated actuator, and so on. To make our work more complete and expandable, we integrated our ontology with an external medical ontology called DOID, linking the wearable to DOID symptoms and diseases.
Structure of Motifs in Seismic Networks

&amp;lt;p&amp;gt;We report detailed statistical results on the structure of motifs in seismic networks covering four distinct terrain regions. Our main finding is that all seismic networks under investigation display motifs which have a distinct scale-free-like structure.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The seismic networks were constructed from public seismic data using a standard procedure which relies on splitting the seismic region into equally sized cubes, which are the nodes of the seismic network. Then, placing each earthquake, in chronological order, into the cube corresponding to its epicenter, we define a link between two nodes as a series of two subsequent earthquakes with epicenters in different cubes. Using these seismic networks we study the occurrence of 3-node and 4-node motifs, which are triangles and tetrahedrons of the network, and report a scale-free-like behavior of the area or volume of these motifs weighted by the total energy released by the earthquakes contained in the nodes of the motif.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The statistical properties of motifs, in particular the scaling exponents of the aforementioned scale-free-like distributions, can be used to assess the differences and similarities between different seismic regions, without taking into account the inner workings of the plate tectonics. Our approach is fully customizable as all relevant parameters of the network (e.g., size of the cubes, magnitude of earthquakes, considered timeframe, coordinates of epicenters) can be changed to accommodate virtually all seismic regions.&amp;amp;#160;&amp;amp;#160;&amp;lt;/p&amp;gt;
A Real-Time Stereoscopic Images Rectification and Matching Algorithm Based on Python

Feature matching is a crucial step in several vision computing applications. To increase the quality of the matching, stereo rectification is needed. It makes the epipolar lines of the stereo images parallel to the horizontal direction (i.e. baseline); therefore, removes the parallax in the vertical direction. A rectified image pairs is helpful for a quick and accurate search of the corresponding points. In this paper, a method to rectify a set of stereoscopic images is proposed, then the Oriented FAST and Rotation Aware BRIEF is used for feature detection and matching. For this, real-time images of the same scene are captured using two similar cameras. A Python algorithm is proposed to extract each camera’s parameters, and to rectify the images. Experiments performed with images taken in real-time prove that the proposed algorithm is effective in rectifying stereo images, detecting and matching the keypoints.
ManEx: The Visual Analysis of Measurements for the Assessment of Errors in Electrical Engines

Electrical engines are a key technology all automotive manufacturers must master to stay competitive. Engineers need to analyze an overwhelming number of engine measurements to improve the manufacturing for this technology. They are hindered in the task of analyzing large numbers of engines, however, by the following challenges: 1) Engines comprise a complex hierarchical structure of subcomponents. 2) Locating the cause of errors along manufacturing processes is a difficult procedure. 3) Large numbers of heterogeneous measurements impair the ability to explain errors in engines. We address these challenges in a design study with automotive engineers and by developing the visual analytics system Manufacturing Explorer (ManEx), which provides interactive interfaces to analyze measurements of engines across the manufacturing process. ManEx was validated by five experts. Our results suggest high usability and usefulness scores and the improvement of a real-world manufacturing process. Specifically, with ManEx, experts reduced scraped parts by over 3%.
Analyzing opinion of different countries for recent events in Afghanistan using text mining

The world&#x2019;s consideration has been attracted to ongoing occasions in Afghanistan due to the potential for them to extend to Iraq and Syria, just as the horrendous treatment of the residents. There is as of now little review to decide whether individuals in Western and Eastern countries consider the new occasions in Afghanistan to be a psychological oppressor bunch and a wellspring of fear, or regardless of whether they see it in an unexpected way. Online media has as of late acquired ubiquity as a device for person-to-person communication and data trade. Twitter is microblogging website that permits clients to send and get brief instant messages (tweets) of 140 characters. These messages can be used for opinion mining to check whether there is a distinction in impression of recent developments in Afghanistan between people from Western and Eastern countries. In this study the opinion analysis of the tweets is conducted to understand whether individuals utilized almost similar words while tweeting about the new occasions in Afghanistan, having similar positive and pessimistic words reflects how individuals consider this association a gathering of psychological oppressors paying little heed to where they are from, and most clients consider the new occasions in Afghanistan to be a wellspring of danger and dread paying little heed to where they are from.
A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis

Text representation is an important topic in the field of natural language processing, which can effectively transfer knowledge to downstream tasks. To extract effective semantic information from text with unsupervised methods, this paper proposes a quantum language-inspired tree structural text representation model to study the correlations between words with variable distance for semantic analysis. Combining the different semantic contributions of associated words in different syntax trees, a syntax tree-based attention mechanism is established to highlight the semantic contributions of non-adjacent associated words and weaken the semantic weight of adjacent non-associated words. Moreover, the tree-based attention mechanism includes not only the overall information of entangled words in the dictionary but also the local grammatical structure of word combinations in different sentences. Experimental results on semantic textual similarity tasks show that the proposed method obtains significant performances over the state-of-the-art sentence embeddings.
Incremental Fuzzy Clustering Based on Feature Reduction

In the era of big data, more and more datasets are gradually beyond the application scope of traditional clustering algorithms because of their large scale and high dimensions. In order to break through the limitations, incremental mechanism and feature reduction have become two indispensable parts of current clustering algorithms. Combined with single-pass and online incremental strategies, respectively, we propose two incremental fuzzy clustering algorithms based on feature reduction. The first uses the Weighted Feature Reduction Fuzzy C-Means (WFRFCM) clustering algorithm to process each chunk in turn and combines the clustering results of the previous chunk into the latter chunk for common calculation. The second uses the WFRFCM algorithm for each chunk to cluster at the same time, and the clustering results of each chunk are combined and calculated again. In order to investigate the clustering performance of these two algorithms, six datasets were selected for comparative experiments. Experimental results showed that these two algorithms could select high-quality features based on feature reduction and process large-scale data by introducing the incremental strategy. The combination of the two phases can not only ensure the clustering efficiency but also keep higher clustering accuracy.
Construction of a Novel Digital Platform for Smart Financial Talent Training Under the Big Data Environment

This article first summarizes the definition of big data and the connotation of internet finance and expounds the relationship between big data and internet finance. Secondly, an overview of Internet financial innovation achievements in the context of the big data era, including the vertical search model of Internet financial service platforms, crowdfunding financing, third-party payment, P2P online lending, and the Internet financial C2B model. Finally, a digital financial talent training platform is designed based on Java and MySQL Cultivate students’ ability to use mathematics and statistics to solve economic and financial problems in professional courses, and vigorously promote discussion class teaching, experimental class teaching and practical teaching, so as to achieve the purpose of cultivating applied financial talents for the financial industry.
Parameter search-based scaling network for self-supervised depth

The selection of the benchmark network in self-supervised monocular depth-based estimation can often only be made using previous networks to select the best performers among them. When there is a change in resources and want to scale the network, it is difficult to find a suitable way to adjust the network quickly if the selected network itself does not give the same series of networks of different sizes. In this paper, we investigate whether the network generated by the Neural Architecture Search method based on search parameter scaling has good robustness in monocular depth estimation based self-supervised, as which the pose estimation network as well as the depth estimation network can have a better improvement in the accuracy of depth estimation. The final experiments show that the generated series perform well on the KITTI dataset, with the best performing EfficientNet-B3 outperforming all previous self-supervised networks.
Ranking the ontology development methodologies using the weighted decision matrix

Purpose The current work provides a framework for the ranking of ontology development methodologies (ODMs). Design/methodology/approach The framework is a step-by-step approach reinforced by an array of ranking features and a quantitative tool, weighted decision matrix. An extensive literature investigation revealed a set of aspects that regulate ODMs. The aspects and existing state-of-the-art estimates facilitated in extracting the features. To determine weight to each of the features, an online survey was implemented to secure evidence from the Semantic Web community. To demonstrate the framework, the authors perform a pilot study, where a collection of domain ODMs, reported in 2000–2019, is used. Findings State-of-the-art research revealed that ODMs have been accumulated, surveyed and assessed to prescribe the best probable ODM for ontology development. But none of the prevailing studies provide a ranking mechanism for ODMs. The recommended framework overcomes this limitation and gives a systematic and uniform way of ranking the ODMs. The pilot study yielded NeOn as the top-ranked ODM in the recent two decades. Originality/value There is no work in the literature that has investigated ranking the ODMs. Hence, this is a first of its kind work in the area of ODM research. The framework supports identifying the topmost ODMs from the literature possessing a substantial amount of features for ontology development. It also enables the selection of the best possible ODM for the ontology development.
Out of the blue: on the suddenness of perceived chance events

People commonly use terms like ‘random’, ‘by chance’, or ‘accidentally’ when they describe occurrences that sidestep the normal course of events, with no apparent causal link to ongoing activities. Such intrusive events are typically perceived as happening all of a sudden. This was demonstrated in seven experiments (N = 1299) by asking people to identify statements they believed belonged to stories about chance events, and by comparing chance vs. non-chance events from their own life and from the lives of others. Suddenness also appeared to be more strongly associated with beginnings than with endings of an episode. Thus, statements about sudden beginnings of a career or a relationship were more readily believed to happen ‘by chance’ than ‘not by chance’. But life-changing events did not become more accidental simply by being placed in the beginning of a ‘life chapter’. These studies highlight a central but neglected feature of perceived randomness.
Emerging clouds

Section 13.1 discusses briefly the obvious challenges cloud computing is expected to address in the immediate future. Artificial Intelligence (AI) and Machine Learning (ML) are the most impactful areas of research for virtually all aspects of human activities requiring computing. Quantum computing provides a glimmer of hope and, theoretically, a practical alternative to silicon as the end of Moore's law approaches. These two topics are covered in Sections 13.2 and 13.3, respectively. The next topic covered is vehicular clouds, which aim to harness computing resources that are mostly wasted today. Prof. Stefan Olariu, one of the pioneers of this field, has graciously provided an overview of this topic in Section 13.4.
R-DCNN Based Automatic Recognition of Indian Sign Language

An implementation of a system that can recognize the sign language in real time would be highly beneficial to the deaf and dumb community. The proposed work aims to develop a system for automatic recognition of hand gestures pertaining to finger spelling in the Indian sign language System (ISL) and thereby overcome the barrier that the disabled faces to communicate. The proposed R-DCNN automatic recognition is the combination of Region of Interest (ROI) and deep convolution neural network (DCNN) layers. Dataset is created with 26 alphabets and 9 numbers of 1200 gestures for each and hence 42,000 gestures are trained. The system proposes to use Three Conv2d layers followed by one dense as the hidden layer. Finally, one dense layer containing 35 nodes is used for recognizing the gesture. The time consuming for training in CNN is overcome by applying Region of Interest algorithm. Hence, the combination of neural networks gives highly efficient and accurate system for sign language recognition in real time.
Existence of the Yule-Simpson Effect: An Experiment with Continuous Data

In today&#x2019;s world, artificial intelligence based smart applications and smart medical devices are developed with big data-based trained datasets. However, what if a training dataset used to train a machine learning module is incorrect and has a statistical paradox. Statistical paradoxes are complicated to observe in data but are very important to analyze in every training datasets. This article discusses Simpson&#x2019;s paradox and its effects on various datasets. We provide that Simpson&#x2019;s paradox is more common in a variety of data and it leads to wrong conclusions potentially with harmful consequences. We provide a mathematical analysis of Simpson&#x2019;s paradox and analyse its effects on continuous data. Experiments on real-world and synthetic datasets clearly show that the paradox severely impacts big data.
Distortion detection and removal integrated method for image restoration

Image restoration has been the focus of research in image processing, and current methods mainly target specific single distortion or hybrid distortion with known distortion types. However, real-world images generally affected hybrid distortions of unknown quantity and type, there is no restoration method applicable to such complex hybrid distortion. Therefore, we propose a distortion detection and removal integrated method. Firstly, the distortion detection module is designed based on the idea of multi-label classification, which can detect the type and level of distortion in the distorted image. The type and level of distortion can be used as a piece of prior information to control the subsequent image restoration process. Then, the image restoration module is designed based on controllable residual connection, which enables the network to restore images with different types and levels of distortion, and the attention parallel convolution block is designed by using a parallel convolution layer and coordinated attention mechanism to enhance the feature extraction ability of the network and improve the quality of the restored image. The experimental results show that the proposed method achieves superior performance.
The parasites of language

Abstract This article examines how “parasitical” political language can help explore a new understanding of oppositional opinions in a global context of political polarization. By parasitical political language, I refer to what speech act theorist J.L. Austin calls “infelicitous speech acts”, language that is used unsuccessfully. Jacques Derrida argues that these “incorrect” utterances can escape from an already determined context and extend to a more liberated kind of communication. To explore the potentially “positive” effect of parasitical language, this article examines the utterances of Hong Kong protesters prior to the 2020 US elections as infelicitous speech acts. Their radical political approach, surprisingly, spawned an anti-globalist activist subculture shared by protesters in other parts of the world.
ARGAN: Fast Converging GAN for Animation Style Transfer

Transformation of real images to the animated image is one of the most challenging tasks in artistic style transfer. In this paper, using a novel architecture for Generative Adversarial Networks (GANs), a faster and more accurate result for style transfer is achieved. There are three common problems regarding animation style transfer. First, the original content of an image is lost during the generation of new images by the network. Second, the generated image does not have an apparent animated style. Finally, the networks are not fast enough, and they require a large amount of memory to process the images. In this paper, ARGAN, a lightweight and fast GAN network for animation style transfer, is proposed. To enhance the quality of the output images, three loss functions related to grayscale style, content style, and reconstruction of the color spectrum in each image are proposed. Also, the training phase of this method does not require paired data. The proposed method transforms real-world images into animated style images significantly faster than similar methods.