Reviewerly/exHarmony huggingface dataset:
Personal account of the employer in the system of evaluation of the student team

Purpose of research. The article describes the mechanism of formation of electronic portfolio and evaluation of the student team at the University and considers the relevance of the use of such a database by employers. Based on the above-mentioned relevance, it is proposed to develop a personal account of the employer as a service over the interuniversity database of the electronic portfolio. Materials and methods. Assessment of students in the personal account of the employer is based on the index method with a floating base on the basis of linear convolution with three-stage normalization. The article also discusses the implementation of this project with a description of technologies and approaches used in software development on the example of business process design based on structural analysis - a diagram of top-level data flows and a description of the project implementation based on service architecture and Single-Page approach is given. Results. At this stage, methods have been developed and tested and a list of key indicators for evaluating students’ activities has been defined, a system for collecting, approving and evaluating student achievement data has been implemented. Based on these data it is possible to obtain a reliable formalized portfolio for students and graduates of the University. The employer’s personal account and data import services from external portfolio systems are at the final stage of development. The completion of this stage will provide employers with access to information about future employees, obtaining a list of the gifted students for the appointment of corporate scholarships, campaigning, attracting students’ attention to professional implementation, selection and appointment of topics of course and diploma projects and some other opportunities. Conclusion. In conclusion, the article discusses the prospects for the development and implementation of this project: assessment of the activities of young employees, testing and questionnaires of students by employers, numerical assessment of the degree of compliance of graduates with the requirements of professional standards.
Single Image Super-Resolution by Parallel CNN with Skip Connections and ResNet

Image super-resolution has always been a research hotspot in the field of computer vision. Recently, image super-resolution algorithms using convolutional neural networks (CNN) have achieved good performance. But the existing methods based on CNN usually has to many (20–30) convolution layers, which has a large amount of calculations. In response to this problem, this paper proposes a lightweight network model based on parallel convolution, skip connections and ResNet. Parallel convolution means that different sizes of convolution kernels are set in the same convolutional layer to extract image features of different scales. In addition, in order to reduce the loss of image details, we combine the input and output of the previous layer as the input of the next layer, which is the skip connection. We also borrowed the idea of Residual Net. The network learns the residuals between high-resolution images and low-resolution images. Therefore, the algorithm proposed in this paper not only achieves the most advanced performance, but also achieves faster calculations.
Automatic high-grade cancer detection on prostatectomy histopathology images

Automatic cancer grading and high-grade cancer detection for radical prostatectomy (RP) specimens can benefit pathological assessment for prognosis and post-surgery treatment decision making. We developed and validated an automatic system which grades cancerous tissue as high-grade (Gleason grade 4 and higher) vs. low-grade (Gleason grade 3) on digital histopathology whole-slide images (WSIs). We combined this grading system with our previouslyreported cancer detection system to build a high-grade cancer detection system which automatically finds high-grade cancerous foci on WSIs. The system was tuned on a 3-patient data set and cross-validated against expert-drawn contours on a separate 68-patient data set comprising 286 mid-gland whole-slide images of RP specimens. The system uses machine learning techniques to classify each region of interest (ROI) on the slide as cancer or non-cancer and each cancerous ROI as high-grade or low-grade cancer. We used leave-one-patient-out cross-validation to measure the performance of cancer grading for classified ROIs with three different classifiers and the performance of the high-grade cancer detection system on a per tumor focus basis. The best performing (Fisher) classifier yielded an area under the receiver-operating characteristic curve of 0.87 for cancer grading. The system yielded error rates of 19.5% and 23.4% for pure high-grade (Gleason 4+4, 5+5) and high-grade (Gleason Score &ge; 7) cancer detection, respectively. The system demonstrated potential for practical computation speeds. Upon successful multi-centre validation, this system has the potential to assist the pathologist to find high-grade cancer more efficiently, which benefits the selection and guidance of adjuvant therapy and prognosis post RP.
The Implementation of Ant Colony Algorithm In Finding The Shortest Travel Route of Palembang Tourism By Android Based

Ant Colony is a methodology produced by observing ants. In this algorithm, ants as agents assigned to find solutions to an optimization problem, like to find the optimal solution on Traveling Salesman Problem (TSP). Therefore, the research will apply ant colony algorithm to search the shortest path of tourism in Palembang City, covering tourist destinations, culinary, hotels and handicrafts. The results of this study, will provide ease of tourists in accessing tourist destination information and information about travel routes easily via mobile applications with android operating system and use of Google Maps API.
Development of an Educational Program Combining an Educational System for University Students with an Educational System for Engineers Working at Actual Companies to Educate an Administrator for Manufacturing System

An administrator of manufacturing factory requires knowledge of manufacturing management to enhance productivity and quality of products. In recent production environment, the administrator requires knowledge of management of manufacturing system including product planning and product design to continue productivity of factory. In this study, we call this administrator as "an administrator of manufacturing system" and we construct educational program to study the knowledge of manufacturing management. This program includes kaizen activity in actual factories by a group of university students and working engineers. In this paper, we explain characteristics of the developed educational program and case study in actual factory.
TAPS: Trust-based Access Control and Protect System

The current network access control solution focuses only on the setup for the connection control of each gateway or VPN equipment and the status monitoring function of each equipment. In addition, each secure connection configuration between the corresponding network access nodes making it easy for the target node to be protected is likely to be exposed to malicious attacks. Therefore, in this paper, we propose a trust-based network access control and protect system (TAPS) that enables the software-driven definition and control of a network tunnel for protection between devices and services. The proposed system was designed to only grant access to specified services to mutually authorized devices by predefining and mapping a list of devices and services. In addition, data traffic between each device and service has been developed to be resistant to a variety of cyber attacks by encrypting it according to its purpose.
Trace form associated to cyclic number fields of ramified odd prime degree

In this work, we computate the trace form [Formula: see text] associated to a cyclic number field [Formula: see text] of odd prime degree [Formula: see text], where [Formula: see text] ramified in [Formula: see text] and [Formula: see text] belongs to the ring of integers of [Formula: see text]. Furthermore, we use this trace form to calculate the expression of the center density of algebraic lattices constructed via the Minkowski embedding from some ideals in the ring of integers of [Formula: see text].
Analysis Of Customer Chat Using Text Mining For Customer Relationship Management (case Study: Paperlust.co)

Abstrak Di era Industry 4.0, persaingan di dunia bisnis semakin menigkat dan mendorong setiap perusahaan untuk selalu berkembang dan beradaptasi dengan perubahan untuk memenangkan persaingan. Pesatnya perkembangan teknologi informasi telah mengubah banyak kegiatan bisnis, salah satunya adalah bisnis berbasis internet. Aplikasi praktis dari teknologi internet dalam dunia bisnis kini semakin berkembang, yaitu perdagangan di dunia maya dengan memanfaatkan internet yang sering disebut dengan e-commerce (perdagangan elektronik)..Penelitian ini bertujuan untuk menganalisis obrolan pelanggan dari Paprlust.Co menggunakan Metode Text Mining. Dalam penelitian ini metode yang digunakan adalah analisis sentimen yang digunakan untuk mengetahui persepsi pelanggan / emosi dan metode pemodelan topik untuk mengekstraksi topik atau masalah yang dibahas dari setiap kelas analisis sentimen berdasarkan data obrolan pelanggan di platform live chat Paperlust. Semoga penelitian ini bermanfaat untuk informasi dalam industri e-commerce khususnya Paperlust.co sebagai wawasan untuk mengevaluasi dan meningkatkan bisnis mereka untuk memberikan nilai lebih dan memperoleh lebih banyak pendapatan. Kata kunci â€” Analisia Sentimen; Pemodelan Topik; Manajemen Hubungan Pelanggan Abstract In the era of Industry 4.0, the competition in the business world is increasing and encouraging each company to always develop and adapt with the change to win the competition. The rapid development of information technology has changed a lot of business activities, on of it is the internet-based business. The practical application of internet technology in the business world is now growing, namely trading in the virtual world by utilizing the internet which is often referred to as e-commerce (electronic commerce). E-commerce business has changed many things in the business world, things that become the focus not only on goods but on services, information and intelligence focus on customers. This research purposed to analysis customer chat of Paperlust.Co using Text Mining Method. In this study the method used is sentiment analysis used to find out customers perception/emotion and topic modelling methods to extract topics or issue discussed from each class of sentiment analysis based on customer chat data in Paperlustâ€™s live chat platform. This research hopefully useful for information in e-commerce industry especially Paperlust.co as insight to evaluate and improve their business to deliver more value and gained more revenue. Keywordsâ€”Text Mining; Sentiment Analysis; Topic Modelling; Costumer Relationship Management
Approach to the Achievement Level Evaluation for the Acquisition Benchmarks

Previous report from Hachinohe Institute of Technology described twenty acquisition benchmarks for measuring subjective learning outcomes of undergraduate students after the course programs. Reliability of assessment was suggested when compared with the graduation achievement degree that was evaluated by company where graduates were working. The present study was designed to develop a grading achievement degree as an objective learning outcomes from grade record of students and relation tables between benchmarks and classes. The relation tables formulated in FY2016 were found to be effective to predict a 33.3% of total relations with correlation coefficients no less than 70%. Subjective achievement degree of whole remaining benchmarks were related to a key benchmark of the curriculum. These techniques enabled us to evaluate the achievement level of 20 benchmarks from grade record of students.
A deep learning framework for context-aware mitotic activity estimation in whole slide images

A proper cancer diagnosis is imperative for determining the medical treatment for a patient. It necessitates a good staging and classification of the tumor alongside with additional factors to predict response to treatment. Mitotic count-based tumor proliferation grade provides the most reproducible and independent prognostic value. In practice, pathologists examine H&amp;E-stained, giga-pixel-sized digital whole-slide images of a tissue specimen for counting the mitotic index. Considering the enormity of the images, focus for analysis is centered on specific, so-called, high-power- fields (HPFs) on the periphery of the invasive parts of the tumor. Selection of the HPFs is very subjective. Additionally, tumor heterogeneity impacts both the region selection and the quality of the area analyzed. Several efforts have been made to automate the tumor proliferation score estimation by counting the mitotic figures in certain regions-of-interest. But the region selection algorithms are inconspicuous and do not ensure to encompass the crucial regions interesting for pathological analysis, thereby, making the grading sub-optimal. In this work, we aim at addressing this problem by proposing to visualize a distance weighted mitotic distribution in the entire invasive tumor region. Our approach provides a holistic view of the mitotic activity and localizes active proliferating regions in the tumor with tissue architecture context, enabling the pathologists to more objectively select the HPFs. We propose a deep learning-based framework to generate the mitotic activity heat-maps. Additionally, in the framework, we develop a number of significant tools for digital pathology; a semi-supervised tumor region delineation tool, a fast nuclei segmentation and detection tool, and a mitotic figure localization tool.
Geographic Pattern as a Determinant Factor of Interlinking Climatic and Hydrological Components of the Natural Resources

Some of the practical implications of the previously identified geographic patterns linking climate rate of surface temperature with the fractal dimension of a multi-year series of runoff, are presented. The statistical data, showing a role of coefficient of kurtosis at classification of distributions of density of probability in a range of air temperatures from –5 to 27 degrees Celsius are given. It is empirically established that statistical distributions of long-term average annual runoff series for the temperature zone with prevailing high values of the fractal dimension correspond to beta-distributions of type II and when plotted on the K. Pearson diagram this type is distinguished from probability density curves in other temperature zones by kurtosis (statistically significant). It is revealed that ignoring the coefficient of kurtosis means not taking into account the climatic component of natural resources. The results of the study can be used to assess the reliability of hydraulic structures and in the field of water management.
Secure Multi-User k-Means Clustering Based on Encrypted IoT Data

IoT technology collects information from a lot of clients, which may relate to personal privacy. To protect the privacy, the clients would like to encrypt the raw data with their own keys before uploading. However, to make use of the information, the data mining technology with cloud computing is used for the knowledge discovery. Hence, it is an emergent issue of how to effectively performing data mining algorithm on the encrypted data. In this paper, we present a k-means clustering scheme with multi-user based on the IoT data. Although, there are many privacy-preserving k-means clustering protocols, they rarely focus on the situation of encrypting with different public keys. Besides, the existing works are inefficient and impractical. The scheme we propose in this paper not only solves the problem of evaluation on the encrypted data under different public keys but also improves the efficiency of the algorithm. It is semantic security under the semi-honest model according to our theoretical analysis. At last, we evaluate the experiment based on a real dataset, and comparing with previous works, the result shows that our scheme is more efficient and practical.
Logistics distribution path optimization research based on adaptive chaotic disturbance flies optimization algorithm

With the economic globalization and fast-changing of information technology, the logistics industry and production efficiency have greatly improved, and the logistics distribution have become an important part of the modern logistics industry. The selection of the distribution path in the logistics is the key issue because the choice of reasonable distribution route have great significance on increasing delivery speed, improving service quality, reducing distribution cost and increasing economic benefit. Aiming at this characteristic in the process of logistics distribution, this paper presents an adaptive chaotic disturbance flies optimization algorithm (ACD-FOA), which can optimize fruit flies algorithm in a state of convergence for global optimization. It also can overcome the shortcoming of flies optimization algorithm which is easy to fall into local faults in the solving process to some extent. Based on this advantage, the convergence speed, reliability and optimization on the accuracy of optimization algorithm are much better than the pure flies. Finally the instance simulation test have been carried out, which proved that the adaptive chaotic flies optimization algorithm has better ability of global optimization. In order to prove that the proposed adaptive chaotic disturbance fly optimization algorithm has higher optimization accuracy in logistics distribution, the results were compared with three other models, which indicates that the ACD-FOA established in this paper is more applicable to the current Logistics distribution path optimization. It also can find the optimal logistics distribution path quickly and reduce the logistics distribution cost effectively at the same time.
Construction of multimodal 3D model of pancreatic cancer tumor

Histopathological imaging and Magnetic Resonance (MR) are two equally important yet very distinct modalities of medical imaging. The high resolution of the first and the non-invasiveness of the later provide complementary information for medical diagnosis and research. Due to their largely different resolutions, the registration between 3D images of these two modalities is challenging. The objective of this paper is to create a multimodal 3D model of pancreatic cancer tumor by performing the registration of a reconstructed 3D pathological image and an MR image from a KPC mouse. The tumor portions were manually segmented and the 3D pathological image was reconstructed using landmark-based non-linear registration. The process starts by registering the outline of the images using the LDDMM non-linear registration method to match the binary labels of the tumor regions. Next, a non-linear B-spline deformation method based on mutual information maximization is used to register the internal structures of the images. Experimental results show that the overall shape of the tumor and its internal necrosis portion could be correctly registered, although the quality of the manual segmentations affects the accuracy of the registration.
Application of comprehensive survey method to water exploration in mountainous area

Looking for groundwater in mountainous areas is faced with many technical problems, which restrict the economic and livelihood of the local people. Based on the practice of water prospecting project in mountainous area, this paper discusses the application of comprehensive prospecting method in Groundwater Prospecting in mountainous area. We use data collection and collation, field investigation and reasoning, geophysical detection and analysis methods to explore the surface to line, line to point, gradually accurate groundwater comprehensive search method. This paper analyzes the significance of the comprehensive survey method, introduc the work flow of comprehensive survey method combined with engineering practice, and put forward suggestions and suggestions on how to use this method. The research results in this paper can be used as guidance and reference for the search of groundwater in mountainous areas.
Study on Application of Bloom’s Taxonomy in Engineering Project Management Course

In recent years, educational reformers have focused on teaching philosophy of Bloom's Taxonomy, studying theory and practice of relevant teaching philosophy at home and abroad in terms of cognition, application of engineering project management teaching models in classroom-based lamination teaching, classroom teaching models, assessment and evaluation.
A Real Time Data Association Prototype System for Multi-Tenants in Big Data

With the development of multi-source heterogeneous data production and multi-application multi-tenant data consumption, the requirement of data association analysis is growing. Current data association analysis technology cannot satisfy the requirements of streaming data analysis and processing in a real scenario. In this paper, in order to achieve the real-time data association analysis, firstly, we solve the key problems of streaming data association; secondly, we construct a data real-time association computing system for multi-tenants for a Kafka-based big data analysis platform. The system has characteristics of high availability and high efficiency.
DGBA: Delay-Guaranteed Bitrate Adaptation for Mobile Broadcaster in Interactive Live Video Streaming

Improving the broadcaster-side performance is crucial for ensuring the video quality experience (QoE) for mobile interactive live streaming. But it is challenging in the presence of mobile network's bandwidth fluctuation, as we need not only to ensure a high video bitrate but also to meet the broadcaster-side constraint. This paper presents Delay-Guaranteed Bitrate Adaptation (DGBA), an algorithm based on the idea of controllable bitrate adaptation used to optimize the broadcaster-side performance. We compare the performance of DGBA with a commercial bitrate adaptation algorithm by implementing these two algorithms in a prototype system. The experiment results show that DGBA can ensure 95.2% broadcaster-side delay constraint satisfaction compared to commercial algorithm's 84.7%, while being more effective in bitrate adaptation.
Model Checking for Turn-Based Probability Epistemic Game Structure

In this paper, a turn-based probability epistemic game structure (TPEGS) is proposed to model knowledge preconditions for actions of system and environment firstly, which is an extension of turn-based synchronous game structures with probabilistic transition. Secondly, we introduce probability operator $$ P_{\sim \lambda } $$ into alternating temporal epistemic logic (ATEL) and define turn-based probability alternating-time temporal epistemic logic (tPATEL) for model checking the properties of TPEGS quantitatively. The probability of agents knowing some precondition before they implement an action can be expressed in tPATEL. Thirdly, we propose a method to compute probability for model checking verification problems of tPATEL based on DTMC and CTMC, and then analyze the time complexity of the method. Then, we are able to convert a part of tPATEL verification problems into the PATL ones by defining the knowledge formula $$ K_{a} \phi ,\, E_{As} \phi $$ and $$ C_{As} \phi $$ as atomic propositions. Finally, we study a flight procedure in STAS using PRISM-games to demonstrate the applicability of the above model checking framework and expand the application field of model checking.