Reviewerly/exHarmony huggingface dataset:
Block Chain Architecture in Financial System for Integrity, Transparency, and Trust-Free Transaction

In previous studies of bit coin it is notified with an objective and mission and vision that a new decentralized economic system termed as Bitcoin. The financial institutions are not included in the solely peer-to-peer electronic cash transactions and would allow online payments to be sent directly from one customer to another customer. The version was practically implemented by the concept of the bit coin, which is a practical real-time study of decentralized autonomous organization (DAO) automatically driven with full-proof transparency, integrity, and decentralization. Decentralization means it is executed in peer-to-peer transactions including nodes in the network for users, and it is fully transparent because the network is open in public domain. All these characteristics taken together are the milestones of trust-free transactions systems.
Visual Monitoring Technology for Substation Vulnerable High-Voltage Electrical Equipment Based on ISSA-LSTM Deep Learning Model Coupling Video Overlay Algorithm

To enhance the visualization effect of substation high-voltage electrical equipment vulnerability, this study proposes an ISSA-LSTM coupled video overlay algorithm-based substation high-voltage electrical equipment vulnerability visualization and monitoring model. Using the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>α</mi> </math> blending algorithm combined with the inverse sampling of video background color, overlaying visible video as well as infrared video, using the improved adaptive weighted two-dimensional principal component analysis (W2DPCA) to fuse the base layer, selecting the detail layer as the final detail layer, obtaining the final fusion frame, and realizing the visualization and monitoring of substation high-voltage electrical equipment vulnerability, and introducing the improved sparrow search algorithm (ISSA) to establish long and short-term memory network prediction model to reduce the prediction error and improve the monitoring accuracy rate. The experimental results show that the monitoring frames obtained by this method can reflect rich details of substation high-voltage electrical equipment, and the texture color and equipment edge contrast are enhanced to facilitate accurate determination of substation high-voltage electrical equipment vulnerability, and the prediction accuracy of ISSA-LSTM model is as high as 99.85%.
Evaluating to Persuade in Statistical Framing

In this chapter, the author explores the importance of moves of evaluation in statistical rhetoric – what he calls “statistical framing,” or the rhetor’s attempt to direct an audience’s interpretation of a statistic’s meaning in the wider context of the text that the statistic is embedded in. By paying attention to the moves rhetors make to frame the meaning of a number, rhetors and audiences alike can be more attuned to the dynamic process between rhetoric and calculation. Throughout this chapter, the author uses methods of amplification from classical rhetoric and aspects of the appraisal framework from systemic-functional linguistics to analyze statistical frames in the news media. Being aware of the presence and usefulness of statistical frames can help rhetors and audiences weigh how to evaluate statistical information from multiple perspectives in order to make explicit connections between rhetorical moves that evaluate and the statistical information evaluated.
The Philosophy of Naive Bayes and its Comparison with the Tree Augmented Naive Bayes (TAN) in Making Predictions (Case Study Using Course Student Data)

This paper aims to present a comparison between the Naive Bayes model and the Tree Augmented Naive Bayes (TAN). This comparison is preceded by a philosophical explanation of the two in the author's way of understanding. Then this comparison was carried out in the case of making a predictive model for the final exam scores of students at the Muhammadiyah University of Ponorogo. In this case, we apply two different models, namely Naive Bayes and Tree Augmented Naive Bayes (TAN) to predict learning outcomes before ending at the end of the semester in terms of lecturers' assessments of students. When the course is in progress, the lecturer needs to continuously evaluate students' understanding of the subject matter being taught. This is so that lecturers can immediately anticipate learning problems in class. Calculations with these two models use only R language with jupyter notebook interface. Validation and testing of the two models used a case dataset in 4 classes of language theory and automata course students even semester 2017-2018 at the Department of Informatics Engineering, Faculty of Engineering, and University of Muhammadiyah Ponorogo with a dataset size of 99 notes (99 students). For the validation and model testing methods, k- fold and hold-out cross-validation are used. Each model is validated and tested with the same k-fold method scheme.
The Effectiveness of Indonesian National Police Strategy in Cybercrime Eradication through ASEAN Ministerial Meeting on Transnational Crime (AMMTC)

Cybercrime has increased along with digital growth in Indonesia in the last decade. There has been a heightened urgency among Indonesian government agencies to eradicate cybercrime. These efforts include a national cybersecurity strategy and taking part in regional cooperation, such as AMMTC. This paper examines the effectiveness of the Indonesian National Police (INP)’s strategy in AMMTC to combat the nation’s cybercrime. By looking at the Global Cybersecurity Index (GCI), this article elaborates on whether the INP’s approach effectively counters cybercrime in Indonesia. This study utilizes transnational crime, cybercrime, cooperative security, and regional organization concepts. It conducts a qualitative research method with a case study approach as a derivative and an analysis descriptive research type. With primary data in the form of interviews with two sources and secondary data that took various related sources, the research conducts triangulation of data. This study shows that although Indonesia was slower in handling cybercrime cases, the effectiveness of INP’s approach on a national and regional scale is starting to look up
Study on community governance during COVID-19

COVID-19 has challenged China's traditional model of community governance. This article discusses the existing problems and optimization strategies of community gridding management, advocates the value concept of diversified governance, solves the problems of low efficiency of community governance, weak community emergency management ability and so on, and improves the resilience of community governance. This article puts forward some strategies, such as paying attention to special groups in communities, actively absorbing social forces, expanding informatization and digitization application scenarios, to establish the operation mechanism of the community and improve the ability of community governance under multiple risks.
A Novel Steganography Method for Halftone Images

Steganography is the common name of methods that aim secret communication. In this conference proceeding, a novel steganography algorithm that hides plaintext payload in halftone images and a payload extraction algorithm that is suitable for messages hidden using this steganography method is presented. Our steganography algorithm uses a modified pattern-based halftone image generation procedure and distributes the payload into multiple output images. The proposed method has proven to be secure and able to hide large payloads. According to the objective and subjective evaluations made, it was seen that the proposed method produces promising results.
Multi-Phase Traffic Classification Based on Payload

While the internet is gaining more and more importance in our daily life, the number of applications used via the internet are increasing at the same speed. Today, fast and accurate classification of data packets transmitted over the network based on the applications has become an important issue in terms of security as well as network management. In this study, with the proposed classification approach, it is aimed to determine which application these network packets belong to, by inspecting their payloads. To classify packets, a multi-phase method based on majority voting is proposed. This method is based on training deep learning-based classifiers using different numbers of packets and updating the classification prediction as the number of packets in the network flow increases. This updated prediction is achieved by majority voting by using the predictions of previous classifiers trained by smaller number of packets from flows. With this approach, more accurate classifications can be made with less number of packages and this allows an early classification without waiting for more packages to arrive. This approach has been tested on real data collected for various applications.
Face Inpainting with Pre-trained Image Transformers

Image inpainting is an underdetermined inverse problem that allows various contents to fill in the missing or damaged regions realistically. Convolutional neural networks (CNNs) are commonly used to create aesthetically pleasing content, yet CNNs have restricted perception fields for collecting global characteristics. Transformers enable long-range relationships to be modeled and different content generated with autoregressive modeling of pixel-sequence distributions using image-level attention mechanism. However, the current approaches to inpainting with transformers are limited to task-specific datasets and require larger-scale data. We introduce an approach to image inpainting by leveraging pre-trained vision transformers to remedy this issue. Experiments show that our approach can outperform CNN-based approaches and have a remarkable performance closer to the task-specific transformer methods.
Natural Language Processing-Based Product Category Classification Model for E-Commerce

In scope of the great dynamism that the world of e-commerce gained with the post-pandemic changes on customer behavior, extending the customer&#x2019;s time on site has become much more valuable. With regards to creating a better understanding of the online shopper&#x2019;s intent on site, an effective search engine is the best tool for improving the user experience. A useful search engine needs to produce fast results and do intent analysis of customers correctly to present successful recommendations. In this study, BERT, ELECTRA and RoBERTa which are language models that give successful results in similar problems and pre-training libraries in Turkish languages are used for developing text classification and customer intent analysis models based on e-commerce product categories. The results of these deep learning models, which were optimized using the product description and comment libraries of the selected e-commerce brand, are shared in a comparative way, and the results of our e-commerce end-user intent analysis model, which was tested on user search history, were detailed on the basis of product category. The developed model can be used for e-commerce product prioritization in Turkish language, as well as creating an infrastructure for the development of intent analysis models that are able to predict as far as the main product that the user wants to display in searches containing multiple products.
Köpek Gezdirici Segmentasyonu Dog Walker Segmentation

In this study dog walkers were separated into clusters according to walkers&#x2019; walk habits. Due to the fact that the distributions were non-normal, normalization algorithms were applied before the onset of clustering. After normalizing, K Means algorithm and Gaussian Mixture Models used for finding optimum cluster count. According to these clusters, walkers&#x2019; consecutive months separated to follow-up their behavioral traits. This part of the study adds value to the project to examine walkers&#x2019; behaviors closer.
Performance Evaluation of Different Optimization Techniques for Image-based Camera Localization

In this study, the performance evaluation of different optimization methods in calculating the change of camera movement, in other words the camera position and angle, was made on images taken from different views with the same camera. For this purpose, gradient-based and population-based optimization techniques have been investigated by using the differentiable rendering methods that have been proposed in recent years for the 3D point cloud created over depth information. It was found that while gradient-based methods are more successful on close movements, population-based methods were more successful in distant changes.
Removal of Gaussian Distributed Noise in Images with Deep Neural Network Models

The removal of noise caused by environmental factors in microscopic imaging studies has become an important research topic in the field of medical imaging. In the medical imaging stage made with any digital microscopy method (Confocal, Fluorescence, etc.), undesirable noises are added to the image obtained due to factors stemming from excessive or low illumination, high or low temperature, or electronic circuit equipment. The most basic noise model formed due to these environmental factors mentioned is the Gaussian normal distribution or a characteristic function close to this distribution. It is widely known that spatial filters (mean, median, Gaussian smoothing) are applied to eliminate Gaussian noise in digital image processing. However, undesirable results may occur in the images obtained when spatial filters are used to remove the noise in the images. In particular, because high frequencies are suppressed in images where spatial filters are applied, details are lost in the final image, and a blurred image is obtained. For this reason, four different convolutional neural network-based models are used for noise removal and to improve the PSNR values in this study. As a result, the modified U-Net improved the PSNR values for different noise levels as follows: +6.23, +7.88 and +10.52 dB
Edge-Based Image Watermarking Method with Weighted Discrete Cosine Transform Coefficients

In image watermarking, hybrid approaches increase imperceptibility and robustness. Also, a scaling factor is used, which should be optimized when combining the cover image and watermark. In this study, discrete wavelet transform and discrete cosine transform (DCT) were used together. The watermark-edge image was obtained by randomly inserting the watermark on the horizontal, vertical and diagonal edge points of the cover image detected with Sobel. The DCT frequency components of the watermark-edge image were weighted with a generated matrix and combined with the DCT of the cover image. According to the obtained results, the proposed method is imperceptible and robust to various attacks, especially JPEG compression and noise attacks.
Learning Narrowband Graph Spectral Kernels for Graph Signal Estimation

In this work, we study the problem of estimating graph signals from incomplete observations. We propose a method that learns the spectrum of the graph signal collection at hand by fitting a set of narrowband graph kernels to the observed signal values. The unobserved graph signal values are then estimated using the sparse representations of the signals in the graph dictionary formed by the learnt kernels. Experimental results on graph data sets show that the proposed method compares favorably to baseline graph-based semi-supervised regression solutions.
An Artificial Neural Network Based Pixel-by-Pixel Lossless Image Compression Method

With the success of deep learning in computer vision applications, deep learning based algorithms have also been proposed for image processing applications. One such application is lossless image compression. Most traditional lossless image compression algorithms use pixel-by-pixel processing algorithms. In the pixel-by-pixel algorithms, each pixel is predicted from the previously coded neighbor pixels and the prediction error is compressed without loss. Deep learning based lossless image compression algorithms can be categorized into two categories, namely prior based algorithms and pixel-by-pixel (or masked convolution based) algorithms. In the pixel-by-pixel algorithms, each pixel&#x2019;s probability distribution is obtained by processing the previously coded neighboring pixels with a neural network, which is then used by an arithmetic coder for lossless compression. This paper explores a deep learning based architecture, which utilizes masked convolutions, to model probability distributions of pixels and also presents a method to improve the parallelization of the algorithms. The obtained compression performance is competitive and is compared to both state-of-the art traditional and deep learning based methods.
Unified Intrinsically Motivated Exploration for Off-Policy Learning in Continuous Action Spaces

Exploration is maintained in continuous control using undirected methods, in which random noise perturbs the network parameters or selected actions. Exploration that is intrinsically driven is a good alternative to undirected techniques. However, it is only studied for discrete action domains. The intrinsic incentives in the existing reinforcement learning literature are unified together in this study by a deterministic artificial goal generation rule for off-policy learning. The agent gains additional reward through this practice if it chooses actions that lead it to useful state spaces. An extensive set of experiments indicates that the introduced artificial reward rule significantly improves the performance of the off-policy baseline algorithms.
Video Object Verification via Meta-learning

Performance of a long term object tracker relies on the object detection accuracy. Although several object detectors are proposed in the literature, robustness to target disappearances and reappearances is still a challenging problem. To deal with this problem, we propose an inference pipeline that integrates an object detector with a meta-learner, both locally trained. This is achieved by replacing the head classification layer of the object detector by a meta-learner that also enables verification of the target. In particular, Mask R-CNN object detector is integrated with SDNet trained end-to-end for object tracking. Improvement achieved by MAML++ meta learner trained as a classifier is also evaluated. Numerical results reported on VOT2020-LT long term video dataset demonstrate that both SDNet and MAML++ meta-learners improve the detection accuracy for unseen object classes. Moreover verification by SDNET provides 7% increase on detection of target disappearance and reappearance frames.
Active Learning for Online Nonlinear Neyman-Pearson Classification

Neyman-Pearson (NP) classification framework is suitable for solving binary classification problems with asymmetric error costs such as network intrusion detection and medical diagnosis. In these kind of applications, type I (detecting non-target as target, false positive) and type II (detecting target as non-target, false negative) errors have different consequences. In this paper, we propose an active learning method for online context tree based ensemble NP classifiers. Proposed method prioritizes training samples that have high uncertainty (greater than a constant threshold) among different classifiers of the ensemble model. We report the performance of the proposed active learning method by measuring the moving true positive rates (TPR) and NP scores with respect to the number of samples used in learning. Experiments are carried out on 4 different datasets and proposed model was compared with random sampling method, where new samples are selected randomly from the training set. In addition, we also show that in order to satisfy target false alarm rate of the NP problem, we need to sample training set with and exploration probability, independent from uncertainty measurement.
Balanced Distributed Augmentation for Multi-Label Few Shot Learning with Prototypical Network

Many methods have been presented as a few shot learners in order to enhance few-shot learners. Some of these methods involve routine-based pre-trained language models and novel pipeline for automating the prompt generation. In this study, we propose a new evenly distributed data augmentation technique, which generates samples according to the probabilistic distribution of the relationship of each label with the mean of a label group. In the labeling phase, we present a semantic sentiment analysis approach in order to increase the realism of the data, in a more semantic augmentation way. The results show that this approach improves the few shot learners. In addition to this, we compare our adaptation approach to other traditional problem transformation methods. The newly developed approach outperforms these traditional methods, especially when the classifier learns from a limited number of samples.
3D Face Animation Generation from Audio Using Convolutional Networks

3D facial animation generation from audio problem is drawing attention as it is demanded for generating artificial characters in games and movies. In the literature, several studies address this problem. However, the generated facial animations are far away from being realistic. In this work, we represent faces with Facial Action Coding System (FACS) and collect a 37-minute-long dataset. We develop convolutional and transformer based models. It is observed that the trained model is able to generate animations that can be used in video games and virtual reality applications, even with novel speaker audio data of speakers it has never seen in the training data.
An Intrinsic Motivation Based Artificial Goal Generation in On-Policy Continuous Control

This work adapts the existing theories on animal motivational systems into the reinforcement learning (RL) paradigm to constitute a directed exploration strategy in on-policy continuous control. We introduce a novel and scalable artificial bonus reward rule that encourages agents to visit useful state spaces. By unifying the intrinsic incentives in the reinforcement learning paradigm under the introduced deterministic reward rule, our method forces the value function to learn the values of unseen or less-known states and prevent premature behavior before sufficiently learning the environment. The simulation results show that the proposed algorithm considerably improves the state-of-the-art on-policy methods and improves the inherent entropy-based exploration.
Optical Flow-based Temporal Video Analysis for Hand Hygiene Monitoring

Touching people or surfaces can increase spread of infectious diseases. Successful hand sanitization performed with correct sequences of hand movements as described by WHO is an important step in maintaining hygiene and decreasing contamination. In this work, deep learning models are trained with inputs enriched with optical flow vectors for the recognition of hand hygiene movements differing from prior approaches. Optical flow describes the apparent movement of pixels in the image. Optical flow vectors are used as extra channels alongside the original image additionally masking them with the hand region of interest or applying image preprocessing techniques. These multi-channel inputs are used to train three different deep learning models. Experimental results show that optical flow improves performance when used along with the original images. The best results are obtained when CNN models are trained on multi-frame images including the angle of optical flow.