Reviewerly/exHarmony huggingface dataset:
Enhancing Image Resolution and Denoising Using Autoencoder

Nowadays, in real time, image processing is involved in various sectors like security, health care, banking, and face recognition. While capturing an image, there is more chance of noise engaged with multiple aspects of the surroundings. To improve the quality of the image and to get better classification results, we need to clean the picture, which is called pre-processing of the image. For the past 30 years, there is tremendous research happening on image processing by many researchers. Deep learning-based autoencoders are producing better results with minimum loss. Image denoising can be achieved with autoencoder architecture. The denoised image is taken as input to the next level to improve the resolution. In this paper, we have considered the popular dataset fashion mnist to denoising the image, which includes the noise. We used back-to-back autoencoders to perform both image denoising and resolution enhancement. In this approach, we can do the pre-processing stage once on the dataset for both image denoising and enhancement of image resolution. We have used binary cross-entropy as loss function to evaluate the performance of the model, and later, we have focussed on improving the resolution to the image. Denoising of an image followed by resolution enhancements in the same process minimizes the time and pre-processing steps separately.
Bert Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets

During the last two decades, we have progressively turned to the Internet and social media to find news, entertain conversations and share opinion. Recently, OpenAI has developed a machine learning system called GPT-2 for Generative Pre-trained Transformer-2, which can produce deepfake texts. It can generate blocks of text based on brief writing prompts that look like they were written by humans, facilitating the spread false or auto-generated text. In line with this progress, and in order to counteract potential dangers, several methods have been proposed for detecting text written by these language models. In this paper, we propose a transfer learning based model that will be able to detect if an Arabic sentence is written by humans or automatically generated by bots. Our dataset is based on tweets from a previous work, which we have crawled and extended using the Twitter API. We used GPT2-Small-Arabic to generate fake Arabic Sentences. For evaluation, we compared different recurrent neural network (RNN) word embeddings based baseline models, namely: LSTM, BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our new transfer-learning model has obtained an accuracy up to 98%. To the best of our knowledge, this work is the first study where ARABERT and GPT2 were combined to detect and classify the Arabic auto-generated texts.
Parameterization of quantum walks on cycles

This study investigates the unitary equivalence classes of quantum walks on cycles. We show that unitary equivalence classes of quantum walks on a cycle with N vertices are parameterized by 2N real parameters. Moreover, the ranges of two of the parameters are restricted, and the ranges depend on the parity of N.
Speaker Diarization using Embedding Vectors

In recent years, with the rapid increase of voice data, solutions are being sought extensively for examination and indexing in the field of speech processing. One of these solutions is the speaker diarization, which is used to examine speech records that include multi-speaker. The speaker diarization system basically splits the speech file into segments using the speech file's silence fields and examines the similarity between the segments. In this study, deep learning based embedding vectors are used for speaker representation. The vector embedding proposed for speaker representation is performed with x-vectors extracted using time-delayed deep neural network and d-vectors extracted using LSTM. Then the system is tested with xd-vectors consisting of the combination of these two vectors. As a result, the effect of representative embedding vectors on the performance of the diarization system is examined in the scope of this paper.
Improving Scalability, Sparsity and Cold Start User Issues in Collaborative Tagging with Incremental Clustering and Trust

Background: Due to huge data in web sites, recommending users for every product is impossible. For this problem Recommender Systems (RS) are introduced. RS is categorized into Content-Based (CB), collaborative Filtering (CF) and Hybrid RS. Based on these techniques recommendations are done to user. In this, CF is the recent technique used in RS in which tagging feature also provided. Objective: Three main issues occur in RS are scalability problem which occurs when there is a huge data, sparsity problem occurs when rating data is missing and cols start user or item problem occurs when new user or new item enters in the system. To avoid these issues here we have proposed Incremental clustering and Trust in Collaborative Tagging. Methods: Here we have proposed a method Collaborative Tagging (CT) with Incremental Clustering and Trust which enhances the recommendation quality by removing the issues of scalability with the help of Incremental Clustering and sparsity and cold start user or item problems are resolved with the help of Trust. Results: Here we have compared the results of Collaborative tagging with Incremental Clustering and Trust (CFT-EDIC-TS) with the baseline approaches of CT with Cosine similarity (CFT-CS), CT with Euclidian Distance and Incremental Clustering (CFT-EDIC) and CT with Trust (CFT-TS). Conclusion: Here we have compare the proposed approach with the baseline approaches and the metrics are used MAE, prediction percentage, Precision and Recall. Based on these metrics for every split CFT-EDICTS shown best results as compared to other baseline approaches.
Traffic Abnormalities Identification Based on the Stationary Parameters Estimation and Wavelet Function Detailization

The article deals with the analysis of the of corporate network traffic properties using statistical methods. The complex method of estimating the stationary of computer network traffic using the autocorrelation function and the Hurst index is proposed. To detect the abnormal behavior of computer network traffic, the wavelet analysis method using the Fourier function, has been developed and tested.
Breast Histopathology with High-Performance Computing and Deep Learning

The increasingly intensive collection of digitalized images of tumor tissue over the last decade made histopathology a demanding application in terms of computational and storage resources. With images containing billions of pixels, the need for optimizing and adapting histopathology to large-scale data analysis is compelling. This paper presents a modular pipeline with three independent layers for the detection of tumoros regions in digital specimens of breast lymph nodes with deep learning models. Our pipeline can be deployed either on local machines or high-performance computing resources with a containerized approach. The need for expertise in high-performance computing is removed by the self-sufficient structure of Docker containers, whereas a large possibility for customization is left in terms of deep learning models and hyperparameters optimization. We show that by deploying the software layers in different infrastructures we optimize both the data preprocessing and the network training times, further increasing the scalability of the application to datasets of approximatively 43 million images. The code is open source and available on Github.
Self-Adaptive Trust-aware Decentralized Information Flow Control, extended version

Modern software systems and their corresponding architectures are decentralized, distributed, and dynamic. As a consequence, decentralized mechanisms are also required to ensure security in such ar ...
Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces

Current state of the art computer vision applications rely on highly complex models. Their interpretability is mostly limited to post-hoc methods which are not guaranteed to be faithful to the model. To elucidate a model’s decision, we present a novel interpretable model based on an invertible deep convolutional network. Our model generates meaningful, faithful, and ideal counterfactuals. Using PCA on the classifier’s input, we can also create “isofactuals”– image interpolations with the same outcome but visually meaningful different features. Counter- and isofactuals can be used to identify positive and negative evidence in an image. This can also be visualized with heatmaps. We evaluate our approach against gradient-based attribution methods, which we find to produce meaningless adversarial perturbations. Using our method, we reveal biases in three different datasets. In a human subject experiment, we test whether non-experts find our method useful to spot spurious correlations learned by a model. Our work is a step towards more trustworthy explanations for computer vision.
WITHDRAWN: A comparative study on a privacy protection in personalized web search

World wide web (WWW) is very popular and very popular The knowledge retrieval tool widely used on the internet. Now-a-days Internet search is a widely used function on the internet. Client obtains Variety of knowledge relevant to their questions. Construct more appropriate and effective performance for consumers, Customization Using technique. Customized web search refers to search Knowledge which is specifically tailored to the needs of a individual By adding the information received about the question. Two fold Specific approaches to customizing the Browser outcomes are the refinement of the app application and reclassification of the search data. Several site search strategies customized to web content, web connection style, history searching, user accounts and shareholder queries. The proposed paper is meant to illustrate studies of specific forms of methods of personalization.
Classiﬁcation of Breast Cancer Histopathology Images by Cell-Centered Deep Learning Approach

Breast cancer is one of the most common cancer types worldwide today. The diagnosis of this cancer is usually made by the intensive work of pathologists on stained biopsy tissue images. In this study, breast cancer tissues are classified into four classes (normal, in situ, invasive and benign) by using the convolutional neural networks. In the training and test process performed on histopathological images, a cell-centered approach is followed instead of using the whole image. The results are examined separately for both image patches and the classification of the whole microscopic image. In addition, the effect of image patch sizes and cell neighborhood relationships on accuracy in different dimensions is investigated. As a result, 75% in four classes and 80% accuracy in cancer/non-cancer two-grade evaluation were achieved with the application which was trained with the training data of BACH dataset and tested with the test data of Bioimaging2015 dataset.
Image Segmentation Algorithm Based on Statistical Properties

Image segmentation, a technology that divides images into parts, is an important technique in image processing, and is used in various disciplines, such as remote sensing, medical imaging, and industrial fields. Several methods and known algorithms are used for image segmentation. This paper proposes a new algorithm for image segmentation that is based on statistical properties. The new algorithm depends on colour contrast between regions of the image, and uses a mathematical equation based on statistical properties (the maximum value, minimum value, and standard deviation) to automatically calculate a global threshold. We find that the algorithm successfully performs image segmentation when applied to two different test images.
Addressing Exposure Bias With Document Minimum Risk Training: Cambridge at the WMT20 Biomedical Translation Task

The 2020 WMT Biomedical translation task evaluated Medline abstract translations. This is a small-domain translation task, meaning limited relevant training data with very distinct style and vocabulary. Models trained on such data are susceptible to exposure bias effects, particularly when training sentence pairs are imperfect translations of each other. This can result in poor behaviour during inference if the model learns to neglect the source sentence. The UNICAM entry addresses this problem during fine-tuning using a robust variant on Minimum Risk Training. We contrast this approach with data-filtering to remove ‘problem’ training examples. Under MRT fine-tuning we obtain good results for both directions of English-German and English-Spanish biomedical translation. In particular we achieve the best English-to-Spanish translation result and second-best Spanish-to-English result, despite using only single models with no ensembling.
How Benign is Benign Overfitting

We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models. When trained with SGD, deep neural networks essentially achieve zero training error, even in the presence of label noise, while also exhibiting good generalization on natural test data, something referred to as benign overfitting (Bartlett et al., 2020; Chatterji & Long, 2020). However, these models are vulnerable to adversarial attacks. We identify label noise as one of the causes for adversarial vulnerability, and provide theoretical and empirical evidence in support of this. Surprisingly, we find several instances of label noise in datasets such as MNIST and CIFAR, and that robustly trained models incur training error on some of these, i.e. they don’t fit the noise. However, removing noisy labels alone does not suffice to achieve adversarial robustness. We conjecture that in part sub-optimal representation learning is also responsible for adversarial vulnerability. By means of simple theoretical setups, we show how the choice of representation can drastically affect adversarial robustness.
Convolutional Neural Network with Inception-like Module for Ball and Goalpost Detection in a Small Humanoid Soccer Robot

The current RoboCup Humanoid rules determine white balls, white goalposts and white field lines. In such an environment, the color-based heuristics algorithms used by RoboCup teams for computer vision do not present good detection performance. Convolutional neural networks (CNNs) are a straightforward solution to this challenge. Among the existing CNN-based techniques for object detection, the YOLO (You Only Look Once) algorithm is designed for real-time execution. In this paper, we created many neural networks architectures based upon YOLO so that we could identify which one had the best overall cost benefit between performance and execution time in a small humanoid robot. We assessed the algorithm's performance mainly by calculating the quantity of correct classifications and the mean average precision (mAP). A Convolutional Neural Network with an Inception-like module has showed promising results, running in real time on the robot's computer with Intel's OpenVINO inference framework. Getting right more than 88% of the classifications and achieving a mAP of 0.81, the developed neural network is prone to work effectively in the robot.
Privacy Preserving Anti-forensic Techniques

In this digital age of mass surveillance and personal identity theft, data security and privacy is a major concern. Although sophisticated tools and software that guarantee privacy are easily available, users are increasingly becoming aware of their privacy concerns and look for solutions that enables them to secure data on their own, owing to its sensitivity and confidentiality. Anti-forensics has been at the forefront in data concealment by using novel data hiding and trail obfuscation techniques which promise great value when it comes to maintaining anonymity, privacy and confidentiality of data. This chapter takes a look at various anti-forensic techniques that promise value when it comes to data obscurity and discusses their potential in ensuring its security and privacy.
Intent Classification and Slot Filling for Turkish Dialogue Systems

The rapid development of systems with artificial intelligence plays significant role in human-machine interaction. The artificial intelligence algorithms that take instructions via natural language, can provide appropriate answers to the commands they receive. The systems with artificial intelligence which are in an interaction with humans are named as dialogue systems. In other foreign languages, especially in English, there are lots of data sets that can be used in dialogue systems. The most popular and preferred data set among these data sets is named ATIS (Airline Travel Information System). Currently, there is no Turkish data set with characteristics required by dialogue systems. With this study, the ATIS data set is adapted to implement Turkish dialogue systems. After converting the data set into Turkish, intent classification and slot-filling tasks are implemented by BERT multilingual model which is one of state of the art natural language processing methods. Micro averaged F1- score values are 89 percent and 88 percent for intent classification and slot filling correspondingly.
Sentiment Analysis of Twitter Data

The World Wide Web has taken seriously new ways for individuals to convey their views and conclusions on different topics, models and issues.Clients create content that resides in a variety of media, such as web gathering, conversation gathering, and weblogs, and provide a solid and generous foundation for gaining momentum in different areas such as advertising and research.Policy, logic research, market forecasts and business outlook.Hypothesis research extracts inferences from information available online and orders the emotions that the author conveys for a particular item into up to three predefined categories (good, negative, and unbiased).Identify the problem.This article outlines a hypothesis review cycle for quickly ordering unstructured news on Twitter.In addition, we are exploring different ways to perform a detailed emotional survey on Twitter News.In addition, it presents a parametric correlation of strategies considered according to recognized boundaries.This work tends to make the case enjoy investigating on Twitter; The values communicated in them represent the tweets: positive, negative or fair.Twitter is an online thumbnail that contributes to a blog and a wide range of interactions, allowing customers to create short 140-character short instructions.It is a fast growing association with more than 200 million subscribers, of which 100 million are dynamic customers and half of them constantly sign up for Twitter, generating around 250 million tweets every day.Due to this overwhelming use, we plan to achieve a biased impression of the public by breaking the estimates communicated in the tweets.Researching public opinion is important for some applications, for example, when companies are looking to respond to their material, predict political careers, and anticipate economic wonders like stock trading.The function of this to build a useful classifier for the command in a precise and programmed way of the stream of fuzzy tweets.
PCA and projection pursuits on high dimensional data reduction

Abstract The flood of multimedia data has affected various fields of research, including multimedia retrieval, database management, data mining, machine learning, social media analysis, image processing and so on. Many digital multimedia data collections are made in various fields and application contexts not only in advertising or journalism, but also often used in the context of cultural heritage applications, web search, or geographical information. Digital multimedia data is created in various formats and media modalities and available worldwide every day. The higher the quantity of data, the higher the complexity, diversity, dimensions and multy modality, so that multimedia data grows exponentially. To reduce the impact of high-dimensional data, data reduction is carried out. Data reduction plays an important role in classification. Therefore the selection of data reduction techniques must be done carefully. Principal Component Transform is widely used in data reduction. This analysis attempts to eliminate correlations between bands and further determines the optimal linear combination of original data dimensions for variations in the value of data pixels. The mathematical principle of Principal Component depends on the decomposition of eigenvalues from the covariance matrix of the data matrix. When multimedia data is smaller or covered by data noise, Principal Component is no longer efficient at reducing. To overcome this, Projection Pursuit was introduced using sphering matrices and invariant translations in the transformations. In Projection Pursuits, only data that is considered interesting will be transformed. Optimization of reduced data selection is based on the maximum value of the projection index generated. The projection index used is Skewness and Kurtosis because it is considered in accordance with the data used, the CASI image of Bogor city.
Featured Hybrid Recommendation System Using Stochastic Gradient Descent

Recommendation system is a branch of information filtering system that aims to predict "preference" or "rating" that a user prefers to give to an item (such as music, movies, or books) or social object (e.g. group or people) that had been unforeseen, using a model created based on the user's social relationship (collaborative filtering model) or the features of an item (content-based model) [1,2].Continuous growth of web and its applications has given a significant importance for recommendation systems [3].However, recommendation system needs to deal with five main research problems: coldstart, data sparsity, accuracy and scalability [4].To deal with this information overload, incremental learning algorithm is the effective solution with Stochastic Gradient Descent (SGD) algorithm [11].However, the non-flexible feature in learning rate SGD require a better approach [2].For this is reason, this study adjusts two mechanisms which are k group and momentum into novel algorithm -k Momentum SGD (KMSGD).
WITHDRAWN: Real-Time monitoring of track and field teaching based on internet of things and sensors

Track and field is a sport made up of several events in IoT. Field teaching events, such as a jump in the execution, and Throw will include track and field teaching. Although most of the event is an individual, some, such as the track relay, accompanied the team. Team event distribution points, and the total points earned by players with a high finish of each event for their team. Track and field teaching is assumed that all these different roles for sports players. For the coach, but it should not be maximum victory gain is completed, training and competition methods of influence in the personal development of aggressive teaching classification level analysis are very difficult in the previous system. To introduced Logistic Tracking Performance (LTP) based IoT, player development, the players' safety, and supporters' participation play a role today. Coach analyses the possibility of shaping the game's strategy. The analysis will customize the experience that sensor to collect the data through a plurality of IoT. An important role in the information monitoring and control system of IoT sensors, the world attaches great importance to use their real time monitoring track and field teaching, new materials and new product development. The proposed algorithm Logistic Tracking Performance (LTP) is used to classify the sports player performance level using IoT and sensors. Track and field teaching enhances the teaching process using the IoT and how coaches and sports players will help understand the effective relationship between them. The LTP algorithm analysis the track and field teaching performance level and classification level.