Reviewerly/exHarmony huggingface dataset:
Regression

An adult brain contains about 100 billion neurons. Each neuron obtains input signals through dendrites and transmits output signals through axons. The neurons are interconnected to form a huge neural network, thus forming the human brain, the basis of perception and consciousness. Figure 2-1 is a typical biological neuron structure. In 1943, the psychologist Warren McCulloch and mathematical logician Walter Pitts proposed a mathematical model of artificial neural networks to simulate the mechanism of biological neurons [1]. This research was further developed by the American neurologist Frank Rosenblatt into the perceptron model [2], which is also the cornerstone of modern deep learning.
Research on Image Semantic Segmentation Model Based on Bidirectional Recursive Double Branch Network

To solve the problem of road safety, express logistics industry, people begin to pay attention to no delivery, distribution and differences apparent change of scene scale is big, this paper presents a bidirectional recursive double branch network, reusable building blocks, in the form of circulation use of pairs of forward and backward jump connection to form the complex relationship between the encoder and decoder, at the same time, the double branch network is used to combine coarse information with fine information in the decoding stage, so as to restore the feature map more precisely, which helps to further improve the accuracy of the network.
Clinical Data Analysis For Recognizing Named Entities

Ever since the introduction of the internet the amount of digital media and documents available easily has grown rapidly. Even though the digital data may be any language and format it does not change the fact that processing such data needs time and effort. This is true especially in the medical field where reports are constantly generated in huge amounts. Recent innovations in a field called as NLP and with the introduction of several efficient language models to analyze such data like BERT shows that the decrease in time to process and analyze such tasks once they are trained. One such approach that is proposed aims at recognizing entities for every word that are present in a clinical text by assigning appropriate IOB tags that indicate the position of the word or tokens and also the proteins and cell types of the clinical entities in the text data chunk by utilizing a BERT model. The results demonstrate that the labels are assigned to every word present in the clinical text. And by obtaining the metrics results that are calculated after every epoch where it is noticed that the validation accuracy and F1 score increases after every epoch. Hence for a BERT model it is concluded that with the increase in training period of the model the efficiency also increases.
DIFFERENCES IN SELF-CONTROL FROM GENDER, AGE AND BIRTH ORDER IN NAHDLATUL ULAMA 2 STUDENTS GRESIK

Students who can control themselves will not do wrong actions that they have experienced because they will understand that taking actions that violate the rules will get punishment consequences. Regarding gender, a person's self-control is seen from gender, including roles, behavior, preferences, and other attributes that explain a particular culture. Based onAs children age, the communities that influence them increase, as well as the many social experiences they experience, children learn to respond to disappointments, dislikes, failures, and learn to control them, so that over time this self- control emerges from within itself. Birth order can affect a person's self-control in various conditions including how the individual views the role that must be carried out. This study aims to determine differences in self-control in terms of gender, age and birth order in students. This study uses a quantitative method with a population of 547 students. In this case, the researcher uses a non-probability sampling technique with a proportionate stratified random sampling technique. The sample obtained is 241 students. The method collected using a questionnaire. This study uses a summative scale measurement, with a Likert scale type with 5 (five) alternative answers. Test the validity of the scale using content validity. The scale reliability test uses the Cronbach alpha technique. The data analysis technique used the t-test data analysis technique, the Independent Samples T-Test. The results showed that there was no significant difference between self-control with Gender, Age, and Birth Order
A Conceptual Proposal of a Hybrid Method for Detecting Fraud in Civil and Military Service Entrance Examinations

A public service examination is an effective manner of selecting civil and military servants for admission to various sectors of public service. These examinations usually attract well trained personnel by presenting highly competitive and meritocratic selection. Nevertheless, they also attract criminals that offer candidates the possibility of easy and illegitimate admission. Aiming to provide better security and trust in the selection process, we propose a conceptual method that uses data mining and statistical techniques to detect fraud in public service examinations. The method analyzes geographic and biographical data of candidates, scores, and similarity of answers of one candidate to those of other candidates, as well as past information, to compose a comprehensive index of suspicion. When the value of the index is above a certain limit, it indicates a degree of suspicion of an approved candidate, which may call for further investigation.
Academic traditions of pre-university training in the system of school-studio education for future students of architects and designers (on the example of the experience of ten studio schools in Krasnoyarsk)

The subject of this research is the methodological approaches in the pre-university career guidance training of future students of architecture and design universities, organized in the system of pre-university or school general education &amp;ndash; on the example of the activities of selected ten studio schools in the city of Krasnoyarsk. The goal of this work is to identify peculiarities of methodological organization of pre-university training for the development of their abilities to master future architectural and artistic professions. The results of the conducted research are presented by a set of revealed methodological features in the system of pre-university architectural and design training in teaching such key art disciplines as&amp;nbsp;drawing, painting, composition (paper plastic, prototyping), and&amp;nbsp;graphics.The novelty lies in&amp;nbsp;the integrated approach towards the analysis of the structure of artistic training in studio schools, combining various visual disciplines, as well as in&amp;nbsp;the comparative study of specific practices of pre-university training in Krasnoyarsk studios aimed at vocational architectural and artistic training. The acquired conclusions indicate the effectiveness of methods for the development of students' artistic abilities through the use of traditional academic methods of training within the framework of the key visual art disciplines, such as&amp;nbsp;drawing, painting, composition, graphics.
Visibility Enhancement in Surveillance images using Deep Multi-scale Feature Fusion

Visibility degradation is expected in adverse weather phenomena such as fog, mist, and haze. Object detection and identification in the surveillance feed are challenging in these weather conditions. Outdoor images can naturally be degraded, or one can intentionally degrade using smoke or bright light to hide identification. Haze, fog, and smoke are pixel-based degradation and can vary based on their thickness and distribution property in an image. In our proposed work, we explore the possibility of identifying and removal hazy pixels using the benefits of multi-scale feature-fusion and In-scale feature progression. We proposed a learning-based end-to-end network for single image dehazing. Our proposed architecture consists of three different modules: (1) Coarse Feature-fusion, (2) Fine Feature-fusion, and (3) Reconstruction module. The Coarse feature-fusion module learns broad contextual information, and the Fine feature-fusion module refines the coarse features by focusing on the channel and pixel-based information. Multi-scale feature fusion is used both in the coarse and fine module to benefit the network stage from the previous stage&#x2019;s output. Extensive experimental results suggest that the proposed approach outperforms other state-of-the-art methods on synthetic homogeneous and non-homogeneous haze data and improves object detection and identification accuracy.
A framework for a systematic survey of known software defects

It is common practice to use third-party software in projects, which can lead to security problems. There are numerous cases when the known vulnerability was fixed in the upstream repository of the project but still exists in other projects. We present a framework for systematic analysis and detection of publicly known vulnerabilities in a large codebase. On the first stage, the vast codebase of open-source projects and known vulnerabilities are collected. On the second stage, for each known vulnerability, we try to find the corresponding source repository and extract the fixing patch. Based on extracted patches we construct possible vulnerable code fragments and try to find all their clones in the collected codebase. During the experimental setup, we have collected more than 42k open-source packages from Debian OS distribution. Analysis of these packages allowed us to detect more than four hundred copies of unfixed vulnerabilities, seven of them have already been approved and fixed by the vendors.
Exploiting Web Snippets for Multi-label Anime Genre Prediction

Anime is a type of animation film attributed with highly stylized, colorful-art, imaginary locations, and mature topics that follow the traditional Japanese 2D animation. It is one of the prominent means of entertainment for the young generation. The widespread use of the Internet has led to large volumes of anime-related data being generated and shared online. But it is difficult to find the proper genres information about particular animes. Though some studies exploited the synopsis for movie genre prediction, very few studies focused on anime genres. Therefore, it is a formidable task to design an effective system regarding this to meet the viewers’ satisfaction. In this paper, we exploit the web-search snippets to distill the anime genres information. Upon extracting a set of web snippets for each anime, we employ the naive preprocessing techniques to remove noises from snippet texts. Next, we make use of the n-gram and embedding-based features for effective data representation. Then, a set of state-of-the-art classifiers are employed in our multilabel anime genre prediction framework. We present a comparative performance analysis among these methods that yields a significant insight of using web snippets on genre prediction. Experimental findings demonstrated the efficacy of deep learning-based approaches for this task.
TADPool: Target Adaptive Pooling for Set Based Face Recognition

A majority of the modern methods used for template aggregation of set-based face recognition systems rely on learning to quantify the quality of images present in a template. While focusing on weighting the feature embedding based on this quality factor, they have overlooked aggregation strategies that can adapt the template's features to the paired template involved in matching. In this paper, we explore the potential of such adaptive methods for feature aggregation. We propose a template feature aggregation strategy that tailors a template's image set to mirror the properties exhibited by the target template. The proposed method produces state-of-the-art results on standard unconstrained face recognition datasets such as IJB-A, IJB-C and YouTubeFaces, validating the advantages of such an aggregation strategy.
Smart Real Time Dataveillance with Artificial Intelligence

Automatically describing the content material of an image can be an essential hassle in artificial intelligence that connects PC imagination and prescient and tongue processing, the problem will increase whilst we attempt to apprehend faces, gadgets and try and have a look at facial feelings with it or indifferent phrases whilst we attempt to generate the records we observe whilst we see something or a person. The idea of this venture after the desired amendment can permit one to maintain tune of suspicious or undesirable activities if passed off without continuously tracking them or without looking at the entire collection to be expecting the wrongdoer because it works in real-time. Also, it could be cross-checked if required because it stores the records that are secure from any leakage as its miles are encrypted and password protected.
Usability Evaluation of Touch Screen Interface with Various User Groups

Currently, touch screen interfaces are adopted in many consumer products in our daily lives. However, there is still little empirical knowledge studied about their interaction, especially in food serving outlets. Therefore, in this paper we investigated how interact with using touch screen interface for self ordering some beverage and dessert. The method of this system consisted of three main parts: 1.) establishing hardware, 2.) developing software, and 3.) evaluating interaction. We used usability method to collect empirical data and measure how users interact. In the experiment, we used factorial design. Factors of user that we concerned are age, gender, and experience with touch screen interface. We found that only two factors that affect the time spending to ordering: 1.) user&#x0027;s experience with touch screen and 2.) interaction between users&#x0027; experience with touch screen and users&#x0027; age. From interviewing, most users stated that they liked this UI because it provides users convenient, friendly, and attractive with many images.
Ghostbusters: How the Absence of Class Pairs in Multi-Class Multi-Label Datasets Impacts Classifier Accuracy

Compositional bias is common in Multi-Class Multi-Label datasets where certain classes frequently co-occur together. Classification performance due to non-iid behavior of Multi-Class Multi-Label datasets is largely unexplored. We evaluate the potential impact of this compositional bias on Multi-Class Multi-Label classifiers, and, propose a novel framework of representing bias through “Degree of Separation”, and demonstrate its effectiveness on two datasets. Consider an image with a cat and a person - our work effectively strives to answer questions along the lines of “Is the classification accuracy of cat impacted by the presence of the person in the image?” and more importantly, “Is the classification accuracy of cat impacted by the absence of the person in the test image?”. Interestingly our experiments show that class pairs that are present in a test image, but do not appear together elsewhere in the training dataset do impact the recognition accuracy and thus we refer to them as ghost class pairs. We make a surprising discovery: lower the degree of separation between classes (based on class pairs appearing within the same images), higher the classification accuracy. Based on this observation, we develop a greedy data augmentation strategy that recommends which missing pairs need to be added to the training dataset in order to improve F1 score with minimal data addition. This ultimately is able to improve classification accuracy by 25–30% in several scenarios.
Transitioning the Teaching/Learning Process to Online Environment During the COVID-19 Pandemic

The adaptation of both teachers and students to the application of pedagogical techniques in remote teaching/learning platforms of a Portuguese technology and engineering school of a higher education institution, due to the confinement imposed by the safety measures taken against the COVID-19 pandemic, is presented in this paper. The teachers’ behavior and students’ attitude concerning the change to remote teaching and learning methods is analysed. The actions taken, the difficulties experienced by the academic community in the transition from face-to-face to remote education, in particular the changeover of technical-scientific and pedagogical components in the course units, the evaluation of the teaching-learning process and the improvement needs are presented. Considerations on possible solutions and some relevant conclusions are also pointed out, showing that both teachers and students are not ready to switch to new digital means of education.
A Coarse-to-Fine Dual Attention Network for Blind Face Completion

In the area of face completion, the missing information within an occluded area is estimated, yielding a realistic face of the same identity. In most previous works, the mask describing the occluded region is known, limiting the scope of application. To alleviate this limitation, we propose a coarse-to-fine network trained as a conditional generative adversarial network. While the coarse network predicts the mask and generates a rough estimation of the semantic content, the subsequent fine network refines the rough prediction into a realistic and identity-persevering reconstruction. This is achieved by incorporating adversarial loss and using features from a pretrained face feature extractor. Unlike previous approaches, we employ two parallel attention mechanisms: 1) a patchwise cross-attention module to substitute information within the occluded patches with patches from the non-occluded region; 2) a pixel-wise global self-attention to allow information exchange within the entire feature map. Our exhaustive analysis, including reconstruction quality and face recognition metrics, shows that our approach outperforms the state of the art in blind face completion, improving the true positive identification rate at rank 1 on the MegaFace benchmark from 36.55 % to 42.48 %. This represents a substantial step towards closing the gap between occluded (29.34 %) and non-occluded faces (52.32 %). In terms of reconstruction quality, we obtain a structural similarity of 0.9639 compared to 0.8526 and 0.9563 for occluded faces and the state of the art, respectively. In addition to previous approaches, we provide an in-depth analysis of the influence of the position, size, and sparsity of the occlusion and use facial landmark prediction to measure reconstruction quality.
Research and Design of Real-Time Dynamic Adaptation System for Web Applications on Mobile Phone

With the continuous development of Internet technology and the wide application of mobile communication technology, mobile terminals have overtaken personal computers as the main terminal devices for accessing Internet resources. At present, most Web page resources on the Web are designed for traditional PCs, and due to the hardware differences between mobile terminals and PCs, using mobile terminals to directly browse Web pages designed for PC terminals will have confusing page layout and other adaptation problems, resulting in poor user experience. In this paper, we propose to design an adaptation system to automatically convert Web pages into pages suitable for mobile browsing by processing Web page segmentation and page conversion technology such as style template matching, so as to realize mobile adaptation of Web pages.
Annotation of Struck-out Text in Handwritten Documents

Annotating handwritten documents for training deep learning models is a major issue in handwritten text recognition. It requires manual effort to annotate each word in a document to specify the ground truth. Often documents contain struck-out text which needs to be ignored by the recognition process. In preparing training data, struck-out text needs to be represented in a way that can help deep learning models to learn to deal appropriately with the strike-outs. The question is how to do this. In this paper, we have investigated two approaches for struck-out text annotation: (1) provide no annotation, thus reducing the annotation burden, and (2) mark the struck-out text with a special symbol, we have used the symbol #. We have trained two models on a synthetically generated dataset using a convolutional neural network and LSTM. We obtained 8.8% and 9.0% character error rates for models one and two respectively. There was no statistically significant difference in the performance of the two models. This indicates that a model trained with minimal annotations can perform as well as a model trained with extra annotations for struck-out text.
Improving the Prediction of Project Success in the Telecom Sector by Means of Advanced Data Balancing

As governments access capital, technology, and managerial expertise from private investors, there is an increasing trend of privatizations in infrastructure projects worldwide. Given the large size of these investments, notably in the sector of telecommunications, investors typically create a consortium with other interested firms, an investment vehicle known as private participation project (PPP). Given the critical repercussions on the rest of the economy, and its large financial losses in case of failure, the prediction of the success of PPPs in telecommunications is of utmost importance. The success of PPPs can be predicted by Machine Learning and it is probed in this article. Hence, widely acknowledged classifiers (k-nearest neighbors [k-NNs], support vector machines [SVMs], and random forest [RF]) are applied to PPPs publicly available data from the World Bank. The results on this highly imbalanced dataset are greatly improved by the application of data balancing techniques. It includes some standard ones (random oversampling, random undersampling, and synthetic minority oversampling technique [SMOTE]), together with some other advanced ones (density-based SMOTE and borderline SMOTE). The satisfactory results validate the proposed application of classifiers on the dataset improved by data-balancing techniques.
Privacy Preserving Image Matching Scheme with Aggregated Local Descriptors

Detecting similar images, without violating their privacy, is a desirable method for many real-world applications. Existing schemes are based either on global features that are too rigid to describe images or local features which achieve more accurate results with higher complexity. The current local feature aggregation methods rely on constructing and sharing a visual word vocabulary, which leads to serious privacy concerns. In this paper, we proposed efficient schemes, of different security guarantees, for matching private images while maintaining their privacy. To do so, we developed a new method for aggregating the local features, by utilizing the locality-sensitive hashing functions without the need for predefined vocabulary. Extensive experiments on real-world datasets demonstrate the practical usage and security of our schemes.
Outperforming Clinical Practices in Breast Cancer Detection: A Superior Dense Neural Network in Classification and False Negative Reduction

Machine Learning applications provide a promising method to support clinical practitioners in Breast Cancer (BC) detection. Currently, Fine Needle Aspiration (FNA) is a commonly applied diagnostic method for BC tumors, which, however, is associated with ominous false negative misclassifications. For this purpose, the present study explores Artificial Neural Networks (ANNs) with the aim of outperforming clinical practices via FNA in classifying benign or malignant BC cases with regard to an improved accuracy and reduced False Negative Rate (FNR) using the Breast Cancer Wisconsin (Diagnostic) Dataset (WDBC). The findings reveal that a dense ANN with a single hidden layer including 15 neurons can reach a testing accuracy of 98.60% and a FNR of 0% on a scaled dataset. In combination with several introduced improvement measures, a high degree of generalizability is associated with the model under the consideration of the relatively small dataset. As a result, this model outperforms not only clinical practitioners but also 72 classifiers from the recent literature.