Reviewerly/exHarmony huggingface dataset:
Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images

Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and visible images. Second, common feature points of infrared and visible images are extracted based on the consistent edge structures. Third, feature descriptions are established according to the edge structure attributes including edge length and edge orientation. Lastly, feature correspondences are calculated according to the distance of feature descriptions. Due to the utilization of consistent edge structures of infrared and visible images, the proposed DDCE method can improve the detection repeatability and the matching accuracy. DDCE is evaluated on two public datasets and are compared with several state-of-the-art methods. Experimental results demonstrate that DDCE can achieve superior performance against other methods for infrared and visible image match.
Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks

Hate speech detection on Twitter is often treated in monolingual (in English generally) ignoring the fact that Twitter is a global platform where everyone expresses himself with his natal language.In this paper, we created a model which, taking benefits of the advantages of neural networks, classifies tweets written in seven different languages (and even those that contains more than one language at the same time) to hate speech or non hate speech.We used Convolutional Neural Networks (CNN) and character level representation.We carried out several experiments in order to adjust the parameters according to our case study.Our best results were (in terms of accuracy) 0.8893 for a dataset containing five languages and 0.8300 for a dataset of seven languages.Our model solves properly the problem of hate speech on Twitter and its results are, compared to the state of the art, more than satisfactory.
A Code-Description Representation Learning Model Based on Attention

Code search is to retrieve source code given a query. By deep learning, the existing work embeds source code and its description into a shared vector space; however, this space is so general that each code token is associated with each description term. In this paper, we propose a code-description representation learning model (CDRL) based on attention. This model refines the general shared space into the specific one. In such space, only semantically related code tokens and description terms are associated. The experimental results show that this model could retrieve relevant source code effectively and outperform the state-of-the-art method (e.g., CODEnn and QECK) by 4-8% in terms of precision when the first query result is inspected.
Session-Based Graph Convolutional ARMA Filter Recommendation Model

With the rapid popularity of Internet shopping, session-based personalized recommendations have become an important means to help people discover their potentially interesting items in real-time. Most existing works only model a session as a sequence and use recurrent neural networks for recommendation. Despite their effectiveness, the results may not be sufficient to capture the potential relationships between items. In this work, we integrate a graph convolutional layer based on Auto-Regressive Moving Average (ARMA) filters into the Graph Neural Network (GNN). In particular, it is a new session-based recommendation framework Graph Convolution ARMA Filter (AUTOMATE), which can capture complex transformations between items through a sequence of sessions modeled as graph-structured data. Each session is then represented as the composition of the current interest and the global preference of that session using an attention network. The rationality and efficacy of the proposed AUTOMATE model are extensively evaluated on two public real-world datasets. Experimental results show that our model is significantly better than other state-of-the-art methods.
The Analysis of Quantum Radar Scattering for the Typical Pyramid Structure

We first developed a new algorithm which can solve the simulation of quantum radar scattering for 3D convex targets. Here we introduced the three-step computation process of orthogonal projected area and verified the method for a typical pyramid structure. Finally, we presented the quantum radar cross section (QRCS) results of a typical pyramid structure and compared them with the classical radar cross section.
Effectiveness, Efficiency and Instruction Appeal of Blended Learning Model

&lt;p class="0abstractCxSpFirst"&gt;The practice of learning, to acquire success, has so far been measured by the level of effectiveness with a percentage range from 0% to 100% and converted into values E to A. While in the science of knowledge, learning outcome is measured by effectiveness, efficiency, and attractiveness. Dwiyogo's research [1][2] and 2015 developed a tuition design model that combines face-to-face schooling using offline resources (print, audio, video, computer) and online knowledge training (internet and smart phones) known as Blended Learning-Based Instruction Design Models. Numerous studies have been conducted to test the potency, capability, and fascination of learning. However, the objectives of this study with respect to Dwiyogo’s model are: (1) examining experts on it usage, (2) applying the model in courses, and (3) testing the productivity, and capability.&lt;/p&gt;&lt;p class="0abstractCxSpLast"&gt;This research makes use of the survey and quasi-experimental design methods. The data consists of a total of 3 experts, 113 students, and 6 teachers with result showing that the blended learning model using eight items produced huge output (= 4.750, SD = 0.361). Based on the components of the expert test, it can be concluded that the learning model developed is appropriate and can be implemented. Student opinion with respect to this model showed a high level of agreement (= 3.415, SD = 0.536). This shows that students who applied blended learning based instruction models responded positively to its implementation. Therefore, this technique can enhance knowledge thereby, making the act of efficient, effective, and instruction appeal.&lt;/p&gt;
Tracking a Decentralized Linear Trajectory in an Intermittent Observation Environment

Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In some practical scenarios of systems, there are certain interruptions in addition to these faults. These interruptions may occur in the form of noise. However it is expected that the decision about the sensor data is difficult in the presence of noise. This is because the noise adversely affects the communication amongst sensors and the processing unit. More complexity is expected when there are faults and noise simultaneously. To deal with this problem, in addition to existing fault detection and isolation schemes, the Kalman filter is employed. Here, a generic discussion is provided, which is equally applicable to other situations. This work addresses various faults in the presence of noise for decentralized tracking systems. Local single faults and multiple faults in the presence of noise are the core issues addressed in this paper. The proposed work is comprised of a general scenario for a decentralized tracking system followed by a case study of a target tracking scenario with and without noise. The presented schemes are also tested for different types of faults. The proposed work presents effective tracking in the presence of noise and faults. The results obtained demonstrate the acceptable performance of the scheme of this work.
Enhancing Writing Vocabulary Using Mentimeter

This paper aimed to examine the effectiveness of using Mentimeter, which is an online virtual platform to enhance the writing vocabulary among pupils. By using Mentimeter, they can access the platform to collaborate with their peers and teacher regardless of the time frame, geographical and self-confident factors. This collaborative discussion platform would expand pupilsâ€™ word bank and understanding about the writing task; thus helping them in accomplishing their writing tasks. An action research based on Kemmis and McTaggart model was carried out with 40 pupils with a marginal passing rate by using pre and post-test, questionnaire and unstructured interviews. The findings showed a significant difference between the results of pre and post-test and positive feedbacks from the pupils. In conclusion, Mentimeter is an effective tool to enhance pupilsâ€™ writing vocabulary. The results of this study may be beneficial to educators in employing Mentimeter in teaching of writing vocabulary. However, this paper is only focused on the effectiveness of using Mentimeter to increase the vocabulary of pupils in writing within the targeted group thus further study is needed to ascertain the efficacy of using Mentimeter and to generalise the findings to a larger population. https://doi.org/10.26803/ijlter.19.3.7
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-Based Approach

In this article, we propose a general framework for the unsupervised fuzzy rule-based dimensionality reduction primarily for data visualization. This framework has the following important characteristics relevant to the dimensionality reduction for visualization: preserves neighborhood relationships; effectively handles data on nonlinear manifolds; capable of projecting out-of-sample test points; can reject test points, when it is appropriate; and interpretable to a reasonable extent. We use the first-order Takagi–Sugeno model. Typically, fuzzy rules are either provided by experts or extracted using an input–output training set. Here, neither the output data nor experts are available. This makes the problem challenging. We estimate the rule parameters minimizing a suitable objective function that preserves the interpoint geodesic distances (distances over the manifold) as Euclidean distances on the projected space. In this context, we propose a new variant of the geodesic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> -means clustering algorithm. The proposed method is tested on several synthetic and real-world datasets and compared with the results of six state-of-the-art data visualization methods. The proposed method is the only method that performs equally well on all the datasets tried. Our method is found to be robust to the initial conditions. The predictability of the method is validated by suitable experiments. We also assess the ability of our method to reject test points when it should. The scalability issue of the scheme is also discussed. Due to the general nature of the framework, we can use different objective functions to obtain projections satisfying different goals. To the best of our knowledge, this is the first attempt to manifold learning using unsupervised fuzzy rule-based modeling.
PhoneCursor: Improving 3D Selection Performance With Mobile Device in AR

This study proposes a novel technology as a creative method to interact with an augmented reality (AR) system called PhoneCursor, which is incorporated onto head-mounted displays. PhoneCursor is designed to offer more intuitive, portable and natural interaction. Its technical realization combines the gyroscope and acceleration sensor based on mobile phone. In view of the target selection ability as a base function of PhoneCursor, it also enhances the performance in solving some difficult selection problems in AR, including far and small object selection, 3D occlusion, dense object selection, and batch selection problem. An experiment based on the ISO 9241-9 standard is conducted to investigate this ability, wherein the PhoneCursor technique is compared with the widely used and well-performing head-movement-based selection technique. Results show that PhoneCursor performs better in terms of movement time and throughput than the conventional technique. Some applications are further proposed to showcase the potential of using the PhoneCursor technique in AR scenarios.
Study A Public Key in RSA Algorithm

To transmit sensitive information over the unsafe communication network like the internet network, the security is precarious tasks to protect this information. Always, we have much doubt that there are more chances to uncover the information that is being sent through network terminals or the internet by professional/amateur parasitical persons. To protect our information we may need a secure way to safeguard our transferred information. So, encryption/decryption, stenographic and vital cryptography may be adapted to care for the required important information. In system cryptography, the information transferred between both sides sender/receiver in the network must be scrambled using the encryption algorithm. The second side (receiver) should be outlook the original data using the decryption algorithms. Some encryption techniques applied the only one key in the cooperation of encryption and decryption algorithms. When the similar key used in both proceeds is called symmetric algorithm. Other techniques may use two different keys in encryption/decryption in transferring information which is known as the asymmetric key. In general, the algorithms that implicated asymmetric keys are much more secure than others using one key. RSA algorithm used asymmetric keys; one of them for encryption the message, and is known as a public key and another used to decrypt the encrypted message and is called a private key. The main disadvantage of the RSA algorithm is that extra time is taken to perform the encryption process. In this study, the MATLAB library functions are implemented to achieve the work. The software helps us to hold very big prime numbers to generate the required keys which enhanced the security of transmitted information and we expected to be difficult for a hacker to interfere with the private information. The algorithms are implemented successfully on different sizes of messages files.
M-Learning: Positioning the Academics to the Smart devices in the Connected Future

M-learning is about using the massive growth of mobile technologies to benefit learners and learning. As computers and the internet become essential educational tools, the technologies become more portable, affordable, effective and easy to use. Using portable computing devices (such as laptops, tablet PCs, PDAs, and smartphones) with wireless networks enables mobility and mobile learning, allowing college teaching and learning to extend to spaces beyond the traditional classroom. Within the classroom, mobile learning gives university instructors and learners increased flexibility and new opportunities for interaction. Decision support systems can play an important role in decision making for the interaction of instructors and students. Mobile technologies can support learning experiences at the university level that are collaborative, accessible, and integrated with the world beyond the classroom educative learning initiative. In the proposed framework we will use a decision support system for taking some decisions using the system. The system will notify students when they need alert of lecture time, low attendance, meeting time, exam schedule notification, result etc. Also, the Framework will take quizzes periodically for improving the quality of learning of each class and send the result to the student as well as faculty and add this result in the record of the student in university. Students can ask general and technical questions from the system, system reply answer of general questions and send technical questions to the faculty, faculty reply through the system. The student also votes at the end of every lecture, if the system found against vote then send the message to the concerned faculty for improvement in the lecture.Â My research will be more helpful for students and university teachers to increase flexibility and interaction with students more effectively.
Machine learning for learner English

Abstract This paper discusses machine learning techniques for the prediction of Common European Framework of Reference (CEFR) levels in a learner corpus. We summarise the CAp 2018 Machine Learning (ML) competition, a classification task of the six CEFR levels, which map linguistic competence in a foreign language onto six reference levels. The goal of this competition was to produce a machine learning system to predict learners’ competence levels from written productions comprising between 20 and 300 words and a set of characteristics computed for each text extracted from the French component of the EFCAMDAT data ( Geertzen et al., 2013 ). Together with the description of the competition, we provide an analysis of the results and methods proposed by the participants and discuss the benefits of this kind of competition for the learner corpus research (LCR) community. The main findings address the methods used and lexical bias introduced by the task.
Geospatial data and artificial intelligence technologies as innovative communication tools for quality education and lifelong learning

The uniqueness of open and distance learning (ODL) lies in its wide reach to a large audience simultaneously in different locations. No better system than geospatial data and artificial intelligence technologies (GDAITs) can achieve this. Globally, the current trend is to use GDAITs to improve the quality of life and productivity. Education is important for any country’s economy as it enhances the overall life expectancy. Application of GDAITs in educational sector, through broadcast digitization, publishing technologies will record greater achievements in the standard of learning and the literacy of populations. At certain ages in life, people develop apathy towards learning, thus, they are cut off from additional education that could provide them with lifelong learning. With GDAITs, they can be reached with quality education anywhere. Students have constraints of time, space, and finance, for acquisition of study materials. GDAITs are able to create and deploy seamless applications which can collapse these constraints and improve the learning curves of learners. This study investigates the exposure of youths to GDAITs and the influence on their learning patterns. Gerbner’s cultivation theory serves as the theoretical framework. A survey of 200 undergraduate Nigerian students was conducted, using random sampling technique. Findings show that Nigerian youths are highly exposed to GDAITs. THw paper concludes that GDAITs contribute positively and negatively to development in diverse human activities. However, it is highly effective in fostering communication education and research in Nigeria. It recommended that information and communication technology should be taught at all levels of education, so that Nigerians can develop critical minds to distinguish what GDAITs can and cannot do. Media houses should continue to establish platforms to check fake news emanating from social media. Also, attention needs to be focused on media content to ensure that there are enough programmes that would enhance communication education in Nigeria, without fake news parasitism.&#x0D; Keywords: GDAITs, Communication education, Learning processes, Social media, Digitization
Different Machine Learning based Approaches of Baseline and Deep Learning Models for Bengali News Categorization

Today's universe is the type of world where everyone thrives to live in virtual life.According to the perspective of the present time, the online news portal holds a major door to that gradually increasing greedy life.So around the globe, the various platform has been developed to fulfill the requirement of mankind.A heavy load of work has been carried out for making this platform autonomous in the English language.That's why the machine learning approach is quite a fully developed field in English in news classification.But it can't be said the same for Bangla language.These put in the inspiration to do a research on this topic.So, here Bangla news which has been collected from newspapers and gathered around to make a Bengali Corpus.After preprocessing the news text, different sorts of procedures to classify the news text using baseline and deep learning models of Machine Learning are applied.
Leveraging Linguistic Context in Dyadic Interactions to Improve Automatic Speech Recognition for Children

Automatic speech recognition for child speech has been long considered a more challenging problem than for adult speech. Various contributing factors have been identified such as larger acoustic speech variability including mispronunciations due to continuing biological changes in growth, developing vocabulary and linguistic skills, and scarcity of training corpora. A further challenge arises when dealing with spontaneous speech of children involved in a conversational interaction, and especially when the child may have limited or impaired communication ability. This includes health applications, one of the motivating domains of this paper, that involve goal-oriented dyadic interactions between a child and clinician/adult social partner as a part of behavioral assessment. In this work, we use linguistic context information from the interaction to adapt speech recognition models for children speech. Specifically, spoken language from the interacting adult speech provides the context for the child’s speech. We propose two methods to exploit this context: lexical repetitions and semantic response generation. For the latter, we make use of sequence-to-sequence models that learn to predict the target child utterance given context adult utterances. Long-term context is incorporated in the model by propagating the cell-state across the duration of conversation. We use interpolation techniques to adapt language models at the utterance level, and analyze the effect of length and direction of context (forward and backward). Two different domains are used in our experiments to demonstrate the generalized nature of our methods - interactions between a child with ASD and an adult social partner in a play-based, naturalistic setting, and in forensic interviews between a child and a trained interviewer. In both cases, context-adapted models yield significant improvement (upto 10.71% in absolute word error rate) over the baseline and perform consistently across context windows and directions. Using statistical analysis, we investigate the effect of source-based (adult) and target-based (child) factors on adaptation methods. Our results demonstrate the applicability of our modeling approach in improving child speech recognition by employing information transfer from the adult interlocutor.