Reviewerly/exHarmony huggingface dataset:
You Are What You Read: The Effect of Corpus and Training Task on Semantic Absorption in Recurrent Neural Architectures

Recurrent neural networks are able to capture semantic meaning within the geometry of the resultant embedding space, but the impact of training corpus and training curriculum on the structure of the learned representations remains poorly understood. This paper sheds some light on the mystery by comparing the performance of a simple recurrent model on three training tasks and two strongly divergent training corpora. The learned representations are compared on tasks including the Semantic Textual Similarity Benchmark, the Stanford Natural Language Inference Corpus, and Google’s Analogical Reasoning Test Set. Results show that context-based training produces the strongest semantic alignment within the embedding space, with reconstruction loss as an interesting close second. Pairwise comparisons of models trained on different corpora show that the choice of corpus also has powerful effects on the learned representations. Most importantly, we observe that the choice of input corpus and training task are not unilaterally independent, but instead interact with each other in interesting ways. This motivates a cautionary position against training neural models by simply throwing as many different training tasks as possible into the mix. Instead, it may be wiser to carefully select only tasks that are compatible with the chosen input corpus, and vice versa.
Improved perturbation technique privacy‐preserving rotation‐based condensation algorithm for privacy preserving in big data stream using Internet of Things

Abstract Several concerns are raised due to the widespread technology of Internet of Things and big data, which possess private and protection of information. Several researchers have analyzed different privacy preserving techniques, which still cannot provide equal stability between the data privacy and the utility and improvement in the scalability and efficiency. Data mining is one of the prominent technologies, which extracts reliable and useful knowledge from vast amount of information. Henceforth, mining of data stream have become a most popular and important research issue. Due to fast growth in the data generation, the mechanism of privacy preserving with high utility and security becomes more necessary. In this research, an improved efficient perturbation method for data stream named privacy‐preserving rotation‐based condensation algorithm with geometric transformation is proposed that delivers high data utility when compared with other existing techniques. This improved method gives high resilience against the attacks during the process of data reconstruction. Simulation result shows that the proposed method can acquire data privacy and improves accuracy during mining of data streams in which the analysis is performed for different datasets in which the proposed technique obtains more than 95% when compared with original dataset.
Pre-Bachelor's Curricular Guidance For Cybersecurity Programs

There is a growing need for a minimum skill set within Cybersecurity Education to ensure graduates are able to meet the job requirements. The ACM CCECC (Committee for Computing Education in Community Colleges) has led creation of a set of guidelines for pre-bachelor's degree cybersecurity programs, called Cyber2yr2020. The Cyber2yr2020 guidelines underwent two rounds of public review, including a draft presentation at ITiCSE'19[1]. This poster presents the final curriculum guidelines, provides perspectives on the use, and highlights cybersecurity programs already aligned with the guidelines.
Accelerating Parallel Applications in Cloud Platforms via Adaptive Time-Slice Control

Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation. Existing methods optimize parallel applications by promoting the priorities of involved VMs. They cannot fully explore the performance of parallel applications, because they ignore the time-slice requirements of different phases of parallel applications. Furthermore, non-parallel applications experience unsatisfied performance because of low scheduling priorities. Given empirical analysis on time-slices of virtual machines (VMs), we find that shortening time-slices can mitigate synchronization overhead which incurs during communication phases, while over-short time-slices cause frequent cache misses in computation phases. Accordingly, we propose an Adaptive Time-slice Control (ATC) mechanism. ATC first detects the phases of parallel applications based on lock latency or cache misses. Then, ATC shortens time-slices during communication phases and prolongs time-slices during computation phases for parallel applications, and sets a uniform time-slice for non-parallel applications. We evaluate ATC using seven well-known benchmarks with 25+ applications. Experiments show that ATC obtains 1.5-75× performance gain for running parallel applications than state-of-the-art solutions, with nearly unaffected impact on non-parallel applications.
Elitist Reinforcement Strategy for Differential Evolution

Differential evolution (DE) is one of powerful optimizers. It has various mutation strategies for solving widely applications. In order to draw on the strong points of different mutation strategy and to offset their weaknesses, the elitist reinforcement strategy is proposed. Thus, in the proposed method, there are more than one vector-group will be involved in DE, the mutation strategy of each vector-group should be different. If one of vector-group cannot find better solution, elitist reinforcement strategy will join a few elitist vectors to replace vectors which with poor performance. The elitist vectors can guide other vectors toward to potential solution space. In order to test proposed method, the CEC 2005 test functions are adopted for experiments. Also, three DE variants are involved for comparison. From the results, it shows that the proposed method performs better than other three DE methods.
Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions

introduction Free AccessIntroduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions Share on Authors: Rossana Damiano Dipartimento di Informatica University of Turin, Turin, Italy Dipartimento di Informatica University of Turin, Turin, ItalyView Profile , Viviana Patti Dipartimento di Informatica University of Turin, Turin, Italy Dipartimento di Informatica University of Turin, Turin, ItalyView Profile , Chloé Clavel LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, France LTCI, Telecom Paris, Institut Polytechnique de Paris, Paris, FranceView Profile , Paolo Rosso Universitat Politècnica de València, València, Spain Universitat Politècnica de València, València, SpainView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 20Issue 2May 2020 Article No.: 8pp 1–5https://doi.org/10.1145/3392334Online:23 May 2020Publication History 0citation285DownloadsMetricsTotal Citations0Total Downloads285Last 12 Months138Last 6 weeks1 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 SiteView all FormatsPDF
Blockchain: Implications of the Impending Token Economy

A growing number of corporations are using blockchain technology in their daily business activities, and the technology is popular with private users as well. However, the technology entails a number of risks that ought to be identified and minimised. Money laundering is a particular issue that has been concerning compliance officials for quite some time. Liechtenstein’s new blockchain act, the ‘Tokens and TT Service Provider Act’ (TVTG), entered into effect on 1 January 2020. The present article discusses transaction systems based on trustworthy technologies (TT Systems) (Government of Liechtenstein 2019: 6) and the developing token economy with regard to potential compliance risks, the TVTG and the amendment to the Liechtenstein Due Diligence Act. In addition, it illustrates how blockchain technology and the token economy could in the near future influence and transform global markets.
An Ambassador for Neural Networks

Neural networks were never a new part of computing, but at one point they needed a dedicated ambassador to move them into computing practice.We should not be surprised to find a tutorial on artificial neural networks in the list of Computer’s most influential articles. Neural networks are now a common subject in computer science, and they have been taught to two or three generations of undergraduates. They would be an obvious topic for a clear and well-organized article, such as the one written by Anil K. Jain, Jianchang Mao, and K.M. Mohiuddin. This article ranks number 30 on on the list of the most influential articles that have been published by Computer. It has 5,128 downloads and 740 citations. Yet, there are several surprises in the article that help us understand both its position in the body of knowledge and the role of Computer in building that body of knowledge. Perhaps the prominent surprise is that the article has never been cited by another article in Computer, the publication that brought it to the field.
Updated index to perform fast regular expression search

Regular expression search is widely used, including in databases. For example, the LIKE operator was included in the SQL standard about thirty years ago. However, the types of indexes commonly used in DBMS are extremely limited for speeding up regular expression search: most of these queries require a full scan of all data. One of the most interesting approaches to developing a specialized index is described in the article [1]. Its authors suggested using a certain subset of substrings of variable - length input data-multigrams-as index keys. In this article, we propose changes to the structure and algorithm for constructing such an index, which allows us to achieve two goals. First, the index becomes applicable to speed up a broader class of queries. Second, the proposed changes made it possible to update the index. We also developed and tested an algorithm for updating the index when inserting new records into the database. This algorithm allows you to get two orders of magnitude lower time for updating the index compared to its complete reconstruction.
Removal of Salt and Pepper noise: A Game Theoretic Approach

Digital images are often corrupted by the salt and pepper noises which eventually deteriorate the quality of images and affect the subsequent processing steps. This paper presents a game theory based approach to remove the salt and pepper noises. A two-player, non-cooperative, non-zero sum 2-in-1 game is designed and implemented at the pixel level to extract features or local information about the image pixels. This game's payoff values are used to detect whether a pixel is noisy or not. Then the noisy pixel is replaced with a proper value, keeping in mind the pattern followed by the non-noisy pixels. Proposed method efficiently removes the noises of density up to 40%, which is commonly observed in digital images. We have applied our method on some standard images by incorporating varied amount of noises and it has been observed that the proposed method outperforms some state-of-the-art methods.
Machine Learning Approach to Predict Facebook Comment Volume

There is an enormous amount of data uploaded to Facebook every day. Therefore, there is an essential need to analyze this data for many purposes. In this paper, a deep analysis on Facebook comment volume prediction have been conducted to demonstrate the effect of the number of comments on marketing. The data used consists of 603,813 observations provided by Facebook. It includes fifty-three input attributes that have been used to predict the number of comments a post is going to have in the next H hours. Moreover, several Machine Learning techniques have been applied to analyze this considerable amount of data. For instance, Stepwise Linear Regression, Gradient Boosting, Neural Network, and Decision Tree models. Findings indicate that Neural Network model outperformed the other models used in previous studies with the smallest error; when Stepwise Linear Regression is used as a variable selection for it. However, Gradient Boosting outperformed other models with a Hits@10 score of 6.7. Additionally, one of the most crucial outcomes of this study is determining the most significant variables. Particularly, it was found that the publication day of the post, number of check-ins, shares or likes on the page, and the number of comments in the last 24 or 48 hours were the most important variables. These variables contribute to garner more comments, which results in more viewers and higher profit for the marketer. The study concludes with some limitations and suggestions for future research.
Dental X-ray Image Enhancement using Evolutionary Algorithms

Dental x-ray radiography is often done with low radiation dosage which results in a slightly dark, low in contrast and noisy x-ray image. The quality of dental radiograph is low due to the amount of x-ray radiation directed is less on purpose to reduce the risk of various health issues. To solve these problems, this paper presents quantitative and qualitative analyses between the three most widely used evolutionary techniques, namely Particle Swarm Optimization (PSO), Cuckoo Search Algorithm (CSA), and Artificial Bee Colony Optimization (ABC). In this research paper, we have evaluated the above-said techniques on periapical dental x-ray images. The quantitative analysis of obtained results is done using quality metrics such as mean signal error (MSE), peak signal to noise ratio (PSNR), and structure similarity index (SSIM). Qualitative analysis was done with the help of a dental expert. The dentist evaluated the quality of results based on the application and diagnostics required in the periapical dental x-ray pathology. Experimental findings show that implemented evolutionary algorithms managed to enhance the pathology better than the original image. Results obtained show that image enhancement achieved using PSO and ABC algorithms is better than CSA algorithm.
Software Estimation Framework for Digital Enhancements and Maintenance Projects

Software enhancements and the maintenance phase is generally the crucial phase of a software application lifecycle. The enhancements and maintenance consume about 20% of the overall software lifecycle effort. Enhancement and maintenance phase of modern digital projects involves many activities such as incident management, application enhancements, generic maintenance, quality improvements such as automation, preventive maintenance, continuous improvement, and such. State-of the-art estimation models and frameworks fall short of factoring all the dynamics involved in the enhancements and maintenance phase. The article proposes a digital project maintenance estimation framework to estimate various activities of a digital maintenance project. The proposed estimation framework provides comprehensive coverage of maintenance activities including incident management, application enhancements, generic maintenance, and quality improvements. The proposed estimation framework was used to predict effort estimate of 5 digital maintenance projects with MMRE of 0.255 and predicted (0.3) of 80%.
Automatic Classification of Woodcuts and Copperplate Engravings

In this paper we introduce two related Bayesian approaches to distinguishing woodcut and copperplate engraving illustrations. Our approach uses simple methods grounded in the characteristics of the techniques to achieve high performance and excellent interpretability. Specifically, we use the distributions of image gradient magnitudes in illustrations to capture differences in the use of lines in each artistic technique, and the output of a probability for each class aides in interpretability. Our experiments demonstrate high accuracy and robustness with respect to parameters for both algorithms.
2015 개정 교육과정에 따른 초등 5학년과 6학년 영어교과서 읽기 지문의 이독성 지수 비교분석

The purpose of this study is to analyze the readability of reading passages used in 5th and 6th grade elementary school English textbooks under the 2015 Revised National Curriculum. For this purpose, all the reading passages of each textbook were calculated with ATOS (Advantage-TASA Open Standard) formula as a readability index. The results of this study indicate that, firstly, the average readability score of 6th grade English textbooks is higher than that of 5th grade ones. Secondly, the readability of 6th grade English textbooks is about six months ahead of that of 5th grade ones. Thirdly, the readability scores are similar in all reading passages for 5th grade, while the readability scores of 6th grade differ from textbooks to textbooks. According to the textbook writers, the readability score of 6th grade textbooks is higher than that of 5th grade ones in four out of five textbooks. Lastly, In terms of the units in each textbook, the readability scores fluctuate and do not increase sequentially as expected.
A Computationally Efficient Tracking Scheme for Localization of Soccer Players in an Aerial Video Sequence

The authors attempted to construct a novel sensor networking system that estimates locations of sensor nodes as locations of humans wearing them via image processing. In this application, computationally efficient human localization is indispensable because the operation should be executed on embedded systems. To actualize human localization at a lower computational cost, this study proposes a novel tracking scheme using only detection results. Experimental results using the computer graphics (CG) dataset that was created using player locations of an actual soccer game showed that the proposed scheme outperformed existing schemes implemented in the OpenCV library.
Performance evaluation of Leap Motion, Myo, and Space Navigator devices for 2D and 3D interactions

We present the performance analysis for the three devices Leap Motion, Myo, and Space Navigator under the framework of Fitts' law in two and three dimensional applications. One experiment was carry on to evaluate the selection task in a 2D software application, in which is request to the user to select a green rectangle. A second experiment was a 3D game in which an object is displaced until the position of the same object form situated in a different position. The usability of each device is evaluated with the aim of a questionnaire. Based on the results of the conducted experiments, we suggest for which 2D or 3D applications are more suitable the use of these devices: LM and Space Navigator devices can be used in 3D applications; LM is avoided to use it by long periods of time; Myo device can be used in 2D applications.
Information Security Management Frameworks in Higher Education Institutions: An Overview

An overview of academic articles focused on information security management (ISM) in higher education institutions (HEIs) is presented. For this purpose, an empirical study was carried out. The articles found were then analyzed following a methodological procedure consisted of a systematic mapping study with their research questions, inclusion and exclusion criteria, selection of digital libraries, and analysis of the respective search strings. As a result, a set of information security management frameworks (ISMF) for HEIs were identified. They are based on standards such as ISO 27000, COBIT, ITIL, NIST and EDUCAUSE.
Errors in Italian as Second Language A Taxonomy Proposal

This essay deals with a series of strategies that second or foreign language learners adopt in an effort to avoid making predictable errors. One of the most significant and least studied aspects in the academic field is the type of strategies that are put in place to make changes in the planning of the discourse, when the elements are not available to cope with the event, the communication need or when a speaker wants to change subject from a little-known conversational or textual sector. We intend to examine the concept of competence with respect to performance, the role and type of errors, ending up eliciting a taxonomy of interventions by learners, aimed at avoiding errors in oral and written production.
Color Stereo Image Retrieval Using A Residual CNN-Based Approach

This paper deals with color stereo image retrieval. The novelty lies in the adopted method to extract salient visual features from the texture and depth data. To this end, we will resort to deep neural networks, and more specifically the residual ones. Experiments on natural stereo image databases indicate the gain achieved by the proposed approach compared to conventional handcrafted feature based approaches.
Search by Image Engine using Local Feature Detectors

Nowadays, modern big data warehouses require the development of effective management algorithms. For large image storages first of all it's important to develop effective algorithms for comparing and searching a similar image from an image, taking into account their possible geometric transformations. In this regard, one of the most promising is the approach based on the use of invariant Local Feature Detectors. The work is carried out experimental research of such detectors’ effectiveness for Search by Image Engine in large image storages.
Research on Data Processing Algorithm of the Same Time Section in Substation Based on Time Scale Information Transmission

Due to the complex operating conditions of the actual power system, non-convergence cannot be avoided in the state estimation, and it is difficult to reproduce the results of the section in the inversion. When the measurement and parameter are adjusted, the fluctuation of the qualification rate index will be caused, and the stable operation of the real-time network analysis application system will also be affected. In order to make up the gap in the measurement of the same section of the data in the substation, a method to measure the same section of the time of the power can be studied by using the curve fitting technology and the power balance of the power grid, and at the same time, what kind of faults of the bus line can be judged and the error range of the estimated data can be estimated according to the data. According to the power balance of the power system and the rated power of the node, the data error is judged, and the curve fitting is used to change the data of each node into the curve on the two-dimensional plane. By setting the time interval, the same section measurement of the power data of each node can be realized intuitively.
Subalgebras of the truth value algebra of type-2 fuzzy sets

In this paper, we first introduce some subalgebras in a reduct of the truth value algebra of type-2 fuzzy sets and prove one of them is a bisemilattice. Then we also obtain three necessary and sufficient conditions under which subalgebras of the truth value algebra of type-2 fuzzy sets form a bisemilattice. Next we investigate some subalgebras of this kind of bisemilattice and their characterizations. Finally, we introduce some subalgebras different from the bisemilattice and show a necessary and sufficient condition that a function is convex.