Reviewerly/exHarmony huggingface dataset:
Contextual Approach In Developing Communicative Competence In Foreign Language Lessons At University

The problem of the contextual approach in the formation of communicative competence by means of foreign language lessons at the technical university informed this research. The purpose of our research is to define the contextual approach feasibility and advantages during the development of communicative competence by means of foreign language lessons at technical university. The appeal of the educational process to the contextual learning system becomes relevant. It assumes the change of ideas about the development of communication skills, skills in higher school education. New educational standards provide a link between fundamental and practical training, between general education and specialized disciplines updating the content and methods, forms and means of education from the position of the complex integrated training process. Practically all academic teachers believe that the formation and development of a professionally-trained graduate of a higher educational institution is impossible without context-based education which forms the graduate’s professional skills using interdisciplinary integration. The contextual approach is important for the communicative culture formation to educate students of technical universities. The submission of educational material occurs in the process of generalization, metaphorization of educational knowledge, the search for mechanisms of their application in practical activities. The ideas of vocational training and self-development of the personality are updated. This idea is especially relevant to foreign languagebecauseit is one of main foundations of future competitive specialist communicative competence.
Automated traffic congestion estimation via public video feeds

Route planning is essential for commuters and whilst many developed companies and urban cities have reliable systems certain areas in the world have to cope with inaccurate systems or the lack of. There is a current surge in public video feeds that are directed on key roads with some being subsidies by governments. Video footage for two arterial and two rural roads have been gathered for 6 days with a total of 154 hours of footage together with video recordings of the traffic congestion level for the same roads as estimated by an industry leading traffic application. In this research, we are presenting a solution that given such a public video feed will automatically provide traffic congestion levels after a 24 hour self-calibration period. We have benchmarked our solution with an industry leading traffic application and have obtained a 16% higher precision and an overall accuracy of 86%. This research is based on image processing techniques and has addressed clear weather conditions during the day and recommend further research into the estimation traffic during challenging scenarios.
Secure Service Computing System Platform Based On Blockchain - A Systematic Literature Review

The decentralized storage system has problems in terms of secure data and access control issues have an important role in storage security on the service computing system platform. New challenges in data storage provide an opportunity for technology blockchain to be able to solve them. At present, there are several methods in the blockchain that have been implemented. Each method used has different technical characteristics. In this paper, we discuss the implementation of blockchain technology to secure a service computing system platform. In this research, it can be used as an understanding to develop a service computing system platform to produce reliable security.. Ethereum blockchain platform is the most used choice in research.
Research for image caption based on global attention mechanism

Convolution Neural Networks (CNN) and Recurrent Neural Networks (RNN), which are the main research methods of image caption, have developed rapidly. Nevertheless lacking of global consciousness in image caption has not been completely solved. Separation from the bottom-up visual attention mechanism and the top-down visual attention mechanism has been widely used in image description and visual question and answers. In this article, we put forward image description based on a global attention mechanism research methods. The global attention prior channel is added to the infrastructure to extract the global information features while learning the local features. The attention to the object and other outstanding level of image region is calculated, and the global image features are enhanced.
Influence-Based Abstraction in Deep Reinforcement Learning

thousands, or even millions of state variables. Unfortunately, applying reinforcement learning algorithms to handle complex tasks becomes more and more challenging as the number of state variables increases. In this paper, we build on the concept of influence-based abstraction which tries to tackle such scalability issues by decomposing large systems into small regions. We explore this method in the context of deep reinforcement learning, showing that by keeping track of a small set of variables in the history of previous actions and observations we can learn policies that can effectively control a local region in the global system.
Information Security Through Encrypted Domain Data Hiding

In this contemporary world of internet, information security is the most important issue because while transmitting and receiving any information there is a chance of hacking. So to tackle the cyber-attack, this paper proposes a new two-layer information security and data hiding algorithm which involves a block scrambling algorithm for an image encryption and an algorithm for incorporating the message bits into an encrypted image. Firstly, the image encryption has been done by dividing image horizontally into two equal blocks followed by block scrambling algorithm and diffusion scheme. After encryption the data hiding algorithm is used for hiding the message bits into an encrypted image. This technique of hiding secret data into an encrypted image is highly secured and on performance analysis of block scrambling algorithm gives the excellent performance.
The Constrained Longest Common Subsequence: Theory and Experiments

A common operation on strings is to calculate a value of similarity. The longest common subsequence (LCS) is a measure of similarity between two sequences. An extension of LCS measure is the constrained longest common subsequence (CLCS). The CLCS consists of calculating the LCS of two sequences, and it is a supersequence of a constrained sequence. This paper is an experimental evaluation of the most important CLCS algorithms. The implemented algorithms have a similar performance and were tested with different kinds of random strings belonging to the alphabets: generated uniformly at random (4, 10, 20, 32, 48 and 64 characters) and English text (239 characters). This study could help in selecting optimal algorithms for a given problem.
GERAKAN LITERASI DIGITAL BAGI GURU-GURU SEKOLAH DASAR

The digital world, which is characterized by massive use of technology in all sectors of life, has an impact on fundamentally changing life systems. The implication of this digital world is the birth of knowledge-intensive and insightful individuals, as well as producing individuals with the term digital native, namely the generation that lives in the digital era where the internet becomes an integral part of life. In the context of education the development of technology and the internet should have a positive impact on the learning process and from the perspective of teacher professional development. Less than ideal conditions occur in SD N 1 Sulanyah and SD N 2 Sulanyah. The results of the situation analysis found that the teacher's digital literacy was still lacking. The solution to overcome these problems is to conduct a Digital Literacy Movement program which consists of three components of activities in the form of training and coaching digital information retrieval strategies, development of technology-based media, as well as writing papers and publications in e-journals. This program is effectively able to develop teacher digital literacy characterized by more than 70% of partner teachers able to search for digital information independently, the media produced are classified as good, and teachers are able to produce and submit class action research articles in e-journals independently.
Evaluating the Performance of Machine Learning Techniques for Cancer Detection and Diagnosis

Machine Learning (ML) techniques find value in healthcare due to its ability to process huge data sets and convert them into clinical insights. These insights help physicians and healthcare providers in planning, quick decision making and providing timely care to patients with higher accuracy, lower costs and increased customer satisfaction. This paper evaluates the performance of ML algorithms for cancer detection and diagnosis. It compares the performance of different cancer detection algorithms for different types of datasets. It also investigates how the performance of the ML algorithms improve with the use of feature extraction methods, specifically for lung cancer. Developing generic feature extraction methods still remain as a challenge. Healthcare providers need to reorient their treatment approach for diseases like cancer with more focus on detecting them at a very early stage so as to maximize the chances of recovery for the patients.
Recommender Systems -The Lifeline Of The Current Streaming Zeitgeist

The modern age is a peculiar anomaly wherein content is being so voraciously consumed at an astonishing pace. Netflix, Amazon Prime and the litany of streaming services have taken it upon themselves to secure exclusive deals with studios to add to their ever-growing entertainment library. But the real secret sauce behind the outrageous watching times of these platforms are recommender systems which efficiently advise the user to watch what to watch. Three contenders come into mind while describing them, Popularity based filtering system, Content Based filtering system, and collaborative based filtering system. The authors have devised a similarity-based approach which adjudges a similarity score or rather a matrix of scores between two movies or items with the help of cosine similarity (for content based as well as collaborative filtering) and the Pearson Correlation method (for collaborative filtering). These methods will be studied in depth and furthermore, there will be comparison between clustering and Euclidean distance similarity with this and the results will be displayed. Also discussed is the scenario when both types of filtering are combined.
Cloud-Native Architecture

In the recent years, the degree and pace of changes on the software scene has accelerated like never before. The open source movement ignited a large number of scattered and often independent teams to enter a race of bringing new software components, tools, platforms, protocols, or services to market at an incredible pace. Cloud computing removed many hardware-related entry barriers by allowing teams to self-provision virtualized hardware resources in a matter of seconds. With cloud-provider-managed, both virtualized and even bare metal, hardware capabilities available at one’s fingertips, nearly anyone can launch and develop a small software project. Containerization briefly covered in the previous chapter often enables new approaches in designing applications and requires the presence of new supplementary tools to make the solutions production-ready. There are old questions to be answered such as how to solve storage, networking, messaging, and service discovery challenges, this time in the age of containers. There are new waters to explore – service mesh, container registries, and schedulers, to name a few. Let’s list the three information age turning points which considerably impact the way we architect new-generation software solutions. These are as follows:
Manual Annotation of Unsupervised Models : Close and Distant Reading of Politics on Reddit

This article offers a methodological contribution to manually-assisted topic modeling. With the availability of vast amounts of (online) texts, performing full scale literary analysis using a close approach is not practically feasible. The set of alternatives proposed by Franco Moretti (2000) under the umbrella term of reading aims to show broad patterns that can be found throughout the entire text collection. After a survey of literary-critical practices that combine close and distant methods, we use manual annotations of a thread on Reddit, both to evaluate an LDA model, and to provide information that topic modeling lacks. We also make a case for applying these techniques that originate in literary more broadly to online, non-literary contexts. Given a large collection of posts from a Reddit thread, we compare a manual, close analysis against an automatic, computational distant approach based on topic modeling using LDA. For each text in the collection, we label the contents, effectively clustering related texts. Next, we evaluate the similarity of the respective outcomes of the two approaches. Our results show that the computational content/topic-based labeling partially overlaps with the manual annotation. However, the close approach not only identifies texts with similar content, but also those with similar function. The differences in annotation approaches require rethinking the purpose of computational techniques in analysis. Thus, we present a model that could be valuable for scholars who have a small amount of manual annotation that could be used to tune an unsupervised model of a larger dataset.
Many-to-Many Voice Conversion using Cycle-Consistent Variational Autoencoder with Multiple Decoders

One of the obstacles in many-to-many voice conversion is the requirement of the parallel training data, which contain pairs of utterances with the same linguistic content spoken by different speakers. Since collecting such parallel data is a highly expensive task, many works attempted to use non-parallel training data for many-to-many voice conversion. One of such approaches is using the variational autoencoder (VAE). Though it can handle many-to-many voice conversion without the parallel training, the VAE based voice conversion methods suffer from low sound qualities of the converted speech. One of the major reasons is because the VAE learns only the self-reconstruction path. The conversion path is not trained at all. In this paper, we propose a cycle consistency loss for VAE to explicitly learn the conversion path. In addition, we propose to use multiple decoders to further improve the sound qualities of the conventional VAE based voice conversion methods. The effectiveness of the proposed method is validated using objective and the subjective evaluations.
Digital video forgery detection based on statistical features calculation

Fake digital information is distributed heavily nowadays using social networks, new and other information sources. Digital forgeries use may lead to an unexpected result and it is quite difficult to detect tampering with just an expert view. A lot of algorithms for digital image forgery detection exist, but video forgery detection is on its early development stage. We propose a new approach for digital video forgery detection, which is based on statistical features calculation on difference shift frames. We selected three types of features for research: CC-PEV, SPAM and MP-486. We also estimated the quality of several classification techniques to detect altered frames: RBF-based SVM, linear ensemble classifier and decision tree. The experimental results showed the best combination of feature and classification algorithms for the video forgery detection problem solution.
A Guideline for Object Detection Using Convolutional Neural Networks

The main purpose of object detection is to detect and locate specific targets from images. The traditional detection methods are usually complex and require prior knowledge of the detection target. In this paper, we will introduce how to use convolutional neural networks to perform object detection from image. This is one of the important areas of computer vision. In order to build up to object detection, we first learn about how we can get the object localization or landmark by a neural network. And then I will give the detail of sliding windows detection algorithm and introduce how to use the convolutional implementation of sliding windows to speed up the process. Then we will introduce the transfer learning and how to prepare your own learning data for training networks.
Subquadratic encodings for point configurations

For many algorithms dealing with sets of points in the plane, the only relevant information carried by the input is the combinatorial configuration of the points: the orientation of each triple of points in the set (clockwise, counterclockwise, or collinear). This information is called the order type of the point set. In the dual, realizable order types and abstract order types are combinatorial analogues of line arrangements and pseudoline arrangements. Too often in the literature we analyze algorithms in the real-RAM model for simplicity, putting aside the fact that computers as we know them cannot handle arbitrary real numbers without some sort of encoding. Encoding an order type by the integer coordinates of a realizing point set is known to yield doubly exponential coordinates in some cases. Other known encodings can achieve quadratic space or fast orientation queries, but not both. In this contribution, we give a compact encoding for abstract order types that allows an efficient query of the orientation of any triple: the encoding uses \( O(n^2) \) bits and an orientation query takes \(O(\log n)\) time in the word-RAM model with word size \(w \geq \log n\). This encoding is space-optimal for abstract order types. We show how to shorten the encoding to \(O(n^2 {(\log\log n)}^2 / \log n)\) bits for realizable order types, giving the first subquadratic encoding for those order types with fast orientation queries. We further refine our encoding to attain \(O(\log n/\log\log n)\) query time at the expense of a negligibly larger space requirement. In the realizable case, we show that all those encodings can be computed efficiently. Finally, we generalize our results to the encoding of point configurations in higher dimension.
A universal middleware streaming framework and data analytics: Analysing their economic feasibility in road transport planning

The individual paths followed by road transport operators in adopting and using information and communication technologies (ICT) result in heterogeneous planning practices. This includes working with multiple IT systems, data sources and applications. There is no coordination or standardisation that ensures a straightforward approach to develop and implement cross-sector IT solutions. Different integrative state-of-the-art solutions are sub-optimal, not flexible and relatively expensive to maintain. The proposed methodology starts by investigating the current working practices of a road transport operator. It reports on an own exploration initiative conducted by researchers in logistics and computer science, and brings together their complementary expertise. It identifies firstly the ICT solutions used at an operational level and points out the necessities for further integration, functionalities or application development. Secondly, it develops a theoretical model that assists road transport operators in checking whether using a uniform integration layer and data analytics tools is economically feasible. The model shows the fleet size from which significant costs reductions are made. These costs reductions are enabled by implementing a middleware framework and data analytics algorithm that assists human operators in operational planning.
Comparing Poisson-Inverse Gaussian Model and Negative Binomial Model on case study: horseshoe crabs data

Abstract Poisson Regression analysis is commonly used for dependent variable that has non-negative value, called count data. Poisson Regression has an assumption that mean of dependent variable equal to its variance. On over dispersion case where the variance is greater than mean, poisson regression is inconvenient to used because it may underestimate the standard error of regression parameters and consequently giving misleading inference. Poisson-Inverse Gaussian and Negative Binomial regression model can be used on over dispersion data. This paper will discuss about Poisson-Inverse Gaussian regression model, Negative Binomial regression model and comparing them in terms of Goodness-of-fit (GOF) statistics on case study of horseshoe crabs data. According to the result, pseudo R-squared value of P-IG regression model is greater than the Negative Binomial regression model. It shows that P-IG regression model is better than Negative Binomial regression model.
Facial Expression Recognition Based on Strengthened Deep Belief Network with Eye Movements Information

Facial expression recognition is an important application in computer vision. Generally, features which are used for facial expression recognition are mostly based on geometric and appearance features of image. This paper presents a novel method to identify facial expressions which exploring eye movements data labels as auxiliary labels to construct classifier, a Strengthened Deep Belief Network (SDBN) in a united cycle framework is constructed. This framework is formed as strong classifier by multi-weak classifiers voted. Experiments on Cohn-Kanade database showed that the proposed method achieved a better improvement in the task of facial expression recognition.
Teaching a robot to draw: hand gesture demonstration based on human-robot interaction

With the development of collaborative robots, robot programming by demonstration (PBD) plays an important role in human-robot interaction, it aims to transfer new skills from observations of tasks demonstrated by humans to robots. In this paper, we proposed a new approach to teach a robot to draw pictures based on human fingertip recognition and hand motion tracking. Combining the robot operating system (ROS), OpenCV and Moveit (motion planning libraries), we capture the finger movement trajectory by using Kinect2 depth camera. Then the trajectory waypoint is sent to the ur5 robotic arm through topic communication to complete the trajectory tracking task. The experiment indicates that the proposed approach allows inexperienced users to efficiently teach a robot to track the demonstrated trajectory.