Reviewerly/exHarmony huggingface dataset:
Tumor-Stroma Classification in Colorectal Cancer Patients with Transfer Learning based Binary Classifier

Computer aided diagnosis (CAD) of the colorectal diseases based on histological images of the cancer tissues have attracted massive interests in digital pathology. In this context, we propose, in this paper, a transfer learning approach for solving binary classification task of the tumor-stroma region of colorectal cancer (CRC) patients. The pretrained convolutional neural networks (CNN) were used with ImageNet dataset as a source dataset, where the features of the bottleneck layer are transferred to the simple classification head. The classification head consists of a global maxpool layer followed by dropout and a single unit dense layer that outputs raw prediction values which are ultimately classified as 0 or 1, each denoting tumor and stroma region respectively. A target dataset containing small number of CRC histological images is used to train the classification head. During inference, the proposed binary classifier accurately classifies the tumor-stroma region and significantly improves the classification accuracy compared to reference schemes.
An Empirical Study on Channel Pruning through Residual Connections

Channel pruning is one of promising techniques for compressing convolutional neural networks due to its usefulness and runtime-agnostic efficiency. Most existing works focus on determining which channel should be removed or kept for only sequentially connected convolution layers inside a residual block due to the hardness of channel pruning through residual connections. Motivated by this, we focus on investigating the layer-wise dynamics of channel pruning through residual connections, and propose simple yet effective pruning methods. These methods do not require any additional training data to compute the importance of channels affected by residual connections. In experiments, we demonstrate that the proposed methods have promising performance despite their simplicity and efficiency. In addition, we achieve interesting observations about the dynamics of channel pruning on layers at different topological positions, which are related to the consistency of layers' filters for evaluating the importance of each out-channel.
Comparison of 3D Scene Construction Technologies in Virtual Tourism

This paper studies the 3D scene construction technologies in virtual tourism and analyzes the models and display technology of typical tourism systems at home and abroad. Based on the analysis, it evaluates the systems as a whole and summarizes the problems in the three aspects of the current virtual tourism services: display effect, human-machine interface, and operation model. Moreover, through the comparative analysis of approaches to obtaining material, production difficulty, real-time performance, interactivity level, and other indicators in the implementation of the modeling technology involved in 3D scene construction, this paper proposes a scheme for selecting modeling methods for scene construction based on different needs. In the end, through the comparative analysis of mainstream modeling software in terms of real-time rendering, model fineness, interactivity, and data volume, the paper identifies the software suitable for building complex and simple scenes, providing new solutions and ideas for the development and application of virtual tourism services.
Research on once-for-VIT network for efficient deployment using specialist visual encoder using neural structures

The challenge of the effective deployment of deep learning models across a number of devices, from general use hardware to specially designed accelerators, is addressed. Traditional approaches are either hand designed or used to search for a specialist visual encoder using neural structures (NAS), then training it for each case from start. It is prohibitive in computing terms and is hence unscalable. Our main concept in order to cut costs is to construct model training and searching for architecture. In this process, we propose to develop a once-for-VIT (OFT) network supporting various architectural options. In a particular deployment situation, a certain sub-network may be immediately achieved without further training by picking from the OFT network. We also presented a new gradual declining technique, which can simultaneously develop a large number of subnetworks, to prevent interferences in the training process between numerous subnetworks. (>10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> ). The performance of OFT on several hardware platforms (compared to MobileNetV3, the accuracy of the ImageNet top1 is 4.0% higher), while lowering GPU hour-and-a-life ordering, is revealed by several trials on different hardware platforms (CPU, GPU, mCPU, FPGA accelerator). OFT achieved in particular the new ImageNet top1 accuracy of 80.0 percent SOTA (<600M FLOPs), based on mobile devices.
Alert System for Face Mask Detection using CNN

The pandemic of the Coronavirus has a significant impact on people's health and lives. There is a question asked to ourselves whether it is possible to return to normalcy? To do this, a risk-free environment must be developed. COVID-19 virus spreads through the respiratory mainly from infected persons who come in close contact with other beings. Wearing a Face Mask will act as a physical barrier for these respiratory droplets. The main aim of this research paper is to create a face mask detection system using TensorFlow, Keras, and OpenCV that identifies whether or not a person is wearing a mask by monitoring a live video feed and notifying them with a beep sound. An alert system has been integrated with face mask detection system in three phases. In the first phase, the dataset is trained with CNN (using Keras/TensorFlow) with an accuracy of 98.38%. The second phase is focused on identifying whether a person is wearing a mask on a live video stream using Haar Cascade Classifier. In the third phase, the alert system is developed. If a person is detected not wearing a mask, he/she gets alerted via beep sound. This face mask detection system can be used at the entrance of universities, airports, hotels, public buildings, Railway Stations, or any major places huge crowds are expected.
Network Construction Using Overlapping Histogram under Local Differential Privacy

Network construction is beneficial to extract global structural information from vector data. However, node-link information is easy to bring the problem of privacy leakage in network construction. Thus, we provided node-link preserving in network construction via overlapping histogram under local differential privacy. First, we present the general model of network construction using overlapping histogram under local differential privacy. Second, we present algorithm of random perturbation-linking in network construction via overlapping histogram to preserve node-link. Finally, our theoretical and experimental results show that the proposed scheme can achieve the tradeoff between node-link protection and network utility. The proposed scheme provides a basic method against node-link leakage in network construction via overlapping histogram.
Multi-resolution Dense Residual Networks with High-Modularization for Monocular Depth Estimation

Deep-learning neural networks (DNN) have been acknowledged to capably solve the ill-posed monocular depth estimation problem in self-drive applications. In this paper, we proposed a dense residual multi-resolution supervised DNN toward accurate monocular depth estimations for traffic landscape scenes. The proposed DNN is constructed by regularly integrating the dense residual short-cut connections into the multi-resolution backbone. However, since some implicitly influential features cannot be viable at the end of learning, the DNN structure for generating the details of estimated monocular depths shall not be too deepened. Basically, the structural depth of DNN can be suppressed by effectively exploiting the functional residual connections. In the proposed DNN structure, the amount of short-cut connections can be moderate through rational employments. Particularly, for achieving high modularization, we address three-layered modules to generate the adequate levels and layers in which the results can easily be controlled to meet a requested prediction/inference accuracy. Therefore, the visualization and quantitative results can demonstrate the superiority of the proposed DNN to other compared DNNs for street landscape experiments.
Multi-Agent Confrontation Game Based on Multi-Agent Reinforcement Learning

This paper studies the multi-agent confrontation game problem, and takes unmanned aerial vehicle (UAV) offense-defense confrontation as the research object. Deep Deterministic Policy Gradient (DDPG) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm are used for policy optimization. The experimental results show that MADDPG can acquire good policy in multi-agent confrontation game environment. In order to make MADDPG suitable for large-scale multi-agent game problems and obtain robust policy, this paper improves MADDPG by introducing Mean Field Theory (MFT) and ‘Minimax’ idea. The experimental results show that the improved algorithms can deal with large-scale multi-agent game problem and obtain robust policy.
Sparse Representation Classifier Embedding Subspace Mapping and Support Vector for Facial Expression Recognition

With the development of integration and innovation of Internet and industry, facial expression recognition (FER) technology is widely applied in wireless communication and mobile edge computing. The sparse representation-based classification is a hot topic in computer vision and pattern recognition. It is one type of commonly used image classification algorithms for FER in recent years. To improve the accuracy of FER system, this study proposed a sparse representation classifier embedding subspace mapping and support vector (SRC-SM-SV). Based on the traditional sparse representation model, SRC-SM-SV maps the training samples into a subspace and extracts rich and discriminative features by using the structural information and label information of the training samples. SRC-SM-SV integrates the support vector machine to enhance the classification performance of sparse representation coding. The solution of SRC-SM-SV uses an alternate iteration method, which makes the optimization process of the algorithm simple and efficient. Experiments on JAFFE and CK+ datasets prove the effectiveness of SRC-SM-SV in FER.
ARChem: Augmented Reality Chemistry Lab

One of the technologies that has been gaining ground in recent years is Augmented Reality (AR), which allows to insert virtual objects into a real-world view using a device's camera and screen. This form of interaction associated with education can improve teaching and experiencing practical knowledge in schools, especially in more difficult subjects such as Chemistry. This study focused on virtual education by providing a platform for students to follow practical oriented subjects like Chemistry. As a result, a mobile application is created with four main functions that assist students during their learning process of Chemistry using the AR technique. The main functions are, AR with Artificial Intelligence (AI), Chemical equation identification and correction with Image Processing, Chabot with sentiment analysis and text summarization. The application is developed by using Machine Learning, AI with Deep Learning and Mobile Application development technologies. ARChem shows 3D models of flasks with important descriptions with the use and also features a Chabot with text summarization for frequently asked questions.
A System Design of English Speech Synthesis

Speech synthesis, also known as text to speech (TTS), is a technology to convert text into sound, which is also an important technology to realize the communication between human and machine. English is an international language. It is necessary to study English speech synthesis technology. Aiming at English speech synthesis, an end-to-end speech synthesis method and system based on convolutional neural network WaveRNN and WaveNet is explained used in this paper. Experiments show that the Mean Opinion Score (MOS) of the synthesized speech is 3.32, and the speech quality is better than that of the general parametric speech synthesis system.
Scrum Modification for Small-scale Web Application

The paper shows how the Scrum method is tailored and engineered to fit small-scale web application development using Situational Method Engineering. Practitioners usually call the term company profiles instead of a small-scale web application. However, the latter term often refers to a larger scope of application development. The modification process starts from identifying the situation’s definition and then translating it into a more robust form, using the contextualization of method components. This contextualized form is then used for analyzing and determining which element of the Essence kernel is prominent and relevant to the characteristics of the situation. The modifying result, the MiniScrum, is assembled after thoroughly analyzing which elements of Scrum could be omitted, altered, or added. Finally, evaluation of the MiniScrum is done with a case study by using an evidence map to identify the role and how impactful the proposed elements of the Essence kernel are, as well as analyzing the case study implementation with qualitative approaches.
Corona hotels in Israel: Care and abandonment under the auspices of digital medicine

Following the onset of the COVID-19 pandemic, Israel established a number of 'corona hotels' - hybrid spaces that were neither fully treatment-oriented nor fully incarcerational, in which people known or suspected to be infected with the coronavirus were confined, sometimes for prolonged and indefinite periods. This paper describes the experience of 25 people who were confined in corona recovery and isolation hotels between March and July 2020. The corona hotels exemplify how remote medical technology and digital medicine together enable a new 'technogeography of care', where care and abandonment are inextricably linked. The paper adds to the growing number of critical studies on digital health by showing how the employed technologies impact the concepts of human embodiment, subjectivity and social relations, as well as how the occupants negotiated the meaning of these technologies and resisted their effects.
Robust gravitation based adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4989" altimg="si145.svg"><mml:mi>k</mml:mi></mml:math>-NN graph under class-imbalanced scenarios

As a typical single parametric graph model, the k-Nearest Neighbor Graph (k-NNG) is characterized by its high efficiency in detecting topology information and remarkable capability for combining global and local features. However, graph-based supervised learning methods, including k-NNG, do not explicitly consider imbalanced class distribution; as a result, they often tend to perform vulnerable robustness in most imbalanced scenarios. This paper presents an adaptive k-NNG-based imbalanced classification method that can automatically determine the k value during graph construction. Our work presents two novel methodologies: (i) two types of adaptive graph construction methods to address the inherent complex characteristic of imbalanced class distribution; (ii) two graph-based gravitational classification rules to overcome the adverse bias towards the majority class in traditional KNN-based methods. The latter can effectively combine local nearest neighbors and subgraph information when calculating the gravitational force between pairs of vertices. Extensive experiments demonstrate the superiority of our method over other classification algorithms in imbalanced scenarios.
Domination in intuitionistic fuzzy incidence graph

Fuzzy incidence graph (FIG) is able to clearly express some of the complexities of uncertain problems. FIG has applications in modern sciences and technology such as neural networks, artificial intelligence and decision making. FIG is an excellent model for interconnection systems having ramps or minor links between node-edge pairs. The concept of domination in graphs is very important both in theoretical developments and applications. This concept is a strong parameter in decision-making. In this article, we have defined the domination on FIGs by the valid edges on it and mentioned the results. We studied domination in terms of valid private neighborhood and irredundant set. Also, we presented an application of domination in this graph.
Research on Automated Information System of Non-sense Attendance Using Face Recognition and Large Database

Abstract In order to meet the needs of today’s large-scale enterprise human resource management attendance business, this paper designs an embedded face recognition attendance system, and designs and analyses the implementation method of the system in detail. The system consists of two parts: face-swiping attendance and attendance management. Among them, face-swiping time attendance uses the Face Boxes model to detect faces, and the Face Net model to identify the person who punches in. Tests have found that the system is simple to operate, has high recognition accuracy, and has a certain practical value.
Information Extraction from Receipts Using Spectral Graph Convolutional Network

Information extraction from receipts is the process to recognize text and extract key texts from scanned receipts. This task plays a critical role in a wide range of applications in finance, accounting and taxation. Graph neural network is a model which is capable of capturing the dependence of graph via message passing between the nodes of graph. This model has demonstrated high effectiveness on various deep learning tasks. In this paper, we applied the spectral graph convolutional network to extract the key information from receipts. Experimental results have shown that spectral graph convolutional network has achieved good results for this task.
Mechanisms of interaction between employers and vocational educational organizations in a situation of uncertainty

In a situation of uncertainty, there is a rapid change of priorities, which is reflected both in the requirements for the level of professional training of specialists and in the requests of employers for new specialties. However, the list of specialties and areas of training of workers for the regional economy in the regional system of secondary vocational education is formed on the basis of applications from employers for a five-year perspective, but in a situation of uncertainty, no one can guarantee that in five years graduates of a particular specialty or profession will be in demand in the labor market. To achieve a balance in this problem, it is necessary to develop and implement new mechanisms for interaction between professional educational organizations and employers. The aim of this study is to determine the list of popular vocational education specialties, the description and identification of effective mechanisms of interaction between professional educational organizations and employers that affect the improvement of the quality of specialists training and the indicators of their employment according to their specialty based on the analysis of the indicators of the admission campaign and graduates employment of vocational education system of the Republic of Buryatia.
Collaborative filtering algorithm combining trust relationship and item preference

To solve of the traditional collaborative filtering recommendation algorithm has some problems, such as sparse data and difficult cold start, we proposed a collaborative filtering (TCF) recommendation algorithm based on trust relation and item preference. The algorithm performs triple processing on user ratings. First it introduces a correction factor to optimize the traditional similarity calculation. Then it uses user similarity to mine potential trust relationships between users. And taking into account the complex real-world relationship between users, using distrust information to filter users and get new ratings. Finally, a new scoring matrix is constructed on the basis of this score, and the improved Tanimoto coefficient is used to calculate the similarity, and the recommendation results are obtained by integrating user trust and item preferences. Experimental results show that the algorithm can effectively improve the quality of recommendations.
Digital Preservation

In recent decades, libraries, archives, and museums have created digital collections that comprise millions of objects to provide long-term access to them. One of the core preservation activities deals with the evaluation of appropriate formats used for encoding digital content. The development of science has entered the 4th paradigm, where data has become much more intensive than in the previous period. This situation raises new challenges in managing research data, especially related to data preservation in digital format, which allows research data to be utilized for the long term. The development of science in the 4th paradigm allows researchers to collaborate with and reuse research datasets produced by a research group. To take advantage of each other's data, there is a principle that must be understood together, namely the FAIR principle, an acronym for findable, accessible, interoperable, and reusable.
Financing of Activities of Modern Educational Organizations Based on Sustainable Long-Term Relations with Clients: Endowments (US Experience)

The article considers the role of endowments as an additional source of financing for the activities of universities. The example of the largest American educational endowments shows the financial and investment strategies of management companies. The average rate of return of funds is given depending on the investment period and the size of the target capital. Issues of replenishment and distribution of endowment funds were analyzed. One of the indicators of efficiency of the target capital in the sphere of education is considered—the ratio of the size of endowment to the number of students. The work uses the method of analysis, comparison and expert assessments. The research is based on the study of data from official American statistics, as well as data from NACUBO and TIAA, information and statistical reports from the largest endowment funds of American universities. Target capital makes it possible to receive stable extrabudgetary funds. The university’s endowment is an important mechanism for financing long-term innovative development, as well as a modern tool to increase the financial sustainability of the university. Thanks to the endowment fund, strategies are being formed for years to come.
Comparing Word Embeddings on Authorship Identification

Authorship identification is a task which has found historical importance and several widespread applications across different fields. It basically functions as a technique to help attribute the ownership of an anonymous piece of text. Embeddings are discrete fixed-length representations of texts which aim to capture maximum information within these numeric representations. The embedding techniques used in this chapter include Doc2Vec, GloVE, and FastText. The primary focus of the work is to estimate the performance of the word embedding techniques for the task of authorship identification. Deep and artificial neural networks (ANNs) are used to help classify these authors on the basis of these embeddings and their respective results have been reported. The experimental results compared among the competing techniques gives fruitful insight into the performance of the embeddings as also the task of authorship identification.