Reviewerly/exHarmony huggingface dataset:
Computational and motivational mechanisms of human social decision making involving close others

Every day, human beings make decisions with social consequences. These social consequences matter most when they impact those closest to us. Recent research has shown that humans exhibit reliable preferences when deciding between conflicting outcomes involving close others - for example, prioritizing the interests of one's family member over one's friend. However, virtually nothing is known about the mechanisms that drive these preferences. We conducted a pre-registered study in a large (maximum N=375) sample to quantify the computational and motivational mechanisms of human social decision-making preferences involving close others. By pairing assessment techniques from behavioral economics and psychological science with computational modeling and random coefficient regression, we show that value-based cognitive computations (e.g., risk and loss aversion) drive social decision-making preferences involving financial outcomes, whereas socioemotional motivations (e.g., relationship quality) underlie preferences involving social outcomes. These results imply mechanistic heterogeneity, underscoring a need for greater attention to contextual specificity in social decision-making.
Onboard Instability Correction of a Flying Wing Unmanned Aerial Vehicle

The development of unmanned aerial vehicles (UAVs) has undergone extensive development in recent years, responsible for reducing operational and manufacturing costs and also preserving the pilot in missions that present risks. The pursuit of greater flight autonomy in fixed wing UAVs is aimed at the aircraft development, which can generate the highest possible lift with the lowest weight and drag. In this context the flying wing is considered the ideal aircraft, but it has instability during flight due to the absence of warping. With the use of a computerized system for corrections, a 1.20m wingspan UAV with flexible characteristics is proposed and developed in this work, considering all its stages: aerodynamic analysis, construction and configuration, remotely controlled tests, and autonomous mission with image acquisition. As a result of its operation, we could perform both remote controlled and autonomous flights acquiring images, which demonstrates the effectiveness of the project and used embedded systems.
Whole slide imaging: The futurescape of histopathology

The last two decades have seen considerable progress in the use of digital technology in histopathology. Digital photography of microscopic slides and the use of static images gave way to robotic microscopes. These technologies had their own limitations that precluded their widespread use. Creation of whole slide scanners that can produce digitized whole slide images (WSI) and the comparable to conventional microscope experience opened multiple avenues for their utilization not only in specific applications such as expert consults, quality assessment programs, education and archiving, but also for routine day-to-day reporting. Industry pressures driven by consumer requirements have led to great development in image quality, speed of scanning, size of stored files, and capital cost of scanners. User-friendly software and analytical algorithms have further enhanced user experience. Challenges that need to be either accepted or overcome would include capital expense not significantly yielding a return on investment, and management of storage space. This review attempts to take the reader through the evolution of WSI scanners and to share the author's experience with WSI for routine histopathology reporting, education, and external quality assessment along with a review of available literature.
Hate Speech Detection in Saudi Twittersphere: A Deep Learning Approach

With the rise of hate speech phenomena in Twittersphere, significant research efforts have been undertaken to provide automatic solutions for detecting hate speech, varying from simple ma-chine learning models to more complex deep neural network models. Despite that, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aims to investigate several neural network models based on Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN) to detect hate speech in Arabic tweets. It also evaluates the recent language representation model BERT on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contains 9,316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, GRU, CNN+GRU and BERT. Our experimental results on our dataset and an out-domain dataset show that CNN model gives the best performance with an F1-score of 0.79 and AUROC of 0.89.
The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene

In this paper, we present emotion lexicons of Croatian, Dutch and Slovene, based on manually corrected automatic translations of the English NRC Emotion lexicon. We evaluate the impact of the translation changes by measuring the change in supervised classification results of socially unacceptable utterances when lexicon information is used for feature construction. We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT). We show significant and consistent improvements in automatic classification across all languages and topics, as well as consistent (and expected) emotion distributions across all languages and topics, proving for the manually corrected lexicons to be a useful addition to the severely lacking area of emotion lexicons, the crucial resource for emotive analysis of text.
Agent-based Self-organized Constructive Heuristics for Travelling Salesman Problem

Travelling salesman problem (TSP) is a classic combinatorial optimization problem and has become a touchstone of many optimization algorithms. Constructive heuristics with the features of low complexity and using problem knowledge are widely used in online decision-making and can provide high-quality initial solution for iteration algorithms. In this paper, from an agent-based self-organization perspective, the constructive processes from nodes to Hamiltonian graph of a feasible solution are studied. Based on different constructive processes, three novel agent-based constructive heuristic methods (ACHMs) are proposed, including multi-nodes-based ACHM, loop-based ACHM and multi-loop-based ACHM. These constructive heuristic methods build different agent models based on node and loop respectively, and set varied agent actions to make global feasible solutions emerge gradually. Finally, compared with nearest neighbor algorithm and self-organizing mapping, the better performances of these algorithms for TSP are verified by the computational experiments.
A feasible k-means kernel trick under non-Euclidean feature space

Abstract This paper poses the question of whether or not the usage of the kernel trick is justified. We investigate it for the special case of its usage in the kernel k-means algorithm. Kernel-k-me...
Multi Pitch Estimation of Piano Music using Cartesian Genetic Programming with Spectral Harmonic Mask

Piano notes recognition, or pitch estimation of piano notes has been a popular research topic for many years, and is still investigated nowadays. It is a fundamental task during the process of automatic music transcription (extracting the musical score from an acoustic signal). We take advantage of Cartesian Genetic Programming (CGP) to evolve mathematical functions that act as independent classifiers for piano notes. These classifiers are then used to identify the presence of piano notes in polyphonic audio signals. This paper describes our technique and the latest improvements made in our research. The main feature is the introduction of spectral harmonic masks in the binarization process for measuring the fitness values that has allowed to improve the classification rate: 10% in the F-measure mean result. Our system architecture is also described to show the feasibility of its parallelization, which will reduce the computing time.
Applying Multilingual and Monolingual Transformer-Based Models for Dialect Identification

We study the ability of large fine-tuned transformer models to solve a binary classification task of dialect identification, with a special interest in comparing the performance of multilingual to monolingual ones. The corpus analyzed contains Romanian and Moldavian samples from the news domain, as well as tweets for assessing the performance. We find that the monolingual models are superior to the multilingual ones and the best results are obtained using an SVM ensemble of 5 different transformer-based models. We provide our experimental results and an analysis of the attention mechanisms of the best-performing individual classifiers to explain their decisions. The code we used was released under an open-source license.
A High-Performance Bidirectional Architecture for the Quasi-Comparison-Free Sorting Algorithm

This paper proposes a high-performance bidirectional architecture for the quasi-comparison-free sorting algorithm. Our architecture improves the performance of the conventional unidirectional architecture by reducing the total number of sorting cycles via bidirectional sorting along with two auxiliary methods. Bidirectional sorting allows the sorting tasks to be conducted concurrently in the high- and low-index parts of our architecture. The first auxiliary method is boundary finding, which shortens the range for index searching by finding the boundaries of the range. The second auxiliary method is queue storing, which stores each useful index in a queue in advance to reduce the number of miss cycles during index searching. The performance of our architecture highly depends on the distribution of input data. For each set of input data to be sorted, five Gaussian distributions of the input data and four standard derivations for each distribution were adopted in our experiments. The results show that at the expense of some additional area cost, the number of sorting cycles and the energy consumption are significantly reduced by our method.
Investigating the Neural Models for Irony Detection on Turkish Informal Texts

Irony is defined as the expression of one's meaning by using language that normally signifies the opposite, typically for humorous or emphatic effect. In the textual context, it can be considered as a specific type of opinion mining problem. However, due to the nature of the problem, it is generally more challenging to detect. Irony detection is useful to understand the semantics of a text. It also helps improving the accuracy of many other text mining tasks. In this paper, we study the irony detection problem on Turkish informal texts. In the literature there are several solutions proposed mostly for English texts. However, the number of studies on Turkish texts is very limited. The reason for using informal text is that ironic expressions are seen more frequently in informal communication media such as blogs, social media posts. Previously, performance of conventional supervised learning based solutions, such as Naive Bayes Classifier and SVM, on Turkish texts have been studied. In this work, we investigate the performance of neural network based models in comparison to conventional supervised learning models.
Implementation of a Digital TRNG Using Jitter Based Multiple Entropy Source on FPGA

In this study, hardware implementation and evaluation of a true random number generator (TRNG) were presented. For the implementation, Field Programmable Gate Array (FPGA) hardware in which numerical processes based on an algorithmic basis are carried out, was used. In the system, free oscillation ring oscillators with similar structures were used as a noise source, and true randomness was obtained by sampling the jitter signals formed on the oscillators. However, the most critical cryptographic disadvantage of jitter-based TRNGs is the statistical inadequacy of the system. At this point, in contrast to existing designs, entropy sources derived from the subsets of ring oscillators were used in the sampling and post-processing stage. The statistical quality of the system was improved by using true random numbers/inputs obtained from these entropy sources in the sampling and post-processing stage. With sampling and post-processing inputs, the use of complex post-processing techniques that limit the output bit rate of the generator in the system was not required. Thus, a high-performance TRNG model, of which resource consumption is reduced in terms of hardware and which is simple and easily adaptable in terms of applicability was obtained. The statistical validation of the TRNG, which was tested in a total of 6 different scenarios for two separate ring oscillator architectures and three different operating frequencies, was performed with the NIST 800-22 test package.
Image Processing Techniques for Detecting Extra Growth of Teeth in Medical Images

— In medical images like X-Rays, specialists and scientists help to solve and identify many real-time health problems in human by showing the structure of the teeth bones, bone abnormalities, tumors, infections, fracture, extra growth of teeth etc. But analysis of medical x-ray images for detection of extra growth of teeth is also challenging task in specialist opinion using image processing techniques. From many decades, Researchers have come up with many techniques using image processing like threshold-based, edge-based and region-based methods for the diagnosis of tooth problems but still automatic diagnosis are not yet expected. In this paper, we explore an automatic system for identification of extra growth of teeth using Point-Based Active Shape Method using image registration. We apply image processing technique and perform geometric transformations like rotation parameters need to be adjusted along with the teeth shape to improve the convergence and robustness of the algorithm. In this paper, the proposed method consists of four stages: the first step is pre-processing stage., the second step uses image processing technique by performing geometric transformations like rotation parameter to adjust the shape of input reference image and sensed image., the third stage uses automatic system for identification of extra growth of teeth using Point-Based Active Shape Method., also in this stage if any extra teeth is detected we segment the extra growth of teeth, and the fourth stage is calculating performance metrics like RMSE, PSNR, Elapsed time as a means of matching dental record.Keywords- X-Ray, Image Processing (IP), Image Registration (IR), Point-Based Active Shape Model (PBASM), Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Elapsed time.
Discriminative and Selective Pseudo-Labeling for Domain Adaptation

Unsupervised domain adaptation aims to transfer the knowledge of source domain to a related but not labeled target domain. Due to the lack of label information of target domain, most existing methods train a weak classifier and directly apply to pseudo-labeling which may downgrade adaptation performance. To address this problem, in this paper, we propose a novel discriminative and selective pseudo-labeling (DSPL) method for domain adaptation. Specifically, we first match the marginal distributions of two domains and increase inter-class distance simultaneously. Then a feature transformation method is proposed to learn a low-dimensional transfer subspace which is discriminative enough. Finally, after data has formed good clusters, we introduce a structured prediction based selective pseudo-labeling strategy which is able to sufficiently exploit target data structure. We conduct extensive experiments on three popular visual datasets, demonstrating the good domian adaptation performance of our method.
Extracting a Bounded Region from a Map Using Flood Fill Algorithm

Extracting the needed portion from a bounded region is an important task in image processing. Editing a map and extracting a region from the map is challenging. It is useful in some contexts to have a region in a separate sheet. In this image processing, we have used the Flood Fill algorithm to extract a region from the image map. To achieve that goal, we had worked in our study to separate a bounded region on a map. Usually, a scanned map may contain a lot of useless information. So we have to process the image to remove useless information from the map. We had quantized the image to a binary one. In the second phase, we have applied a gray color to separate the desired position from a map. Our main objective of the study to extract a bounded region from mapping an image that contains useless information and removes it. We have experimented with several maps and it works successfully.
Bio-Inspired Algorithms for Preserving the Privacy of Data

Security of the data is also concerned with the privacy of the data since the data or the information can be easily disclosed. Data sharing also plays a key role in security. Recently, patterns are disclosed using associative rule mining and the sensitive information are one of the imposing threats to the security aspects in data mining. Preserving the data as well as the privacy of the user using several PPDM approaches leads to provide authorized access for such sensitive information. The security threats for preserving privacy are provided by developing a sanitization process. The sanitization process is considered to be one of the biggest challenges in the mining of data. In this paper, different approaches such as GA-based and PSO based algorithms are surveyed and analyzed for preserving the privacy of data. The purpose of data sanitization and the use of Bio-Inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are discussed.
Environmental Informatics and Educational Opportunities in Post Graduate level—Indian Potentialities based on International Scenario

The academic sector is changing rapidly and different components have been added in the academic components. The education systems including academic bodies, programs, educational institutes change different aspects in the recent past. The interdisciplinary sciences are booming internationally. Among the interdisciplinary subjects, Environmental Sciences is an important one. On the other hand, Information Science is also truly interdisciplinary. This Information Science is also called as Informatics. The fusion or combination of these two subjects led the new academic body called ‘Environmental Informatics’. Environmental Informatics is very close with exiting field Geo-Informatics and thus it is also called as an extension of Geo-Informatics. Environmental Informatics is the solution to different environmental affairs, issues, problems, etc. Internationally, many universities have started academic programs on Environmental Informatics leading to Certificate, Diploma, Bachelors, Masters, MPhil, PhD in the subject. The present paper is an overview of Environmental Informatics including its features, functions, stakeholders emphasizing the availability of Post Graduate Programs in the field. Paper highlighted the course contents, job prospects, carrier opportunities in brief. Paper also proposed the possible programs at Masters levels; which can be a good solution both for abroad and the Indian education system.
APLIKASI INTERAKTIF TEKNOLOG LAYANAN JARINGAN SEBAGAI MEDIA BELAJAR SISWA BERBASIS ANDROID

The revised 2013 curriculum is a curriculum that requires teachers and students to be more creative in implementing learning at school. One of the subjects contained in the 2013 curriculum is Network Service Technology which contains material about communication systems in the world. However, during learning there are often problems including students not having textbooks, being left behind, damaged, and missing. With this problem, the authors design an application that can facilitate educators and students to carry out learning activities at school, namely interactive media applications with Android smartphones that are adjusted to the 2013 curriculum. is mobile so easily accessible anytime and anywhere. In the application design stage, the author uses the Multimedia Development Life Cycle (MDLC) method which is carried out in several stages and is designed using the Smart Apps Creator software. The results of this application are distributed to students of the Vocational School of Computer and Network Engineering Department and also to teachers who teach Network Service Technology subjects as additional media for teacher administration in schools.
Knowledge Graph Construction of High-Performance Computing Learning Platform

Abstract With the development of intelligent education, it has become one of the more efficient learning schemes to construct the knowledge graph which can excavate the knowledge base. People generally use RDF triples and use languages such as OWL to construct knowledge graphs, but this method has problems such as limited expression ability and too much manual annotation. In this paper, we propose a framework that combines statistical language models, neural network language models, and clustering and clipping algorithms. When processing unstructured text data, unsupervised extraction of representative key words as features of the structured graphs’ entities, so that the processed entity information has more accurate semantics and human learning relevance, and the method of clustering and calculating the learning rate is used to further clarify the learning order and mastery of knowledge points. We have conducted extensive experiments to collect data from the two major modules of HPC high-performance computing courses and domains as datasets, and used this framework to build many knowledge graphs which can provide practical learning. The knowledge graph of this paper can be obtained from: https://v2.easyhpc.net:10000/knowledge Knowledge Graph Module.
Interaction Effect of System, Information and Service Quality on Intention to Use and User Satisfaction

The use of academic information systems is now essential in the teaching and learning process at every university. The Indonesian College of Economics (STESIA) is one of the universities that implement an academic information system to support the teaching and learning process at the university. An evaluation stage is needed to improve the performance of the academic information system. Improving the performance of academic information systems can be done by improving the quality of information, the quality of the system, and the quality of its services. These three aspects are related to increasing user attention and satisfaction. This study will evaluate following the conceptual model of the results of the development of the Delone & Macline information system success model. The method used is a quantitative method using SPSS and WarpPLS. The results of the study state that the service quality aspect is the aspect that provides the most significant contribution in increasing attention to use and user satisfaction.