Reviewerly/exHarmony huggingface dataset:
Cell Segmentation in Triple-Negative Breast Cancer Histopathological Images Using U-Net Architecture

In this study, cell segmentation study is performed on histopathology dataset obtained from triple-negative breast cancer patients. The images present in the triple-negative breast cancer (TNBC) nuclei segmentation dataset are reproduced by the artificial data generation method. By using groundtruth images in the dataset, a second groundtruth image set containing only cell boundaries is created. The dataset, which was separated as training and test data, is passed through histogram equalization process and divided into patches in size of 64×64, 128×128 and 512×512 pixels. A total of three different datasets and two different groundtruths are evaluated separately and training is conducted with U-Net. The output image obtained as a result of the training is improved by thresholding. Output images obtained from 64×64 and 128×128 pixel sizes are combined with a technique similar to the ensemble learning method and converted into 512×512 pixel images. The accuracies of all steps performed during the study are presented in tables.
Speech Act of Flaming in Twitter Status

Speech acts are a way to conceptualize speech as an action. Speech act of flaming is an utterance that expresses insults; swearing; and hateful; intense language in hostile online interaction. Flaming is an expressive speech act and often leads to the trading of insults between members within a certain conversation. Social media is one of the platforms that has been used to express, comment and voice out emotions. In this study, the speech act of flaming in a Twitter status will be analysed based on the Malaysian context. The data of this study is a compilation of Twitter statuses consisting of 2.5 million words and is labelled as Malaysian Twitter Status Context corpus (MTSC). The tweets were analysed based on 14 subcategories of flaming (Bansal, Nittin, Siddhartha, Kapil, Anuj, Sheenu, Kanika, Kunal, Kunal, Manav, 2012 and Revathy &amp; Norizah, 2017). This study adopted the qualitative approach through contextual analysis. Findings of this study showed that flaming in Malaysian context can be direct or intentional; and indirect based on the intentions (illocutionary force) to offend other tweeters. Findings of this study show that language usage among youngsters specifically the tweeters are not in line with the values and ethics that have been practiced in our culture and society.&#x0D; &#x0D; Keywords: Expressive, Flaming, Malaysian context, Speech acts, Twitter
Text Matters but Speech Influences: A Computational Analysis of Syntactic Ambiguity Resolution

Analyzing how human beings resolve syntactic ambiguity has long been an issue of interest in the field of linguistics. It is, at the same time, one of the most challenging issues for spoken language understanding (SLU) systems as well. As syntactic ambiguity is intertwined with issues regarding prosody and semantics, the computational approach toward speech intention identification is expected to benefit from the observations of the human language processing mechanism. In this regard, we address the task with attentive recurrent neural networks that exploit acoustic and textual features simultaneously and reveal how the modalities interact with each other to derive sentence meaning. Utilizing a speech corpus recorded on Korean scripts of syntactically ambiguous utterances, we revealed that co-attention frameworks, namely multi-hop attention and cross-attention, show significantly superior performance in disambiguating speech intention. With further analysis, we demonstrate that the computational models reflect the internal relationship between auditory and linguistic processes.
Deep learning based virtual staining of label-free ultraviolet (UV) microscopy images for hematological analysis

Deep ultraviolet microscopy (UV) enables high-resolution, label-free imaging of biological samples and yields diagnostically relevant quantitative molecular and structural information. We recently demonstrated that deep UV microscopy can serve as a simple, fast, and low-cost alternative to modern hematology analyzers that assess variations in the morphological, molecular, and cytogenetic properties of blood cells to monitor and diagnose blood disorders. We also introduced a pseudocolorization scheme that uses multi-spectral UV images (acquired at three different wavelengths) to generate images whose colors accurately recapitulate those produced by conventional Giemsa staining, and can thus be used for visual hematological analysis. Here, we present a deeplearning framework to virtually stain single-channel UV images acquired at 260 nm, providing a factor of three improvement in imaging speed without sacrificing accuracy. We train a generative adversarial network (GAN) using image pairs consisting of single-channel UV images of blood smears and their corresponding pseudocolorized images to generate realistic, virtually stained images. The virtual stained images are post-processed to improve contrast and yield consistent background colors. We quantify the performance of our framework in terms of the structural similarity index (SSIM) for each color channel. Our virtual staining scheme is the first step towards a completely automated hematological analysis pipeline that includes segmentation and classification of different blood cell types to compute metrics of diagnostic value. Our method eliminates the need to acquire images at different wavelengths and could potentially lead to the development of a faster and more compact label-free, point-of-care hematology analyzer.
A Self Supervised Defending Mechanism Against Adversarial Iris Attacks based on Wavelet Transform

In biometric applications, deep neural networks have presented significant improvements.However, when presenting carefully designed input training data known as adversarial examples, their output is severely reduced.These types of attacks are termed as adversarial attacks, and any biometric security system is greatly affected by these attacks.In the proposed work, an effective defensive mechanism has been developed against adversarial attacks which are introduced in iris images.The proposed defensive mechanism is following the concept of wavelet domain processing and it investigates the mid and high frequency components of wavelet domain components.Based on this, the model reproduces the various denoised copies of input iris images.The proposed strategies are intended to denoise each sub-band of the wavelet domain and assess the subbands most likely to be affected by the adversary using the reconstruction error measured for each sub-band.We test the effectiveness of the proposed adversarial protection mechanism against various attack methods and analyzed the results with other state of the art defense approaches.
Moving object properties-based video saliency detection

Video saliency detection, which suffers from the interference of complicated motion and complex background, aims at discovering the motion-related and the most noticeable object in a video sequence while maintaining the spatiotemporal consistency of saliency maps. We propose a video saliency detection approach by constructing the moving object properties, meaning that motion is in concurrence with a general object for detected regions. With the assistance of different key frame strategies, the consistency propagation, which is respectively conducted to refine the obtained results of motion saliency and object proposals to improve low-quality detections and avoid the excessive accumulation of errors, is orderly implemented through sparse reconstruction based on the constructed adjacent relationships. Meanwhile, by integrating the refined object proposals with the refined salient motion detection, a pixel-based fusion strategy is applied to locate the most trustworthy motion object regions and suppress detection noises, such as dynamic background and stationary objects. Moreover, a Bayesian fusion framework that incorporates the global features obtained using low-rank constraints is employed to further enhance the accuracy and global temporal consistency for the obtained initial motion object regions. Then based on the obtained priors of motion objects and background regions, the spatial saliency map is estimated using the geodesic distances transform to discover more complete and spatially salient motion objects. Finally, an energy optimization function is proposed to intuitively integrate multiple saliency clues (i.e., spatial saliency, global features, and motion objects) and generate the global spatiotemporal consistency saliency map. Experimental results on three different benchmark datasets demonstrate that the proposed method successfully infers video moving objects and extracts the most salient regions, outperforming the state-of-the-art methods.
Parasite Chain Detection in the IOTA Protocol.

In recent years several distributed ledger technologies based on directed acyclic graphs (DAGs) have appeared on the market. Similar to blockchain technologies, DAG-based systems aim to build an immutable ledger and are faced with security concerns regarding the irreversibility of the ledger state. However, due to their more complex nature and recent popularity, the study of adversarial actions has received little attention so far. In this paper we are concerned with a particular type of attack on the IOTA cryptocurrency, more specifically a Parasite Chain attack that attempts to revert the history stored in the DAG structure, also called the Tangle.In order to improve the security of the Tangle, we present a detection mechanism for this type of attack. In this mechanism, we embrace the complexity of the DAG structure by sampling certain aspects of it, more particularly the distribution of the number of approvers. We initially describe models that predict the distribution that should be expected for a Tangle without any malicious actors. We then introduce metrics that compare this reference distribution with the measured distribution. Upon detection, measures can then be taken to render the attack unsuccessful. We show that due to a form of the Parasite Chain that is different from the main Tangle it is possible to detect certain types of malicious chains. We also show that although the attacker may change the structure of the Parasite Chain to avoid detection, this is done so at a significant cost since the attack is rendered less efficient.
Exploring Variables Affecting Adolescents’ Reading Literacy and Life Satisfaction: PISA 2018 International Comparison of Korea and Finland

The purpose of this study is to explore and compare the variables that explain adolescents’ reading literacy and life satisfaction in Korea and Finland. To do this, the random forest method, machine learning technique, was applied to PISA 2018 data to explore the important variables among student or school related 262 variables, and multilevel modeling was utilized to test the significance of the selected important variables for Korean adolescents(n=6,650) and Finnish adolescents(n=5,649). Main results are as follows. The findings of the random forest revealed that 14 variables were commonly selected as important variables for both Korea and Finland; eight additional variables for Korea only and seven additional variables for Finland only were selected in reading literacy. Similarly, 13 variables were commonly selected for both Korea and Finland, five additional variables for Korea only and seven additional variables for Finland only were selected in life satisfaction. The results of the multilevel modeling revealed that ‘ICT available at home’ and ‘Student behaviour hindering learning’ were statistically negatively related to reading literacy for Korean adolescents. Moreover, ‘Family wealth’, ‘Attitude towards school: learning activities’, ‘Perception of cooperation at school’ and ‘Use of ICT at school in general’ were statistically positively related to Korean adolescents’ life satisfaction while ‘Afraid’ emotion was statistically negatively related to Korean adolescents’ life satisfaction. Based on the findings of the current study, implications for improving Korean adolescents’ literacy and life satisfaction as compared to Finnish adolescents are discussed.
An Experimental Study on Performance of Text Representation Models for Sentiment Analysis

Sentiment analysis in social networks has been an active research field since 2000 and it is highly useful in the decision-making process of various domains and applications. In sentiment analysis, the goal is to analyze the opinion texts posted in social networks and other web-based resources to extract the necessary information from them. The data collected from various social networks and web sites do not possess a structured format, and this unstructured format is the main challenge for facing such data. It is necessary to represent the texts in the form of a text representation model to be able to analyze the content to overcome this challenge. Afterward, the required analysis can be done. The research on text modeling started a few decades ago, and so far, various models have been proposed for performing this modeling process. The main purpose of this paper is to evaluate the efficiency and effectiveness of a number of commons and famous text representation models for sentiment analysis. This evaluation is carried out by using these models for sentiment classification by ensemble methods. An ensemble classifier is used for sentiment classification and after preprocessing, the texts is represented by selected models. The selected models for this study are TF-IDF, LSA, Word2Vec, and Doc2Vec and the used evaluation measures are Accuracy, Precision, Recall, and F-Measure. The results of the study show that in general, the Doc2Vec model provides better performance compared to other models in sentiment analysis and at best, accuracy is 0.72.
Design of the Reconnaissance UAV based on TGAM

Abstract The application of UAV in military reconnaissance field is mostly fixed-wing aircraft, such as global hawk and predator. But they have no fixed wing aircraft bulky, need a runway to land, the reconnaissance mission is confined to the reconnaissance range larger operations, is now considering the individual special operations to the demand of the UAV reconnaissance, is adopted to helicopter down small four rotor design, convenient carrying, used to fill a vacancy low-altitude reconnaissance a small scale. In addition, considering the clarity and planning of the purpose of the reconnaissance mission, the UAV can carry out the reconnaissance mission of aerial photography and return according to the predetermined route completely, and no excessive human intervention is needed in this process. Therefore, the small quadrotor is mainly designed for autonomous flight system. But fully autonomous flight can not be very good to adapt to the complex environment, for the sake of it, and the principle of liberation operators hands as far as possible, decided to adopt a simple electrical control of auxiliary control flight, to complete after being spotted by enemy UAV height of rise and fall, immediately return to action, such as to ensure the practicability of the reconnaissance drones.
Silhouette Pose Feature-Based Human Action Classification Using Capsule Network

Recent years have seen a rise in the use of various machine learning techniques in computer vision, particularly in posing feature-based human action recognition which includes convolutional neural networks (CNN) and recurrent neural network (RNN). CNN-based methods are useful in recognizing human actions for combined motions (i.e., standing up, hand shaking, walking). However, in case of uncertainty of camera motion, occlusion, and multiple people, CNN suppresses important feature information and is not efficient enough to recognize variations for human action. Besides, RNN with long short-term memory (LSTM) requires more computational power to retain memories to classify human actions. This research proposes an extended framework based on capsule network using silhouette pose features to recognize human actions. Proposed extended framework achieved high accuracy of 95.64% which is higher than previous research methodology. Extensive experimental validation of the proposed extended framework reveals efficiency which is expected to contribute significantly in action recognition research.
A novel optimal multi-pattern matching method with wildcards for DNA sequence

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.
Of Moments and Matching: Trade-offs and Treatments in Imitation Learning.

We provide a unifying view of a large family of previous imitation learning algorithms through the lens of moment matching. At its core, our classification scheme is based on whether the learner attempts to match (1) reward or (2) action-value moments of the expert's behavior, with each option leading to differing algorithmic approaches. By considering adversarially chosen divergences between learner and expert behavior, we are able to derive bounds on policy performance that apply for all algorithms in each of these classes, the first to our knowledge. We also introduce the notion of recoverability, implicit in many previous analyses of imitation learning, which allows us to cleanly delineate how well each algorithmic family is able to mitigate compounding errors. We derive two novel algorithm templates, AdVIL and AdRIL, with strong guarantees, simple implementation, and competitive empirical performance.
Study of single photon source efficiency based on cavity quantum electrodynamics

Considering the pure dephasing process and external-to-internal cavity loss ratio, we calculated the efficiency of a single photon source of quantum dots coupled to an arbitrary detuned single-mode cavity based on cavity quantum electrodynamics. The results show that there is an optimal external-to-internal cavity loss ratio to maximize the efficiency of the single photon source, and in the proper external-to-internal cavity loss ratio range, the pure dephasing process can improve the efficiency of the device in a detuning system. The relationship between efficiency and temperature was further studied and it was found that the efficiency at higher temperature could be higher than that at lower temperature by proper design. This is of great significance for the preparation of high-efficiency single photon sources.
Affine subspace clustering with nearest subspace neighbor

In this paper, we consider the problem of affine subspace clustering, which requires to estimate the corresponding subspaces and assign the corresponding labels to data points on or near a union of low-dimensional affine subspaces. To address this problem, we propose a framework based on Nearest Subspace Neighbor (NSN). NSN is originally designed to estimate the geometric structure of clusters that can not be adequately performed by conventional approaches based on a general distance metric such as K-means, and has been applied in solving the linear subspace clustering problem. However, in real-world scenarios, the vast majority of data exist in the affine subspace rather than linear subspaces. To make better use of NSN, we construct an affinity matrix by incrementally picking the points considering affine subspaces in a greedy fashion. Statistical experiments demonstrate that our method outperforms both the original NSN and an affine subspace clustering method.
Mining Interesting Association Rules with a Modified Genetic Algorithm

Association Rules Mining is an important data mining task that has many applications. Association rules mining is considered as an optimization problem; thus several metaheuristics have been developed to solve it since they have been proven to be faster than the exact algorithms. However, most of them generates a lot of redundant rules. In this work, we proposed a modified genetic algorithm for mining interesting non-redundant association rules. Different experiments have been carried out on several well-known benchmarks. Moreover, the algorithm was compared with those of other published works and the results found proved the efficiency of our proposal.
The Impact of Population Growth on Economic Growth: Evidence from Ethiopia using ARDL Approach

There is no consensus on the impact of population on economic growth as economists are torn with different varying theories and empirical findings.This study therefore analyzed the impact of population growth on economic growth in Ethiopia using annual time series data over the period 1980/81-2018/19 in a multivariate framework.The study is theoretically based on neo-classical growth model and empirically on the ARDL Methodology along with Philips Peron Unit root tests and other important time series techniques.Results from ARDL Bounds testing indicated that there exists co-integration among population, export, import, trade openness, government expenditure and economic growth for real GDP is taken as dependent variable.The empirical model revealed population growth, export growth and import growth having significant positive impact on economic growth of Ethiopia both in the short-run and long-run while trade openness has significant negative effect on same and government expenditure growth has insignificant impact.Moreover, Toda-Yamamoto and Dolado-Lutkepohi as well as Innovative Accounting Techniques (that is, IRFs and FEVD) approaches to Granger causality analysis showed the existence of strong bidirectional causality between population growth and economic growth.The implication of the finding is that population is an asset rather than a burden for the country, and therefore; a carefully planned and productive population advance along with well managed macro variables would sustain the growth of the its economy.
Human Competency Assessment for Software Configuration Management

Late product delivery is a common problem in software projects. Late delivery can be attributed to software change especially in the development and maintenance phases, leading to more work than originally planned, thus increasing project completion time. One approach for managing software change is Software Configuration Management (SCM). SCM was first introduced in the 1970s to ensure the timely delivery of software products. To date, SCM implementation is supported by recognized standards, best practice, and countless commercial and proprietary tools. However, after more than 50 years, the issue of late software product delivery still prevails, questioning the practice of SCM in software organizations. One aspect of SCM that has received little focus in mainstream research is the human aspects. This study aims to identify how the competency of SCM practitioners can be assessed through the identification of SCM competency criteria and the development of a competency assessment framework. The framework was validated through expert reviews and case studies involving practitioners from the public sector, industry, institutes of higher learning and international organizations. Results confirmed the reliability of the SCM competency criteria and the plausibility of the competency assessment framework.