Reviewerly/exHarmony huggingface dataset:
Performance of Principles of the Individual Project by Senior High School Students: Normative and Organizational Aspects

The article discusses the regulatory and organizational aspects of the implementation of individual project by senior high school students. This type of activity, according to the federal state educational standard of secondary general education, is mandatory for students in grades 10-11. The goal of an individual project in the form of educational, research and project activities is the improvement of the ULA and the achievement of metasubjective educational results by high school students. At the level of general secondary education, the formation of a project, the methods and methods of its implementation by the senior pupils is possible. In the curriculum should be provided for the implementation of the individual project students. It is carried out by students for one year or two years as part of the study time specifi cally set aside by the curriculum in the part formed by the participants in educational relations. An individual project can serve as an addition to the curriculum of a particular profile.
Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster

This paper presents an algorithm, ParGenFS, for generalizing, or “lifting”, a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing the number of introduced “head subjects” and related errors, the “gaps” and “offshoots”, differently weighted. This leads to a generalization of the topic set in the taxonomy. The usefulness of the method is illustrated on a set of 17685 abstracts of research papers on Data Science published in Springer journals for the past 20 years. We extracted a taxonomy of Data Science from the international Association for Computing Machinery Computing Classification System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection, lift them in the taxonomy, and interpret found head subjects to comment on the tendencies of current research.
Leveraging Vision Reconstruction Pipelines for Satellite Imagery

Reconstructing 3D geometry from satellite imagery is an important topic of research. However, disparities exist between how this 3D reconstruction problem is handled in the remote sensing context and how multi-view reconstruction pipelines have been developed in the computer vision community. In this paper, we explore whether state-of-the-art reconstruction pipelines from the vision community can be applied to the satellite imagery. Along the way, we address several challenges adapting vision-based structure from motion and multi-view stereo methods. We show that vision pipelines can offer competitive speed and accuracy in the satellite context.
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses.

Machine-learning (ML) algorithms or models, especially deep neural networks (DNNs), have shown significant promise in several areas. However, researchers have recently demonstrated that ML algorithms, especially DNNs, are vulnerable to adversarial examples (slightly perturbed samples that cause misclassification). The existence of adversarial examples has hindered the deployment of ML algorithms in safety-critical sectors, such as security. Several defenses for adversarial examples exist in the literature. One of the important classes of defenses are manifold-based defenses, where a sample is ``pulled back into the data manifold before classifying. These defenses rely on the assumption that data lie in a manifold of a lower dimension than the input space. These defenses use a generative model to approximate the input distribution. In this paper, we investigate the following question: do the generative models used in manifold-based defenses need to be topology-aware? We suggest the answer is yes, and we provide theoretical and empirical evidence to support our claim.
Facial Emotion Identification Based on Local Binary Pattern Feature Detector

Objectives:The emotion detection is one of the important fields in computer human interaction and this study plays significant a role for identification facial expression from the images.To identify the single emotion, need a various variability of human shapes such as pose, color, texture, expression, posture and orientation.In this study, we implement Local Binary Pattern (LBP) based filters for identifying the dynamic face textures.And moreover, this approach also provides extension and simplification.Methods/Statistical Analysis: We used built-in FER2013 datasets, the database consisting seven classes (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy).The dataset is divided into three parts testing, validation and training (15% and 70%).The Convolution neural network is trained with feature Descriptor Local Binary Pattern.Findings: The experimental results have demonstrated that local LBP representations are effective in spatial dynamic feature extraction, as they encode the information of image texture configuration while providing local structure patterns.The advantages of our approach include local processing, robustness to monotonic grayscale changes and simple computation.The results show that, the performance LBP based Convolution Neural Network (CNN) model is better than conventional CNN.This research study further helps in image classification and image processing fields.Application/Improvements: It is recommended that LBP should be used for finding the local regions or pattern from the image.The LBP computation and local processing is quite better with robustness and monotonic changes.
Accelerating ElGamal Partial Homomorphic Encryption with GPU Platform for Industrial Internet of Things

Industrial Internet of Things (IIoT) is a paradigm shifting technology used for enhancing the manufacturing process. Under this technology, cloud server is widely used to store sensor data collected through various sensor nodes. The stored data is being used for various analysis and computation, including machine learning and statistics, in hope of finding useful inferences for industrial purpose. However, some of these sensor data and analysis results are sensitive, which should be handled properly to protect the user privacy. In addition, the cloud server environment may not be fully trusted, due to potential cyber security attack by adversaries. Encryption is one of the most straightforward way to protect user privacy, but encrypted sensor data prohibits the cloud server to perform any further analysis. Homomorphic encryption is useful to provide encryption to sensor data, yet allow the third party (cloud server) to perform computation on the encrypted data. However, homomorphic encryption algorithm is usually complex and require a lot of computational effort. In this paper, we propose implementation technique to accelerate the ElGamal partial homomorphic encryption in GPU platform. This implementation allows homomorphic multiplication to be performed on cloud server for IIoT applications at high performance, yet able to protect the user privacy.
Exploration of probable hydrocarbon micro-seepage accumulations in Area-3, Northern Palmyrides, central Syria by using airborne gamma-ray spectrometric technique

Aerial gamma-ray spectrometry technique is used herein as an indirect approach for petroleum prospecting to investigate possible hydrocarbon stratigraphic and structural traps of Area-3, Northern Palmyrides, Syria. This indirect approach is based on the thorium Th normalization technique, oriented towards suppressing the influence of the regional geological signatures. The use of the thorium normalization technique in the study Area-3 allows to detect new subtle micro-seepage anomalous patterns. The detected micro-seepage occurrences are characterized by both low residual potassium values termed as KD (%) and high uranium residual values in relation to potassium, termed as DRAD (%) values. DRAD (%) results from the subtraction of potassium from uranium residual values. Eight probable zones are identified and mapped over the study area grace to the application of the thorium normalization technique, that might indicate a prospective possibility for feasible subsurface hydrocarbon accumulations and oil-bearing pay zones at Area-3.
Traditional Methods of Anomaly Detection

In this chapter, you will learn about traditional methods of anomaly detection. You will also learn how various statistical methods and machine learning algorithms work and how they can be used to detect anomalies and how you can implement anomaly detection using several algorithms.
USING JEOPARDY GAME TO IMPROVE STUDENTS’ VOCABULARY KNOWLEDGE

Learning english vocabulary using jeopardy game technique make the studentsfeel interesting and grasping new words easily. Therefore, the purpose of this researchis to find out whether or not there is significant effect of jeopardy game on grade sevenstudents’ vocabulary knowledge. The sampels were taken using cluster randomsampling technique, with the total number of sampels are 74 students which aredivided into two classes. The location is at MTs Al-Ihsan Bekasi. The research methodadopted was quasi experimental with research design was post-test only control group.To collect data, the students were given instruments in the form of objective, multiplechoice each numbering 30 for both control class and Experimental class. The findingsshowed that the jeopardy class students got mean score 80 higher than non-jeopardyclass students who got 77. It indicates that there is a significant difference in each classand can be seen from the analysis using one way ANOVA that produced (Sig.) valueof 0.002 was smaller than (&lt;) Alpha 0.05, Ho is rejected and HA is accepted. Itshowed tha there is significant effect of jeopardy game on grade seven students’vocabulary knowledge at Mts Al-Ihsan Bekasi in the Academic Year of 2016-2017.
Cryptography in Quantum Computing

Quantum cryptography is an area of intense interest, as quantum computers contain the potential to break many classical encryption algorithms. With so much on the line, it is imperative to find a new quantum encryption method before quantum technology catches up with current cryptography. This study examines one of the very few experiments on encryption that has already been conducted and analyzes the results of the tests run on the IBM Cloud Server. It attempts to recreate the sample experiment and make comprehensive adjustments for a real-world environment. This study also looks at the possible application of quantum public key encryption and the theoretical importance of quantum key distribution.
Faulting in the laboratory

This chapter describes the behaviour of faults in the laboratory. The chapter is organised from small scale to large scale experiments, introducing the reader to general and less general observations of faulting and friction, and showing how these observations are linked to faulting processes occurring in nature. The first section introduces cm-scale friction experiments on gouge materials including the concept of rate-and-state friction, i.e., how velocity affects friction in the quasi-static regime. The following section is devoted to dynamic friction, i.e., observations of friction at high velocity as well as observations of dynamic rupture. The third section discusses the evolution of discrete faults and fault zones in up to meter-scale physical analogue experiments, their dependence on material properties and their significance for the study of large-scale tectonic structures. Finally, the various microstructural features and their possible link to fault stability obtained in the quasi-static regime will be discussed.
Examining the effect of organizational innovation on employee creativity and firm performance: Moderating role of knowledge sharing between employee creativity and employee performance

This study sets out to investigate the influence of organisational innovation (innovation speed, innovation quality and innovation quantity), extrinsic rewards, and intrinsic motivation on employee creativity and firm performance. We advance the body of knowledge on this subject and examined the moderating role of knowledge sharing between employee creativity and employee performance. Data was collected from employees working in Saudi public sector. For empirical findings structural equation modelling (SEM) was applied. The results show that employee creativity was jointly predicted by organisational innovation, extrinsic rewards and intrinsic rewards and explained R2 66.5% variance in employee creativity. Therefore, firm performance was predicted by employee creativity and showed R2 60.5% variance in firm performance. Furthermore, this study confirmed the moderating role of knowledge sharing between employee creativity and employee performance such that the positive relationship between employee creativity and employee performance will be stronger when knowledge sharing is higher.
Realistic Breast Mass Generation Through BIRADS Category

Generating realistic breast masses is a highly important task because the large-size database of annotated breast masses is scarcely available. In this study, a novel realistic breast mass generation framework using the characteristics of the breast mass (i.e. BIRADS category) has been devised. For that purpose, the visual-semantic BIRADS description for characterizing breast masses is embedded into the deep network. The visual-semantic description is encoded together with image features and used to generate the realistic masses according the visual-semantic description. To verify the effectiveness of the proposed method, two public mammogram datasets were used. Qualitative and quantitative experimental results have shown that the realistic breast masses could be generated according to the BIRADS category.
Threshold Changeable Ramp Secret Sharing

Threshold changeable secret sharing studies the problem of changing the thresholds of a secret sharing scheme after the shares of the initial scheme have been distributed to players. We focus on the most studied scenario of dealer-free threshold increase in the absence of secure channels with an outsider adversary. Previous theoretical works in this scenario only consider an unchanged privacy threshold and define optimal threshold changeable secret sharing schemes as ones meeting the bounds in this case. We highlight increasing the privacy threshold as an independent design goal on top of increasing the reconstruction threshold. We prove new bounds for the above threshold increase scenario with respect to a new privacy threshold that is possibly bigger than the initial privacy threshold. We similarly define an optimal threshold changeable secret sharing scheme as one that achieves equality in all these bounds. A trade-off between the new privacy threshold and the required combiner communication complexity is discovered and new optimal schemes for the case when privacy threshold also increases are identified. These theoretical results put our new construction of threshold changeable secret sharing on a firm ground. Our threshold changeable ramp scheme does not need a priori knowledge of the targeted thresholds to design the protocol and allow the conversion into a ramp scheme with arbitrary new reconstruction thresholds while the privacy threshold grows proportionally as the reconstruction threshold grows. Previous such schemes were only known from lattice-based constructions that use a non-standard privacy definition. Our new schemes are statistical secret sharing schemes that guarantee indistinguishability of shares up to the new privacy threshold.
Chinese Named Entity Recognition for Clothing Knowledge Graph Construction

Abstract Clothing knowledge graph is a kind of vertical domain knowledge base constructed for the description of clothing knowledge in the field of textile and apparel. In this paper, based on the limitations of the clothing knowledge graph in the effect of entity extraction, the deep learning model and the statistical model are combined. A Chinese named entity recognition method based on CNN-BiLSTM-CRF is proposed. Firstly, the convolutional neural network(CNN) is used to extract the text features, and the character-level vectors with morphological features of the words are trained. Then the bi-directional long short term memory networks(LSTM) is used to learn the context features, and the vector representation of the context of each word is output. Finally, the conditional random fields(CRF) model is used for self-learning. Get the best tag sequence for the sentence. The method can automatically recognize the text, and does not rely on the artificial feature to obtain the semantic category information. Finally, the experimental data and evaluation methods are introduced. The experimental results show that the Chinese named entity recognition method based on CNN-BiLSTM-CRF is superior to other models in all indicators, indicating the effectiveness of the method.
Prediction of SINS/GPS Navigation Information by ELM Algorithm during GPS outages

Abstract SINS/GPS integrated navigation has received great attention recently due to its impressive navigation accuracy on missile-borne platforms. However, it remains an ongoing challenge to address the problem of GPS signal loss. Furthermore, the limited space and power consumption of a projectile have made it challenging to carry out time-consuming iterative calculations. In this paper, ELM is proposed for the first time to predict the navigation information on the missile-borne platform for its simpler neural network structure and no iterative calculation. Comparing it with the algorithms of BP and GABP, the simulation results reveal that the ELM algorithm is more time-saving with higher prediction accuracy than the others. More importantly, the number of parameters needed to be set is much less than those of the other two algorithms, which is beneficial for its practical application.
Research on Movie Box Office Prediction Model with AHP Method

Based on the Chinese film market, the author analyzes movie box office factors from deferent ways combining with the big data of social media and search index, and use expert survey and Delphi Method to establish the predict indicators system and movie offce box including actors, director, screenwriter, Investment, type, awards, publicity, reputation, publisher, schedule and distribution Mode. And then use AHP method to establish the movie box office prediction model, in combination with the actual data of the film market (from 2012 through 2016 in China) along with the empirical analysis and verification to provide valuable investment reference for film's risk control and movie investment decisions.
Geochemistry of Dalma metavolcanic Suite from Proterozoic Singhbhum Mobile Belt, Eastern India: Implications for Petrogenesis and Tectonic Setting

The Dalma metavolcanic belt is composed of ultramafics at the base, tuffs on top and basalts in-between. It extends for about 200kms east-west in the middle of the Proterozoic Singhbhum Mobile Belt. Ultramafics are chiefly composed of actinolite, hornblende, relic pyroxene and olivine. The main mineral constituents of basalts are actinolite, hornblende, chlorite, epidote, clinozoisite and plagioclase whereas; tuffs are composed of xenocrysts of biotite, quartz, k-feldspar, plagioclase and iron oxides. Ultramafic samples have high MgO (> 21 wt.%) and low SiO2 (< 42 wt.%), Al2O3 (< 10 wt.%) and Na2O+K2O (< 1 wt.%). CaO shows large variation in these samples from 3.91 to 7.77 wt. %. In studied basalt samples SiO2, TiO2, Al2O3, MgO and alkalis (Na2O+K2O) show variation like (45.7–48.3 wt.%), (0.64–0.97 wt.%), (9.3–13.9 wt.%), (8.5–13.6 wt.%) and (0.95–4.64 wt.%) respectively. In studied tuffs the SiO2, TiO2, Al2O3, MgO and alkalis show variation from (48.9–49.8 wt.%), (0.7–0.8 wt.%), (13.6–13.9 wt.%), (8.4–9.1 wt.%) and (2.39–2.85 wt.%) respectively. In general, studied samples show light rare earth element depleted patterns and flat patterns for heavy rare earth elements similar to MORBs on chondrite normalized REE diagrams. The overall enrichment of LILEs (e.g., Rb, K and Th) and depletion of HFSEs (e.g., Nb-Ta trough) on primitive mantle normalized multi-element diagrams are similar to subduction zone or islamd arc basalts. Ratios like Ti/Y, Ti/Zr, Zr/Nb and Ti/V are also similar to those found in arc related volcanic suites. Sm/Yb vs La/Sm diagram indicates generation of melts from the spinel lherzolite mantle source. On tectonic setting discrimination diagrams like Zr vs Zr/Y, Zr vs Ti and Ti vs V majority of studied samples clearly show island arc tectonic affinity.
Mobile augmented reality game-based learning: teacher training using the EduPARK app

With the mobile technology widespread, new functionalities are being integrated in devices, namely augmented reality (AR), which can be used to enhance learning. Combining mobile AR learning with game-based approaches supports new forms of instruction in the outdoors and students’ engagement and motivation for learning are enhanced. However, new practices require teacher training. This is the case of the EduPARK workshop that aims to support teachers in the development of innovative outdoor practices, using mobile devices, AR and games. In this exploratory case study, qualitative and quantitative data were collected from five editions of the EduPARK workshop through a mandatory questionnaire and a voluntary reflexive self-account. Results show that teachers value this training for: i) the access to new educational resources, ii) the practices changing support, iii) knowledge update, although not necessarily in what concerns mobile learning, AR and game-based approaches, and iv) learning about cross-subjects approaches to teaching.
E-learning through Google Hangouts: a tool in the teaching-learning process of EFL

As today’s world becomes a networked society, Information and Communication Technologies (ICT) have become powerful contributors towards the acquisition of a foreign language. The aim of this study is to examine the feasibility of implementing a technology-based program: the English as a Foreign Language (EFL) students’ satisfaction with the program and the perceived benefits in improving their English skills (speaking, listening, reading, and writing). A one-group, pretest-posttest design was used. A six-week technology-based program, with 30 min sessions, was implemented once a week. A total sample size of 20 students enrolled in a one-day advanced EFL course. There was a high satisfaction towards the program (80%) and towards the facilitator (100%). Fifty percent of participants considered the Hangout sessions as very helpful in developing their speaking skill, 75% considered them to be very helpful for their listening skill., and 50% reported the Hangout sessions to be somewhat helpful in developing their reading skill and their writing skill. Even though taking time to work outside of the classroom had its challenges, the virtual environment led to the collaboration between students, who demonstrated high commitment during the sessions, allowing their writing, reading, speaking and listening skills to improve.
A Content-Based Approach for Analysing Cyberbullying on Sina Weibo

Cyberbullying has become a global phenomenon within the heightened use of Information and Communication Technologies. Previous studies have largely explored the detection and prevention of cyberbullying in English, and focus has been on the causes and consequences of cyberbullying among adolescents. Yet research on cyberbullying in Chinese has previously been extremely limited, despite the fact that cyberbullying has become a significant problem in China. With this context in mind, this paper will use Sina Weibo as a case to analyse cyberbullying, including language features and detection. It aims to contribute to the academic understanding of cyberbullying, as well as media regulation and governance. The findings will help expand the field of cyberbullying detection, and refine theories.