Reviewerly/exHarmony huggingface dataset:
Research on User Behaviors and Tolerance of Faulty Web Interactions

Even if we think that all the computer systems that are in operation work perfectly, the background might not be as it seems.We might face some faulty web interactions on a popular website or software as well.User behaviors are vital for developers in creating a satisfying computer system. in the aim of this study was threefold.Firstly, to determine if users' tolerance of different kinds of faulty web interactions changes depending on the environment, and then to find how users' behaviors differ when they encounter a faulty web interaction.Lastly, to detect how faulty web interactions shape users' perceptions.To achieve these aims, we conducted a test on a manipulated mobile e-commerce website with 11 tasks including five faulty ones.Participants were not informed that the test includes faulty tasks.Faulty tasks consist of different kinds of web errors: Not Responding, Blank Page, Connection Timeout (HTTP-500), Not Found (HTTP-404), and Redirect (HTTP-301).The other tasks were organized as dummy tasks, and they were not examined.In the results of this study, we reached quantitative (for the collection of quantitative data, we used a Tolerance Evaluation Scale (TES) that we developed for this study) and qualitative findings.According to the quantitative findings, there is no difference between the tolerance levels of users for different environments.On the other hand, it was determined that when there is an error that includes feedback, user tolerance is affected positively.In addition to this, it can be seen that users have a low tolerance towards giving another chance to any kind of website which has a faulty interaction.In terms of qualitative findings, participants emphasized that it does not matter what purpose a website serves, the errors give an amateur impression by damaging usability and professionalism.
The entropy based goodness of fit tests for generalized von Mises-Fisher distributions and beyond

We introduce some new classes of unimodal rotational invariant directional distributions, which generalize von Mises–Fisher distribution. We propose three types of distributions, one of which represents axial data. For each new type we provide formulae and short computational study of parameter estimators by the method of moments and the method of maximum likelihood. The main goal of the paper is to develop the goodness of fit test to detect that sample entries follow one of the introduced generalized von Mises–Fisher distribution based on the maximum entropy principle. We use kth nearest neighbour distances estimator of Shannon entropy and prove its L2-consistency. We examine the behaviour of the test statistics, find critical values and compute power of the test on simulated samples. We apply the goodness of fit test to local fiber directions in a glass fibre reinforced composite material and detect the samples which follow axial generalized von Mises–Fisher distribution.
ROC Analysis of Extreme Seeking Entropy for Trend Change Detection

This paper is dedicated to the evaluation of the ROC curve of recently introduced Extreme Seeking Entropy algorithm. The ROC curve is evaluated for a trend change in the signal that contains additive Gaussian noise. The resulting ROC curve of the Extreme Seeking Entropy algorithm is compared with other adaptive novelty detection methods, namely Learning Entropy and Error and Learning Based Novelty Detection as those algorithms are also evaluating the adaptive weights increments. The ROC curves are evaluated for multiple noise variances and area under those ROC curves is estimated.
Modified Hough Transformation for ROI

Region of interest (ROI) plays major role in the field of Radiology. It's a field of medicine that characterized for clinical health care and transmission of medical images and videos. The aim of ROI extraction is to decide which part of the brain image is affected. For transference of high resolution images, good compression techniques are needed. This is an important aspect in compressing resolution of images by avoiding process of unwanted image points. It provides better extraction techniques for better image extraction. In this paper, the modified Hough transformation technique which is the combination of both manual and automatic techniques is used. This technique was compared with different other techniques and provided good results. Some of the ROI classifications are discussed in literature review and implemented free-hand technique and automatic technique for extraction of important region in images.
Privacy-Preserving Data Generation and Sharing Using Identification Sanitizer

In this paper, we propose a practical privacy-preserving generative model for data sanitization and sharing, called Sanitizer-Variational Autoencoder (SVAE). We assume that the data consists of identification-relevant and irrelevant components. A variational autoencoder (VAE) based sanitization model is proposed to strip the identification-relevant features and only retain identification-irrelevant components in a privacy-preserving manner. The sanitization allows for task-relevant discrimination (utility) but minimizes the personal identification information leakage (privacy). We conduct extensive empirical evaluations on the real-world face, biometric signal and speech datasets, and validate the effectiveness of our proposed SVAE, as well as the robustness against the membership inference attack.
Action Languages and COVID-19: Lessons Learned

We recently conducted an exercise in which we evaluated the use of RAC and action languages to formalize policies related to covid19 In this paper, we summarize the most salient lessons we learned from this exercise We believe our findings are relevant not only to this specific domain, but also to policy formalization in general and possibly even to tasks beyond policy formalization © 2020 CEUR-WS All rights reserved
Parametrized Fixed Points and Their Applications to Session Types

Parametrized fixed points are of particular interest to denotational semantics and are often given by “dagger operations” [Stephen L. Bloom and Zoltán Ésik, Fixed-Point Operations on ccc's. Part I, Theoretical Computer Science (ISSN 0304-3975) 155 (1996), 1–38, https://doi.org/10.1016/0304-3975(95)00010-0; Stephen L. Bloom and Zoltán Ésik, Iteration Theories. The Equational Logic of Iterative Processes, in: EATCS Monographs on Theoretical Computer Science, Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-78034-9, 1993, xv+630 pp., https://doi.org/10.1007/978-3-642-78034-9; Stephen L. Bloom and Zoltán Ésik, Some Equational Laws of Initiality in 2CCC's, International Journal of Foundations of Computer Science 6 (1995) 95–118, https://doi.org/10.1142/S0129054195000081.]. Dagger operations that satisfy the Conway identities [Stephen L. Bloom and Zoltán Ésik, Fixed-Point Operations on ccc's. Part I, Theoretical Computer Science (ISSN 0304-3975) 155 (1996), 1–38, doi: https://doi.org/10.1016/0304-3975(95)00010-0.] are particularly useful, because these identities imply a large class of identities used in semantic reasoning. We generalize existing techniques to define dagger operations on ω-categories and on O-categories. These operations enjoy a 2-categorical structure that implies the Conway identities. We illustrate these operators by considering applications to the semantics of session-typed languages.
Design of Rapid Image Mosaic Based on CUDA by 100-Megapixel Optical System

To avoid the difficulty of image processing in the gigapixel optical system, this paper proposes a CUDA-based acceleration strategy for the rapid mosaic of a large-scale image and realizes the rapid mosaic of a large-scale image using the collaborative processing of CPU and GPU. Finally, the author designed an electronic hardware and software system and a fast splicing algorithm suitable for the rapid processing of large-scale image-based on a 100-megapixel monocentric multiscale optical system. And the real-time processing of data from nine 12 million pixel sub-cameras at 7.5 frames per second is completed to realize the real-time splicing of the video in the whole field of view.
Handwriting Recognition via Visual Observation of Pen Movements

Handwriting recognition is a common problem in applications such as converting handwritten documents to digital documents and authentication. Existing approaches utilize the handwritten document as the most important clue in recognition process. In this work secondary observation is used instead of the written document. Secondary observation becomes the only source of information when handwritten document cannot be observed directly or additional clues are needed for authentication. Secondary observation refers to the observation of the movement of the pen or the hand during writing. Three deep learning models are proposed to predict what is written by observing only the pen movement. The proposed solutions are the first for this problem. The result of the experiments presents a comparison of the success of three proposed models and shows that secondary observation is useful to solve the problem. Among the proposed models, the best results are obtained by using LSTM architecture which is frequently used to learn from temporal data.
Logical Unit Design using Reversible HSG1 Gate and Its Application in ALU Design

Due to reduced size and increase in component density, controlling heat dissipation is becoming a big challenge for chip designers and manufacturers and thus researchers, scientists, and engineers throughout the world are looking for newer technologies for remedy. One such technology that is providing a solution to the above-stated problem is Reversible Logic Technology (RL T). RLT is a new paradigm and finding its application in Quantum Computing, Nano-Electronics, Cryptography, Low-Power Low-Dissipation circuit designs. In this paper based on RLT, 16×16 Harsh Shiva Gate 1 (HSG1) is proposed. HSG1 gate can be programmed for performing 16, 1024, and 16384 logical output operations using 4, 10, and 14 programmable input pins respectively on two operand A and B in a single clock cycle. HSG1 gate has 2 Output Modes - Simultaneous and Cascade Mode. In Simultaneous Mode, a total of 14 programmable and 2 operand input pins are available for performing 65536 logical output operations at the output pins from x0 to x15. While in Cascade Mode, based on 4 selection input pins, X, Y, N, and P, 16 logical output operations are performed at the output pin L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OUT</inf> . The main advantage of cascade mode is using L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OUT</inf> as the input to the control unit of ALU when HSG 1 gate is used as a logical unit in designing of ALU. It has been also proved that the proposed HSG1 gate is better than its counterpart for implementing logical unit in terms of garbage output, the number of gates, and logical operations to be performed. All the results have been verified using VHDL simulation.
Charaterizing RDF graphs through graph-based measures – framework and assessment

The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs. Linguistics). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains.
Graph Based Keyword Extraction for Similarity Identification among Born-Digital News Contents

Increasing influence of internet has led to huge amount of born-digital news articles being published on the internet. It is also becoming increasingly difficult to forage through the vast warehouse of these documents for preventing duplicity. Keywords are the most salient words in any textual document. We have introduced a graph-based approach for keyword extraction, using term co-occurrence in the textual news articles and integrating weighted closeness centrality (CC) with weighted clustering coefficient (WC). We have also proposed a metric namely co-occurrence index (CI) based on the extracted keywords for finding the amount of similarity between any two textual news articles. Our proposed method is independent of the ‘bag-of-word model’ and has shown significant performance improvement over the other existing methods.
Reinforcement Learning for Enhanced Content Based Video Frame Retrieval System in Low Resolution Videos

Over the past two decades, evolutional advancement took place in the field of computer, communication and multimedia technologies which led to the production of massive video data production and large image repositories. This research focuses on the video frame retrieval using popular feature extraction method of computer vision called Speeded Up Robust Features (SURF) and Reinforcement Learning (RL) based Point of Interest (POI) calculation and frame reduction. The proposed method uses the concept of Content-Based-Image-Retrieval (CBIR) which retrieves similar images from the collection of large image databases. In video processing, a challenging task is to remove redundant frames as most of the videos have 30 Frames per Second (FPS). This research study consists of the removal of redundant frames and retrieving of similar frames from remaining non-redundant frames based on a query image. Validation for the proposed scheme has been achieved by simulating the system for a standard dataset as well as videos captured ourselves. The results depict the efficacy of the system and provide a platform for benchmarking.
A Spatial Fusion Enhancement Algorithm for Underground Images

Aiming at the problems of uneven image illumination, strong noise, and blurred edges caused by the acquisition environment or point light sources of downhole images, a spatial fusion enhancement algorithm for downhole images is proposed. First, HSV transforms the source image to obtain the luminance component, and uses a smoothing guided filter to denoise it, perform edge feature extraction on the image, and then perform non-downsampling shear wave on the extracted edge feature image, NSST transform and decompose the luminance component after noise reduction. The high-frequency sub-band uses the PCNN model to fuse the maximum value of the total ignition amplitude to fuse the high-frequency features of the image. The low-frequency sub-band is fused using an improved energy maximum method, which is obtained by inverse NSST and HSV inverse transformation. Finally, the image is enhanced. Experiments show that the improved algorithm enriches the local detail information of the downhole image and achieves good results in objective evaluation indicators.
ROetry: First Steps Towards Computer Generated Poetry in Romanian Language

Over the past decades, the scientists in the field of technology dreamt of a utopia in which computers are human-like machines, capable of comprehending emotions and feelings. Poetry, the sign of culture and intelligence, could be the final test to determine if computers are capable of expressing themselves. Can a computer write poetry? This is the question whose answer could make art and science, the two spheres of knowledge that have seemed to always exclude each other, finally meet.This paper approaches the state of automatic poetry generation by studying the current achievements and the methodologies developed so far for this purpose. We propose a Romanian-based application developed by using both Markov chain and LSTM recurrent neural network models. The goal is to generate a text with metrical constraints (rhyme, number of syllables), that can be read as poetry.
Recursion in SPARQL

The need for recursive queries in the Semantic Web setting is becoming more and more apparent with the emergence of datasets where different pieces of information are connected by complicated patterns. This was acknowledged by the W3C committee by the inclusion of property paths in the SPARQL standard. However, as more data becomes available, it is becoming clear that property paths alone are not enough to capture all recursive queries that the users are interested in, and the literature has already proposed several extensions to allow searching for more complex patterns. We propose a rather different, but simpler approach: add a general purpose recursion operator directly to SPARQL. In this paper we provide a formal syntax and semantics for this proposal, study its theoretical properties, and develop algorithms for evaluating it in practical scenarios. We also show how to implement this extension as a plug-in on top of existing systems, and test its performance on several synthetic and real world datasets, ranging from small graphs, up to the entire Wikidata database.
Image Search System for Indonesian Cultural Paintings with Impression Context Recognition

In the current search system we often encounter difficult in determining an impression on the painting, it is caused by the impression of a painting that often invites a variety of people's perception, the most appropriate way to know the impression is to ask the pronunciation. But not all painters still exist, Indonesian cultural paintings have historical value and have been there since ten and even hundred years, it is impossible to get the right painting impressions. In this paper, we conducted a new study to obtain impressions from Indonesian cultural paintings, using impression context recognition based on the context that exists on color features of the paintings. Then we calculate the features of the painting on the impression that has been determined in previous research. 3D Color Vector Quantization is used to extract color features from paintings. In this experiment, we used 605 paintings obtained from 3 museum sources in Indonesia, namely the Indonesian National Museum (Jakarta), Affandi Museum (Yogyakarta) and the Puri Lukisan Museum (Bali) combined with 30 impressions from previous research. The results of this study can facilitate culturalists, artists, historians or the public who want to know the value contained in Indonesian cultural paintings through the impression created by creating the basic features of paintings.
Generated Installation of Fuzzy Linear Automaton

The article considers the linear automata whose fuzziness is embedded in the equations of their functioning. The method is proposed for constructing generalized sequences that lead them to a certain set of known initial states. The concepts of optimality sequences by various criteria are introduced and the method of their construction is proposed. This problem is very relevant for the diagnosis of digital systems.
Parallel Multiobjective Feature Selection for Binary Classification

Feature selection has become a prominent step for many research studies as available data increases continuously with the advances in technology. The objective of feature selection is two-fold: minimizing the number of features and maximizing learning performance. Therefore, it requires a multiobjective optimization. In this study, we utilize the multicore nature of a regular PC in the feature selection domain. For this purpose, we build three models that exploit the parallel processing capability of a modern CPU. We execute the feature selection task on a single processor in the first model as a baseline. In other models, we execute the feature selection task in four cores of the CPU, in parallel. Specifically, in the second model, we decrease the population size per processor and explore whether we can achieve comparable solution sets in less amount of time. The third model preserves the population size and explores a more extensive search space. We compare the results of these models in terms of accuracy, number of features and execution time. Experiment results show that parallel processing in the feature selection domain leads to faster execution and better feature subsets.
Searching Ball Around ROI to Increase Computational Processing of Detection

Wheeled Indonesian Soccer Robot Contest (Wheeled KRSBI) is a national competition focused on the development of wheeled soccer robots. EEPIS Robot Soccer On Wheeled (ERSOW) is one of KRSBI's wheeled soccer robots. ERSOW has Artificial Intelligent (AI) for detecting balls, dribbling, and avoiding opponents. Currently, the object detection system in ERSOW uses image processing, this will be a heavy computation because it is done thoroughly in all pixel area. This research focused on making local detection to reduce heavy image processing. Local detection works by searching for local objects in the area around the detected object. It creates the ROI for local detection. From the results of tests conducted when the ball is detected, local detection system can increase 4 EPS from the full frame detection system, whereas in the test results when the ball is not detected, the local detection system is 0.2 FPS slower than the full frame detection system.
Image encryption using a new 2D bit reversed logistic map

A new 2D chaotic map is proposed using a recently proposed bit reversal operation to enhance the classic 1D logistic map. The map's is subjected to some standard tests so as to validate it's pseudo-random number generation capabilities. The bit reversed 2D logistic map is used to generate a sequence of a pair of random numbers with a continuous variation of the enhanced logistic map's parameter. The 2D map s then used to implement an image encryption schema. A sequence of simulation runs are carried out to verify the over all effectiveness of the method in terms of parameters like sensitivity to initial key and sensitivity to input plain image. Comparisons with previously reported related algorithms are also shown.