Reviewerly/exHarmony huggingface dataset:
On Quality Assessment of Websites Offering Mobile Applications

The primary objective of this article is to conduct a comparative analysis and evaluation of the quality of the largest and the oldest websites offering mobile applications from clients’ perspective at two points in time in 2014 and five years later, i.e. in 2019. The analysis, similarly to the one performed five years earlier, was limited to three services: Google Play, App Store and Windows Phone. The research problem which the authors tried to solve by means of this comparison consisted in identifying the changes which occurred with regard to clients’ assessment of the selected websites and providing recommendations concerning this area of activity. The research sample, as in the case of the previous study, was limited to the students of two Warsaw universities. The survey was made available to randomly selected didactic groups. After the verification of the survey using a pilot study, the questionnaire has been modified and better adapted to the requirements of the respondents. The comparison has been carried out with the application of a scoring method, and the evaluation was performed with the use of a simplified Likert scale. The obtained findings enabled the authors to draw conclusions concerning increasingly worse scores given for the quality of websites providing applications, which seems to be the result of clients’ growing awareness of the possibilities offered by e-commerce and the functionalities of mobile devices. The tendencies that have been confirmed by the study included Google Play moving towards a leading position, the platform’s sharing the market with App Store and the decreasing role of Windows Phone in this ranking. In particular sections of the article, the authors presented the research problem and the method they used in their study, characterised and justified the selection of the study sample, presented and discussed the findings and, finally, commented on the detailed conclusions drawn from the study.
Covert Cycle Stealing in a Single FIFO Server

Consider a setting where Willie generates a Poisson stream of jobs and routes them to a single server that follows the first-in first-out discipline. Suppose there is an adversary Alice, who desires to receive service without being detected. We ask the question: what is the number of jobs that she can receive covertly, i.e. without being detected by Willie? In the case where both Willie and Alice jobs have exponential service times with respective rates $\mu_1$ and $\mu_2$, we demonstrate a phase-transition when Alice adopts the strategy of inserting a single job probabilistically when the server idles : over $n$ busy periods, she can achieve a covert throughput, measured by the expected number of jobs covertly inserted, of $\mathcal{O}(\sqrt{n})$ when $\mu_1 < 2\mu_2$, $\mathcal{O}(\sqrt{n/\log n})$ when $\mu_1 = 2\mu_2$, and $\mathcal{O}(n^{\mu_2/\mu_1})$ when $\mu_1 > 2\mu_2$. When both Willie and Alice jobs have general service times we establish an upper bound for the number of jobs Alice can execute covertly. This bound is related to the Fisher information. More general insertion policies are also discussed.
A Study on the Effects of Combining Different Features for the Recognition of Handwritten Bangla Characters

Longest run feature Chain code histogram feature Gabor filter Feature combinationsThis paper studies and compares the effectiveness of four different features and their combinations on the recognition accuracy of handwritten Bangla characters.The longest run, chain code histogram, shadow, and Gabor filter based features and their eleven (11) combinations were tested on a standard Bangla database of 15, 000 basic handwritten characters to compare their recognition performances.From the experiments performed, it was observed that the combination of the longest run, chain code histogram, and the shadow features (having feature vector sizes of 20, 20, and 16 respectively) produce the highest recognition accuracy of 84.01%.Furthermore, inclusion of a feature with a large vector size compared to the other features in the combination generally dominates the recognition accuracy.In our case, inclusion of the Gabor filter-based features with a vector size of 1024 in the combination produced a recognition accuracy of 69.71%, which is worse than the accuracy obtained using the other three features.The analysis of the results indicates that the combinations of different feature vectors produce better accuracy as long as the sizes of each individual feature vector is comparable with each other in the combination.
A SEMI- AUTOMATIC DATA SCRAPING METHOD FOR NEWS EVENT DETECTION

A simple Approach for news extraction using web scrapping smart parser is useful system which can be used to get the top stories from the top news sites. As the internet and World Wide Web continue to expand more users, increased rate of crime occurs. Web Scraping tools is used to extract and analyse the information on these web sites to identify traffickers. The proposed system for this project is a web scraper that is able to access and extract data from web sites using a web application as an interface for user interaction. The extracted data is then stored in a database as the web application allows the user to search through and query the saved findings. when the system has been fully implemented, it restructures the data and displays to the user.
Studying the Relationship between Transformational Leadership Style and Organizational Citizenship Behavior in Egyptian Hotels

This study aims to investigate the relationship between transformational leadership style and Organizational Citizenship Behavior (OCB) in five-star hotels in Cairo and Alexandria. To achieve this aim, a questionnaire form was developed and distributed to a random sample of employees in the investigated properties. The data were statistically processed. The findings of field study illustrated a significant relationship between the transformational leadership style and OCB. Also, there was a significant impact of transformational leadership dimensions on organizational citizenship behavior. On the other hand, there were no significant differences among respondents regarding transformational leadership style or organizational citizenship behavior due to respondents’ demographics .
Offline development of active alignment based on empirical virtual environments

Active or sensor guided alignment presents a promising production approach for high-quality optical products and helps to overcome challenges in the tolerance chain. Application fields such as fast-axis collimation increasingly deploy automated productions solutions in order to improve and ensure stable quality even with relatively low production volume. The advantages are offset by the high demand towards the engineering as upfront cost, common to all automated production solutions with integrated evaluation of the system function. In this paper, we present our approach to overcome this problem. We propose the use of virtual environments, which are derived from empirical data generated with minimized effort in early phases of the product development. We will present options for building the necessary dataset and how our solution can derive an empirical simulation due to the application of artificial intelligence. Our solution is independent from ray tracing simulations and reflects defects, deviations and tolerances as observed in the actual product samples. This allows developing alignment algorithms offline, without the need for costly machine time and the risk of damage due to manual errors. We will present validation results, which demonstrates the capability to transfer active alignment algorithms from the virtual environment to actual automation equipment. Next to FAC alignment and the high-power laser industry our virtual environment solution is of special interest to application in the field of diffractive optical elements (DOE) and free-form optics, where low volume or changing designs demand adaptable automation solutions.
The Evolution of the Earth-Moon System Based on the Dark Matter Field Fluid Model

The evolution of Earth-Moon system is described by the dark matter field fluid model with a non-Newtonian approach proposed in the Meeting of Division of Particle and Field 2004, American Physical Society. The current behavior of the Earth-Moon system agrees with this model very well and the general pattern of the evolution of the Moon-Earth system described by this model agrees with geological and fossil evidence. The closest distance of the Moon to Earth was about 259000 km at 4.5 billion years ago, which is far beyond the Rocheâs limit. The result suggests that the tidal friction may not be the primary cause for the evolution of the Earth-Moon system. The average dark matter field fluid constant derived from Earth-Moon system data is 4.39 × 10-22 s-1m-1. This model predicts that the Marsâs rotation is also slowing with the angular acceleration rate about -4.38 × 10-22 rad s-2.
Vzaimosviaz' illiustrirovaniia literaturnykh proizvedenii i metodov ikh izucheniia

The main aspects of the interrelation of illustrating the text and methods of their perception are revealed in the article. For a more convincing and full-fledged visualization of a literary work, a comprehensive and in-depth study of episodes of the text is necessary. The purpose of the article is to identify the interrelation between the visual arts and philology – the methods and form of studying a literary work for its further illustration. In connection with this the following tasks were set out: 1) to identify the main methods and ways of a literary work perception; 2) to analyze and compare specific ways of illustration; 3) identify the features of book design creation. The following features of illustration were revealed during the study: its interrelation with the text, functions of replenishment and explanations, the influence of visual perception, integrity and semantic logic of creating drawings for text, taking into account the purpose of illustration (age, scientific character, visibility, etc.), the use of appropriate technology of illustration.
Design and Development of Innovative Software for Student Union

College student union is the link among college students. It is an organization that serves students and unites them. According to the demand, prototype design and implementation process of the student union, this paper analyzes the starting point from the management level (group worker), the service level (member of student union) and the student level, putting forward some methods of student union management and work through the form of "Internet +" and develops a software to meets the demand. It solves some problems existing in the student union of colleges and universities, improving the work efficiency of the student union and make its work more transparent and open.
Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

In the context of intelligent maintenance, spare part prediction business scenario seeks promising return-oninvestment (ROI) by radically diminishing the hidden costs at after-sales customer services.However, the classification of class-imbalanced data with mixed type features at this business scenario is not straightforward.This paper proposes a hybrid classification model that combines C4.5, Apriori algorithms and weighted k-Nearest Neighbor (kNN) adaptations to overcome potential shortcomings observed at the corresponding business scenario.While proposed approach is implemented within CRISP-DM reference model, the experimental results demonstrate that proposed approach doubles the human-level performance at spare part prediction.This highlights a 50% decrease at the average number of customer visits per fault incident and a significant cutting at the relevant sales and distribution costs.According to best runtime configuration analysis, a real-time spare part prediction model has been deployed at the client's SAP system.
VISUAL IMAGERY STRATEGY IN ENCHANCE STUDENTS’ READING COMPREHENSION

The objective of the research were to find out whether or not the use of Visual Imagery Strategy can enhance the students’ reading comprehension at the Eighthof MTs Pondok Pesantren Al-Urwatul Wutsqa Benteng.This research employed a quantitative method that applied one class. The population of this research was the eighth grade students in academic year 2019-2020. There were 253 students spread in eighth classes (VIII.A, VIII.B,VIII.C, VIII.D, VIII.E, VIII.F, VIII.G, and VIII.H) and each class consist appropriately 27-37 students. The researcher took simple random and chose VIII.C as the representative and the number of total sample is 23 students.The data of the research were collected by using one kinds of instruments, namely reading comprehension test. Reading test was used to obtain data of the students’ reading comprehension ability in Reading Comprehension by using visual Imagery strategy.The result of data analysis showed that there was significant difference between the students’ score after they were taught by using Visual Imagery Strategy and before they were taught by Visual Imagery Strategy. It was proved by the mean score of the post-test which was higher than the mean score of pre-test (62.04>53.86). Furthermore, the result of the p-value (0.004) was lower than level of significant (α = 0.05); which means that H1 was accepted. Based on data analysis, the research concluded that: the use of Visual Imagery Strategy effective in improves students’ reading comprehension.
Analysis of the Economic Effects of Jeju Regional by Hosting the International Seaweed Symposium

Purpose: The purpose of this study is to clarify the significance of the International Seaweed Symposium held in Jeju Island and to calculate the ramifications of the community in Jeju in the context of quantitative and qualitative effect. Through this, it aims to have initiatives in the marine, environment, and biodiversity related industries. Research design, data and methodology: First of all, the detailed measurement items of qualitative effect were searched and classified into quantitative effect and qualitative effect, and secondly, the relative importance between quantitative effect and qualitative effect was measured. Then, the importance between detailed measurement items within the qualitative effect was calculated and based on the result, the qualitative effect was quantified based on the quantitative effect. In order to reflect the opinions of experts on qualitative effects as much as possible, an expert survey was conducted. In this study, Delphi and AHP (Analytic Hierarchy Process) methodologies were applied. We also analyzed the short answer items included in the questionnaire using Nvivo 11.0. Results: The quantitative effect (economic effect) of the symposium analyzed in this study was estimated to be about KRW 6.21 billion in production, KRW 62.2 billion in added value, and 34 persons in employment. In qualitative effect, the promotion effect of Jeju area can be estimated to be about 12.7 billion won, and the promotion of Jeju marine industry can be estimated at 10.8 billion won. The combined effect of these quantitative and qualitative effects was KRW 33.3 billion, and the B / C ratio was 6.88, suggesting that the economic ripple effect of supporting the symposium was secured. Conclusions: The implications of holding a symposium in this study suggest the future possibility of the Korean seaweed industry, and it can be expected to develop the industry. In addition, the Jeju Ocean Environment Declaration was proclaimed in front of seaweed experts from around the world. In conclusion, through this, it can be used to verify the validity of local expenses of international events. The results of this study are expected to be used as basic data for judging whether to attract similar international events.
Library: Science Mentorship Program: Library 101: Home

This guide was developed for students in the Science Mentorship program, but is a useful resource for any student who wants to learn more about the library and about library research.
Bancroft LibGuides: Bookends: Digital Resources for Reading: Home

Did you know that you have access to reading materials from the Bancroft School's Garfield and Prouty Libraries, the Boston Public Library (BPL), and your local Public Library? All from home? All for free?!Look here to learn more.
CONDITIONAL ATTRIBUTE OR IDENTITY BASED BROADCAST PROXY RE-ENCRYPTION OF DATA AND ITS APPLICATION TO CLOUD.

Disseminated handling has obtained visionary because of its preference of prodigious putting away and colossus enlisting limits. Ensuring and securing information sharing is basic to cloud applications. Beginning late, remarkable character based imparts delegate re-encryption plans have been proposed to choose the questions and issues. In any case, the IB-BPRE requires a cloud client who needs to offer information to an miscellany of clients to take an interest the get-together shared key reclamation process since Alice's private key is an essential for shared key age. This, regardless, doesn't use the advantage of dispersed reckoning and causes the weight or load for cloud clients. Along these lines, a novel security thought named revocable character based give middle person re-encryption is appeared to address the issue of key waiving in this work. In a RIB-BPRE conspire, a go-between can deny a lot of specialists, named by the mandators, from the re-encryption key generation. The presentation appraisal reveals that the intended arrangement is competent and down to business.
Two-dimensional Comprehensive Meteorological Observation Platform Based on Combination of Timeline and Observation Data Products

The one-dimensional time axis on which the current meteorological observation platform depends is transformed into a two-dimensional coordinate system, i.e. one dimension, multi-source observation instruments, data and products are added. This dimension shows the observation occurring at a certain time node. The multi-source observations at the same time are vertically arranged to be distinguished by icons or colors. In this study, real-time display and rendering of scenes are realized through real-time efficient local multi-level of detail loading and adaptive multi-level caching of large-scale 3D spatial data and the dynamic interaction of multi-source observation information is realized by combining the CPU/GPU graphics hardware accelerated rendering technology of OpenGL+Shader. The platform is upgraded from one dimension of time to two dimensions of time and observation, which greatly expands the selection and change space of independent variables, thus the capacity of dependent variables is also greatly increased. Theoretically, it can accommodate all kinds of observation data and products. Through seamless connection on one page, it can freely schedule, switch and superimpose all kinds of observation information, enabling business researchers to efficiently and intensively obtain comprehensive meteorological observation data products, and carry out analysis and application.
Neural Mesh Refiner for 6-DoF Pose Estimation

How can we effectively utilise the 2D monocular image information for recovering the 6D pose (6-DoF) of the visual objects? Deep learning has shown to be effective for robust and real-time monocular pose estimation. Oftentimes, the network learns to regress the 6-DoF pose using a naive loss function. However, due to a lack of geometrical scene understanding from the directly regressed pose estimation, there are misalignments between the rendered mesh from the 3D object and the 2D instance segmentation result, e.g., bounding boxes and masks prediction. This paper bridges the gap between 2D mask generation and 3D location prediction via a differentiable neural mesh renderer. We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation. By leveraging geometry, we demonstrate that our technique significantly improves direct regression performance on the difficult task of translation estimation and achieve the state of the art results on Peking University/Baidu - Autonomous Driving dataset and the ApolloScape 3D Car Instance dataset. The code can be found at \url{https://bit.ly/2IRihfU}.
NETWORKING CASE STUDY IN STEM EDUCATION - IP FRAGMENTATION

M. Mikac1, M. Horvatić2, V. Mikac2 1Inter-biz, Informatic Services (CROATIA) 2University North (CROATIA)
A classical attack on the coherent one way protocol for quantum key distribution

We propose a way to retrieve the secure key generated by the coherent one way protocol without reading the information transmitted on the quantum channel.
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme

Sentiment Analysis is a branch of Affective Computing usually considered a binary classification task. In this line of reasoning, Sentiment Analysis can be applied in several contexts to classify the attitude expressed in text samples, for example, movie reviews, sarcasm, among others. A common approach to represent text samples is the use of the Vector Space Model to compute numerical feature vectors consisting of the weight of terms. The most popular term weighting scheme is TF-IDF (Term Frequency - Inverse Document Frequency). It is an Unsupervised Weighting Scheme (UWS) since it does not consider the class information in the weighting of terms. Apart from that, there are Supervised Weighting Schemes (SWS), which consider the class information on term weighting calculation. Several SWS have been recently proposed, demonstrating better results than TF-IDF. In this scenario, this work presents a comparative study on different term weighting schemes and proposes a novel supervised term weighting scheme, named as TF-IDFC-RF (Term Frequency - Inverse Document Frequency in Class - Relevance Frequency). The effectiveness of TF-IDFC-RF is validated with SVM (Support Vector Machine) and NB (Naive Bayes) classifiers on four commonly used Sentiment Analysis datasets. TF-IDFC-RF shows promising results, outperforming all other weighting schemes on two datasets.
ICSAC: Towards Outliers Rejection and Multi-Model Identification in Keypoint-Based Matching of Partial Near-Duplicates

In this paper, we propose a novel modification to the Random Sampling Consensus (RANSAC) algorithm, called Iterative Comprehensive Sampling Consensus (ICSAC), enabling it to identify more than one transformation between images containing multiple matching partial near-duplicates (sub-images, objects), within a sufficient level of accuracy. The proposed method makes use of the k-means clustering algorithm in order to locate consensus in the parameter space of the transformation matrix. The method is developed primarily for outlier rejection after the feature matching step, where a geometric consistency assumption is enforced on all matching pairs. The method was verified against a variety of diversified images and demonstrates promising results and a clear ability of outlier rejection and accurate localization of multiple transformations within the same pair of matched images.