Reviewerly/exHarmony huggingface dataset:
Psychological Features of The Formation of Creative Abilities in Preschool Children

This research paper describes the development of the creative abilities of preschool children. There is a gradual adjustment of images over each other; play and children's stories are a direct reflection of the surrounding reality in various forms. The development of creative abilities is closely related to the child's speech development, since a delay in speech development leads to a delay in imagination, which, accordingly, leads to a low level of development of children's creative abilities.   The formation of creative abilities depends on the level of development of verbal imagination, creativity, imagination, variability of thinking, the ability to associate and such indicators as: fluency, originality, flexibility, the degree of remoteness of the idea from the stimulus, emotional expression, artistic and aesthetic elements; the number of images accumulated by children in the process of participation in educational activities and everyday life; from gender differences.
Measurement-induced entanglement phase transitions in variational quantum circuits

Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many-body systems and improve machine learning algorithms using near-term quantum computers. Prominent challenges associated with this family of quantum-classical hybrid algorithms are the control of quantum entanglement and quantum gradients linked to their classical optimization. Known as the barren plateau phenomenon, these quantum gradients may rapidly vanish in the presence of volume-law entanglement growth, which poses a serious obstacle to the practical utility of VQAs. Inspired by recent studies of measurement-induced entanglement transition in random circuits, we investigate the entanglement transition in variational quantum circuits endowed with intermediate projective measurements. Considering the Hamiltonian Variational Ansatz (HVA) for the XXZ model and the Hardware Efficient Ansatz (HEA), we observe a measurement-induced entanglement transition from volume-law to area-law with increasing measurement rate. Moreover, we provide evidence that the transition belongs to the same universality class of random unitary circuits. Importantly, the transition coincides with a "landscape transition" from severe to mild/no barren plateaus in the classical optimization. Our work paves an avenue for greatly improving the trainability of quantum circuits by incorporating intermediate measurement protocols in currently available quantum hardware.
Integrated Flight/Propulsion Optimal Control for DPC Aircraft Based on GPR-PSO

The distributed propulsion configuration (DPC) is a new aircraft design concept proposed for the future and the design causes the tight coupling between aerodynamics, flight and propulsion system and creates the redundancy of control input. Therefore, an integrated optimal control that can improve flight performance is required. In this paper, particle swarm optimization (PSO) based on Gaussian Process Regression (GPR) (GPR-PSO) is proposed to solve the optimization control problem. GPR has obvious advantages in small sample, high dimensional, and non-linear modeling problems. GPR is used to model L/D and control inputs based on sample data. PSO is a global optimization method with high accuracy and fast speed. The model given by GPR is used as the objective function for PSO to search for the optimal solution in the feasible region, and a control allocation strategy can be determined based on the optimization result. Simulation results of example and flight/propulsion integrated optimization control show that GPR-PSO leads better accuracy compared to PSO based on piecewise linear interpolation and their convergence speed is similar.
Hierarchical ensemble learning method in diversified dataset analysis

Abstract The remarkable advances in ensemble machine learning methods have led to a significant analysis in large data, such as random forest algorithms. However, the algorithms only use the current features during the process of learning, which caused the initial upper accuracy’s limit no matter how well the algorithms are. Moreover, the low classification accuracy happened especially when one type of observation’s proportion is much lower than the other types in training datasets. The aim of the present study is to design a hierarchical classifier which try to extract new features by ensemble machine learning regressors and statistical methods inside the whole machine learning process. In stage 1, all the categorical variables will be characterized by random forest algorithm to create a new variable through regression analysis while the numerical variables left will serve as the sample of factor analysis (FA) process to calculate the factors value of each observation. Then, all the features will be learned by random forest classifier in stage 2. Diversified datasets consist of categorical and numerical variables will be used in the method. The experiment results show that the classification accuracy increased by 8.61%. Meanwhile, it also improves the classification accuracy of observations with low proportion in the training dataset significantly.
Research on Blockchain Privacy Protection of Enterprise Internal Control Evaluation in the Big Data Era

Privacy protection and information security are two key issues facing the advanced big data era in the future, especially for the internal control evaluation system of enterprises with rich core data exchange, and blockchain technology provides a variety of privacy protection algorithms. This paper mainly studies the privacy protection of internal control evaluation of enterprises based on blockchain technology and big data, and provides an effective system and platform framework for enterprise managers to carry out internal control work.
Research and Practice of Software Development Technique for Electric Power Application

With the development of business, there always exists a large demand for application software in electric power industry. From a technical point of view, there has been no revolutionary changes in the technical architecture of such software in recent years. On the other hand, application softwares are required to meet the electric power industry standard like the security standard, and pass the security tests. Theoretically, the above two features make the development works of various electric application softwares have a high similarity. But in the practice, the amount of time and personnel spent on software development does not show a significant decrease. To simplify the development of electric application software and improve the efficiency, in this paper, an approach is introduced. First, the requirements specification of an application and its expression are clarified, with the reusable part in the development work separated out based on softwares’ similarities. Then a general application server is implemented, which can be instantiated with business requirements for various application softwares. At last, we build two application servers to see the effectiveness.
Visually Analyzing the Structure and Code Quality of Component-based Web Applications

Monitoring code quality and dependencies is an important task to keep software maintainable. While generally well researched, only little work on visually analyzing code quality of component-based front-end web applications exists that considers the specifics of such software systems. We propose an approach to visualize dependencies and code quality metrics of component-based JavaScript React applications. Our prototype implementation uses a node-link diagram for dependency visualization, tailored to the specific component structure of React applications and enriched with various visual cues. It is linked with different panels to show code quality and exact source code locations. Recommendations on how the quality of the system under analysis can be improved and refactored are provided. We evaluated our prototype in a small user study with four participants and found that it helped in program comprehension tasks and finding refactoring opportunities.
ParaShoot: A Hebrew Question Answering Dataset

NLP research in Hebrew has largely focused on morphology and syntax, where rich annotated datasets in the spirit of Universal Dependencies are available. Semantic datasets, however, are in short supply, hindering crucial advances in the development of NLP technology in Hebrew. In this work, we present ParaShoot, the first question answering dataset in modern Hebrew. The dataset follows the format and crowdsourcing methodology of SQuAD, and contains approximately 3000 annotated examples, similar to other question-answering datasets in low-resource languages. We provide the first baseline results using recently-released BERT-style models for Hebrew, showing that there is significant room for improvement on this task.
Automation of Question-Answer Generation

In this paper, an automatic system is proposed to generate different kinds of questions and answers from the input text. Question answer generation systems have been an interesting field of research for over decades. From generating questions for educational purposes to preparing answers to questions that could be asked in a legal proceeding, the purpose of question answer generation(QAG) systems is to reduce the tedious task of going through large texts. In our system, question-answer pairs from a given input text are generated using linguistic and statistical knowledge of text. Initially, those sentences are identified from the input text on which questions can be framed and in further steps, identified sentences are ranked in an order of importance. Built specifically for assessment purpose, the system generates multiple choices, fill-ups, true-false, binary and wh type questions based on high ranked sentences in the last step. In performance evaluation of the proposed system, it is found that the system is performing well and it is close to state of art research in the standard linguistic approach.
Chapter 11. Computational representation of FrameNet for multilingual natural language generation

Multilingual natural language generation, the process of producing written or spoken utterances in parallel languages from either structured or unstructured representations requires large amounts of syntactic and semantic information to generate an expression that is tailored to the target audience. This information is offered by FrameNet-like resources, which have been developed for a number of languages. In this chapter, we present a computational FrameNet grammar resource for multilingual natural language generation. We compare between English and Swedish framenets to illustrate how these can be unified under a shared computational representation using Grammatical Framework. We demonstrate how the grammar was exploited in two practical multilingual natural language generation applications to facilitate tourist communication and empower museum users with coherent artwork descriptions.
ASMaaS: Automatic Semantic Modeling as a Service

Traditionally the integration of data from multiple sources is done on an ad-hoc basis for each analysis scenario and application. This is an approach that is inflexible, incurs high costs, and leads to “silos” that prevent sharing data across different agencies or tasks. A standard approach to tackling this problem is to design a common ontology and to construct source descriptions which specify mappings between the sources and the ontology. Modeling the semantics of data manually requires huge human cost and expertise, making an automatic method of semantic modeling desired. Automatic semantic model has been gaining attention in data integration [5], federated data query [14] and knowledge graph construction [6]. This paper proposes an service-oriented architecture to create a correct semantic model, including annotating training data, training the machine learning model, and predict an accurate semantic model for new data source. Moreover, a holistic process for automatic semantic modeling is presented. By the usage of ASMaaS, historical semantic annotations for training machine learning model used in automatic semantic modeling can be shared, reducing costs of human resources from users. By specifying a well defined interface, users are able to have access to automatic semantic modeling process at any time, from anywhere. In addition, users must not be concerned with machine learning technologies and pipeline used in automatic semantic modeling, focusing mainly on the business itself.
PENGARUH MODEL PEMBELAJARAN COOPERATIVE LEARNING TIPE EXAMPLE NON EXAMPLE TERHADAP HASIL BELAJAR PADA MATA PELAJARAN EKONOMI KELAS XI SMA NEGERI SEKAMPUNG TAHUN PELAJARAN 2018/2019

As for the problem in the research conducted by researchers, “ there are still many students who have not experienced learning completeness in the learning outcomes of the XI SMA Negeri 1 Sekampung in the academic year 2018/2019”. Based on these problems, the problem statement is, Is there a positive influence on the use of cooperative learning model type example non example on learning outcomes in economic subjects in class XI SMA Negeri 1 Sekampung in the academic yaer 2018/2019 ?The purpose of this study was “ to find out the effect of using cooperative learning models &#x0D; for example non example types on learning outcomes in economic subjects in class XI of SMA Negeri 1 Sekampung in the academic year 2018/2019. Based on the results of the research conducted, the hypothesis is accepted, because the value of tcount&gt; t table can be seen in the appendix list G, at a significance level of 5%, namely 9,50&gt; 1.70. Students who are declared complete with KKM are (72) after being given treatment with the inside outside circle model which is as many as 25 students or with a percentage of 83,33% and those that are not complete are 5 students with a percentage of 16,67%. Thus it shows that the Cooperative Learning Model Type Example Non Example has a positive influence on the economic learning outcomes of class XI students of State High School 1 Sekampung Academic Year 2018/2019.
Optimal Configuration of Alarm for Ship Engine Room Based on Modified Invasive Weed Optimization Algorithm

In the event of an accident when a ship is sailing at sea, the sound of the alarm is the only way to notify people to evacuate. Therefore, the location of the alarm is important. The general method is usually equally distributed in space, but the best performance is still not achieved. This paper proposes an optimal spatial configuration method of modified invasive weed optimization algorithm based on neighbor-optimal diffusion, reverse mapping reproduction, and regeneration mechanism. The lack of diversity in individual updates method of traditional invasive weed algorithm makes it easy to fall into the optimal local solution. Neighbor-optimized diffusion and reverse mapping propagation are introduced to improve the diversity of algorithm updates. In addition, a regeneration mechanism is proposed to avoid falling into the optimal local solution. Finally, it is applied to the optimal spatial configuration problem of cabin alarms to achieve the optimal performance of average loudness.
Optimization Design of Computer Information Management Software under the background of Big Data

With the increasing growth and progress of the big data science, information management software has been widely used, but also highlights the security risks. At present, it is particularly easy to have information theft, information leakage and many other situations. This will have a serious negative impact on the overall system application efficiency and application effect. In the set preprocessing, this paper uses the filling method for the missing values in the collected data, and establishes an effective evaluation mechanism for the missing value filling scheme. By comparing various filling schemes, this paper finally chooses the fitting method based on Gaussian distribution to fill the missing values. Experimental data reflect the usefulness of the proposed method.
Chapter 7. NLP for resource building

We evaluate several lexicon-based and corpus-based methods to automatically induce new lexical units for Swedish FrameNet, and we see that the best-performing setup uses a combination of both types of methods. A particular challenge for Swedish is the absence of a lexical resource such as WordNet; however, we show that the semantic network Saldo, which is organized according to lexicographical principles quite different from those of WordNet, is very useful for our purposes.
A new agent‐based framework combining authentication, access control and user behavior analysis for secure and flexible cloud‐based healthcare environment

Abstract Cloud computing is an emerging paradigm for hosting and delivering Internet services. This technological trend is increasingly attracting individuals and organizations in different disciplines. Healthcare is at the top of fields where cloud becomes widely adopted to provide remote services and real‐time monitoring for patients and medical stakeholders. However, as sensitive health data are sent over insecure cloud networks and stored in the cloud anywhere across the globe, a reliable system meeting security requirements is of utmost importance, particularity in terms of authentication, access control and traceability. In this article, we propose an innovative security‐aware framework typically dedicated for cloud‐assisted healthcare environments. Our framework is mainly based on three efficient mechanisms. First, we introduce an anonymous authentication process based on mobile agents and elliptic curve cryptography to ensure a secure and flexible communication between the cloud and users. The second mechanism designs a fine‐grained data access control model, which beneficially associates the notions of attribute, role, task, constraint, and session. Moreover, we integrate a user behavior analysis performed by mobile agents, which perceive and trace users behaviors to depict potential vulnerabilities and anomalous activities on sensitive resources. The security analysis and the experiments being conducted demonstrate that our contribution is provably secure and shows very promising features.
Seeds of SEED: Efficient Access Pattern Obfuscation for Untrusted Hybrid Memory System

The emerging non-volatile memory (NVM) is considered a well-qualified candidate for main memory due to its competitive performance, high density, and near-zero idle power. However, the low write endurance hinders NVM from being widely used as the main memory. So hybrid memory architecture (HMA), composed of DRAM and NVM, is used to mitigate this limitation by storing frequently written data in DRAM. Meanwhile, the HMA still has security vulnerabilities, such as side-channel attacks, through memory access patterns. Oblivious RAM (ORAM) can eliminate access patterns leakage to prevent such attacks. However, with massive extra write operations for obfuscation, even in HMA, ORAM can significantly reduce the lifetime of NVM. Providing access pattern protection is still a challenge for a system with NVM. This work aims to propose an efficient access pattern obfuscation framework for a hybrid NVM memory system. We use a lightweight data shuffling scheme to obfuscate NVM’s access pattern. We propose a two-level structure to store the data in NVM while the shuffled mapping information is stored in the DRAM with Path ORAM structure to ensure security. The experimental results show that we improve performance 1.67× compared to Path ORAM, and reduce 96% extra writes that can mitigate the endurance problem of NVM.
Activity Monitoring of the Potential COVID’19 Individuals in Quarantine Facility

The rapid spread of Novel CoronaVirus Pandemic 2019 (COVID’19) has severely affected the lives of millions of people around the globe. This effect is not just limited to the widespread of the disease, but it has also altered the ways of industrial operations to the day-to-day lives of individuals. In this paper, we have analyzed the societal impacts of COVID’19 on individuals’ specifically those who are quarantined as a potential COVID’19. To address the problem of stress and anxiety among individuals in quarantine, latest technologies such as Artificial Intelligence (AI), Internet of Things (IoT) and Data Analytics are utilized to propose a solution based on activity monitoring of individuals in quarantine. A smartphone based accelerometer sensor is used for collecting activity data, and to classify between activities, we have used latest deep learning technology i.e. Convolutional Neural Networks (CNN). The proposed solution achieved highest accuracy up 95% on validation data.
Physical Layer Authentication Based on Gaussian Mixture Model Under Unknown Number of Attackers

Message authentication based on wireless physical layer channel information has gained significant attention in recent years. In existing studies, there are several channel based authentication methods to deal with the single attacker scenario. However, in the real wireless environment, there may be several attackers and we do not know the exact number of the attackers. To solve the physical layer authentication problem in such a multi-attackers scenario, we propose a variational Bayesian algorithm based authentication scheme using Gaussian mixture model. We show that even without having a complete prior knowledge and the number of the attackers, our algorithm can identify the received messages to determine whether they are from the legitimate transmitter or the attackers. We experimentally demonstrate the performance of our proposed method and show that the variational Bayesian algorithm has a low miss detection rate.
PapyGreek Treebanks: A Dataset of Linguistically Annotated Greek Documentary Papyri

The PapyGreek Treebanks dataset contains documentary texts written in Postclassical Greek (ca. 300 BCE–700 CE), morphosyntactically annotated according to Dependency Grammar. The source of the texts is the Duke Databank of Documentary Papyri (DDbDP), which preserves the modern editorial treatment of the documents in TEI Epidoc XML encoding. Aiming to expose linguistic variation in the DDbDP, we have annotated two versions of a selection of documents: the plain transcription and an editorially corrected version. The dataset also comprises metadata about the documents’ dating and provenance, text type, and the persons involved. Furthermore, it facilitates linguistic research on these texts.
Machine Learning for Generic Energy Models of High Performance Computing Resources

This article presents an study of the generalization capabilities of forecasting techniques of empirical energy consumption models of high performance computing resources. This is a relevant subject, considering the large energy utilization of modern supercomputing facilities. Different energy models are built, considering several forecasting techniques and using information from the execution of a benchmark over different hardware. A cross-evaluation is performed and the training information of each model is gradually extended with information about other hardware. Each model is analyzed to evaluate how new information impacts on the prediction capabilities. The main results indicate that neural network approaches achieve the highest quality results when the training data of the models is expanded with minimal information from new scenarios.