Reviewerly/exHarmony huggingface dataset:
Automatic hippocampus segmentation based on convolutional neural networks and level set model

The hippocampus has a key role in a number of neurodegenerative diseases, such as Alzheimer's Disease. While many automatic segmentation methods exist, their performances are still challenged due to the poor image contrast of hippocampus in the MR images. In this paper, we present a fast and robust hippocampus segmentation method using convolutional neural networks and level set model. First, images are cropped which reserve the hippocampus region. The convolutional neural network (CNN) is then trained to segment the whole hippocampus region which can be taken as the initial level set contour. The final segmentation is achieved by the evolution of the level set contour. Our method performs better than other state-of-the-art methods. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.864.
Human Parsing with Edge Enhancement

Human parsing, which aims at segmenting human images according to body parts, attracts many researchers&#x0027; attention because of its various application perspectives. The problem requires accurate object edge detection on segmentation masks. However, the edge information has not been fully explored in the existing methods for this issue. This paper focuses on enhancing body&#x0027;s edge segmentation performance of the human parsing problem. The proposed enhancement method includes three aspects, which are feature extraction, context information, and edge refinement. To this end, we introduce an edge enhancement module that focuses on edge enhancement and refines the object edges from a coarse predicted segmentation mask. The edge enhancement module, together with a context embedding module and a feature extraction module, is integrated into a state-of-the-art human parsing framework. Our proposed method can significantly improve the performance in edge estimation. The experimental results on LIP dataset and PASCAL-Person-Part dataset validate the excellent performance of our method.
Association Concept Clustering Method Based on User-Generated Data

In order to overcome the inaccurate results in the traditional extraction and clustering of domain concepts, the paper proposes a concept clustering algorithm fusing the associations between concepts based on user-generated data. This method takes full advantage of user preferences derived from user-generated data, and effectively transforms the user preferences implied between concepts into the concept associations using mutual information. Considering that concept's own semantic is an important role for the similarity-based clustering, we propose a comprehensive semantic similarity measure method, which calculates the similarities in terms of distance, depth, density, coincidence degree and information content exploiting the principal analysis technique. On the basis of the above, we fuse the similarities of concepts with associations of that, making the original clustering results without concept associations get beneficial supplement in practical applications. The experimental results show the proposed method is effective and efficient. Compared with other existing methods, the precision is improved by 8%, the recall by 12%, and the F-Measure by 10%.
Liar, liar, pants on fire! Social desirability bias in software piracy research

Measures of ethical behaviours in research may be subject to social desirability bias (SDB). Most behavioural aspects of software piracy research are a subset of ethical research. Few studies of software piracy have explicitly addressed the issue of SDB. In this article, the extent of SDB that may be present in software piracy is empirically assessed. This was done by comparing SDB in software piracy under three conditions using the 40-item Balanced Inventory of Desirable Responding (BIDR) scale. First, software piracy behaviour was examined in an experimental setting with actual money at stake. This allowed subject behaviour to be observed rather than inferred from self-report responses. Second, a survey was conducted to test the extent of software piracy based on subject self-report responses. Third, the extent of software piracy was also assessed based on peer-report responses (i.e.: the subject reported whether their peers would pirate). A comparison of the responses in the three conditions allows us to draw inferences about SDB in software piracy research. Self-report surveys exhibited high levels of SDB. The behaviour in the experimental condition, and responses in the peer-report survey method, did not exhibit SDB.
Automatic Evolutionary Settings of Machine Learning Methods for Buildings’ Thermal Loads Prediction

Due to climate change, buildings can consume 30% more energy by 2040, with energy performance being the critical element for achieving sustainable development in the civil construction sector. One way to solve this evaluation problem is by applying Machine Learning Methods that can assist specialists in civil construction in analyzing scenarios even in the initial phase of the project. The present work evaluates the application of the Elastic Net, Extreme Learning Machine, and Extreme Gradient Boosting models for the prediction of heating and cooling loads in residential buildings. The database used has 768 samples, with eight geometric input variables and two thermal output variables. Differential Evolution optimization algorithm was applied to select method parameters to find the sets of hyperparameters that reinforce the predictive capabilities of the models. The comparisons of the results occurred using the metrics MAE, MAPE, RMSE, and R\(^2\). The results showed that the Extreme Gradient Boosting method obtained a better performance among the tested methods than the literature, presenting the lowest values for the error metrics and significant differences in the statistical tests. Thus, combining Differential Evolution and Extreme Gradient Boosting methods, thermal loads can be predicted, assisting projects that aim at energy savings and sustainability
A Lattice-based approach for Mining Non-Redundant High Utility Association Rules

High utility itemsets mining provides more information, which can help manager to make better decisions. However, there are too many redundant high utility itemsets which makes it difficult for users to find useful information in a large set of high utility itemsets. To solve this problem, this paper proposed an approach for mining non-redundant high-utility association rules (NR-HAR) efficiently in both dense and sparse datasets, which make more timely decisions on business strategy. This approach includes two phases: (1) building an extended high utility itemsets Lattice(EHUIL) and identify generator and closed itemsets within this. (2) Proposing an efficient algorithm for mining NR-HAR from the built EHUIL. The new algorithm is based on utility-confidence framework and the lattice structure, obtaining semantic relationships among HUIs. Extracting results from EHUIL, which having closed flag and generator flag, determining to find rules with child nodes to be stopped or continued. We carry out experiments to show the efficiency of the proposed method. The experimental results show that this new approach has a faster runtime on different types of datasets than existing methods, especially in sparse database.
Construction of De Bruijn Sequences from l-sequences

De Bruijn sequences and l-sequences are defined to be the maximum-period sequences generated by feedback shift registers (FSRs) and feedback with carry shift registers (FCSRs), respectively. In this paper we give some relationships between de Bruijn sequences and l-sequences, and we propose an efficient method to generate de Bruijn sequences from l-sequences. The experimental results indicate that this method should be able to generate de Bruijn sequences of any order.
End States Guided Matching Network for Retrieval-based Multi-turn Conversation

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper choice. However, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after, so there is no need to provide any context-related information after that. Thus, in addition to match the response with context, it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching result. Evaluation results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.
Digital Technologies for Analysis of Restoration of Serial Numbers

In daily activities, serial numbers represent one way to range finished products of different types of production fields and also tracking those objects during their lifetime – ever since their exit of the production site until their recycling. In this paper are presented several types of imprinting techniques as well as a case study for restoration of a modified serial number.
Visualization of Relationship between Facial Features and Impression Evaluations

Impression of facial images strongly relates to the tones and shapes of the faces. This paper presents our study on the visualization of relationships between facial features and impression evaluations. Specifically, we generate clusters of faces and then visualize the relationships between the clusters and impression evaluation results. This study applies a set of artificial facial images generated with a machine learning model and then extracts facial regions. We apply a clustering algorithm with features of the faces calculated based on pixel values and aspect ratios of the faces. Meanwhile, we conduct impression evaluations based on the SD method on the five-point Likert scale. This study integrates the clustering and impression evaluation results and visualizes the dataset by using our own hierarchical data visualization technique.
An Intelligent Customer Service System for Securities Industry based on Compound Corpus and Text Vector

Customer service is one of essential tasks for securities companies. The traditional manual service mode has disadvantages such as high labour intensity and poor customer service experience. This paper proposes an intelligent customer service system based on Fasttext and word vectors. By constructing the securities knowledge database, and using Fasttext to train the word vector model from the Chinese compound corpus, the performance of “sentence vector + cosine distance” and “word vector similarity matrix + CNN feature extraction” methods in calculating semantic similarity are analyzed based on single corpus and compound corpus, respectively. The results show that “sentence vector + cosine distance” has higher performance on accuracy rate.
The influence of theoretical concepts on the practice of assessing the quality of knowledge in the 1920-1930s

A significant component of higher education didactics is the assessment of the quality of students’ knowledge, which allows determining the effectiveness of the work of the participants in the educational process. Assessment of the quality of students’ knowledge, considered at various levels, is of interest not only from the standpoint of modern didactics, but also in the historical aspect. Theoretical representations of teachers of the 1920-1930s on the assessment had an impact on the practice of organizing the work of universities. The selected chronological framework (1917-1939) is characterized by active transformations in the educational sphere, significant development of pedagogical thought. The study aims to substantiate the development trend of the practice of assessing the quality of students’ knowledge on the basis of theoretical ideas of teachers of the period under review. The work employs a general theoretical modeling method aimed at building basic models for assessing the quality of students’ knowledge. In addition, special historical and pedagogical methods were used: historical-structural, historical-comparative and constructive-genetic ones. The employment of these methods makes it possible to describe the main structural components of assessment that occur during the studied period of change and identify the main trends. The result of the work was the identification of the main models for assessing the quality of students’ knowledge at various stages of the period under consideration: authoritarian control, control based on the “teacher-student” interaction and self-control, as well as control based on the “teacher-student” interaction with the leading role of the teacher. The representations described in the pedagogical literature of that period, which posed the basis for building these models, had a significant impact on the practice of higher education. The novelty of the work consists in identifying the main tendency of strengthening the teacher’s priority when assessing the quality of students’ knowledge in the “teacher-student” interaction.
CNN-DBN: Quality Assessment and Optimization of Content-Based Image Retrieval Services

The quality assessment of image retrieval services can master the quality of retrieval results in real-time, and provide valuable information for users. Therefore, the quality assessment of image retrieval, especially content-based image retrieval(CBIR) services, has attracted the attention of researchers. However, the traditional quality assessment approach of CBIR services had some problems, for example, they ignored the high-level semantics information of the image, used shallow learning models, etc. To address these problems, this paper proposes a quality assessment approach of image retrieval, namely CNN-DBN, it extracts image features by Convolutional Neural Network(CNN) and establishes a quality assessment model using Deep Belief Network(DBN). Based on the quality assessment results, this paper proposes a scheme to optimize the quality of CBIR. To evaluate the performance of this approach, design a set of experiments to verify the performance and practicability of the proposed approach. To a certain extent, it can evaluate the quality of image retrieval and optimize the quality of retrieval results, demonstrate the importance of image retrieval quality assessment for users.
Panorama sobre Archival Intelligence no cenário internacional

It aims to understand and explore the discussions about Archival Intelligence in the international literature, seeking to broaden and consolidate the discussions about Information Literacy and Archival Science in the Brazilian scenario. Systematic Bibliographic Review was used as a methodological option to identify the presence of the term Archival Intelligence in the Periodical Portal of the Coordination for the Improvement of Higher Education Personnel. Eighteen articles were retrieved, all of which were analyzed in full. The results point out the importance of Archival Intelligence as well as Information Literacy for Primary Sources and Primary Source Literacy to deepen the studies on archival instructions, foster the creation of teaching and learning standards to primary sources and infer their practical application to archival users. The role of archivists in this process is necessary, as well as its partnership with other professionals in related fields, as well as librarians, historians and teachers in the area.
Recovery of Simulated Fuel Debris by Remote Control Robot for Decommissioning Work

This research aims to realize a remote control robot that can play an active role in decommissioning work. We have developed a remote control robot consisting of a main-unit and a sub-unit. The robot has a function to retrieve fuel debris. The main-unit is mainly responsible for raising and lowering the sub-unit by 3.2[m]. The sub-unit mainly performs the task of grasping the fuel debris. We participated in the Creative Robot Contest for Decommissioning with the robot we created. The Creative Robot Contest for Decommissioning is a robotics contest that aims to develop human resources for decommissioning work. We were able to complete all the tasks given to us in the contest. Therefore, we were able to demonstrate the potential of our robot to contribute to decommissioning work.
Task scheduling based on computing resource usage prediction in large-scale cluster

To further improve the computing performance of the cloud for complex computing environments, efficient task scheduling strategies suitable for container deployment can improve the computing performance of the system. The current task scheduling algorithm is based on the current situation of the computing environment and the internal logical relationship between tasks to ensure that performance indicators are met. In this article, we first use lstm and attention algorithms to extract features from the historical data of processor operation to obtain better scheduling strategies in different states, and improve the existing processor scheduling strategies based on this. The method proposed in this paper has been experimentally verified in kubernate, which proves that the method in this paper is reasonable and effective.
Analysis of WEB Browsers of HSTS Security Under the MITM Management Environment

The transactional websites and services on the cloud, have actually become the most used browsers, thanks to their portability and ease of use, with a significant increase in the development of cloud solutions, implementing digital contexts under the 4.0 web, which generated an increase of possibilities for transactions of different types. However, every time more security issues arise. Due to this problem, the computer security is a rising trend, generating new possibilities to mitigate vulnerabilities when handling the information in a transactional web site; an analysis is made of performance, weaknesses and strengths of the HSTS standard, as a security complement of the SSL/TLS protocol. Different tests scenarios are verified under a man attack in the MITM environment, to intercept or capture the traffic sent and received during web transactions. That is how we identify if the standard can prevent that intrusion, which is of vital importance for the different transactional environments actually used, such as bank entities or online purchases; vulnerabilities of the standard are verified upon making the first request to a website, which strengthens and secures transactions done from the beginning of the transaction to its ending, in an encrypted way. Browsers analyzed - Mozilla Firefox, Google Chrome and internet Explorer, under controlled corporate and personal environments; The security importance of the browser is outlined, Google Chrome being the best one in performance under an internet hacking. The other browsers present some shortcomings during the first interconnection request, during some milliseconds under the point to point model, for the initial phase of information interchange.
Weierstrass points on modular curves <i>X</i><sub>0</sub>(<i>N</i>) fixed by the Atkin–Lehner involutions

Purpose The authors have determined whether the points fixed by all the full and the partial Atkin–Lehner involutions W Q on X 0 ( N ) for N ≤ 50 are Weierstrass points or not. Design/methodology/approach The design is by using Lawittes's and Schoeneberg's theorems. Findings Finding all Weierstrass points on X 0 ( N ) fixed by some Atkin–Lehner involutions. Besides, the authors have listed them in a table. Originality/value The Weierstrass points have played an important role in algebra. For example, in algebraic number theory, they have been used by Schwartz and Hurwitz to determine the group structure of the automorphism groups of compact Riemann surfaces of genus g ≥ 2. Whereas in algebraic geometric coding theory, if one knows a Weierstrass nongap sequence of a Weierstrass point, then one is able to estimate parameters of codes in a concrete way. Finally, the set of Weierstrass points is useful in studying arithmetic and geometric properties of X 0 ( N ).
Decentralised task allocation using GDL negotiations in Multi-agent system

In large distributed systems, the optimization algorithm of task scheduling may not meet the special requirements of the domain control mechanism, i.e. robustness, optimality, timeliness of solution and computational ease of processing under limited communication. In or- der to satisfy these requirements, a novel decentralized agent scheduling method for dynamic task allocation problems based on Game Descrip- tion Language (GDL) and Game Theory is proposed. Specifically, we define the task allocation problem as a stochastic game model, in which the agent's utility is derived from the marginal utility, and then prove that the global optimal task allocation scheme resides in the Nash equi- librium set by the non-cooperative game. In order to generate an optimal solution, we define Multi-agent Negotiation Game (MNG), in which ne- gotiations are held between agents to decide which tasks to act on next. Building on this, we make a simple extension to adopt GDL more suit- able for negotiations and propose to use it to model such negotiation scenarios. Finally, we use a negotiation example to show that our ap- proach is more amenable to automatic processing by autonomous agents and of great practicality than a centralized task scheduler.
A Technical Research on UAV for Object Detection

According to a Forbes report published in 2018, US drone industry has witnessed a dramatic commercial growth from merely $40 million in 2012 to a billion in 2017 as documented in the study conducted by MCKinsey and Company in December 2017. It is estimated by MCKinsey that by 2026 the commercial drones will annually impact $31 billion to $46 billion on country’s gross domestic product.Drones have been widely adopted for data capturing that can be used in the fields of  defense, agriculture, emergency response and disaster management, conservation of endangered species, healthcare etc. For most of the applications of UAV, Real-time object detection is extremely crucial. In the last few years, considering the growth of drone industry and the interest of around 300 companies who are making substantial investments of time and resources in drone many technologies have been emerged for making advancement in the field of UAV focusing on object detection and recognition for UAV.This paper summarizes a number of object detection techniques proposed till date by researchers. It reports the characteristics and requirements of UAV from object detection viewpoint. The objective of our research is to understand different architectures that are capable of detecting objects from aerial images. The main goal of this survey is to create an insight of an architecture that is accurate, fast, robust and utilizes low computation power.
Students Roll Call at Vietnam Universities with Convolutional Neural Networks (Cnn)

In this article, we introduce the construction and training of a CNN model for student face recognition for automatic roll call. Collection of training and testing data, data preprocessing, model training and result evaluation are demonstrated and explained in detail. The model has been tested on the faces of students in a university in Vietnam and yields positive result, which can be applied in practice for automatic roll call. At the same time, the authors have built a learning management system for classes at a university in Vietnam, including automatic roll call.