Reviewerly/exHarmony huggingface dataset:
Content loss and conditional space relationship in conditional generative adversarial networks

In the machine learning community, generative models, especially generative adversarial networks (GANs) continue to be an attractive yet challenging research topic.Right after the invention of GAN, many GAN models have been proposed by the researchers with the same goal: creating better images.The first and foremost feature that a GAN model should have is that creating realistic images that cannot be distinguished from genuine ones.A large portion of the GAN models proposed to this end have a common approach which can be defined as factoring the image generation process into multiple states for decomposing the difficult task into several more manageable sub tasks.This can be realized by using sequential conditional/unconditional generators.Although images generated by sequential generators experimentally prove the effectiveness of this approach, visually inspecting the generated images are far away of being objective and it is not yet quantitatively showed in an objective manner.In this paper, we quantitatively show the effectiveness of shrinking the conditional space by using the sequential generators instead of utilizing single but large generator.At the light of the content loss we demonstrate that in sequential designs, each generator helps to shrink the conditional space, and therefore reduces the loss and the uncertainties at the generated images.In order to quantitatively validate this approach, we tried different combinations of connecting generators sequentially and/or increasing the capacity of generators and using single or multiple discriminators under four different scenarios applied to image-to-image translation tasks.Scenario-1 uses the conventional pix2pix GAN model which serves as the based line model for the rest of the scenarios.In Scenario-2, we utilized two generators connected sequentially.Each generator is identical to the one used in Scenario-1.Another possibility is just doubling the size of a single generator which is evaluated in the Scenario-3.In the last scenario, we used two different discriminators in order to train two sequentially connected generators.Our quantitative results support that simply increasing the capacity of one generator, instead of using sequential generators, does not help a lot to reduce the content loss which is used in addition to adversarial loss and hence does not create better images.
A novel hybrid algorithm for morphological analysis: artificial Neural-Net-XMOR

In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results show that we improved the morphological analysis performance for optical character recognizer (OCR) and social media data. Thus, for the new hybrid algorithm with LSTM, the accuracy reached 99.91% for the OCR dataset and 99.82% for social media data.
Automated question generation and question answering from Turkish texts

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources.Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation.However, compared to automatic question answering (QA), QG is a more challenging task.In this work, we fine-tune a multilingual T5 (mT5) transformer in a multitask setting for QA, QG and answer extraction tasks using Turkish QA datasets.To the best of our knowledge, this is the first academic work that performs automated text-to-text question generation from Turkish texts.Experimental evaluations show that the proposed multitask setting achieves state-of-the-art Turkish question answering and question generation performance on TQuADv1, TQuADv2 datasets and XQuAD Turkish split.The source code and the pretrained models are available at https://github.com/obss/turkishquestion-generation.
Automatic keyword assignment system for medical research articles using nearest-neighbor searches

Assigning accurate keywords to research articles is increasingly important concern.Keywords should be selected meticulously to describe the article well since keywords play an important role in matching readers with research articles in order to reach a bigger audience.So, improper selection of keywords may result in less attraction to readers which results in degradation in its audience.Hence, we designed and developed an automatic keyword assignment system (AKAS) for research articles based on k-nearest neighbor (k-NN) and threshold-nearest neighbor (t-NN) accompanied with information retrieval systems (IRS), which is a corpus-based method by utilizing IRS using the Medline dataset in PubMed.First, AKAS accepts an abstract of the research article or a particular text as a query to the IRS.Next, the IRS returns a ranked list of articles to the given query.Then, we selected a set of documents from this list using two different methods, which are k-NN and t-NN representing the first k documents and documents whose similarity is greater than the threshold value of t, respectively.To evaluate our proposed system, we conducted a set of experiments on a selected subset of 458,594 PubMed articles.Then, we performed an experiment to observe the performance of AKAS results by comparing with the original keywords assigned by authors.The results we obtained showed that our system suggests keywords more than 55% match in terms of F-score.We presented both methods we used and results of experiments, in detail.
Transmorph: a transformer based morphological disambiguator for Turkish

The agglutinative nature of the Turkish language has a complex morphological structure, and there are generally more than one parse for a given word.Before further processing, morphological disambiguation is required to determine the correct morphological analysis of a word.Morphological disambiguation is one of the first and crucial steps in natural language processing since its success determines later analyses.In our proposed morphological disambiguation method, we used a transformer-based sequence-to-sequence neural network architecture.Transformers are commonly used in various NLP tasks, and they produce state-of-the-art results in machine translation.However, to the best of our knowledge, transformer-based encoder-decoders have not been studied in morphological disambiguation.In this study, in addition to character level tokenization, three input subword representations are evaluated, which are unigram, bytepair, and wordpiece tokenization methods.We have achieved the best accuracy with character input representation which is 96.25%.Although the proposed model is developed for Turkish language, it is not language-dependent, so it can be applied to a larger set of languages.
Subject tracking robotic manipulator using pan-tilt mechanism

In this paper, the implementation of a real-time framework which can monitor RGB targets, with a fewer camera in a dynamic environment, is suggested. The applications which involve object tracking are quite crucial for military, surveillance system and operational robot applications and are becoming more significant gradually. The implemented design allows to track an object using fewer cameras. The three main parts of the design are object detection, mapping and tracking the object. When the camera captures the object, it first senses the color and applies different filters, and then detects the object's circular outline or boundary. And in accordance to the information obtained, an algorithm is used to calculate the centroid of the object. Object centroid is decided using coordinates of the circular boundary and then tracking process is initiated by activating the servo motors. The motors mounted on a robotic platform are called the pantilt platform, which is also roughly the spatial manipulator of the 2DOF. It can turn 270 degrees on x-axis and 180 degrees on y-axis. Viola-Jones algorithm was implemented for facial detection and tracking of the detected face with a PI control was designed.
Significance of fidelity deviation in continuous-variable teleportation

Performance of quantum teleportation is typically measured by the average fidelity, an overlap between the input and output states. Along with the first moment, we introduce the second moment of fidelity in continuous-variable (CV) teleportation, i.e., the fidelity deviation as the figure of merit to assess the protocol's efficiency. We show that CV states, both Gaussian and non-Gaussian, can be better characterized by considering both average fidelity and fidelity deviation, which is not possible with only average fidelity. Moreover, we shed light on the performance of the teleportation protocol in two different input scenarios: One is when input states are sampled from constrained uniform distribution while the other one is Gaussian suppression of the input states which again leads to a different classification of CV states according to their performance. The entire analysis is carried out in noiseless and noisy scenarios with noise being incorporated in the measurement and the shared channels. We also report that one type of noise can make the protocol robust against the other one, which leads to a ``constructive effect,'' and identify the noise models which are responsible for decreasing average fidelity and the increment in fidelity deviation.
Weakly Supervised Object Detection with Self-Attention Distillation and Instance-Aware Mining

Abstract Weakly supervised object detection (WSOD) has been widely concerned in the field of computer vision due to its inexpensive way for learning object detectors. However, existing WSOD methods are either limited on localization accuracy or require complex pipelines. In this paper, we propose a single network training based framework for WSOD task. The layer-wise self-attention distillation is performed in our framework so as to improve representation learning of the detection network. As the complementary composition, a context addition unit is designed to prevent the detection network focusing on the most discriminative parts of object. Moreover, an instance-aware mining algorithm is proposed to generate more precise pseudo-labels, and thereby further enhance the performance of the detection network. We have evaluated the proposed method on popular benchmarks, i.e., PASCAL VOC 2007 and VOC 2012, and the experimental results show that it can accurately and efficiently locate objects in images without complex data augmentation and well-annotated auxiliary dataset.
Document-Level Sentiment Analysis of Course Review Based on BG-Caps

With the development of the Internet in various fields, the combination of education and the Internet is close. Many users choose courses they are interested in to study on the MOOC platform and leave text reviews with emotional colors. However, the traditional word vector representation method extracts text information in a static way, which ignores text location information. The convolutional neural network cannot fully utilize the semantic features and correlation information, so the results of text sentiment analysis are inaccurate. To solve the above problems, this paper proposes a sentiment analysis method based on BG-Caps MOOC text review. The ALBERT pretraining model was used to obtain the dynamic feature of the text. Combined with the BiGRU and capsule network model, the features were trained to obtain deep semantic features. We evaluated our mode on the MOOC review dataset. The results show that the proposed method achieved effective improvement in accuracy.
A Low Spectral Bias Generative Adversarial Model for Image Generation

We propose a systematic analysis of the neglected spectral bias in the frequency domain in this paper. Traditional generative adversarial networks (GANs) try to fulfill the details of images by designing specific network architectures or losses, focusing on generating visually qualitative images. The convolution theorem shows that image processing in the frequency domain is parallelizable and performs better and faster than that in the spatial domain. However, there is little work about discussing the bias of frequency features between the generated images and the real ones. In this paper, we first empirically demonstrate the general distribution bias across datasets and GANs with different sampling methods. Then, we explain the causes of the spectral bias through the deduction that reconsiders the sampling process of the GAN generator. Based on these studies, we provide a low-spectral-bias hybrid generative model to reduce the spectral bias and improve the quality of the generated images.
Towards Single-Pass and Transferable GAN-Based Attacks Against Object Detectors

Object detection using deep learning is a computer vision task related to predicting bounding boxes and class labels for one or more objects in an image. Although, deep neural networks have demonstrated huge success, recent studies have shown that they are vulnerable to carefully crafted adversarial attacks, which can lead to misclassifications, object suppression, and object fabrication. This fact has led to works that have successfully generated adversarial examples against object detectors using iterative optimization methods. However, such attacks need to repeatedly query the target model in order to achieve the attack objectives. In this paper, we design an adversarial attack against object detection using generative adversarial networks. To emphasize that such adversarial attacks are possible in practical conditions, we show that the attack works in a single pass over the target image and does not need to query the target object detector in order to function. We demonstrate the transferability of our attack and its resilience towards basic defenses by evaluating it on two different datasets and on several defended black-box detectors, respectively.
Region of interest in JPEG

This paper describes three methods to encode a region of interest (ROI) of arbitrary shape in the JPEG standard. The ROI is part of the image encoded with higher quality than the rest of the image. Two of the described methods are based on thresholding of DCT coefficients. The last method is based on the early termination of the entropy coding phase. All methods are fully compatible with existing JPEG decoders. Using ROI, it is possible to reduce the bitrate to only a fraction of the original value. The results in this paper show that thresholding of original DCT coefficients provides superior performance in terms of the PSNR-B index.
Filipino Students' Active-Passive Voice Preference in Invitation Letters

Discussions on active and passive use are rich in literature. However, there is a remarkable dearth of studies showing students' actual voice preferences after all concepts of the voice have been introduced. This study aims at ascertaining Filipino university students' actual preference of voice in invitation letters written in 2012, 2014, and 2015. It also looks at the tense-aspect combinations and the semantics achieved in the two voice categories. Three groups taking technical writing course from three universities in Manila produced 135 letters. Letters were run using AntConc and UAM Corpus tools. Results confirm the dominance of active voice, with passive almost nil in the corpus. The preference is found to be statistically significant. The dominance of active voice may suggest that foregrounding the doers is important. Moreover, there is no significant difference between the long and agentless passive. Although the results came from the parochial Philippine context, implications respecting students' preference of voices may be universal. Limitations and trajectories are offered accordingly.
Model for wireless image correlation assisted by sensors based on 3D display technology

The utilization of 3D display systems offers significant advantages to end-users. Sequencing technologies are essential for accessing an autonomous, auto-balanced, non-glass 3D world with greatly improved visual acuity. To have both social and emotional depth cues on a 3D display is difficult. Because of the theoretically constant feature vectors provided, certain 3D overall volume display approaches cannot produce shadows or structures. Hence, in this paper, a Wireless Sensor Assisted Image Correlation framework (WSAICF) has been proposed to enhance the current competitiveness, performance experience, and better visual understanding of the 3d display systems. In 3D presentations, the Steep Learning algorithm is used to enhance social depth estimations rapidly. The WSAICF method is integrated with the Deep Learning algorithm to enhance the emotional depth indicators of 3D display systems. The simulation result shows that an image correlation system achieves 97 % reliability, 99 % performance, 96 % viability, 11 % resilience, and 95 % efficiency.
Detecting Malicious Facebook Applications

With daily installs, third-party Apps may be a very important cause for the recognition and attractiveness of Facebook or any on-line social media. Sadly, cyber criminals get return to appreciate the potential of victimisation apps for spreading spam and malware. we have a tendency to notice that a minimum of thirteen of Facebook apps within the dataset ar sometimes malevolent. but with their findings , many problems like fake profiles, malicious applications have put together grown There are not any potential solutions to those issues. throughout this project, we have a tendency to tend to return up with a framework that automatic detection of malicious applications is possible and is economical. Suppose there is a Facebook application, can the Facebook user verify that the app is malicious or not. actually the Facebook user cannot establish that so The key contribution is in developing the primary tool dedicated to detection deceitful apps on Facebook is FRAppE-Rigorous Facebook's Application authority. we have a tendency to tend to leverage information nonheritable from the posting behaviour of Facebook applications seen by uncountable Facebook users to develop FRAppE. initial we have a tendency to establish a collection of options that facilitate United States to research malicious from benign ones. Second, investing these identifying options ,where we have a tendency to show that FRAppE will discover malicious apps with ninety five.9% accuracy. Finally, we have a tendency to explore the ecosystems of malicious Facebook apps and establish mechanisms that these apps use to unfold. Key Words:apps, malicious, on-line social networks.
A tool for modeling and analysis of relationships among feature model views

High-tech systems development involves several models and artifacts, each focusing on one or more aspects or parts of the system. In product lines, managing the common and variable characteristics of these development artifacts typically involves feature models. Yet for high-tech systems, management is complicated by the large number of features and constraints. Using multiple feature models, each with a reduced number of features relevant only for specific artifacts, has been identified as a possible solution to deal with this complexity. However, to use multiple feature models effectively, the relationships among feature models need to be known. While several approaches that support multiple feature models exist, they do not explicitly provide concepts for representing structural relationships between features in different feature models. Explicitly documenting these relationships improves understanding of the complete variability model and supports maintainability of the variability model under evolution. In this study, we present two domain specific languages---one for relationship modeling, one for change analysis which enables users to express change impact analysis queries using the relationship modeling language terminology---, which we implemented in TReMAFeM, a tool for relationship modeling among feature models. Moreover, we demonstrate the use of the tool on the industry-related case of the Extended Twilight System product line and explain how it can be used to support typical change impact analysis scenarios.
A Roomy Survey of Image Filtering Techniques

Filtering is most important and critical task to exquisite information from the noisy image and can be used in different and diverse engineering problems such as signal processing, image processing(like edge detection, object identification, brightness, contrast etc) , electronic, physics electronic, etc. Filters are used for de-blurring and smoothing in image processing and analysis,. In addition with the help of the filters some details which are clear and fair in the original image will be blurred and the ultimate final image will be enhanced. In this paper we present different types filtering techniques and have simulated some of them in Matlab.
Spin-Wave Quantum Computing with Atoms in a Single-Mode Cavity

We present a method for network-capable quantum computing that relies on holographic spin-wave excitations stored collectively in ensembles of qubits. We construct an orthogonal basis of spin waves in a one-dimensional array and show that high-fidelity universal linear controllability can be achieved using only phase shifts, applied in both momentum and position space. Neither single-site addressability nor high single-qubit cooperativity is required, and the spin waves can be read out with high efficiency into a single cavity mode for quantum computing and networking applications.
Effect of Syntax Surgery strategy on Achievement of Secondary School Student's

The research aims to identify the effect of the Syntax Surgery strategy on the achievement of secondary school students in English grammar.The researcher used the experimental method in two groups, one of them is experimental, which is studied according to The Syntax Surgery strategy, and the second is a control group, which is studied according to the usual method.The achievement test is used as a research tool.The experiment was implemented in Somaya Secondary School for Girls in the first course of the academic year 2019-2020.After completing the application, the research applied tool, and the researcher observed superiority of the experimental group that studied according to the Syntax Surgery strategy over the control group that studied according to the traditional method.The study recommended the necessity of deception according to the strategy used with the experimental group.It also suggested generalizing Syntax Surgery strategy to other subjects such as the Arabic language and other stages in the intermediate stage.