Reviewerly/exHarmony huggingface dataset:
Temporal indexing of medical entity in Chinese clinical notes

The goal of temporal indexing is to select an occurred time or time interval for each medical entity in clinical notes, so that all medical entities can be indexed on a united timeline, which could assist the understanding of clinical notes and the further application of medical entities. Some temporal relation shared tasks for the medical entity in English clinical notes have been organized in the past few years, such as the 2012 i2b2 NLP challenge, 2015 and 2016 clinical TempEval challenges. In these tasks, many heuristics rule-based and machine learning-based systems have been developed. In recent years, the deep neural network models have shown great potential on many problems including the relation classification. In this paper, we propose a recurrent convolutional neural network (RNN-CNN) model for the temporal indexing task, which consists of four layers: input layer – generates representation for each context word of medical entities or temporal expressions; LSTM (long-short term memory) layer – learns the context information of each word in a sentence and outputs a new word representation sequence; CNN layer – extracts meaningful features from a sentence and outputs a new representation for medical entity or temporal expression; Output layer – takes the representations of medical entity, temporal expression and relation features as input and classifies the temporal relation. Finally, the time or time interval for each medical entity can be directly selected according to the probability of each temporal relation predicted by above model. To investigate the performance of our RNN-CNN model for the temporal indexing task, several baseline methods were also employed, such as the rule-based, support vector machine (SVM), convolutional neural network (CNN) and recurrent neural network (RNN) methods. Experiments conducted on a manually annotated corpus (including 563 clinical notes with 12,611 medical entities and 4006 temporal expressions) show that RNN-CNN model achieves the best F1-score of 75.97% for temporal relation classification and the best accuracy of 71.96% for temporal indexing. Neural network methods perform much better than the traditional rule-based and SVM-based method, which can capture more semantic information from the context of medical entities and temporal expressions. Besides, all our methods perform much better for the accurate time indexing than the time interval indexing, so how to improve the performance for time interval indexing will be the main focus in our future work.
Proposed narrowband biphoton generation from an ensemble of solid-state quantum emitters

We explore a mechanism for producing time-frequency entangled photon pairs (termed a biphoton) from an ensemble of atom-like solid-state quantum emitters. Four distinct energy levels of the solid-state system render four spin-conserving optical transitions as observed in color centers. This feature opens up the possibility to generate a four-wave mixing biphoton based on an electromagnetic induced transparency (EIT) for long-coherence quantum communication as demonstrated in cold atomic systems. We propose a narrow EIT window below a lifetime-limited linewidth of a SiV− in diamond, assuming a few hundred MHz. Consequently, the EIT-induced narrowband guarantees biphoton coherence time to be at least a few tens of a nanosecond without a cavity. Assessing the criteria of solid-state parameters applicable to the existing biphoton model from cold atoms will accelerate solid-state biphoton source research. This study shows that a realization of negligible ground state dephasing of a solid-state sample will be a crucial step toward a solid-state biphoton generation for more than a 100 ns time scale with a subnatural atomic linewidth of a few MHz.
Clustering analysis of learning style on anggana high school student

The inability of students to absorb the knowledge conveyed by the teacher is’nt caused by the inability of understanding and by the teacher which isn’t able to teach too, but because of the mismatch of learning styles between students and teachers, so that students feel uncomfortable in learning to a particular teacher. It also happens in senior high school (SHS/SMAN) 1 Anggana, so it is necessary to do this research, to analyze cluster (group) of student learning style by applying data mining method that is k-Means and Fuzzy C-Means. The purpose was to know the effectiveness of this learning style cluster on the development of absorptive power and improving student achievement. The method used to cluster the learning style with data mining process starts from the data cleaning stage, data selection, data transformation, data mining, pattern evolution, and knowledge development.
Dynamic prediction of hospital admission with medical claim data

Congestive heart failure is one of the most common reasons those aged 65 and over are hospitalized in the United States, which has caused a considerable economic burden. The precise prediction of hospitalization caused by congestive heart failure in the near future could prevent possible hospitalization, optimize the medical resources, and better meet the healthcare needs of patients. To fully utilize the monthly-updated claim feed data released by The Centers for Medicare and Medicaid Services (CMS), we present a dynamic random survival forest model adapted for periodically updated data to predict the risk of adverse events. We apply our model to dynamically predict the risk of hospital admission among patients with congestive heart failure identified using the Accountable Care Organization Operational System Claim and Claim Line Feed data from Feb 2014 to Sep 2015. We benchmark the proposed model with two commonly used models in medical application literature: the cox proportional model and logistic regression model with L-1 norm penalty. Results show that our model has high Area-Under-the-ROC-Curve across time points and C-statistics. In addition to the high performance, it provides measures of variable importance and individual-level instant risk. We present an efficient model adapted for periodically updated data such as the monthly updated claim feed data released by CMS to predict the risk of hospitalization. In addition to processing big-volume periodically updated stream-like data, our model can capture event onset information and time-to-event information, incorporate time-varying features, provide insights of variable importance and have good prediction power. To the best of our knowledge, it is the first work combining sliding window technique with the random survival forest model. The model achieves remarkable performance and could be easily deployed to monitor patients in real time.
Neural Networks: A Methodology for Modeling and Control Design of Dynamical Systems

This chapter presents different approaches where neural networks (NNs) have been successfully employed for the modeling and control of nonlinear dynamical systems. NNs have allowed to deal with complex and nonlinear systems, which are assumed to be uncertain, disturbed, unknown, or difficult to model; therefore, the resulting neural models become robust schemes against such uncertainties. The flexibilities of the NN are exploited and highlighted in the designs with the aim of proposing well-established models, which guarantee the system properties and with mathematical structures such that convenient control methodologies can be synthesized. This chapter covers both the discrete-time and the continuous-time framework for modeling and control of uncertain systems, where recurrent NNs (RNN) are used to develop the artificial neural models, which a posteriori are employed for the design of two neural control schemes (sliding mode block control and nonlinear optimal control). Finally, the applicability of the neural methodology is illustrated through two practical examples.
Common Paragraph Writing Errors Made by Saudi EFL Students: Error Analysis

Relying on Error Analysis, this article presents an empirical study that scrutinizes comprehensively the types and frequencies of errors in paragraph compositions made by 80 EFL students, Preparatory Year Deanship, at Prince Sattam bin Abdulaziz University. In addition, it aims at exploring the differences between male and female students in regards to these errors as well as investigating whether or not native and non-native English teachers affect significantly the types and frequencies of errors committed in writing. After analyzing 80 paragraphs at the sentence-level and the paragraph-level based on a particular rubric, the results reveal that the 80 students committed 1580 errors in total. At the sentence level, the subjects made 1316 errors in grammar (42.15%), Punctuation (16.14%), spelling (14.81%) and capitalization (10.19%). At the paragraph -level, the participants produced 264 errors illustrated in the following order: Errors in paragraph development (5.13%), Errors in paragraph coherence (4.87%), Errors in paragraph unity (3.80%) and Errors in inconsistency of point of view (2.91%). The results found that except for errors of grammar, there are no statistically significant differences between male and female students at the preparatory Year in Prince Sattam bin Abdulaziz University in the types of errors in paragraph writing. Moreover, the statistical analysis showed that there are statistically significant differences between university students taught by English native-speaker instructors and university students taught by non-native English instructors in some types of errors in paragraph writing i.e. errors in grammar, spelling and punctuation.
Self-dual additive $ \mathbb{F}_4 $-codes of lengths up to 40 represented by circulant graphs

In this paper, we consider additive circulant graph codes which are self-dual additive $ \mathbb{F}_4 $-codes. We classify all additive circulant graph codes of length $ n = 30, 31 $ and $ 34 \le n \le 40 $ having the largest minimum weight. We also classify bordered circulant graph codes of lengths up to 40 having the largest minimum weight.
Automating Privacy Compliance Using Policy Integrated Blockchain

An essential requirement of any information management system is to protect data and resources against breach or improper modifications, while at the same time ensuring data access to legitimate users. Systems handling personal data are mandated to track its flow to comply with data protection regulations. We have built a novel framework that integrates semantically rich data privacy knowledge graph with Hyperledger Fabric blockchain technology, to develop an automated access-control and audit mechanism that enforces users' data privacy policies while sharing their data with third parties. Our blockchain based data-sharing solution addresses two of the most critical challenges: transaction verification and permissioned data obfuscation. Our solution ensures accountability for data sharing in the cloud by incorporating a secure and efficient system for End-to-End provenance. In this paper, we describe this framework along with the comprehensive semantically rich knowledge graph that we have developed to capture rules embedded in data privacy policy documents. Our framework can be used by organizations to automate compliance of their Cloud datasets.
A multiseed-based SVM classification technique for training sample reduction

A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets for multiclass classification. The main contributions of the paper are (i) an efficient multiseed technique for selection of seed points from circular/elongated class training samples, (ii) adjacent class pair selection from the set of multiseeds by using the minimum spanning tree, and (iii) extraction of support vectors from class pair seed equivalent regions to manage multiclass classification problems without being computationally expensive. Experimental results on a variety of datasets showed better performance compared to other sample-reducing methods in terms of training and testing time. Traditional support vector machine (SVM) solution suffers from $O(n^{2})$ time complexity, which makes it impractical for very large datasets. Here, multiseed point technique depends on the estimated density of each data, and the order of computation is $O(n$ log $n)$. Using the estimated density, the computational cost of the seed selection algorithm is $O(n)$. So, this is the only burden for reducing the sample. However, reducing the sample takes less time with the proposed algorithm compared to the clustering methods. At the same time, the number of support vectors has been abruptly reduced, which takes less time to find the decision surface. Apart from this, the classification accuracy of the proposed technique is significantly better than other existing sample reduction methods especially for large datasets.
How Adaptive Is an Adaptive Test: Are All Adaptive Tests Adaptive?

The primary distinguishing feature of computerized adaptive testing (CAT) is that sets of items that are administered to examinees are specifically selected for each examinee during the test process. This implies that examinees who respond differently to items on a test will get different sets of test items. However, if an item bank is small, if there are strict content constraints, or if there is strong exposure control, there might not be much adaptation during the test. Examinees who perform differently might get many of the same test items. The research reported here recommends some statistical indicators of how much adaptation is taking place and shows how these indicators vary for different kinds of adaptive test designs. Guidelines are provided for the values of the statistics indicating that a CAT is strongly adaptive.
Clone-Based Encoded Neural Networks to Design Efficient Associative Memories

In this paper, we introduce a neural network (NN) model named clone-based neural network (CbNN) to design associative memories. Neurons in CbNN can be cloned statically or dynamically which allows to increase the number of data that can be stored and retrieved. Thanks to their plasticity, CbNN can handle correlated information more robustly than existing models and thus provides better memory capacity. We experiment this model in encoded neural networks also known as Gripon-Berrou NNs. Numerical simulations demonstrate that memory and recall abilities of CbNN outperform state of the art for the same memory footprint.
Free-Space-Optical Quantum Key Distribution Systems: Challenges and Trends

Nowadays, high security levels are required to transmit critical information for government, private and personal sectors.As a countermeasure, the Quantum Key Distribution systems are the best option in order to protect this information because it provides unconditional security.In addition, increasing the transmission distance is a highlight.Therefore, the Free-Space Optical Quantum Key Distribution systems (FSO-QKD) present an innovative way for sharing secure information between two parties located at ground stations, spacecraft or aircraft.However, these scenarios present several challenges regarding the hardware, protocols and techniques used that must be solved in order to enhance the performance parameters (security level, distance link, final secret key rate, among others) for any QKD system; although, in particular, a high transmission performance is required for both the classical and quantum channels.These issues impose the roadmap and trends in the research, academic and manufacturing sectors around the world.
A Bayesian grouplet transform

In the signal processing literature, wavelet transforms have been widely used for compression, restoration or texture processing. In this sense, grouplet transforms have been proposed to account for the geometrical image regularities. A grouplet transform (basis or frame) is based on an a priori fixed association field that groups image coefficients according to geometrical considerations. In this paper, we propose a method for estimating this association field in a Bayesian way. The resulting association field is therefore adaptive to the processed image content. A hierarchical Bayesian model is proposed and the inference is conducted using a Markov Chain Monte Carlo (MCMC) algorithm. The proposed method is tested on standard images in terms of association field quality and quantitative properties of the obtained wavelet coefficients. Specifically, the proposed method provides coefficients with low correlation level, and for which the highest level of energy is concentrated within the 20% most significant. These promising results confirm the potential of the proposed method for several image processing applications such as compression, denoising or restoration.
English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language.A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper.Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic.To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory.The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.
A multiobjective adaptive approach for the inference of evolutionary relationships in protein-based scenarios

Complex optimization problem solving is a constant issue in a wide range of scientific domains. Robust bioinspired procedures with accurate search capabilities are therefore required to address the challenge that such optimization problems represent. This work explores different design alternatives for the metaheuristic Multiobjective Shuffled Frog-Leaping Algorithm, a novel method that combines parallel searches and swarm-based operators to undertake the processing of complex search spaces. Three variants of the metaheuristic are adopted: a dominance-based approach, an indicator-based alternative, and an adaptive proposal that incorporates both multiobjective strategies (dynamically assigning during the execution more resources to the most successful strategy). The performance of the proposed designs is examined when tackling, as a case study, the inference of ancestral relationships from protein data, using different multiobjective metrics and bio-statistical testing procedures. Experimental results show the additional robustness that the adaptive technique provides to the metaheuristic, allowing its search engine to exploit the most fitting multiobjective approach according to the status of the optimization process.
A leading cyber warfare strategy according to the evolution of cyber technology after the fourth industrial revolution

In February 2018, the US foreign ministry reported that the United States has made massive preparations for a massive cyber-attack on North Korea over the past six months.Although it is focused on the preemptive strike on the North Korea, which is called the "Kofi Operation" of the United States, the Donald Trump administration's first attack on the North Korean will be a cyber-operation, not a military attack, the media said [1].The United States is developing from the concept of defensive cyber operations to the concept of offensive cyber operations for effective cyber warfare of cyber command.The other development countries as well as the United States are shifting the concept of cyber operation from defensive concepts to offensive or preemptive attacks.Recently, developed countries have shifted cyber warfare strategy from a defensive strategy to offensive strategy.If innovative science technologies and cutting-edge information & communication technologies (ICT) are applied to cyber battlefield, cyber-assets cannot be effectively protected by defensive operations anymore.
A Distributed Distribution and Scheduling Algorithm of Educational Resources Based on Vector Space Model

To explore the distributed dispatching and scheduling algorithm of educational resources, based on the vector space model, the user and resource description model is built. In addition, the user interest representation method based on the background and tense vector space model (BTVSM) is proposed, and the distributed architecture based on the scheduling server is used to provide the personalized distribution service to the resource users. At the same time, the task scheduling model and algorithm are analyzed. The task scheduling optimization based on ant colony algorithm is mainly used in the scheduling server, and the simulation experiment is designed to verify the effectiveness of distributed dispatching and task scheduling algorithm for educational resources. The experimental results prove the validity of the application of distributed dispatching and scheduling algorithm based on VSM in specific systems, and it can effectively solve the allocation problem between the resource user group and the distributed dispatching center.
Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization.