Reviewerly/exHarmony huggingface dataset:
Developing English For Front Office Material for XII Grade Students at SMK N 2 Singaraja

The aim of this study is to develop materials for English for front office use by the XI grade students in first semester at SMK N 2 Singaraja. This study is to develop as supplementary materials based on the student’s need analysis using the theory suggested by Hutchinson and Waters (1987). The subjects of this research included one the front office teacher, 40 students of the school and one of the front office staff who worked at Dash Hotel in Seminyak, Bali. Design and development method (D&amp;D) by Richey &amp; Klein (2017) were applied in this study. A questionnaires and a checklist were used as the instruments in collecting the obtained data of the study. Three steps which include: 1) Design (Conducting Needs Analysis), 2) Develop (Developing the Product), and 3) Evaluation (Judging the quality of the product) were past in in the materials development. The findings of this study show that there were 5 topics needed to be developed which include: (1) Handling incoming calls (2) Handling of telephone calls (3) Reservation process (4) Handling individual reservations (5) Handling group reservations to be develop. Each of which will be developed into a units which consist of input, content focus, language focus, and task. The design materials was categories as a very high validity, therefore it is suggested to be used as the supplementary materials for the school.
An Aerial Image Stitching Algorithm Based on Long-distance Features

Aerial image stitching is very important in obtaining UAV information. The Speeded Up Robust Features Algorithm (SURF) is an image matching method with high robustness. However, the SURF algorithm subjects to the problems of inexact edge positioning and low matching accuracy. In order to obtain high-precision aerial stitching images with high efficiency, an aerial image stitching algorithm based on long-distance features is proposed in this paper. First, the Canny-SURF algorithm is used for feature detection. After that, the Long-Fast Retina Keypoint (L-FREAK) binary symbol is used to describe and match the feature points. Finally, the Random Sample Consensus Algorithm (RANSAC) is used to compute the projection transformation model, and the weighted average fusion algorithm is used to fuse the pixels. Experimental results show that the proposed algorithm is outperform the SIFT, SURF, ORB, and BRISK algorithms. The proposed algorithm has good stitching accuracy, and can stitch aerial images with wide rotation characteristics.
Wavelet Numerical Solutions for a Class of Elliptic Equations with Homogeneous Boundary Conditions

This paper focuses on a method to construct wavelet Riesz bases with homogeneous boundary condition and use them to a kind of second-order elliptic equation. First, we construct the splines on the interval [0,1] and consider their approximation properties. Then we define the wavelet bases and illustrate the condition numbers of stiffness matrices are small and bounded. Finally, several numerical examples show that our approach performs efficiently.
A Bayesian Approach to Linking a Survey and a Census via Small Areas

We predict the finite population proportion of a small area when individual-level data are available from a survey and more extensive household-level (not individual-level) data (covariates but not responses) are available from a census. The census and the survey consist of the same strata and primary sampling units (PSU, or wards) that are matched, but the households are not matched. There are some common covariates at the household level in the survey and the census and these covariates are used to link the households within wards. There are also covariates at the ward level, and the wards are the same in the survey and the census. Using a two-stage procedure, we study the multinomial counts in the sampled households within the wards and a projection method to infer about the non-sampled wards. This is accommodated by a multinomial-Dirichlet–Dirichlet model, a three-stage hierarchical Bayesian model for multinomial counts, as it is necessary to account for heterogeneity among the households. The key theoretical contribution of this paper is to develop a computational algorithm to sample the joint posterior density of the multinomial-Dirichlet–Dirichlet model. Specifically, we obtain samples from the distributions of the proportions for each multinomial cell. The second key contribution is to use two projection procedures (parametric based on the nested error regression model and non-parametric based on iterative re-weighted least squares), on these proportions to link the survey to the census, thereby providing a copy of the census counts. We compare the multinomial-Dirichlet–Dirichlet (heterogeneous) model and the multinomial-Dirichlet (homogeneous) model without household effects via these two projection methods. An example of the second Nepal Living Standards Survey is presented.
Designing a Deep Neural Network Model for Finding Semantic Similarity Between Short Persian Texts Using a Parallel Corpus

Text processing, as one of the main issues in the field of artificial intelligence, has received a lot of attention in recent decades. Numerous methods and algorithms are proposed to address the task of semantic textual similarity which is one of the sub-branches of text processing. Due to the special features of the Persian language and its non-standard writing system, finding semantic similarity is an even more challenging task in Persian. On the other hand, producing a proper corpus that can be used for training a model for finding semantic similarities, is of great importance. In this study, the main purpose is to propose a method for measuring the semantic similarity between short Persian texts. To do so, first, we try to build an appropriate corpus, and then propose an efficient approach based on neural networks. The proposed method involves three steps. The first step is data collection and building a parallel corpus. In the next step, namely the pre-processing step, the data is normalized. Finally, Semantic similarity recognition is done by the neural network using vector representations of the words. The suggested model is built upon the produced corpus made of movie and tv show subtitles containing 35266 sentence pairs. The F-measure of the proposed approach on PAN2016 is 75.98% with 4 tags and 98.87% with 2 tags. We also achieved an F-measure of 98.86% for our model tested on the parallel corpus with 2 tags.
Research on the summer meteorological support method of Yan Liang Airport for flight test

Abstract Based on the demand of the aircraft flight test of wet braking distance, wiper function and high temperature subject flight test on the meteorological conditions. The surface meteorological observations of Yan Liang Airport were used to statistically analysis the characteristics of summer precipitation and high temperature weather. Based on the demand of the flight test management, organization and command department to implement the airborne meteorological radar detection performance flight test, formulate the risk prevention plan of thunderstorm and other strong convective weather to ensure the safety of flight test. The surface meteorological observations of Yan Liang Airport and Doppler weather radar detection data of Xi’an Jing Yang Station were used to statistically analysis the characteristics of summer thunderstorm weather at Yan Liang Airport and airspace. In recent years, it has provided strong technical support to ensure the quality, efficiency, progress and safety of the flight test.
Adaptive Video Streaming Using Dynamic Server Push over HTTP/2

With the increasing popularity of video services, HTTP adaptive streaming (HAS) has become the mainstream technology for media streaming distribution. In traditional HAS over HTTP/1.1, the HAS server responds to each request from the client individually. This process adds additional round-trip time, resulting in underestimation of available bandwidth. As a result, the HAS client chooses a lower bitrate, which reduces network utilization and the user&#x0027;s quality of experience. In recent years, the HTTP/2 protocol has emerged, which allows server to actively push multiple data segments to the client. Pushing multiple segments can reduce the negative impact of network latency on estimating available bandwidth, thereby increasing the user&#x0027;s request bitrate and video quality. However, when the network is unstable, the more video segments that are pushed by the server, the more challenges the client encounters in responding to network fluctuations i n time, causing playback stalling and poor user experience. Therefore, this paper proposes a dynamic server push algorithm over HTTP/2, which chooses a different number of segments for server push according to network fluctuations. For the evaluation results, relative to its benchmarks, the proposed approach improves the average video request bitrate while minimizing the probability of playback stalling.
MoSen: Sensor Network Optimization in Multiple-Occupancy Smart Homes

Smart home solutions increasingly rely on a variety of sensors for behavioral analytics and activity recognition models to provide context-aware applications and personalized care. Optimizing the sensor network is one of the most important approaches to ensure classification accuracy and the system's efficiency. However, the trade-off between the cost and performance is often a challenge in real deployments, particularly for multiple-occupancy smart homes. To aid in accelerating the adoption of practical sensor-based activity recognition technology, we design MoSen, a framework to simulate the interaction dynamics between sensor-based environments and multiple residents. We explore and quantify the trade-offs between the cost of sensor deployments and the expected labeling accuracy in different scenarios with 2-5 residents. By evaluating the factors that affect the performance of any target sensor network, MoSen is able to provide the sensor selection strategy and design metrics for the sensor layout in real deployments. Using our selection strategy in a 5-person scenario as the case study, we demonstrate that MoSen can significantly improve overall system performance without increasing the deployment costs.
FASER: Seismic Phase Identifier for Automated Monitoring

Seismic phase identification classifies the type of seismic wave received at a station based on the waveform (i.e., time series) recorded by a seismometer. Automated phase identification is an integrated component of large scale seismic monitoring applications, including earthquake warning systems and underground explosion monitoring. Accurate, fast, and fine-grained phase identification is instrumental for earthquake location estimation, understanding Earth's crustal and mantle structure for predictive modeling, etc. However, existing operational systems utilize multiple nearby stations for precise identification, which delays response time with added complexity and manual interventions. Moreover, single-station systems mostly perform coarse phase identification. In this paper, we revisit the seismic phase classification as an integrated part of a seismic processing pipeline. We develop a machine-learned model FASER, that takes input from a signal detector and produces phase types as output for a signal associator. The model is a combination of convolutional and long short-term memory networks. Our method identifies finer wave types, including crustal and mantle phases. We conduct comprehensive experiments on real datasets to show that FASER outperforms existing baselines. We evaluate FASER holding out sources and stations across the world to demonstrate consistent performance for novel sources and stations.
Self-supervised Metric Learning in Multi-View Data: A Downstream Task Perspective

AbstractSelf-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is used in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, k-means clustering, and k-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the article. Supplementary materials for this article are available online.Keywords: k-meansk-nearest neighborMetric learningTwo-sample testing Supplementary MaterialsWe provide some extra results and prove all the theorems and relevant lemmas in the online supplementary materials. All analyses for numerical experiments can be found under https://github.com/lakerwsl/SSTMetric-Manuscript-Code.AcknowledgmentsWe thank the editor, associate editor and referees for valuable suggestions.Additional informationFundingThis project is supported by the grant from the National Science Foundation (DMS-2113458).
Towards an Automatic Approach to the Design of A Generic Ontology for Information Security

Ontology modeling plays an important role in broadening the scope of cognitive Artificial Intelligence and Data Science. In this paper, we propose an automatic approach to the design of a generic Information Security ontology. Starting from a comprehensive information security online dictionary, we automatically explore dictionary content to extract security related concepts and their hierarchical relationships. The process is fully automatic and leads to a concept-wide complete ontology. It encompasses web scrapping, concepts classification, pre-processing, archetypes identification, and ontology design. We use classification method and Natural Language Processing techniques to identify and regroup concepts. The obtained ontology is formed by several graph components. Each gives a security concern, which is a concept that is a root in a hierarchical relationship with other concepts. Isolated concepts are called trivial concerns. The ontology contains 2518 concepts and attributes regrouped into 1166 trivial and non-trivial concerns. It is captured in an XML format that can be automatically transformed into a graphML format. These two formats enable the total or partial usage, and the browsing of the ontology. Then, using graph theory terminology, we define the notions of archetypes and concept-attributes. The obtained ontology while it is concept complete, it needs further enrichment with other than hierarchical relationships that can be obtained from security corpuses and information security standards.
Аxiological features of formation of leadership qualities in the process of obtaining higher education

Based on the analyzed literature, it is established that leaders play an important role in the society. The sources have shown that each period of development of the society has its own leaders. This correspondence is due to the fact that leaders have the qualities that the society, social group or team wants to see. The use of methods of analysis of philosophical, psychological and relevant literature, phenomenological, dialectical method, systematization, generalization, hypothetical-deductive modeling made it possible to establish that only some qualities are instinctive and focus on self-preservation, self-realization, etc., but there are qualities that are acquired during the study in a higher education institution, in their combination they form a leader not only in the public, professional, political sphere, but also in life. Taking into account the information period of society development, its openness and uncertainty requires a person to be a leader. Having formed own «I», having mastered universal and professional values, developing leadership qualities, a person enters the whirlpool of professional life, enters a social professional group, with which the person began to identify himself or herself during the period of mastering the profession. Particular attention is paid to student self-government, which, in accordance with current legislation, creates all the conditions for the development of leadership qualities that are provoked by such values as life, people, justice, freedom, responsibility. Focusing on these and other values promotes the development of the following qualities: initiative, creativity, sociability, comradeship, sensitivity, professionalism, erudition, responsibility, ability to predict actions and predict their consequences. Having developed these qualities, the student becomes ready for life’s challenges, able to lead a social group, take responsibility for decisions, and so on. That is, the leader who is being formed today in educational institutions is ready to organize collective action to achieve the goal and master the values that are needed for a social group, team, society.
Active–Passive Fusion Technology Based on Neural Network-Aided Extended Kalman Filter

The information fusion algorithm based on extended Kalman filter is widely used in the field of information fusion because of its relatively simple algorithm and solid theoretical foundation. This paper uses the advantages of neural networks and their advantages in solving nonlinear and time-varying systems to improve the traditional extended Kalman filtering method, improve the speed and accuracy of filtering, and improve the quality of active and passive data fusion.
Corporate Strategy and Firm Performance: Case for the Multinational Banks in Sub Saharan Africa

Various studies have empirically examined the effect of corporate strategy on firm performance but not to the level of this paper’s methodological scope regarding time, geographic, and variable constructs. To achieve external validity of research findings, this paper uses a more modern estimation procedure-the system Generalized Method of Moments GMM, on a panel data (2007-2017) for multinational banks in sub-Saharan Africa. It adopts a more relevant corporate strategy constructs for firms with operations in many environments. The paper investigates the role of corporate strategies: diversification, debt and equity financing on firm’s performance as indicated by return on assets ROA. Key provoking question motivating this inquiry is: Does it profit a firm to have operations in many countries, or raising funds through debt and equity? Such corporate strategies are expected to have positive returns. Findings show that corporate strategy affects the performance of multinational banks. Specifically, unlike debt financing strategy, geographical diversification and equity financing positively affect bank profitability in terms of ROA. The underlying implication for researchers, investors and corporate managers is that corporate decisions regarding where to go for investment, and source of funds, should be well planned if banks are to be successful.
Privacy Preserving Verification Scheme for Cloud Platform Using DML

Abstract Deduplication technology is familiar in cloud-based services. It is used to reduce the space and the bandwidth requirements as a result, it reduces the redundancy and stores only the original copy. Deduplication finds its usage when a group of users stores the same data to the cloud storage service, but by using this method there is an issue regarding the ownership and security, and before getting into this process many users first encrypt their data and then share it with the cloud storage so that there is minimum or no privacy issue found. Recently so many models were released to solve this problem. That was actually done by sharing the same encrypted data for the same data which is shared by each owner. But by following this described method many flaws were found. We present a novel server-side deduplication model for all encrypted data in this paper. By following this model there is a proper control over the delegated data even after the control changes from person to person. So, Random convergent encryption and stable ownership key distribution are used. Through this method, the data theft can be prevented not only to the reward user but also to the old user who previously owned that data.
Knowledge Map and Its Application in University Library

The biggest contribution of the knowledge map is to construct the relationship between knowledge, so that disordered knowledge information is presented to users in an orderly appearance, and the utilization rate of knowledge is improved. Modern information technology has promoted the shift from document services and information services to knowledge services in university library. The introduction of knowledge map into knowledge services is conducive to the effective use of explicit knowledge and tacit knowledge visualization in the collection, and provides users with a full range of knowledge services. Provide guarantee for teaching and scientific research in universities. This article studies the composition, function, classification, and conversion of knowledge map, and points out the specific applications of knowledge map in university library: establishing scientific library knowledge management model, assisting users in automatically searching information resources, and providing cooperative digital references consulting services and providing personalized information recommendation services.
Localization and Identification of Copy-Move Forgery using SIFR and FQRHFM

In an image, if a particular part or region is copied and further pasted in another part of the image, it is a type of image forgery known as region duplication or copy-move. This technique is commonly used to either conceal an object or to overemphasize on a particular object. There are a number of detection methodologies introduced over the years to identify copy-move images. But when the image is hidden in smooth background, it proves to be difficult to identify and localize. A novel method of localization of the forgery has been proposed with the help of FQRHFM (Fast Quanternion Radial Harmonic Fourier Moments) and SIFER (Scale-invariant feature detection with error resilience). The initial step involves splitting the image into segments that can be represented as uniform superpixel and non-overlapping blocks. Based on the extraction of key points, SIFER is used by incorporating color invariance and super pixel content. The next step involves using the extracted image key points to build an image with connected Delaunay triangle such that every triangle is formulated with gradient entropy and FQRHFMs. The last step would be to use the extracted features to match with the triangles and those that do not match will be removed as duplicated regions.