Reviewerly/exHarmony huggingface dataset:
Unsupervised Lesion Detection via Image Restoration with a Normative Prior

Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. Recently, this problem has received increased attention from the research community following the advances in unsupervised learning with deep learning. Such advances allow the estimation of high-dimensional distributions, such as normative distributions, with higher accuracy than previous methods.The main approach of the recently proposed methods is to learn a latent-variable model parameterized with networks to approximate the normative distribution using example images showing healthy anatomy, perform prior-projection, i.e. reconstruct the image with lesions using the latent-variable model, and determine lesions based on the differences between the reconstructed and original images. While being promising, the prior-projection step often leads to a large number of false positives. In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. The probabilistic model punishes large deviations between restored and original images, reducing false positives in pixel-wise detections. Experiments with gliomas and stroke lesions in brain MRI using publicly available datasets show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin, +0.13 (AUC), for both glioma and stroke detection. Extensive model analysis confirms the effectiveness of MAP-based image restoration.
Microresonator-enhanced, Waveguide-coupled Emission from Silicon Defect Centers for Superconducting Optoelectronic Networks

Superconducting optoelectronic networks could achieve scales unmatched in hardware-based neuromorphic computing. After summarizing recent progress in this area, we report new results in cryogenic silicon photonic light sources, components central to these architectures.
Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles

Future unmanned aerial vehicles (drones) will be shared by multiple users and will have to operate in conditions where their fully-autonomous function is required. Calculation of a drones trajectory will be important but optimal trajectories cannot be calculated unless mass and flight speed are taken into account. This article presents the case for on-drone trajectory planning in a multi-user dynamic payload mass scenario, allowing a drone to calculate its trajectory with no need for ground control communication. We formulate and investigate on-drone trajectory planning under variable payload mass and flight speed awareness, in cases where it is shared by multiple users or applications. We present efficient solutions using a combination of heuristic and optimization algorithms. To support this investigation, we present a new model for the power dissipation of drone propulsion as a function of speed and payload mass. We evaluate our proposed algorithmic solution on contemporary embedded processors and demonstrate its capability to generate near-optimal trajectories with limited computational overhead (less than 300 milliseconds on an ARM Cortex-A9 SoC).
Research on Network Attack Effect Evaluation Based on Confrontational Perspective

Abstract The cyberspace battlefield is full of strategic confrontations between attack and defense sides. However, the existing evaluation methods do not consider the impact of defense strategies on attack effect, and only carry out unilateral attack effect evaluation from the perspective of attacker, which cannot reflect the dynamic changes of attack effect against different defense strategies. To solve this problem, we propose the Confrontation Oriented Attack Effect Evaluation Model (CO-AEEM) to evaluate the attack effect from the perspective of both attack and defense sides. By creating CO-AEEM, we establish a multi-level evaluation index system covering all-assests-elements of the target object, and propose an attack effect evaluation algorithm to reduce the impact of subjective factors and adapt to the complex characteristics of network confrontation, then design the comparison and evaluation process of attack test and confrontation test according to the changes of defense measures. Finally, the simulation experiments in various confrontation scenarios are carried out in network range, which show that the model can not only describe the actual attack effect in confrontation with different defense strategies, but also the effectiveness degree of defense strategies against various types of attacks. Thus, the evaluation results of CO-AEEM can provide scientific guidance of action decision making and equipment development in network counterwork. CCS Concepts •Security and privacy➝Formal methods and theory of security •Computing methodologies➝Modeling and simulation.
Deep Learning as Substitute for CRF Regularization in 3D Image Processing

For calculating 3D information with stereo matching, usually correspondence analysis yields a so-called depth hypotheses cost stack, which contains information about similarities of the visible structures at all positions of the analyzed stereo images. Often those cost values comprise a large amount of noise and/or ambiguities, so that regularization is required. The Conditional Random Field (CRF) regularizer from Shekhovtsov et al. [Sh16] is a very good algorithm among various methods. Due to the usual iterative nature of those regularizers, they often do not meet the strict speed and memory requirements posed in many real-world applications. In this paper, we propose to substitute Shekhovtsov’s CRF algorithm with an especially designed U-shaped 3D Convolutional Neural Network (3D-CRF-CNN), which is taught proper regularization by the CRF algorithm as a teacher. Our experiments have shown, that such a 3D-CRF-CNN is not only able to mimic the CRF’s regularizing behavior, but - if properly setup - also comprises remarkable generalization capabilities compared to a state-of-the-art 2D-CNN that is trained on a slightly different, yet equivalent, task. The advantages of such a CNN regularizer are its predictable computational performance and its relatively simple architectural structure, which allows for easy development, speed up, and deployment. We demonstrate the feasibility of the concept of training a 3D-CRF-CNN to take over CRF’s regularizing functionality on the basis of available test data and show that it pays off to invest special effort into tailoring an according CNN architecture.
Recognition and Analysis of Opinion Questions in Standard Arabic

Nowadays, most question-answering systems have been designed to answer factoid or binary questions (looking for short and precise answers such as dates, locations), however little research has been carried out to study complex questions. In this paper, we present a method for analyzing medical opinion questions. The analysis of the question asked by the user by means of a pattern based analysis covered the syntactic as well as the morphological levels. These linguistic patterns allow us to annotate the question and the semantic features of the question by means of extracting the focus and topic of the question. We start with the implementation of the identifying rules and the annotation of the various medical named entities. Our named entity recognizer tool (NER) is able to find references to people, places and organizations, diseases, viruses, as targets to extract the correct answer from the user. The NER is embedded in our question answering system. The task of QA is divided into four phases: question analysis, segmentation, and passage retrieval & answer extraction. Each phase plays a crucial role in the overall performance. We use the NooJ platform which represents a valuable linguistic development environment. The first evaluations show that the actual results are encouraging and could be deployed for further question types.
Strategies and Performance Analysis of Queries Associated with Cloud Database

In the present day, cloud computing plays a vital role towards technologies associated with service. The primary objective of cloud computing is to make people compute and store resources properly and effectively. Therefore to improve the performance in the cloud, it may require optimization towards processing data. It is obvious that cloud computing enhances sharing computing power as well as storage for a number of applications towards the database with heterogeneity. But it has been observed that the way a number of applications is influenced by various cloud platforms, the high scale generated data the data generated may be increased as well as consumed during the applications. Accordingly with the availability of virtual machines, cloud computing may enable users for the usage of resources to execute complex queries efficiently on large-scale data. The complete autonomy towards each node in the large database environments may be expected towards the services through external communication along with the experimentation towards optimizing query terms. Accordingly, unifying and authorization linked with the desired problem may be partially linked with specific points towards information retrieval along with its characteristics. In that scenario, the large database may be linked with the virtual server towards providing services to the relevant data. Also the database associated with the cloud may be associated with the various instances linked with different heterogeneous databases. Many techniques have already been presented linked to processing queries in cloud databases. In this paper, it has been proposed to optimize query processing linked with virtual data associated with virtual servers. Accordingly, the generation of queries along with the execution query plans may also attempt to optimize the performance of virtual databases.
Bi-Decoder Augmented Network for Neural Machine Translation

Neural Machine Translation (NMT) has become a popular technology in recent years, and the encoder-decoder framework is the mainstream among all the methods. It's obvious that the quality of the semantic representations from encoding is very crucial and can significantly affect the performance of the model. However, existing unidirectional source-to-target architectures may hardly produce a language-independent representation of the text because they rely heavily on the specific relations of the given language pairs. To alleviate this problem, in this paper, we propose a novel Bi-Decoder Augmented Network (BiDAN) for the neural machine translation task. Besides the original decoder which generates the target language sequence, we add an auxiliary decoder to generate back the source language sequence at the training time. Since each decoder transforms the representations of the input text into its corresponding language, jointly training with two target ends can make the shared encoder has the potential to produce a language-independent semantic space. We conduct extensive experiments on several NMT benchmark datasets and the results demonstrate the effectiveness of our proposed approach.
3D OBJECTS SEPARATION: UNSUPERVISED SEGMENTATION AND CLASSIFICATION

Object segmentation for the purpose of object and pattern recognition has been a long-standing subject of interest in the field of machine vision. Despite the significant attention given to the development of segmentation and recognition methods, the critical challenge of separating merged objects did not share the spotlight. In the work presented in this paper, we propose a simple yet novel approach to overcome this hurdle, by developing a methodology for unsupervised classification and separation of objects in 3D. Lower dimensionality classifiers are joined to provide a powerful higher dimensionality classification. The robustness of this approach is illustrated through its implementation on two case studies of merged objects. Applications of this methodology can further extend from structural classification to general problems of clustering and classification in various fields.
Design for Elliptical Micropillar Triplets for a Highly Efficient Quantum-Dot Entangle Photon Pair Source

A design of quantum-dot-based polarized entangle photon pair source by implementing three overlapping elliptical micropillars, leading to >80% collection efficiency and high Purcell factor for both exciton and biexciton states, is presented and discussed.
Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings

In this paper, we focus on recommendation settings with multiple stakeholders with possibly varying goals and interests, and argue that a single evaluation method or measure is not able to evaluate all relevant aspects in such a complex setting. We reason that employing a multi-method evaluation, where multiple evaluation methods or measures are combined and integrated, allows for getting a richer picture and prevents blind spots in the evaluation outcome.
A Blockchain-Encryption-Based Approach to Protect Fog Federations from Rogue Nodes

People have used cloud computing approach to store their data remotely. As auspicious as this approach is, it brings forth many challenges: from data security to time latency issues with data computation as well as delivery to end users. Fog computing has emerged as an extension for cloud computing to bring data processing and storage close to end-users; however, it minimizes the time latency issue but still suffers from data security challenges. For instance, when a fog node providing services to end users is compromised, the users' data security can be violated. Thus, this paper proposes a secure and fine-grained data access control scheme by integrating the CP-ABE algorithm and blockchain concept to prevent fog nodes from violating end users' data security in a situation where a compromised fog node is being ousted. We also classify the fog nodes into fog federations, based on their attributes such as services and locations, to minimize the time latency and communication overhead between fog nodes and cloud server. Further, the exploitation and integration of the blockchain concept and the CP-ABE algorithm enables fog nodes in the same fog federation to perform the authorization process in a distributed manner. In addition, to solve time latency and communication overhead problems, we equip every fog node with an off-chain database to store most frequently accessed data files for specific time, and with an on-chain access control policies table (On-chain Files Tracking Table) which must be protected from being tampered by malicious (rogue) fog nodes. Therefore, blockchain plays a vital role here as it is tamper-proof by nature. We demonstrate our scheme's efficiency and feasibility by designing algorithms and conducting a security analysis. The provided analysis shows that the proposed scheme is efficient and feasible in ousting malicious (rogue) fog nodes.
SERVIÇO DE DISSEMINAÇÃO SELETIVA DE INFORMAÇÕES: mediação da informação em Bibliotecas Jurídicas.

The study presents as object the effectiveness of Selective Dissemination of Information(ISD) in the process of mediation of legal information in the Joao Freire Medeiros library ofthe Regional Court of Maranhao (TRT-MA). It proposes a case study in order to answer towhat extent the ISD in the Joao Freire Medeiros library can be considered to have effectivelyfulfilled the role of mediating legal information with the user community. It aims to analyzethe effectiveness of the TRTMA Library's ISD, in order to provide data that contributes toinformation management. It presents concepts and perceptions related to informationalmediation, such as intermediation and disintermediation. Demonstrates typologies ofmediation, such as implicit and explicit mediation. It addresses the relationship betweenmediation, selective dissemination of information and referral service, pointing out that ISD isone of mediation alternatives to meet information needs, while integrating the third line of theReferral Service. It discusses the ISD, presenting the history, conceptual and operationalaspects, theories and methods that give theoretical support for its implementation. Discussesthe application of the ISD service in the legal field, highlighting concepts and typologies oflegal information. In order to achieve the proposed objective, a field research is applied, basedon the bibliographic and documentary research, using as questionnaire research techniquesand systematic observation. Indicates that the DSI is fully automatic. It shows that the servicemeets some requirements of the specialized literature, requiring only a feedback action, whichallows to evaluate the performance for decision making. As a result, it points out that usersare fully satisfied with the form of registration, and with the amount of legal informationnotified. Notes that respondents consider informed topics fully appropriate to theirprofessional practices and information needs. It concludes that the studied ISD positivelyimpacts the professional performance of users, while being efficient and effective.
Case Base Reasoning (CBR) and Density Based Spatial Clustering Application with Noise (DBSCAN)-based Indexing in Medical Expert Systems

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ≥ 90.
Doppler Radar for Dynamic Hand Gesture Recognition based on Signal Image Processing

From previous researches, Doppler radar was used to detect signal for implement with many applications. Nevertheless, it is difficult to analyze for recognize object. At present, technique of deep learning in terms of signal processing and image processing are using in many researches to classify categories of data. In this paper, signal image was used by deep learning to classify hand gesture by receiving signals from 24GHz transceiver: BGT24MTR11. We transformed the signals to images for 3 categories including Spectrogram, Time domain from original signal and feature MFCC graph. After that those of converted image will be trained by Deep learning for classify the hand gesture types. From the result of this experiment has been shown that signal image can be used to recognize hand gesture and Spectrogram graph makes the highest accuracy as 94%.
Analisis Faktor-faktor yang Mempengaruhi Penentuan Jenis Pemeliharaan Embung Irigasi

Maintenance of good and sustainable irrigation ponds have an impact on ensuring the sustainability of the irrigation system. For this reason, it is necessary to develop a reservoir maintenance model based on multicriteria analysis relating to the type of maintenance. The method used Structural Equation Model (SEM) with the WarpPLS approach. The criteria used Structural Stability, Economy / Finance, and Physical &amp; Environmental. The findings obtained variable structural stability has a very significant effect on the maintenance of irrigation ponds (r &lt;0.01 and β = 0.73). Economic / financial variables have not significant effect on reservoir maintenance where r = 0.3 and β = 0.03. Physical and environmental variables also did not directly influence the maintenance of ponds with r = 0.29 and β = 0.03. However, the physical &amp; environmental variables significantly influenced structural stability with r &lt;0.01 and β = 0.32. Physical and environmental variables also significantly influenced economic/financial where r &lt;0.01, and β = 0.45. This finding supported various previous studies for structural stability variable but different for economic/financial variables that did not significantly affect by the determination of reservoir types.
Artificial God Optimization – A Creation

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.
Influences of Childcare Center Directors’ Authentic Leadership on childcare Teachers’ Job Satisfactions

본 논문은 어린이집 원장의 진성리더십이 보육교사의 직무만족에 어느 정도 영향을 미치는가에 대한 의문으로부터 출발하고 있다. 연구 대상은 구미지역 어린이집에 근무하는 보육교사 258명을 대상으로 설문조사를 실시하였고, 설문지는 SPSS 23.0을 통하여 기술통계, 상관관계 및 다중회귀분석을 실시하였다. 본 연구의 결과는 다음과 같다. 첫째, 진성리더십과 직무만족, 조직문화 간에는 비교적 높은 상관을 보였고 유의미한 긍정적인 영향을 미치는 것으로 나타났다. 둘째, 원장의 진성리더십은 보육교사의 직무만족도에 유의미한 긍정적인 영향을 미치는 것으로 나타났다. 넷째, 조직문화는 원장의 진성리더십과 보육교사의 직무만족도 관계에서 유의미한 매개효과를 갖는 것으로 나타났다. 이러한 분석결과를 토대로 본 연구는 조직문화의 매개효과를 검증하고 직무만족에 미치는 영향을 실증적으로 연구함으로써 향후 진성리더십에 대한 연구의 폭을 넓혔다는 의의를 가진다. 조직문화의 매개효과를 살펴본 결과, 조직문화가 진성리더십과 종속변수의 관계에서 매개역할을 하는 것으로 밝혀졌다. 특히 조직문화 하위 구성요소 중에서 합리문화에 가장 영향력을 미치는 것으로 나타났다.The childcare center directors should maintain high morality through reflection on oneself, collect opinions based on transparency and authenticity in the relationship with the childcare teachers, and handle the work smoothly through reasonable information collection and analysis. The results of this study are as follows. First, there was a relatively high correlation between the authentic leadership, job satisfaction, and organizational culture, and it showed a significant positive effect. Second, the authentic leadership of childcare center directors has a significant positive effect on the job satisfaction of the childcare teachers. Third, organizational culture has a significant mediating effect on the authentic leadership of the childcare center directors and job satisfaction of the childcare teachers.