Reviewerly/exHarmony huggingface dataset:
Non-word Attributes’ Efficiency in Text Mining Authorship Prediction

Abstract Literature scripts can be compared to paintings, in an artistic way as well as in the perspective of financial value, whereas the value of these scripts rise and fall depending on their author’s popularity. Authors’ scripts represent a specific style of writing that can be measured and compared using a text mining field called Stylometric. Stylometric analysis depends on some features called authorship attributes, and these attributes or features can be used in special algorithms and methods to reach that aim. Generally, each method selected in the Stylometric field uses a variety of attributes to reach higher prediction accuracy. The aim of this research is to improve the accuracy of authorship prediction in literary works based on the artistic writing style of the authors. To achieve that, a new set of attributes will be used with the Stylometric Authorship Balanced Attribution method, which was chosen in this research among several other machine language methods because of its delicateness in authorship prediction projects. The attributes that have been used by most of the researchers were word frequencies (single word, pair of words, or trio of words), which led to some prediction mistakes. In this research, a new set of attributes is used to decrease these mistakes. These proposed non-word attributes are named sentence length, special characters, and punctuation symbols. The results obtained by using these proposed attributes were excellent.
Web Service Ranking and Selection Based on QoS

With the quick evaluation of Web services on the web, we find that quality of Web services plays a significant role for the selection of Web services. When a user has a list of functionally match services then service selection decisions are often made based on the non-functional attributes value such as reliability, availability, price, and rating. and the values of these non-functional attributes of the Web services are given by the user. Different decision strategies may be followed by different users for selection of Web service. In this paper, we discuss the different approaches introduced by many authors for selection of Web services based on QoS and the issues in the existing work after that we proposed a selection and ranking system based on multiple decision strategies.
Bayesian mixture models and their Big Data implementations with application to invasive species presence-only data

Due to their conceptual simplicity and flexibility, non-parametric mixture models are widely used to identify latent clusters in data. However, when it comes to Big Data, such as Landsat imagery, such model fitting is computationally prohibitive. To overcome this issue, we fit Bayesian non-parametric models to pre-smoothed data, thereby reducing the computational time from days to minutes, while disregarding little of the useful information. Tree based clustering is used to partition the clusters into smaller and smaller clusters in order to identify clusters of high, medium and low interest. The tree-based clustering method is applied to Landsat images from the Brisbane region, which were the actual sources of motivation for development of the method. The images are taken as a part of the red imported fire-ant eradication program that was launched in September 2001 and which is funded by all Australian states and territories, along with the federal government. To satisfy budgetary constraints, modelling is performed to estimate the risk of fire-ant incursion in each cluster so that the eradication program focuses on high risk clusters. The likelihood of containment is successfully derived by combining the fieldwork survey data with the results obtained from the proposed method.
Integrated Detection and Tracking for ADAS Using Deep Neural Network

The recent advancements in computer vision technology have ensured that it has an increasingly important position in intelligent transportation. This paper proposes an integral system, including object detection and tracking, to recognize multiple objects in dynamic and complex real-world scenes. A backbone network of the single shot multi-box detector (SSD) is implemented using an improved SqueezeNet for performance improvement. The object detector is followed by an online object tracker that fuses multiple information features, including the appearance feature extracted by CNNs, motion information, and shape information. Both the detector and tracker can well balance accuracy and processing time. The proposed system shows acceptable performance, especially the detector demonstrates the best performance among real-time models on the KITTI test benchmark.
Experimental learning of quantum states

The exponential scaling in sample complexity which characterizes quantum tomography can be circumvented using a computational learning theory approach, reducing it to a linear one. Here we experimentally demonstrate this linear scaling in optical systems with up to 6 qubits.
Learning in Twitter Streams with 280 Character Tweets

Social networks are a thriving source of information and applications are pervasive. Twitter has recently experienced a significant change in its essence with the doubling of the number of maximum allowed characters from 140 to 280. In this work we study the changes that come from such modification when learning systems are in place. Results on real datasets of both settings show that transferring models between both scenarios may need special treatment, as bigger tweets are harder to classify, making such dynamic environments even more challenging.
Analysis of finite fluctuations as an approach to mathematical remodeling

The paper introduces the interpretation of Analysis of Finite Fluctuations as an approach to Mathematical Remodeling. Fitting based on a given multivariable function a new function, connecting the response fluctuation with the fluctuations of its arguments, refers to Mathematical Remodeling problems and is a standard problem in Mathematical Analysis, which is usually solved approximately under the assumption of smallness of fluctuations. Some important applications have identified an importance of mathematical problems to represent finite (generally speaking, not small) fluctuations of a function (response) via finite fluctuations of its arguments. The way to solve the problem in this current formulation comes from classical Mathematical Analysis in the form of Lagrange and Bonnet theorems, operating with finite fluctuations.
A New Fair Identity Based Encryption Scheme

In 1984, Shamir presented the first Identity Based cryptography scheme resolving the public key sharing using signatures. Identity Based Encryption (IBE) remained an open problem for many years. Boneh and Franklin or Cocks presented some IBE schemes. IBE scheme was improved by Malek with a sender authentication in addition to the encryption. We improve this solution by solving the key escrow problem. The trusted center has access on a partial private key in our solution. This method allows to guarantee the data confidentiality between the sender and the recipient. Even if an attacker retrieves the master key of the trusted center, the data confidentiality is guaranteed for the previous and next exchanges.
From behaviour to design: implications for artifact ecologies as shared spaces for design activities

Researchers are increasingly exploring collaborative behaviour in complex socio-technical systems through in-the-wild investigations to understand, evaluate and technology. The space configuration and tools available in such activities are crucial for the successful collaboration of a group. This work offers an in-the-wild examination of six groups tackling a design project working in an artifact ecology, a space rich in physical and digital artifacts. We delve into the physical and digital space of each of the groups during a 3-month duration to obtain a rich understanding of their collaborative activities. The aim of this work is two-fold; provide summative narrations of each one of the five models of DiCoT to extract design implications and evaluate the usefulness of DiCoT as an analytical tool for understanding artifact ecologies. Through a rich dataset – interviews, focus groups, reflective diaries, online interactions, and video recordings – we construct a summative description of the group behaviour based on the methodological framework of Distributed Cognition for Teamwork. Drawing on these narrations, we provide design implications on the use of an artifact ecology as a shared space for design activities. Both outcomes are then used to evaluate the usefulness of DiCoT as an analytical tool for artifact implications.
Enhanced Face Recognition System

Image processing has enormous applications and bio-metrics is one of them that has become a focal point for researchers, as well as for developers. The most common application of bio-metrics is the face analysis. The face analysis is an efficient method to detect and verify the faces of people. In this research article we have the proposed techniques are CRC and KNN. Generally, CRC (Collaboration representation based classification) relies on the collaboration among various classes to represent an image sample. KNN (K-Nearest Neighbor) it is a category of classification approach that utilized to access regression purposes. The experiment is performed on the Yale database and the results are acquired from the simulation tool MATLAB. The performance parameters are accurate, processing time, random noise and random occlusion. A comparison of performance is described and it is proven that the proposed method results give the enhancement in the overall performance of face recognition and accuracy value is 99%.
A Novel Performance Enhancing Task Scheduling Algorithm for Cloud-Based E-Health Environment

The fast-growing internet services have led to the rapid development of storing, retrieving and processing health-related documents from a public cloud. In such a scenario, the performance of cloud services offered is not guaranteed, since it depends on efficient resource scheduling, network bandwidth, etc. The trade-off which lies between the cost and the QoS is that the cost should be variably low on achieving high QoS. This can be done by performance optimization. In order to optimize the performance, a novel task scheduling algorithm is proposed in this article. The main advantage of this proposed scheduling algorithm is to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost. The proposed work is simulated in Aneka and shows better performance compared to existing paradigms.
Theme-Based Partitioning Approach to Decision Tree: An Extended Experimental Analysis

Decision tree is a well-established technique for classification in data mining and machine learning. Induction of a decision tree using high-dimensional dataset may lower the performance of the decision tree in terms of classification rate and stability. Vertical partitioning is a novel paradigm to avoid these issues; it divides the features of a dataset into subsets and creates a subset-based classifier ensemble. In our previous work, we proposed a theme-based decision tree classifier ensemble using vertical partitioning for teacher recruitment modelling. In this paper, we extend our previous work in terms of exhaustive experimental analysis to address both high-dimensionality and instability issues of decision tree using five standard datasets. The performance of the theme-based method is evaluated using classification rate, standard deviation and misclassification rate. Our experimental analysis confirms the superiority of the theme-based approach over traditional decision tree approaches.
Comparative Analysis of Different Machine Learning Algorithms in Classification of Suitability of Renewable Energy Resource

Renewable energies are one of the most important energy resources in this modern era not only due to deficiency of other energy resources but also because they are friendly to the environment. Their efficient utilization must be implemented to gain the most out of them. In this paper, a comparative analysis of the performance of different machine learning algorithms is presented for finding the suitable type of renewable resource for a location. A classification model of the best performing algorithm is implemented on the google earth engine, and their results are discussed.
How to Understand the Basic Graphic Analysis Method

This paper aims to apply the understanding model to understand the Basic Graphic Analysis Method. The method steps are as follows: 1. Apply model A to understand its old terms, such as: Graphic, Natural and Symbolic Language; 2. Apply model B, to understand new terms, such as: the Basic Graphic Analysis Method and the Elementary Periodic Table as the set of basic graphics; 3. Apply model C, to understand the combination or expressions through following steps: step 1, decompose the graphic, natural and symbolic language contained in the geometric language in response to the actual problems on different cognitive levels of geometric language; step 2, analyze the visibility of graphic objects; step 3, deductive construct of graphic features. The result is to understand the Basic Graphic Analysis Method based on the recognition, analysis and application of basic graphs. The significance lies in the usage of the Elementary Periodic Table in the field as the set of basic graphics, to deconstruct any geometry problem and simplify the process, so that the geometry problem understanding could be more standardized, concise and traceable. Both human’s cognitive structure and machine’s information processing levels by the geometry problem could be therefore improved.
BEST PAPER AT SIGCSE 2019 IN THE CURRICULUM INITIATIVES TRACK

other Share on BEST PAPER AT SIGCSE 2019 IN THE CURRICULUM INITIATIVES TRACK: ')DROP TABLE textbooks;--: an argument for SQL injection coverage in database textbooks Authors: Cynthia Taylor Oberlin College Oberlin CollegeView Profile , Saheel Sakharka University of Illinois at Chicago University of Illinois at ChicagoView Profile Authors Info & Claims ACM InroadsVolume 10Issue 2June 2019 pp 58–64https://doi.org/10.1145/3324897Published:25 April 2019Publication History 0citation163DownloadsMetricsTotal Citations0Total Downloads163Last 12 Months12Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Local Experts Finding Across Multiple Social Networks

The local experts finding, which aims to identify a set of k people with specialized knowledge around a particular location, has become a hot topic along with the popularity of social networks, such as Twitter, Facebook. Local experts are important for many applications, such as answering local information queries, personalized recommendation. In many real-world applications, complete social information should be collected from multiple social networks, in which people usually participate in and active. However, previous approaches of local experts finding mostly focus on a single social network. In this paper, as far as we know, we are the first to study the local experts finding problem across multiple large social networks. Specifically, we want to identify a set of k people with the highest score, where the score of a person is a combination of local authority and topic knowledge of the person. To efficiently tackle this problem, we propose a novel framework, KTMSNs (knowledge transfer across multiple social networks). KTMSNs consists of two steps. Firstly, given a person over multiple social networks, we calculate the local authority and the topic knowledge, respectively. We propose a social topology-aware inverted index to speed up the calculation of the two values. Secondly, we propose a skyline-based strategy to combine the two values for obtaining the score of a person. Experimental studies on real social network datasets demonstrate the efficiency and effectiveness of our proposed approach.
Automatic Analog Pointer Instruments Reading Using Panel Detection and Landmark Localization Networks

Analog pointer instruments are widely used in industrial automation. However, reading their values by human is time and labour consuming. Most current pointer reading algorithms lack universality and can not handle complex and varying application scenarios. Therefore, this paper proposes a general pointer instrument reading scheme based on CNN. Our main contributions are two folds. First, we combine two sub-networks into one backbone, which has shortened the inference time. Second, we propose a light-weighted sampling decoder, which makes our scheme achieve a higher accuracy. Comparing to other algorithms, our scheme can deal with the case in which the instrument is too difficult to read, e.g., there is more than one similar pointer in one panel. Also, our scheme only needs less than 100 ms on a typical laptop for a 512 $$\times $$ 512 image. It is fast enough and can satisfy most requirements in industrial automation.
ggparliament: A ggplot2 extension for parliament plots in R

ggparliament is a R (R Core Team, 2018) package for visualizing legislative chambers.It allows for easy visualization of the party composition of a legislative body or for plotting election results using syntax from the tidyverse, a set of packages for cleaning, modelling, and visualizing data (Wickham, 2017).The package is one of the first ggplot2 (Wickham, 2016) extensions for visualizing political data.It is considerably more detailed than most visualization tools for graphing legislatures in that it is designed to handle many different legislatures irrespective of size, number of parties, or characteristics of legislatures.Legislature visualizations are also known as parliament plots or parliament charts.
UNIVERSITY LIBRARY IN THE DIGITAL AGE: INVITATION TO DISCUSSION

The challenges of the digital revolution need critical rethinking the usual functions of the university libraries and to establish what characteristics it must have in order to adapt effectively to the new technological situation. The article analyzes the reasons for the frequent lack of demand by researchers of digitized textual heritage and presents the idea of a problem-thematic online resource “Reconstructing the Reader: a Rare Book Collection in the Regional Cultural Landscape” developed by the Laboratory for Library and Communicative Studies (Tomsk State University). Conceptually, its implementation aims at overcoming the positivistic approach to the library as a “repository of information on paper and electronic medium”, and in practice — at identifying the ways to integrate the university libraries into a new format of scientific knowledge and scholarly communications.
Development of Leadership Skills of University Students in the Educational Process

Development of leadership skills has been an issue of many discussions for a long time. A number of sources in management, psychology, and sociology emphasize the role of a leader for economic sustainability and social well-being. Many scholars have been developing and classifying leadership evaluation models. These models differ in their basic criteria of leadership—professionalism, personal motivation, responsibility, initiative, and cognitive abilities. Development of leadership skills is a long and painstaking process, for which training and educating will play a crucial role. Having analyzed the long-term experience of teaching humanities at a university of technologies, the authors have elicited a significant potential for developing leadership skills within the Daniel Goleman’s concept of emotional intelligence, methodological recommendations on knowledge management by Nonaka (The knowledge-creating company. Harvard Business Press, Boston, 68 p, 2008), and practical recommendations on the development of leaders by Adizes. In addition, the authors have described the principles of methodological support for the processes of creating and developing leadership skills in the educational process.
Supervised Reinforcement Learning via Value Function

Using expert samples to improve the performance of reinforcement learning (RL) algorithms has become one of the focuses of research nowadays. However, in different application scenarios, it is hard to guarantee both the quantity and quality of expert samples, which prohibits the practical application and performance of such algorithms. In this paper, a novel RL decision optimization method is proposed. The proposed method is capable of reducing the dependence on expert samples via incorporating the decision-making evaluation mechanism. By introducing supervised learning (SL), our method optimizes the decision making of the RL algorithm by using demonstrations or expert samples. Experiments are conducted in Pendulum and Puckworld scenarios to test the proposed method, and we use representative algorithms such as deep Q-network (DQN) and Double DQN (DDQN) as benchmarks. The results demonstrate that the method adopted in this paper can effectively improve the decision-making performance of agents even when the expert samples are not available.
Developing eXeLearning application through project-based learning

This study aims to develop web-based teaching materials using eXe to know the students' responses toward the eXeLearning in mathematics learning on linear program topic using the Project-Based Learning. This study employed 4D method (Define-design-develop-disseminate) as the research method and the instruments involved in this study were: teaching media, and questionnaire to get students' responses. The subjects of this study were ten students of class XI of SMA Negeri 1 Pundong and ten students of class XI of SMK Multimedia Buluspesantren who were randomly selected. Descriptive qualities technique was employed in analysing the data. The results show that the students' responses toward the mobile learning is at a level of 74.94%., which indicates mobile learning applications developed in good categories and suitable to be used in learning activity.
Distance-based Global Descriptors for Multi-view Object Recognition

Summary The paper reports on a new multi-view algorithm that combines information from multiple images of a single target object, captured at different distances, to determine the identity of an object. Due to the use of global feature descriptors, the method does not involve image segmentation. The performance of the algorithm has been evaluated on a binary classification problem for a data set consisting of a series of underwater images.