Reviewerly/exHarmony huggingface dataset:
Image classification of vaginal microecology detection based on gabor texture and LSTM model

Gynecological diseases threaten women's health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists.In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model.Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images.The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%.Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.
ON THE ESSENCE OF THE CRISIS IN HIGHER EDUCATION

In this work, the problem of the crisis of higher education in the world educational process is actualized. The significance of this problem demanded from the author a comprehensive analysis of the literature, which reveals the essence of the phenomenon under study. The paper speaks of the inappropriateness of the representation of the higher education system as being in deep crisis. The characteristic features of modern society are revealed, which testify to the opposite.&#x0D; The role of higher education is presented as the main source of the development of intelligence, the intellectual potential of a person, outside of which scientific and technological progress would be impossible. It is argued that there is no crisis in the higher education system, there are permanently arising contradictions (the driving forces of the development of nature, society and man) that require their systemic resolution. The need to improve the psychological, pedagogical and methodological culture of a teacher of higher education as significant components in the structure of their personality is noted
Analysis of Crossover Techniques in Modification of Grasshopper Optimization Algorithm

This article made a description of a novel approach that presents the utility of the various crossover techniques in the Grasshopper algorithm. The Grasshopper optimization algorithm is referred as one of the swam intelligence algorithm in the recent years. This algorithm was already applied in several field of engineering optimization. In order to provide further reinforcement to quality of the results without increasing the complexity of optimization, the crossover technique is applied in this paper. A comparison of several crossover techniques is done with a variety of benchmarking functions in order to provide a comparable platform for different category of optimization. All the technique of crossover are followed by Gaussian mutation for the enhancement of quality of results. The simulation results show the demonstration of the modified versions of the algorithm in the domain of unimodal as well as multimodal category of optimization. The results presented in this paper verified the quality of the outcome presented after suggested modification of the original algorithm. This paper helps the researchers with an elaborate idea about the planned algorithm and can act as base algorithm for several optimization applications.
Hands Are People Too: Reflections on the Value of Hands (and How to Study Them)

Did you know that hands have bodies, relationships, and minds of their own? In the coming years as a new wave of technologies focused on our hands is under development, and as AR/VR may include haptics as a key mode of interaction, we need to design for hands as we would for people – keeping the technology in the background to ensure hands can learn, collaborate, and shine. We conducted a study in 2020 about what gives hands unique value to people. The ambition was to understand hand‐based skills across contexts and domains of practical expertise. We asked practitioners to record themselves using their hands, analyzed the video footage, and watched the recordings together with each practitioner. We asked practitioners to reflect on their hands and compare how their skills might apply to other contexts. Through this process, we uncovered that the hands have bodies, relationships, and minds of their own. These fundamental observations help us to imagine and anticipate future technology interactions that are not only relevant and useful, but that respect and enable the aspects of our hands that make us feel most human. Note: The presentation was intended to feature GIFs from fieldwork video, but only still images are allowed per the rules of the pecha kucha format; we therefore storyboarded stills from the recordings as the visuals, to highlight the haptic movements.
Loss Function of GAN to Make a Clear Judgment

In general, in GAN training, the Discriminator learns a loss function whose value becomes smaller when the expected label matches the correct label. However, if we do not make a clear decision about the authenticity of the image, the GAN will continue to learn about ambiguous images, and the resulting images will be less clear. In this experiment, we propose a loss function that penalizes the Discriminator when it makes an ambiguous decision by adding a regularization term to the existing loss function Binary Cross Entropy(BCE), and verify its superiority through experiments.
Introduction to Agile Frameworks

In this chapter, we will discuss different delivery models that are defining agile ways of working and that are also introducing new roles into the system. The topics that will be covered in this chapter are as follows:
Tutorial: The Correctness-by-Construction Approach to Programming Using CorC

The Correctness-by-Construction tutorial focuses on a structured programming approach for correct software development. Besides functional correctness, also non-functional properties such as security properties can be guaranteed using the CbC approach. In this tutorial, the participants learn a good practice to develop software that is midway between formal approaches and a &#x201C;hack into correctness&#x201D; style.
Recognition of Facial Expressions Based on Detection of Facial Components and HOG Characteristics

People were able to convey intentions and feelings through non-verbal languages such as gestures and facial expressions. However, recognizing facial expressions is a very difficult task. Numerous factors such as light, posture, and distortion can cause complications. The proposed system is a good method for facial emotion detection problems. The system, which considers the elements of the face, will help to predict emotions from an image. Then histogram of oriented gradient (HOG) is used to encode these facial elements as features. A linear support vector machine is then used to identify facial expressions. The final results of this experiment show the accuracy of our prediction.
Semi-supervised siamese network using self-supervision under scarce annotation improves class separability and robustness to attack

Self-supervised learning approaches were shown to benefit feature learning by training models under a pretext task. In this context, learning from limited data can be tackled using a combination of semi-supervised learning and self-supervision. In this paper we combine the traditional supervised learning paradigm with the rotation prediction self-supervised task, that are used simultaneously to train a siamese model with a joint loss function and shared weights. In particular, we are interested in the case in which the proportion of labeled with respect to unlabeled data is small. We investigate the effectiveness of a compact feature space obtained after training under such limited annotation scenario, in terms of linear class separability and under attack. The study includes images from multiple domains, such as natural images (STL-10 dataset), products (Fashion-MNIST dataset) and biomedical images (Malaria dataset). We show that in scenarios where we have only a few labeled data the model augmented with a self-supervised task can take advantage of the unlabeled data to improve the learned representation in terms of the linear discrimination, as well as allowing learning even under attack. Also, we discuss the choices in terms of self-supervision and cases of failure considering the different datasets.
A Supervisory Mask Attentional Network for Person Re-Identification in Uniform Dress Scenes

Person re-identification (Re-ID) aims at retrieving a person's identification across multiple non-overlapping cameras. While recent Re-ID methods have achieved significant success on a number of benchmark datasets, most of them are still insufficient in the scenes with unified or highly similar dressing like construction sites, schools and factories. To address this problem, we propose a supervisory mask attention network (SMA-Net). Our approach combines two key components: (1) ROI mask mapping (RMM) is a supervisory branch to provide ROI mappings that divide a person region into several parts; (2) Partial mask attention (PMA) integrates channel and space attention mechanisms that focus on local features and different accessories in each ROI. Therefore, the network can pay more attention to the local features and different accessories herein. Compared with the state of the art methods, SMA-Net demonstrates excellent performance on our dataset of construction scenes, with improvement of 6.65% in mAP and 3.8% in Rank-1 accuracy.
From Semantics to Types: the Case of the Imperative lambda-Calculus

We propose an intersection type system for an imperative λ -calculus based on a state monad and equipped with algebraic operations to read and write to the store.The system is derived by solving a suitable domain equation in the category of ω-algebraic lattices; the solution consists of a filter-model generalizing the well known construction for ordinary λ -calculus.Then the type system is obtained out of the term interpretations into the filter-model itself.The so obtained type system satisfies the "type-semantics" property, and it is sound and complete by construction.
Annotation Guidelines for Narrative Levels and Narrative Acts v2

The concept of narrative levels is widely applied in Literary Studies but often based on different theoretical foundations.To operationalise the concept with a reproducible category for a machine learning approach, these guidelines focus on two core definitions of narrative levels, namely Genette's concept of a narrator change and Ryan's proposal of illocutionary and ontological boundaries between levels.We separate the notions of "level" and "narrative" into dedicated subcategories for the narrative level, which reflects a vertical dimension, and the narrative act that encompasses horizontally aligned stories.Furthermore, supplementary aspects, like the boundary type between narrative levels or related phenomena like metanarration and metalepsis, are captured as attributes in conjunction with the annotation category to obtain additional knowledge that might be relevant as training data.The guideline is divided into a first part that discusses narratological theory to define the annotation category as well as the attributes and a second part that gives annotation instructions along with textual examples.
FDMCA: A Novel Authentication Technique using Face Detection and Gaze-based Morse Code Entry

With the rise in password theft attacks and threat to user's security, using conventional methods of entering passwords to authenticate users is not enough. There is a need for secure, user-friendly methods to authenticate users to login to any application. In order to ensure complete security to the users and their information, this paper proposes a solution which is more reliable, secure, user-friendly and a cost-effective method of user authentication. The two-step authentication system proposed includes real time face recognition as the first step. The second step includes authentication using Morse code. The Morse code authentication incorporates a gaze based virtual keyboard. The user inputs the password in the form of Morse code by blinking his eyes at the screen. This strong form of authentication provides security against various password theft attacks such as shoulder surfing, thermal tracking and keylogger attacks.
Multimedia Databases and Data Mining

This chapter explains the characteristics of multimedia data types and other approaches to database management that further expand on this universal technology strategy. Multimedia database systems are expected to coherently handle these disparate data types and provide content-based access to the data. Content-based multimedia information retrieval (CBMIR) means that the system retrieves information according to the textual, visual, auditory, and semantic contents of the media. Multimedia data management can be broadly categorized into approaches: image processing and pattern recognition, database, and information retrieval. The image processing and pattern recognition approach to multimedia data management views multimedia data as unstructured. The information retrieval approach views multimedia data as semistructured, which is primarily in the form of keywords, specially crafted captions, or free-form text. Multimedia representations and associated operations for content-based retrieval vary widely across applications.
A Faster and Less Parameterized Detector of Arbitrary Shapes Text Detection

Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. The existing text detection algorithms can be roughly divided into three categories: the Connected Component (CC) strategy, regression method and segmentation method. These methods are either costly for character annotation or are determined by the interaction of multiple phases and components in the pipeline, which is generally not suitable for real-time applications. This paper presents a simple and powerful pipeline for fast and accurate detection of arbitrarily shaped text in natural scenes. Our contribution is threefold: 1) Use a single neural network to directly predict the text of any shape in the whole image, without intermediate steps (such as candidate set and word division), and greatly improves the detection speed of the text of any shape. 2) The B-spline Curves were proposed to fit the curved text boxes, which require fewer predicted parameters compared with the existing curved bounding boxes. The algorithm was tested on full-text and CTW1500 arbitrary shape standard datasets, and the average score improved by nearly 2% with almost no additional computational overhead, achieving a balance between speed and accuracy.
Light Source Selection in Primary-Sample-Space Neural Photon Sampling

This paper proposes a light source selection for photon mapping combined with recent deep-learning-based importance sampling. Although applying such neural importance sampling (NIS) to photon mapping is not difficult, a straightforward approach can sample inappropriate photons for each light source because NIS relies on the approximation of a smooth continuous probability density function on the primary sample space, whereas the light source selection follows a discrete probability distribution. To alleviate this problem, we introduce a normalizing flow conditioned by a feature vector representing the index for each light source. When the neural network for NIS is trained to sample visible photons, we achieved lower variance with the same sample budgets, compared to a previous photon sampling using Markov chain Monte Carlo.
Comparing Load-Balancer Algorithms Performance on Virtualized Environment

The need for proper workload balancing arise as there are significant increase on data traffic volume during peak time, which may cause workload imbalance or server overloading. A proposed solution to this problem is to implement a load balancer system with a load balancing algorithm that has the best performance based on incoming workloads. This paper presents a performance comparison of Round Robin, Least Connection, and Weighted Least Connection load balancing algorithms. Analysis on the experiment results is done using a machine learning model comprising three machine learning algorithms, which are AdaBoost, and Random Forest. AdaBoost predicted WLC is the best algorithm on 8 out of 10 or 80% of sampled instances. Random Forest predicted WLC is the best load balancing on 10 out of 10 or 100% of sampled instances. kNN predicted WLC is the best load balancing algorithm on 8 out of 10 or 80% of sampled instances. Based on the timestamp, amounts of threads received, and session receive rate sampled in the test dataset, the analysis found that WLC had the best performance compared to RRand LC.
University and Enterprise Cooperation—Effective Solution in the Digital Economy

The article considers the improvement of the system of education and training of qualified personnel in the conditions of digitalization of society, which today covers all fields of life. As an effective solution to this problem, such a form of interaction as cooperation between a university and enterprises is being proposed. The active interaction of the enterprise and the university will significantly reduce the costs of training specialists, but at the same time significantly improve the quality, since it will be possible not only to take into account the needs of a particular enterprise, but also to conduct the training process on advanced, unique modern equipment. A positive example of such cooperation is the experience of cooperation between universities and enterprises in the design and implementation of an interdisciplinary export-oriented advanced educational program of higher education 05.03.01 Geology, profile “Oil and gas geology and geophysics” using a network form of the educational process. The project is implemented by three entities: the Federal State Budgetary Institution of Higher Education “Ufa State Oil Technical University” together with scientists and teachers of the Federal State Autonomous Educational Institution of Higher Education “Novosibirsk National Research State University,” included in the TOP-200 of subject global ratings, and production partner—joint-stock company “Bashneftegeofisika”, an advanced enterprise of the Russian oilfield service industry, which has more than 80 years of experience in prospecting for oil and gas in any geological and natural conditions using methods of seismic exploration and geophysical research of wells. As a result of the cooperation, the parties managed to create a unique program, according to which the training will make it possible to form a comprehensive specialist—a geologist-geophysicist, who professionally knows both the skills of processing and interpreting seismic data in the oil and gas industry, and the knowledge and competencies of a geologist-oil scientist.
A Systematic Study on Machine Learning Techniques for Predicting Software Faults

Fault tolerance and prediction are some of the interesting quality assurance activities of software engineering. Fault prediction algorithms assist the project manager in identifying the parts of the system that are prone to failure and provide the benefit of reducing the time required to test the entire application. The fault-prediction method attempts to detect faulty software modules so that they are used subsequently in the software development process. There are numerous prediction approaches available in the domain of software engineering for detecting defect-prone blocks. Few of them are stable models, and a few are unstable models, which may not be enough to handle all of the possible scenarios and thus fail to produce the earliest prediction. This paper provides an extensive review of existing predictive models for software defect prediction, identify flaws in existing techniques, and address the need for a new prediction approach in the era of fault prediction. The Classification was commonly used to differentiate among faulty and non-faulty modules. This paper proposes two sub modules for prediction of faults. One submodule to identify the accuracy are implemented and discussed. To achieve better and more accurate results, we intend to build a machine learning model, which can automate the complete process.
Can We Spot Energy Regression using Developers Tests?

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Keynote:Timofey Bryksin

The International Conference on Software Maintenance and Evolution is the premier international forum for researchers and practitioners from academia, industry, and government to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution.
Breast tissue density influences tumor malignancy perception and decisions in mammography.

High density breast tissue has been found to reduce radiologists’ accuracy in detecting and classifying mammogram abnormalities. The current research examines the perceptual and decisional components that underlie diagnostic classification (independent of detection) in a sample of novices. Mammograms were varied along two dimensions: Breast tissue density (low/high) and the nature of an identified mass (benign/malignant). In two experiments, participants learned to classify images into 4 categories created by factorial combination of these dimensions. In low density tissue, accuracy was higher for benign than for malignant masses. Surprisingly, and in contrast to the mammography literature, accuracy for malignant masses was higher in high-density than in low-density tissue. Cognitive modeling based in general recognition theory (GRT) indicated that low-density/benign category accuracy was largely due to high perceptual discriminability for that category. The high accuracy level for malignant masses in high density tissue was accounted for by decision bound slopes that favored the “malignant” response for items in the high-density malignant category. Because GRT can provide insight into perceptual and decisional determinants of observed classification accuracy, as well as individual-difference level parameters about attention allocation, GRT may be a means to obtain a more detailed understanding of diagnostic classification of complex naturalistic stimuli.