Reviewerly/exHarmony huggingface dataset:
Tracking Eye Movements To Predict The Valence of A Scene

Studying human bio signals such as eye movements and tracking them can help in identifying and classifying the emotional essence of a scene. The existing methods employed to evaluate the reaction of the eyes based on exposure to a scene or image often use a classifier to extract features from eye movements. These extracted features are then evaluated to determine the valence of a scene. On the contrary, as much as eye movement has proved to be a reliable source in scene or image detection, factors such as how each feature affects the outcome of the prediction have not been explored. For the determination of the emotional category of images using eye movements, images are categorized into pleasant, neutral and unpleasant images and then these images are shown to the test subjects to record their response. Features of eye movement like fixation count, fixation frequency, saccade count, and saccade frequency among others, along with a machine learning approach was used for scene classification.
Video Coding (MPEG-2, MPEG-4/AVC, HEVC)

The data rates of uncompressed digital video signals range from 270 Mbit/s (SDTV) through 1.5 Gbit/s (HDTV) or 3 Gbit/s (HDTV) to 12 Gbit/s (UHDTV). However, these data rates are too high for efficient storage or transmission, so they need to be first compressed using a so-called perceptual coding to reduce the amount of data. This process is called source coding. In digital TV broadcasting this compression is one of the tasks of the so-called headend, but such operations are sometimes also performed when the video signals are recorded (reportage camera, home video camera, mobile phone, etc.).
Robust Reversible Watermarking for Grayscale Medical Images

Robust reversible watermarking algorithms appliedDutta, Tanima to protectBagi, Randheer medical imaging fromGupta, Hari Prabhat misdiagnosis for any slight distortion. It suited for tasks like copyright protection while preserving the visual quality of watermarked images. It also shows robustness against transmission errors. We propose a novel robust reversible watermarking technique for grayscale medical images. We investigate the high embedding capacity watermarking technique also keeping low distortion in the watermarked images. We demonstrate the accuracy of the reversibility even in error-prone transmission channels. Our simulations show that the proposed technique effectively retains the perceptual quality.
MEDICAL IMAGE WATERMARKING FOR AUTHENTICATION, CONFIDENTIALITY, TAMPER DETECTION AND RECOVERY

This paper presents a region based blind medical image watermarking (MIW) scheme for ensuring authenticity, integrity and confidentiality of medical images. Medical image is segmented into region of interest(ROI) and region of non interest (RONI). ROI is watermarked for tamper detection and recovery in the spatial domain. For providing confidentiality and authenticity, electronic patient record (EPR) and hospitals logo is embedded as a robust watermark in RONI using IWT-SVD hybrid transform. Various experiments were carried out on different medical imaging modalities for performance evaluation of the proposed scheme in terms of imperceptibility, robustness, tamper detection and recovery. Evaluation results show that the visual quality of watermarked image is good and it is robust under common attacks. A comparison with well known schemes has been performed to show superiority of the proposed method.
Bengali Named Entity Recognition: A survey with deep learning benchmark

Sequence labeling is a complex task in natural language processing where the data set used to be biased to a specific class mainly to “not named entity” class. Previously several machine learning approaches were harnessed for Bengali named entity recognition where additional information like Parts Of Speech (POS) tag, suffix value, optimal number of context words were required. This study aspires to give an overview of past methods on Bengali named entity task along with leveraging different neural networks on a new dataset at an easy way. A dataset consisting of 96697 tokens were annotated in the house where 67554 tokens were applied for training and 29143 words were for testing purposes. Then several deep learning methods were exploited where Bidirectional Gated Recurrent Unit (BGRU) came up victorious with f1 score 72.66%. The value may not be promising after comparing with other methods but different studies calculated the precision and recall value differently. Increasing the number of training data could raise the performance metrics along with other forms of word embedding.
Synthesis of feature spaces ensuring the quality and reliability of recognition

Solution to the problem of the synthesis of feature spaces that ensure the quality and reliability of recognition is discussed in this paper. The methods of feature selection is inherent for each class of initial properties of the objects, the maximum allowable dimension of the feature space are found, taking into account the size of the training sample, the required quality values and recognition reliability. Based on the proposed methods, an algorithm and software have been developed. Computational experiments on a computer are given and the results are presented in the form of decision rules used for object recognition. The general findings of the study are also given.
DPGAN

This paper is directed against the image super resolution problem that is an extraordinary topic in the field of computer vision. We proposed a new model PReLU Used in Deep Convolutional Generative Adversarial Networks (DPGAN) which has designed a pre-training structure, and the generator and the discriminator are cross-optimized to form a stable network structure. In the proposed model, the activation function in the generator uses the PReLU[1] innovatively. The experimental results demonstrate that the images generated by the proposed model have higher resolution, which is compared with previously studied models.
Face Recognition Method Based on Probabilistic Neural Network Optimizing Two-Dimensional Subspace Analysis

Abstract If there is noise in the original image of face recognition, the efficiency of face recognition will be affected. In this paper, a face recognition method based on probabilistic neural network optimizing two-dimensional subspace analysis was proposed. Firstly, discrete wavelet variation was used to preprocess the image, and then two-dimensional linear discriminant analysis was used for feature extraction. Finally, the probabilistic neural network was used to complete the face classification. According to the results of experiments conducted on ORL and Fei general face database and the database collected independently, the recognition rate can also be as high as 98.9% when noise is added, and compared with several new identification methods, this method can achieve better identification performance.
Artificial Intelligence Painting The Bigger Picture For Copyright Ownership

To receive copyright protection in Australia works must be original, amongst other requirements. The originality standard involves ‘independent intellectual effort’ that originates from an actual person. The reality of today’s creativity domain is that works are not always originating from actual persons. Due to impressive advancements in technology, some works are being created by artificial intelligence and without the involvement of an actual person. These works cannot meet copyright requirements under current law and subsequently do not receive copyright protection. This paper endeavours to answer a two-tiered question posed by the challenges artificial intelligence works have on traditional concepts of copyright. Firstly, should copyright subsist in works created by AI? Secondly, who would possibly be the copyright owner for such works? Answering these questions involves a discussion of utilitarian and natural rights theories and references to US and UK discussions on the conversation around copyright and artificial intelligence.
Are College Students Really Digital Natives? : An Exploratory Case Study of South Korea

This study implements a case study in order to investigate the behavior of Korean college students as digital natives. Our finding is that most of Korean college students show the dependency to digital technologies in terms of affective dependence to digital technologies. In the area of game-preference behavior, a considerable portion of first and second interviewees indicates that they tend to play diverse kinds of video games. However, some of junior and senior interviewees do not show the equivalent fever in the digital entertainment. In case of multi-tasking behavior, responses are, broad-speakingly, categorized into three groups: first group where participants shows multi-tasking behavior; second group where participants do not show multi-tasking behavior; third group where participants show multi-tasking behavior depending on the nature of activities. In view of the fact that Korean students have been surrounded by digital technologies, there is a distinctive segment of college students who do not enjoy multi-tasking behavior. Our findings suggest that there are heterogenous pattern in multi-tasking behavior. On the choice between the monitor and book, all of students are known to choose the computer monitor instead of visiting the library and finding books containing the necessary information to write a report. In the realm of the side effect of the Internet, a couple of students reply that they recognize the side effect of the Internet such as security problem.
Traveling Salesman Problem Solution using Genetic Algorithm

Artificial intelligence is significantly increased with technological advancements. It can be seen in many applications of artificial intelligence in technological development. Many artificial intelligence technologies solve cases. One of the problems that can be solved by artificial intelligence is the Traveling Salesman Problem. In the Traveling Salesman Program, a salesperson must travel to several destinations with optimal mileage. For a small amount of destination, the optimum distance can be reached with a minimum distance. However, in more extensive cases, the optimum distance can be obtained with a very minimal distance in the search process. The genetic algorithm can find the optimum distance by regenerating each population so that it produces the minimum value. This value is not the minimum value in all points, but it can be the optimum global value. By applying the science of artificial intelligence in the case of Traveling Salesman Problem, the amount of mileage will be optimized.
To buy or to borrow? Evaluating the cost of an eBook in TalTech library

Purpose The purpose of this paper is to find out how much the purchasing and lending of individual electronic books really cost. Additionally, this paper investigates which kind of approach would be cheaper and less time-consuming for library staff as well as library patrons – purchase or short-term loan. Design/methodology/approach This study was conducted at the Tallinn University of Technology (TalTech) Library. This is the only university library in Estonia where the Ebook Central platform is adapted on a large scale. For background information, all statistical data of expenditures and average prices of purchases and short-term loans during April 2013 and December 2018 were calculated and analysed. Through a case study, the time-driven activity-based costing (TDABC) method was used – all activities related to acquisition and lending of eBooks were identified, recorded in detail and analysed. More specifically, the study concerned eBooks offered in the Ebook Central platform and covered purchasing and short-term loan processes, such as receipt of order request, communication with the patron (if necessary) making a purchase or short-term loan, and feedback to the patron. Findings While analysing the results, it appeared there are many additional activities libraries can avoid during the eBook short-term loan process compared to purchasing. As a normality in TalTech library, purchase is always followed by a cataloguing process which increases the time and cost of this process in turn. On the basis of the current study, it can be said that short-term loan is a cheaper way to use eBooks; many activities related to the short-term loan of eBooks take remarkably less staff time and financial resources than eBooks acquisition/purchasing activities. When analysing the literature reviewed as well as collected statistical data, the problem may arise when the decision-maker librarian is not experienced, professional or long-sighted enough to understand the future behaviour of the patron or the usage of the specific eBook. When the usage reaches a certain point, it becomes an indicator of continuing future usage and so it makes sense to purchase the eBook, as the library pays no further charges once an eBook is owned. Originality/value Most studies reviewed by the author are based on the statistical data collected about expenditure, costs, usage, cost-per-use, etc. of short-term loans and purchases. While acquisitions costs, average cost per acquired item per year and cost per usage are easy to identify, it has been difficult to measure associated costs of acquisition, cataloging and circulation. The TDABC methodology seems to be one of the best tools for understanding cost behaviour and refining a cost system for university libraries. Based on the information known to the author, there is no study carried out using the TDABC methodology for analysing costs of eBook programmes.
Credit Card Fraud Detection using Data mining and Statistical Methods

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-sensitive learning for credit card fraud detection. In the first step, useful features are identified using genetic algorithm. Next, the optimal resampling strategy is determined based on the design of experiments (DOE) and response surface methodologies. Finally, the cost sensitive C4.5 algorithm is used as the base learner in the Adaboost algorithm. Using a real-time data set, results show that applying the proposed method significantly reduces the misclassification cost by at least 14% compared with Decision tree, Naive bayes, Bayesian Network, Neural network and Artificial immune system.
An Improved Method of Detecting Infrared Weak and Small Targets Based on Deep Learning

The convolution network is a very powerful visual model that can be used to detect objects in an image. Traditional target detection frameworks are generally divided into anchor-based object detector and anchor-free object detector. Among them, SSD is a single-stage anchor-based object detector that can detect objects quickly and efficiently. In order to detect the infrared weak and small objects, we improve the SSD network for our object detection tasks by using an improved backbone network. We use the open UAVs dataset and achieve highly training and testing accuracy in the open dataset.
Implementasi Data Mining Untuk Memprediksi Penyakit Jantung Mengunakan Metode Naive Bayes

Heart disease is a disease with a high mortality rate in the world of health. The disease is usually rarely realized the cause. However, there are several parameters that can be used to predict whether a person has a risk of heart disease or not. As for this study, researchers will use several indicators including Age, Sex, Chest pain type, Trestbps, Cholesterol, Fasting blood sugar, Resting ECG, Max heart rate, Exercise-induced angina, Oldpeak, Slope, Number of vessels coloured, and Thal This research will perform calculations using the Data Mining method with the Naive Bayes Algorithm. The results of this study get an accuracy of 86% for the 303 datasets tested.
Social Media Sentiment Analysis with Context Space Model

In this article the description of algorithm of an assessment of mood of the statement is presented with the accent on the context of user’s messages in social media. The article focuses on the fact that messages containing identical sentiment objects have different meaning that affects onto the evaluation of the sentiment of the message. An additional research objective is the identification of formal criteria for assigning messages to classes “core”, “periphery”, “non-relevant” to denote the role of the research relevance of the object key in the message. In this article, we have given several examples of authentic messages for each group. The method was tested on the empirical basis of more than 10,000 messages to assess the relationship of users of the social network VKontakte to the object of tonality – a form of employment “freelance”. The research methodology presupposes the use of basic and additional methods of data preprocessing, data augmentation, comparative analysis of the application of classification methods. The article includes comparative description of results of application logistic regression, support vector machines, naive Bayesian classifier, nearest neighbor, random forest.
A Multiple Triplet-Ranking Model for Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of matching an input sketch with a specific photo containing the same instance. The key challenge of learning a FG-SBIR model is to bridge the domain gap between photo and sketch. Most existing approaches build a joint embedding space where two domains can be directly compared. They only focus on the highly abstract features in final fully connected (FC) layer, ignore some low-level semantic concepts in convolutional layers. In this paper, we propose a multiple triplet-ranking model in FG-SBIR task. Specially, we introduce an auxiliary supervision loss function in the convolutional layer, and we use the fusion of features from convolutional layer and final FC layer to build the joint embedding space. Extensive experiments show that the proposed multiple triplet-ranking model significantly outperforms the state-of-the-art.
THE COMPARISONS OF OCR TOOLS: A CONVERSION CASE IN THE MALAYSIAN HANSARD CORPUS DEVELOPMENT

Optical Character Recognition (OCR) is a tool in computational technology that allows a recognition of printed characters by manipulating photoelectric devices and computer software. It runs by converting images or texts that are scanned beforehand into machine-readable and editable texts. There are a various numbers of OCR tools in the market for commercial and research use, which are obtainable for free or restrained with purchases. An OCR tool is able to enhance the accuracy of the results which as well relies on pre-processing and subdivision of algorithms. This study intends to investigate the performances of OCR tools in converting the Parliamentary Reports of Hansard Malaysia for developing the Malaysian Hansard Corpus (MHC). By comparing four OCR tools, the study has converted ten reports of Parliamentary Reports which contains a number of 62 pages to see the conversion accuracy and error rate of each conversion tool. In this study, all of the tools are manipulated to convert Adobe Portable Document Format (PDF) files into Plain Text File (txt). The objective of this study is to give an overview based on accuracy and error rate of how each OCR tools essentially works and how it can be utilized to provide assistance towards corpus building. The study indicates that each tool possesses a variety of accuracy and error rates to convert the whole documents from PDF into txt or plain text files. The study proposes that a step of corpus building can be made easier and manageable when a researcher understands the way an OCR tool works in order to choose the best OCR tool prior to the outset of the corpus development.
Adversarial Samples for Improving Performance of Software Defect Prediction Models

Software defect prediction (SDP) is a valuable tool since it can help to software quality assurance team through predicting defective code locations in the software testing phase for improving software reliability and saving budget. This leads to growth in the usage of machine learning techniques to SDP. However, the imbalanced class distribution within SDP datasets is a severe problem for conventional machine learning classifiers, since result in the models with poor performance. Over-sampling the minority class is one of the good solutions to overcome the class imbalance issue. In this paper, we propose a novel over-sampling method, which trained a generative adversarial nets (GANs) to generate synthesized data aimed for output mimicked minority class samples, which were then combined with training data into an increased training dataset. In the tests, we investigated ten freely accessible defect datasets from the PROMISE repository. We assessed the performance of our offered method by comparing it with standard over-sampling techniques including SMOTE, Random Over-sampling, ADASYN, and Borderline-SMOTE. Based on the test results, the proposed method provides better mean performance of SDP models among all tested techniques.
Comovement in the Cryptocurrency Market

This study examines the comovement between 17 of the most active cryptocurrencies. We are unable to statistically reject the presence of perfect comovement between Bitcoin and six of the 16 non-Bitcoin cryptocurrencies. Consistent with the friction-based explanation for the presence of comovement, once the CBOE introduced futures contracts on Bitcoin, we find that all 16 cryptocurrencies comove with Bitcoin. These results suggest that introducing futures contracts improves the informational environment of the entire cryptocurrency market, which helps explain the unusual comovement in the cryptocurrency market.
Development of an Online Sales Information System for SMEs Using Incremental Methods

Home Industry Shoes is one of the SMEs that are engaged in the production and sale of shoes. Shoes SMEs in marketing and sales have been still relying on customers from surrounding areas of production activities. Current business competition is stringent, thus making SME shoes demanded to be more advanced in marketing and sales to be able to stay afloat. Shoes SMEs have problems with promotional media and sales, which does not yet have an independent online sales system. Therefore, the authors feel the need to help SMEs by creating an online sales information system that can be used as a promotional media as well as a media for selling independently and easy to use. In this study, the authors apply the incremental method in system development and also observation in gathering data on the needs of the system developed. Achievements in this study, the authors, produce an online sales information system for SMEs, in particular, the production of home industry shoes that can be used independently by SMEs to carry out promotions and sales online.
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations.

Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsupervised approach for segmentation applications with prior knowledge of the objects' shapes. We propose and investigate different strategies to generate simulated label data and perform image-to-image translation between the image and the label domain using an adversarial model. Specifically, we assess the impact of the annotation model's accuracy as well as the effect of simulating additional low-level image features. For experimental evaluation, we consider the segmentation of the glomeruli, an application scenario from renal pathology. Experiments provide proof of concept and also confirm that the strategy for creating the simulated label data is of particular relevance considering the stability of GAN trainings.