Reviewerly/exHarmony huggingface dataset:
DETECTION OF FASHION LANDMARKS BASED ON POSE ESTIMATION AND HUMAN PARSING

Locating fashion items on any input images is referred as 'fashion image understanding', which is often a preliminary or initial step that supports various other visions tasks.The study of fashion image understanding has been widely benefited from the rapid advancements in deep learning-based new models and the availability of large datasets covering real-world fashion images.However, the real value of these datasets is in doubt, because different annotation strategies have been adopted in these datasets, resulting in different landmarks for different clothing styles and low compatibility of these datasets.In this paper, we propose a pose-aware segmentation-based method to locate key points of fashion items on fashion images by taking advantage of clear correspondence between clothing and human body, that can be applied to locate key points of fashion items and to re-annotate existing datasets for cross dataset learning.The validity of the method was validated on a subset of the DeepFashion2 dataset.
Distributed Text Representations Using Transformers for Noisy Written Language

This work proposes a methodology to derive latent representations for highly noisy text. Traditionally in Natural Language Processing systems, methods rely on words as the core components of a text. Unlike those, we propose a character-based approach to be robust against our target texts’ high syntactical noise. We propose pre-training a Transformer model (BERT) on different, general-purpose language tasks and using the pre-trained model to obtain a representation for an input text. Weights are transferred from one task in the pipeline to the other. Instead of tokenizing the text on a word or sub-word basis, we propose considering the text’s characters as tokens. The ultimate goal is that the representations produced prove useful for other downstream tasks on the data, such as criminal activity in marketplace platforms.
Predict the Complexity of Literary Passages Based on Deep Learning

Graded reading is becoming increasingly important in everyday life. For example, recommending books suitable for the reading difficulty of the current age to children can bring great convenience to parents. At the same time, it can also increase children’s interest in reading and prevent them from being stressed because of the difficulty of reading. However, using humans to judge the reading difficulty of texts and then grading them requires more manpower and material resources. We need to use deep learning methods to automatically judge the reading difficulty of text. Our paper proposes a Roberta-based model to rate the difficulty of a given text. First, the data set is preprocessed, and then use the training data set and the validation data set to train and optimize the model. In addition, our model is compared with LSTM and RNN. Experiments show that the model proposed in this paper has better performance on the task of text reading difficulty classification.
Problems of training and advanced training of scientific and pedagogical staff of higher education

The purpose of the study is to consider the current problems of training and advanced training of scientific and pedagogical staff of higher education and identify ways to overcome them. The article discusses the main trends of an integrated approach to the designated problem, substantiates the relevance of their development and implementation. The scientific novelty consists in a comprehensive consideration of ways to overcome existing problems in the field of organization of work on training and advanced training of scientific and pedagogical staff of higher education. As a result, it is established that the improvement of the system of training and advanced training of scientific and pedagogical staff of higher education is one of the main ways to implement the ongoing reform of the education sector, aimed, among other things, at creating conditions that will contribute to more effective professional development and self-development of higher school specialists. In this regard, the issue of revising the basic foundations of the system of training and advanced training of scientific and pedagogical staff of higher education as an integral part of the system of continuing education becomes relevant.
Minimizing the Misinformation in Social Networks using Heuristic Greedy Algorithm

In recent years, online social media has grown in popularity, and a vast volume of information has circulated over social media platforms, altering people's access to information. The credibility of information material is being questioned, and various types of misinformation are using social media to propagate quickly. The importance of network space administration and maintaining a trusted network environment cannot be overstated. In this paper, we look at a new challenge termed the activity minimization of misinformation influence (AMMI) problem, which involves removing a group of nodes from the network in order to reduce the total amount of misinformation interaction between nodes (TAMIN). To put it another way, the AMMI challenge is to choose K nodes from a given social network G to block in order to minimize the TAMIN.We demonstrate that the objective function is neither submodular nor supermodular, and we suggest a heuristic greedy algorithm (HGA) for removing the top K nodes.
Multi-face Synchronous Recognition Method for Civil Aviation Security Inspection Based on Deep Migration Learning

There is a large flow of people in the civil aviation security inspection process, and the traditional face recognition method is inefficient in synchronously recognizing multiple faces. To this end, a synchronous recognition method of multiple faces in civil aviation security check based on deep transfer learning is designed. Collect multiple face image data, and perform face image feature processing. Using deep transfer learning to build a multi-face synchronous recognition model for civil aviation security inspection, and then realize the multi-face synchronous recognition of civil aviation security inspection. The comparison experiment is used to verify that the face synchronization recognition effect of the new method is better, which is of great promotion value.
Grouping the spread of the covid-19 virus based on the positive case number, population and area width using the k-means clustering method

The spread of the Covid-19 virus began to enter Indonesia in early 2020. Until now, the spread of the virus is still occurring in several parts of Indonesia, although it has begun to decline in many areas. ). The possibility of direct human contact depends on the number of confirmed positives, the population and the area. Therefore, this study looks for the characteristics of the city/district area group based on the population, area and number of residents who are confirmed positive for the Covid-19 virus using the K-Means Clustering method. The clustering process begins with finding the best number of clusters (K) using the elbow method and testing cluster members using the silhouette and davies-bouldin index methods. The best K value results obtained by the elbow method are 3 clusters (K=3). The results of K-Means Clustering with 3 clusters show that in cluster 1, a city or district with a small area with a large population, the number of positive COVID-19 residents is large. In cluster 2, a city or district with a small area, with a moderate population, the number of people who are positive for COVID-19 is small. And in cluster 3, a city or district with a large area, with a very small population, the number of positive COVID-19 residents tends to be moderate.
Prior Incubation as a Tool to Overcome Understaining of Leishman Stain: A Cross-sectional Study

Introduction: A well stained peripheral smear is an integral part in the diagnosis and management of many clinicohematologic conditions. Leishman stain is recommended universally by the International Committee for Standardisation in Haematology (ICSH). However, a commonly encountered problem in tropical countries, especially during the rainy season, is under staining by Leishman stain as a result of interference by the atmospheric moisture. Hence, incubation of slides and/or buffer prior to standard Leishman staining protocol could bring out best staining qualities. Aim: To assess the effects of prior incubation of slides and/or buffer on standard Leishman staining technique. Materials and Methods: The cross-sectional study was conducted over a period of 8 weeks in the Central Laboratory, Kempegowda Institute of Medical Sciences, Bangalore in the months of October 2020 to November 2020. Blood samples were received in the central laboratory for routine haematological investigations. Total of 100 samples of left over non haemolysed Dipotassium Ethylenediaminetetraacetic Acid (K2-EDTA) anticoagulated blood irrespective of age, gender and cell counts were included in the study. Slides were stained following prior incubation of slides and/or buffer at 37° C and compared with the standard staining technique. The staining characteristics of nucleus, cytoplasm and granules of White Blood Cells (WBCs), platelets and erythrocytes were observed. One-way Analysis of Variance (ANOVA) test and p-value of &lt;0.05 was considered statistically significant and higher f value (&gt;104.35152) rejected the null hypothesis. Results: Prior incubation of slides and/or buffer resulted in better stained smears which were found to be statistically significant (p-value&lt;0.05) as compared to standard Leishman staining technique. Conclusion: Minor modifications when applied to standard Leishman staining technique such as prior incubation of slides and/ or buffer at 37° Centigrade produced best staining characteristics to overcome the effects of humidity which normally interfere with staining of slides.
Setting Up TPM Attestation

In Chapter 14 we looked at setting up Always Encrypted with secure enclaves using host key attestation. Microsoft recommends using host key mode in dev/test scenarios or where you don’t have a physical TPM (Trusted Platform Module) – though you can use TPM mode in some virtualized scenarios. A TPM is usually a physical chip that sits on the motherboard of your computer, but you can have virtualized TPMs on VMs. In this chapter we go over the steps of setting up TPM mode with Always Encrypted.
DEVELOPMENT OF CREATIVE THINKING AS A CONDITION OF PROFESSIONAL FORMATION OF A TEACHER OF VOCATIONAL TRAINING

The article considers scientific approaches to the concept of creativity, creative thinking and the conditions for its development in the conditions of organizing competitive activities of university students. The authors emphasize the binary nature of the process of education a teacher of vocational training: from one side, the students’ own creativity and active mental activity, from the other, the opportunity to teach the methods of creative activity to others. The authors support the position of scientists that creativity and intelligence interact in the process of development and encourage to the making of professional consciousness.
Statistical Analysis of Online Voting System Through Blockchain and ML Techniques

A digital voting system is a process that allows people to vote while sitting at their homes and is based on their face recognition identification. The votes will be counted and saved in a blockchain-based structure which is secure and immutable, thus giving availability with security in a system. The traditional voting system does not allow people to vote sitting at their home. Considering the situation of covid, everything is going digital. Questions on EVM from losing parties regarding some malfunctioning.
Design of algorithms for automated collection of IT assets

The main goal of information security is compliance with established security policies, norms, and standards. Maintaining and improving security affects all business aspects from infrastructure to information itself. All computing entities such as routers, switches, cloud storage, firewalls, servers, databases or software and information can be called by one name, IT assets. All these IT assets must be protected in terms of integrity, availability, and confidentiality. These three aspects are addressed in the risk management process for the purpose of information security. Risk management is closely related to information security, and it is relatively complex and expensive activity. In most cases, even today, the risk management and information security are approached manually. In addition, various software is also used that partially facilitate some sub-processes, but still require manual human intervention to operate them to achieve the desired outputs. In this study, we focus on one of the basic sub-processes of information security management, namely the identification of IT assets, which is the basis of the entire process. We will describe new algorithms for efficient and automated collection of IT assets gathered over the network. At the end of the study, we address and describe the possible use of collected information and its connection to the IT asset assessment sub-process, which is another of the information security risk management sub-processes.
EFEKTIFITAS SITUS BERBASIS KECERDASAN BUATAN DALAM MENDESAIN TES KETERAMPILAN MENDENGAR TOAFL

Abstract: This study aims to determine the advantages of the site and how to use it to produce TOAFL audio text understanding audio, as well as some of the obstacles and solutions to its utilization. This study uses a qualitative approach with descriptive type, using triangulation method in processing and analyzing the results, and data collected through documentation and interviews. The results of the study show that: (1) the site pampers its users with several voice options based on gender and nationality, of which 14 Arabic native voices are currently available; (2) there are features that make it easy for site users to get the desired recordings through speed, volume settings, formats, effects, tones, sizes, and sample rides; (3) the site can be accessed for free with a maximum limit of 400 words per recording and 30 languages with 70 different natives, as well as cloud storage is available; 4) the steps for using the site start with logging in go to the url address, login with email, type the text to be recorded on the white board provided, select the driving engine, language and native, give a touch according to your taste through the settings feature provided. available, press the record button then download the results; 5) The obstacles that are often found are the pronunciation of harakat, the pronunciation of nakirah and maâ€™rifah, and the pronunciation of two words into one word. all of this can be overcome by placing the harakat according to the i'rab rules and above the alif lam qamariyah (makrifah) and placing the second word on a new line below it.Keywords: Listening Skill, VoiceMaker, Artificial Intelligence
Segmentation of 6-month infant brain tissues from multi-spectral MRI records using a U-Net neural network architecture

The accelerating rise of the amount of daily produced medical image data and the high costs of human expert training are strongly motivating the efforts dedicated to the development of reliable and efficient image segmentation and interpretation methodology. This paper proposes a fully automatic method to provide reliable segmentation of brain tissues from volumetric multi-spectral magnetic resonance image data. The proposed method provides a U-Net neural network based decision making, that is trained with infant brain MRI records originating from the iSeg-2017 challenge, which were preprocessed only to normalize the intensity histograms of individual records. The accuracy of the proposed method is evaluated using statistical indicators. The quality of the segmentation is characterized by overall correct decision rate exceeding 89%, and average Dice scores of 92.7%, 88.7%, and 87.0% for cerebro-spinal fluid, grey matter, and white matter, respectively.
The Briar Patch

Abstract Bob opened Big Briar for business, but one-off projects were still his bread and butter. He restored Herb Deutsch’s original prototype so Herb could auction it off, but it had to be donated to the Henry Ford Museum. Bob made a small synth, the Spirit, for the Italian firm Crumar. His 1982 catalog offered prototypes of three Big Briar Controllers; his main project was the Multiply-Touch-Sensitive Keyboard for composer John Eaton, a project that ate up family properties and landed Bob in debt. Dave Luce and a partner bought Moog Music from Norlin and unveiled a synth prototype, the SL-8, with MIDI compatibility. But it was overshadowed by the Yamaha DX7 with advanced digital FM Synthesis technology, signaling the dominance of Japanese synths. With Big Briar insolvent, Bob accepted consulting work with Kurzweil Music Systems and was offered a full-time position with the Boston company. The Moogs prepared to move yet again.
Feature Enhanced Fully Convolutional Network for Ship Detection

Focusing on the problem of high false detection and missed detection in the existing ship detection methods, which is affected by the background noise on sea surface and the various scales of different types of ships, a novel feature enhanced fully convolutional network is proposed for ship detection. First, a multi-scale feature enhancement mechanism is introduced to improve the feature descriptive power of the model for ships. In each single-scale feature extraction stage, the statistical information is calculated and utilized to suppress the interference of background noise, such as wave and clutter, on the feature description of ships. Meanwhile, in the multi-scale feature fusion stage, two steps feature adaptive fusion strategy is utilized to improve the network awareness of multi-scale ships. Furthermore, an improved object detection head network is devised under the regression analysis framework, which optimizes the calculation of centerness through an attention enhancement mechanism in regression branch to better suppress the low-quality bounding boxes, and improve ship detection performance while ensuring detection efficiency. The mean average precision of the proposed method reaches 91.6% on the large-scale maritime surveillance dataset SeaShips, and the detection speed is 9 frames per second. Experimental results demonstrate the proposed method has good detection accuracy and robustness.
Intention Recognition Based on Multi-layer Attention and Label Embedding

Realizing the intention according to the questions raised by user is one of key issues in dialogue Systems. To address the issue of the accuracy of user intent recognition, this paper proposes the Label Embedding Hierarchical Attention Networks (LE _HAN) algorithm with reference to the multi-layer Attention model and the introduction of prior knowledge in semantic extraction. The method first uses word granularity to embed the text vector. Then the semantic context information is extracted by bidirectional GRU and the important information in hidden state is extracted by Self_attention. Thirdly, fuse the information using bidirectional GRU and map the label information to the same vector space of the GRU hidden state as the query vector. Fourthly, the query vector is computed with the GRU hidden state as the attention vector. Finally, the query vector and the GRU hidden state are computed by attention calculation to complete the fusion of sentence semantics and label information. The results of the comparison experiments show that the algorithm achieves a high accuracy rate on the selected Chinese experimental dataset and has certain advantages over other algorithms.
Convolutional neural networks for obstacle detection on the road and driving assistance

Generally, a driver has moments of inattention, that can cause considerable damage. To deal with this issue, we have to detect obstacles on the road automatically. To do that, several challenges appear. Firstly, we have to locate the region of interest, which is the road part in the frame, then we have to detect the objects inside the region of interest. In this work we propose an improved driver assistance system using a camera on the front of the car. Acquired images from this camera feed our system. In the frames to be processed, we reduce the region of interest to the area of the road. Obstacles on the road are sought in this region of interest. At the same time, we take care of the driver by detecting whether he is drowsy. Experimental results were evaluated using KITTI Vision Benchmark Suite and short videos recorded on streets in Batna.
Face Identification and Verification in Thermal Images

Surveillance has become a crucial role in recent years, owing to the rise in crime rates. Existing daytime surveillance research has improved performance by employing deep learning algorithms to recognize and track objects. However, due to low illumination and/or poor weather conditions, achieving the same performance for night vision are difficult. Face detection is a crucial task in surveillance. A face detection module based on merging of thermal and visual pictures is proposed. The encoder-decoder network of the fusion module extracts effective features from the given thermal and visual images using depth-wise convolution. By recording radiation information at a greater wavelength, the thermal face solves most of the limitations that visual faces have. The range of facial imageries that can be recognized is expanded by developing a method that recognizes thermal faces. Face recognition in thermal images is having high research scope, and it has been discovered that when thermal images are trained, it performs better.
THE USE OF THE CONVERGENT CROSS MAPPING METHOD IN THE PROBLEMS OF STUDYING THE CROSS-DEPENDENCE OF TEMPERATURE SERIES

The dynamics of temperature series in two problems of temperature measurement by distributed optical sensors and determination of the measure of the dynamic dependence of heat consumption of residential buildings and outdoor temperature are investigated by the method of convergent cross mapping. It is shown that the convergent cross mapping method has prospects in energy efficiency evaluation and can be used to compare buildings by their characteristics, as well as to further solutions to energy efficiency improving.
FUNet: Flow Based Conference Video Background Subtraction

Video background subtraction has applied widely in video conferencing. However, it still cannot handle well for motion blurring, e.g., shaking the head or waving hands. To overcome the motion blur problem in background subtraction, we propose a novel optical flow-based encoder-decoder network (FUNet) that combines both traditional Horn-Schunck optical-flow estimation technique and autoencoder neural networks to perform robust real-time video background subtraction. We concatenate the optical flow motion feature and original image’s appearance feature, and pass the concatenated value into an encoder-decoder structure to perform video background subtraction. We also introduce a video and image subtraction dataset: Conference Video Segmentation Dataset. Code and pre-trained models are available on our GitHub repository: https://github.com/kuangzijian/Flow-Based-Video-Matting .