Reviewerly/exHarmony huggingface dataset:
Compressed-Domain Video Object Tracking Using Markov Random Fields with Graph Cuts Optimization

We propose a method for tracking objects in H.264/AVC compressed videos using a Markov Random Field model. Given an initial segmentation of the target object in the first frame, our algorithm applies a graph-cuts-based optimization to output a binary segmentation map for the next frame. Our model uses only the motion vectors and block coding modes from the compressed bitstream. Thus, complexity and storage requirements are significantly reduced compared to pixel-domain algorithms. We evaluate our method over two datasets and compare its performance to a state-of-the-art compressed-domain algorithm. Results show that we achieve better results in more challenging sequences.
Design and Implementation of 3D Effect Web Page Based on JavaScript Technology

With the development of Internet technology, people's production and life are greatly influenced. Web design is widely used in Internet world. Web design and user experience are inseparable. In the pursuit of webpage layout and aesthetics, but also to add user interaction functions, Dynamic effects technology based on JavaScript can achieve user interaction requirements. The perfect combination of JavaScript dynamic effects and interactive features is the best way to achieve webpage effects. This paper expounds the application method of JavaScript, and studies the special effect application of JavaScript technology in webpages through examples.
Answer selection using Word Alignment based on Part of Speech Tagging in community question answering

This paper explain about answer selection using word alignment based on POS tagging in Community Question-Answering (CQA). This online community allowed the user to ask and reply related to the question problems which has no restrictions. This causes inappropriate comments with the question problems proposed before. To solve these problems, combining lexical and semantic features has been developed with result conclude that the approach more adequate for similarity task rather than question answering. According to the previous research, there is several problems that can be enhanced. First, vector representation counts exactly matched words, so it does not effective to cover other words that have relatedness between two pairing words. Second, noun overlap for similarity measure in pairing words can't define that the two words are similar. So, it must be define that the pairing POS tag is the same meaning or relatedness. In this study, unsupervised lexical and semantic similarity method employed with different approach from previous method in verbatim and contextual similarities. The data was taken from SemEval 2017 competition which focus on Question-Answer Similarity task. The experiment result for precision (Mean Average Precision) score shows the improvement from 0.674 to 0.6845, 1.03 % higher than previous research in CQA. This improvement comes from lexical similarity, which is not just from noun pattern but also taken from verb pattern. Furthermore, semantic similarity has an important role in determining which words that have same pattern and meaning to define relevancy between them.
Data Security and Storage Module in Cloud

This chapter describes a Cloud-based data security and storage module. The module is simple in design and verified in the Cloud environment. Qualitative comparison shows that the module is a better candidate in Cloud environment.
Three Dimensional (3D) Simulation for Ubiquitous and Inclusive Learning in a Digital Era

With the advances in web technologies, learning of scientific phenomena has taken a new dimension. Emergence of 3D Simulation, virtual and augmented reality has added a new dimension to the way we learn a particular concept. These digital technologies provide us an opportunity to create representations of scientific, technical and historical phenomena. Pictorial models have been reported to be effective in representing molecular reactions to explain concepts which involve invisible structures and dynamic characters. Researchers have suggested that a combination of virtual labs and physical experiments can enhance students' conceptual understanding of scientific phenomena more than the use of virtual tools or lab experiments alone. This chapter will explain the development of the São João da Bahia Virtual Museum, a project consisting of a three-dimensional (3D) modeling and imaging program as a means of ubiquitous and inclusive learning in a digital era.
Aspect Specific Opinion Expression Extraction Using Attention Based LSTM-CRF Network

Opinion phrase extraction is one of the key tasks in fine-grained sentiment analysis. While opinion expressions could be generic subjective expressions, aspect specific opinion expressions contain both the aspect as well as the opinion expression within the original sentence context. In this work, we formulate the task as an instance of token-level sequence labeling. When multiple aspects are present in a sentence, detection of opinion phrase boundary becomes difficult and label of each word depend not only upon the surrounding words but also with the concerned aspect. We propose a neural network architecture with bidirectional LSTM (Bi-LSTM) and a novel attention mechanism. Bi-LSTM layer learns the various sequential pattern among the words without requiring any hand-crafted features. The attention mechanism captures the importance of context words on a particular aspect opinion expression when multiple aspects are present in a sentence via location and content based memory. A Conditional Random Field (CRF) model is incorporated in the final layer to explicitly model the dependencies among the output labels. Experimental results on Hotel dataset from Tripadvisor.com showed that our approach outperformed several state-of-the-art baselines.
The Post-Material Values of Young People and Interactive Technologies in the Formation of a Culture of Scientific Dialogue in the Student Environment

The task of reforming and improving the social and humanitarian component of modern education is related to the understanding of time as an existential experience associated with the phenomenon of age. According to the theory of generations of N. Howe and W. Strauss, age is defined as a measure of awareness of one's own living life. It is a methodological approach of this study. The generations of Y and Z are the subject of special attention in the study being the group that actually forms the requests for the educational policy of the state. Considerable attention is paid to the formation of skills and culture of conducting scientific dialogue in the student environment (the development of certain norms of behavior, the adoption of the normative requirements for scientific dialogue, the assimilation of various formats for discussions, projective activities, etc.).
A Hybrid Approach for Image Segmentation in the IoT Era

Spectral clustering is a class of graph theoretic procedure, which is popular for finding natural groupings. Over the last decade, it has become a widely adopted tool – utilized in solving image segmentation problems, via normalized cut (NCut) methodology. Few challenges faced by image segmentation based on spectral clustering include its inability of processing large images due to high computational cost and memory requirements and its sensitivity to irrelevant and noisy data. This chapter presents an unsupervised image segmentation technique using spectral clustering, aimed at salient object detection, followed by extraction. The presented technique addresses all of the aforementioned challenges by means of a weighted binary tree-based fast spectral clustering (WBTFSC). The algorithm integrates dimensionality reduction with spectral clustering by introducing an effective preprocessor, comprising two fundamental steps of color quantization and unique pixels selection. The experiments, performed on color images using the proposed algorithm, show improved performance in extracting objects of interest with high accuracy. We also test the algorithm on several noisy images; the obtained results reveal better performance in comparison to few existing techniques.
An Evaluation of the Password Practices on Leading e-Commerce Websites in South Africa

Despite the emergence of numerous authentication methods, passwords have remained the dominant authentication mechanism for e-commerce websites. However, password authentications if often widely criticized, especially due to the ease with which it can be compromised by end-users as they often have poor password security behaviors. Nevertheless, a plethora of evidence suggests that the blame should not only be placed on the users as many engage in poor password security practices because they lack sufficient guidance and support on how to maintain good password security behaviors. Indeed, many researchers over the years have shown that user password security behaviors can be significantly enhanced by provided guidance and support on how they can create and maintain strong passwords. Yet, it remains uncertain how well e-commerce website providers have learned these essential lessons. As such, this study is aimed at evaluating the password practices of e-commerce websites in South Africa (SA). After evaluating 37 leading e-commerce websites in the country, it was observed that the majority (92%) of the websites had poor password practices with over 81% offering no guidance for users to enhance their password behaviors. This problem is certainly worse than it should be in this day and age. Consequently, there is an urgent need for e-commerce service providers in SA to improve their password security practices as this is vital for enhancing the password behaviors of their website’s users.
Data Extraction in Malignant Environments

Deliberate or inadvertent escape of classified data is undoubtedly one among the premier extreme security dangers that associations look inside the advanced period. The risk right now stretches out to the private lives: an abundance of private information is out there to interpersonal organizations and great telephone providers and is in a roundabout way exchanged to undependable outsider and fourth gathering applications. amid this work, a bland data genealogy structure LIME (Data Lineage in the Malicious Environment), is utilized for data stream over different elements that take two trademark, guideline parts (i.e., proprietor and buyer). It characterizes the exact security ensures required by such a data heredity component toward recognizable proof of a blameworthy substance, and decide the disentangling non-denial and trustworthiness suspicions. At that point create and break down a totally special dependable data exchange convention between two substances among noxious surroundings by expanding upon unmindful exchange, solid watermarking, and mark natives. At long last, an exploratory investigation to exhibit the helpfulness of convention what's more, apply structure to the important data run projections of learning outsourcing and the informal organizations. In general, LIME(Data Lineage in the Malicious Environment),lineage structure for information exchange, to be a key advance towards accomplishing responsibility by plan.
Doc ApprovED

email: emailFigureNeanderthals are to Homo sapiens as Gmail is to Superhuman. Superhuman is an email client that is by far the most enjoyable software I have ever used other than Zapier and Civilization. If you are willing to pay $30 a month for the most incredible email experience, read on. Superhuman is designed for true superusers who want to blow through hundreds of emails and hit inbox zero at breakneck speed. The entire interface is designed for speed, which by definition means keyboard shortcuts instead of the mouse. The Superhuman team is obsessed with having keyboard shortcuts for everything and making it super easy to learn and use the shortcuts, so they created a retro Tetris-like game for users to memorize the shortcuts. Every time you do something in Superhuman, an unobtrusive text field shows you the keyboard shortcut you could have used instead. But keyboard shortcuts do not a great software make. The Superhuman team has figured out all the barriers to triaging email, like seeing your calendar for the day mentioned in the email and automating access to that information. When you reference a day such as “next Monday” in your email, for example, Superhuman will automatically pull up your calendar for that day. If you forget a shortcut, your single go-to for all actions is Command-K. If you are in an email and realize you need to add somebody to the cc field, instead of using your mouse and editing the field, just type “+” and start writing the name of the person you want to add. The list of genius time-saving tricks is endless. I encourage all productivity-obsessed folks to try out Superhuman and be prepared to be blown away. (https://superhuman.com.)
Data Classification with Decision Trees

In this chapter, we will develop the theory for binary decision trees. Decision trees can be used to classify data, and fall into the Learning category in our Autonomous Learning taxonomy. Binary trees are easiest to implement because each node branches to two other nodes, or none. We will create functions for the Decision Trees and to generate sets of data to classify. Figure 7.1 shows a simple binary tree. Point “a” is in the upper left quadrant. The first binary test finds that its x value is greater than 1. The next test finds that its y value is greater than 1 and puts it in set 2. Although the boundaries show square regions, the binary tree really tests for regions that go to infinity in both x and y.
Behavioral Analysis of Users for Spammer Detection in a Multiplex Social Network

There are now a growing number of social networking websites with millions of users, creating a fertile ground for “spammers” to abuse opportunities in these websites for their own gain through constant exposure of malicious communications to other users. The variety of interactions afforded by these social networks has resulted in a Multiplex Network of interactions. In these networks, malicious users evade detection by frequently changing the nature of their activities. This makes it challenging to analyse users’ interactions to capture anomalous behaviours. In this paper, we aimed to detect spammers in a large time-evolving multiplex social network called Tagged.com. For this purpose, we used four different sets of features: (i) a set of light-weight behavioural features to capture the structural behaviour of users in their neighbourhood network; (ii) a set of bursty features and (iii) sequence-based features for capturing the temporal behaviour of users; and (iv) a set of profile-based features which was used as a side information. In addition, we also employed an unsupervised Laplacian Score based approach for feature selection and space dimensionality reduction. The experimental results showed an accuracy of over 88% in spammer detection with a lower empirical time complexity for feature extraction. Implementing behavioural and bursty features in a relational data management system makes the proposed approach more practical since most of the real-world networks store their data in relational databases.
Takagi-Sugeno fuzzy approach for forecasting solar radiation in Timisoara using an exogenous variable – The air temperature

In this paper the Takagi-Sugeno fuzzy approach is used for computing the daily global solar irradiation. A new model for one day ahead forecasting of the daily global solar irradiation using air temperature data is reported. To improve forecasting accuracy, the model uses as inputs the measured values of solar irradiation together with an exogenous variable – the daily air temperature extremes. The results demonstrate that fuzzy models can use the information from past measurements for an actual forecast with acceptable accuracy and the Takagi-Sugeno fuzzy models constitute a useful instrument for the forecasting of solar irradiation.
Correlation Distributions between an &lt;i&gt;m&lt;/i&gt;-Sequence and Its Niho Decimation Sequences of Short Period

Let d=2pm-1 be the Niho decimation over $\mathbb{F}_{p^{2m}}$ satisfying $\gcd(d,p^{2m}-1)=3$, where m is an odd positive integer and p is a prime with p ≡ 2(mod 3). The cross-correlation function between the p-ary m-sequence of period p2m-1 and its every d-decimation sequence with short period $\frac{p^{2m}-1}{3}$ is investigated. It is proved that for each d-decimation sequence, the cross-correlation function takes four values and the corresponding correlation distribution is completely determined. This extends the results of Niho and Helleseth for the case gcd(d, p2m-1)=1.
Nonlinear sequential fusion estimation for clustered sensor networks

Abstract This paper is concerned with distributed fusion estimation problem for discrete‐time nonlinear systems with asynchronous sampling data in the clustered sensor networks. A nonlinear sequential fusion method consisting of a sequential measurement fusion (SMF) method and a sequential state fusion (SSF) method is proposed. It is shown that the SMF estimator using the Unscented Kalman Filtering (UKF) method can handle the asynchronous measurement data sequentially and the SSF estimator which uses sequential matrix weighting method can get a close performance as the centralized batch matrix weighting method but has a lower computational complexity. The proposed measurement fusion method is able to deal with asynchronous measurements and has lower computational complexity as compared with the augmentation method. A simulation example shows the effectiveness of the proposed nonlinear sequential fusion method.
Data Mining Algorithms for Risk Detection in Bank Loans

This article proposes a new approach on detection of fraudulent credit operations applying computational intelligence techniques. We use a dataset of historical data of customers from a financial entity and we split it to train a classification and clustering algorithm. We train a radial basis function network to classify clients that commit or not credit fraud. Then, we build a Fuzzy c-means clustering to group data points to create customer profiles. This algorithm has the capacity of grouping the data inside clusters and assigning a degree of membership to the points outside the clusters. Subsequently, the trained classification algorithm is applied to the clusters to provide additional information about customer profiles. We demonstrate good performance for fraudulent credit operations and identification of customer profiles.
State of the Art in the Contribution of an Ontology-Oriented Knowledge Base to the Development of a Collaborative Information System

This article reviews the state of the art of implementing collaborative information systems that guarantee interoperability using ontologies as a semantic dimension. It presents the implementation of different systems of collaborative information and collaborative platform. It also presents the various limitations that this type of information systems still suffers from and works that have tried to overcome its limitations and finally it gives our perspective to this topic.
Implementation Of Cooperative Learning Model Snowball Throwing To Increase Activity And Learning Outcomes

Compared to other classes, students in grade X IIS 1 majority of students have less enthusiasm or activity. After the teacher gives the materials and the students are asked to ask questions about the material that has not been understood. However, when teachers provide evaluation questions to measure students' understanding it is known that their learning outcomes are still below the predetermined KKM 75 so that some students are declared unfinished. This research uses Classroom Action Research with a qualitative approach. The data in this study was taken through observation, technical tests, documentation, and field notes. The research procedure of each cycle consists of the planning, implementation, and observation of actions and reflection actions. The results showed that the improvement of teachers' success in applying the Snowball Throwing learning model increased from the first cycle by 79.44% to 90.05%. Comparison of student activity also shows improvement from cycle I to cycle II with the average percentage of the cycle I 59,84% and become 76,08% in cycle II, so it can be concluded there is increase 15%. It can be concluded that the learning outcomes of grade X students of IIS 1 SMA Brawijaya Smart School increased by 3%.
Enhanced Two-Stage Multi-person Pose Estimation

In this paper we introduce an enhanced multi-person pose estimation method for the competition of the PoseTrack [6] workshop in ECCV 2018. We employ a two-stage human pose detector, where human region detection and keypoint detection are separately performed. A strong encoder-decoder network for keypoint detection has achieved 70.4% mAP for PoseTrack 2018 validation dataset.
Internet Daemons: Digital Communications Possessed. FenwickMcKelvey. Minneapolis, MN: University of Minnesota Press, 2018. 336 pp. $28.00 (Paperback). (ISBN 9781517901547)

Journal of the Association for Information Science and TechnologyVolume 71, Issue 1 p. 121-123 BOOK REVIEW Internet Daemons: Digital Communications Possessed. Fenwick, McKelvey. Minneapolis, MN: University of Minnesota Press, 2018. 336 pp. $28.00 (Paperback). (ISBN 9781517901547) William H. Dutton, William H. Dutton Global Cyber Security Capacity Centre, Computer Science, University of Oxford, Oxford, United KingdomSearch for more papers by this author William H. Dutton, William H. Dutton Global Cyber Security Capacity Centre, Computer Science, University of Oxford, Oxford, United KingdomSearch for more papers by this author First published: 04 February 2019 https://doi.org/10.1002/asi.24204Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume71, Issue1January 2020Pages 121-123 RelatedInformation
Analyzing Scientific Corpora Using Word Embedding

The bibliographic databases have abstract and citations of scientific articles, the summary being the most consulted section of an article. In order to classify and address the entries in a system of indexing and retrieval of information in the databases of a manuscript, there are keywords, which in many cases this information should not achieve greater dissemination. This paper presents an evaluation of the semantic relatedness between the abstract of scientific papers and their keywords. This analysis will be using word2vec that is a predictive model, and it will find the nearest words. Thus, this study is focused on the metadata quality assessment through the similar semantics between two words that allow the accuracy in relation to metadata of scientific databases.