Reviewerly/exHarmony huggingface dataset:
Application of fuzzy logic in video recommendation system for syllabus driven E-learning platform

Video sharing websites today generate petabytes of data every second. Determining the educational value and quality of these videos is challenging. The paper presents a syllabus driven E-Learning platform and challenges in ranking videos from multiple platforms and solutions.Acharya is a syllabus-driven E-learning platform that allows the public to upload a new video or link/map videos uploaded in video sharing websites to the syllabus topics. The approach made it possible to provide multiple videos on the same topic to the public. However, this presents a new challenge, How to rank these videos? What metrics are to be considered? How to utilize existing ratings of the video to our website?.A user may map a video uploaded on Youtube to the Acharya E-learning platform. It may already have a lot of views, comments, ratings, etc. The video may be old, newly uploaded, or duplicate. How do we integrate a new video into the platform?. Another challenge is in determining the relevance of the video to the topic mapped. A popular video from Youtube may contain many likes but maybe only loosely associated with the topic in the syllabus.This paper proposes a fuzzy-based ranking system that considers video creation time, total watch time, video length, comments, rating in the parent website and integration time, etc. in our platform. We use Sentiment analysis to understand the domain, which will also be input. We combine this information to rank the videos.
On the pitfalls of learning with limited data: A facial expression recognition case study

Deep learning models need large amounts of data for training. In video recognition and classification, significant advances were achieved with the introduction of new large databases. However, the creation of large-databases for training is infeasible in several scenarios. Thus, existing or small collected databases are typically joined and amplified to train these models. Nevertheless, training neural networks on limited data is not straightforward and comes with a set of problems. In this paper, we explore the effects of stacking databases, model initialization, and data amplification techniques when training with limited data on deep learning models' performance. We focused on the problem of Facial Expression Recognition from videos. We performed an extensive study with four databases at a different complexity and nine deep-learning architectures for video classification. We found that (i) complex training sets translate better to more stable test sets when trained with transfer learning and synthetically generated data, but their performance yields a high variance; (ii) training with more detailed data translates to more stable performance on novel scenarios (albeit with lower performance); (iii) merging heterogeneous data is not a straightforward improvement, as the type of augmentation and initialization is crucial; (iv) classical data augmentation cannot fill the holes created by joining largely separated datasets; and (v) inductive biases help to bridge the gap when paired with synthetic data, but this data is not enough when working with standard initialization techniques.
Development and Testing of Garbage Detection for Autonomous Robots in Outdoor Environments

In Japan, there is a growing concern about labor shortages due to the declining birthrate and aging population, and there are high expectations for robots to help solve such social problems and create industries. However, due to the prohibition of public road tests in Japan, there are few examples of actual applications of robots. Therefore, considerations and problems in the practical application of robots are still unclear. In this paper, by focusing on the implementation of garbage collection technology, we have developed an autonomous garbage collection robot using deep learning. In addition, we have verified the usefulness of our garbage detection technology in outdoor environments by conducting actual demonstrations at HANEDA INNOVATION CITY, which is a large-scale commercial and business complex belonged private property, Utsunomiya University, and Nakanoshima Challenge 2019, which is a field of demonstration experiment in the outdoor environment. Our garbage detector was designed to detect cans, plastic bottles, and lunch boxes automatically. Through experiments on test data and outdoor experiments in the real-world, we have confirmed that our detector has a 95.6% Precision and 96.8% Recall. Conparisons to other state-of-the-art detectors are also presented.
An Algorithm for Semantic Vectorization of Video Scenes - Applications to Retrieval and Anomaly Detection

Video scene retrieval and anomaly detection are important problems in the area of computer vision that share a common concept, of vectorization of the input image frames. We propose here a new vectorization approach and a fast object tracking algorithm. First step is to use any of the existing methods for recognition of objects in image frames. The subsequent step is our key contribution, to use information inside these objects, tracking and generation of semantic vectors for a sequence of image frames. We introduce a novel way of ultra high speed object tracking using a density based clustering of local vector representation of objects across video frames. The vectorization results in semantic features involve object types, their identifiers and movement information. The algorithm has been validated for its ability to retrieve scenes having the highest similarity, over a subset of the YouTube 8M data set on about 1200 videos having 36651 sub-scenes. One type of validation is, among the frames that were closest to the query scene, the fraction of them having common picture characteristics is 90%. The second type of validation is, among the successive frames with a little time gap, a similarity of more than 90% is recorded as desired. The vectorization algorithm is tested for its usefulness to a different problem scenario of anomaly detection in video frames. The vectorization performed well for qualitative and quantitative evaluation on standard and customized anomaly detection data sets of videos and results are reported here.
Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec)

In Collaborative Filtering methods, tailored recommendations cannot be obtained when the user-item matrix is sparse (i.e., has low user-item interactions such as item ratings or purchases). Conventional recommendation systems (ChoiRec12, HPCRec18, HSPRec19) utilizing mining techniques such as clustering, frequent and sequential pattern mining along with click and purchase similarity measures for item recommendation cannot perform well when the user-item interactions are less, as the number of items keep increasing rapidly. Additionally, they have not explored the integration of semantic information of products extracted from customers' purchase histories into the item matrix and the pattern mining process. To address this problem, this paper proposes (SEMSRec) which integrates semantic information of E-commerce products extracted from purchase histories into all phases of recommendation process (pre-processing, pattern mining and recommendation). This is achieved by i) learning semantic similarities between items from customers' purchase histories using Prod2vec model, ii) leveraging this information to mine semantically rich sequential purchase patterns and, iii) enriching the item matrix with semantic and sequential product purchase information before applying item based collaborative filtering. Thus, SEMSRec can provide Top-K personalized recommendations based on semantic similarities between items without the need for users' ratings on items. Experimental results on publically available E-commerce data set show that SEMSRec provides more relevant recommendations over other existing methods.
Blockchain-Based Transparent and Traceable Halal Food Supply Chain Management Systems

Halal food is gaining attention among Muslims and non-Muslims alike due to its nature of ensuring food is free from any impurities or contamination and hygienically prepared. The growing demand for Halal food has resulted in several food-producing companies to certify their products as Halal. However, with existing supply chains, there is no authenticity of these products being Halal. To ensure Halal food authenticity, the technology of blockchain is proposed as a viable solution. In this chapter, the applicability and usability of blockchain technology in food supply chain management systems is studied and highlighted. The study depicts that how trackability and traceability of the blockchain networks can effectively aid in maintaining the Halal integrity of food products by presenting various use cases. Technological shift for food supply chains over blockchains will result in more transparent, secure, and resilient supply chains. This will bring variety of health and economic benefits to food producing business and consumers.
SECI-GAN: Semantic and Edge Completion for dynamic objects removal

Image inpainting aims at synthesizing the missing content of damaged or corrupted images to produce visually realistic restorations; typical applications are in image restoration, automatic scene editing, super-resolution, and dynamic object removal. In this paper, we propose Semantic and Edge Conditioned Inpainting Generative Adversarial Network (SECI-GAN), an architecture that jointly exploits the high-level cues extracted by semantic segmentation and the fine-grained details captured by edge extraction to condition the image inpainting process. SECI-GAN is designed with a particular focus on recovering big regions belonging to the same object (e.g. cars or pedestrians) in the context of dynamic object removal from complex street views. To demonstrate the effectiveness of SECI-GAN, we evaluate our results on the Cityscapes dataset, showing that SECI-GAN is better than competing state-of-the-art models at recovering the structure and the content of the missing parts while producing consistent predictions.
Research on Image Classification of Lightweight Convolutional Neural Network

Convolutional neural networks are widely used in related tasks in the image field. However, in order to achieve high accuracy, modern convolutional neural networks often contain a large number of hidden layers and training parameters, resulting in a substantial increase in computing costs. Training and deployment of convolutional neural network models require a large amount of computing and storage resources, which is difficult to apply to smart mobile devices, embedded devices and IoT devices. In order to make the network more efficient, the existing lightweight convolutional neural networks often have shallow depth and limited feature extraction capabilities, and there is still a large room for improvement in accuracy. In response to these problems, a lightweight convolution unit is designed to replace the traditional convolution operation in this paper, and a lightweight convolutional neural network LCUNet based on the VGG-16, VGG-19 and ResNet network structure are designed. These networks are used to do image classification experiments on CIFAR-10 dataset. The experimental results show that the lightweight convolutional neural networks designed in this paper are better than the current efficient lightweight convolutional neural network MobileNet series and ShuffleNet series, and the amount of parameters and calculations are more less.
Detection of small tumors of the brain using medical imaging

Nowadays, there is much research aimed at detecting cancer, most of it based on medical image processing. This chapter presents a software application, developed by the authors, for detecting cancer by means of image processing algorithms applied to the images contained in the Digital Imaging and Communications in Medicine (DICOM) files. The purpose of the software application thus created is to develop a technology by which physicians can automatically analyze and detect cancerous formations, otherwise undetectable by classical imaging techniques. The application provides the user with a simple interface through which the DICOM image set can be selected. The images are processed to detect tumors, especially of small size, to obtain a certain diagnosis. Another functionality of the software is that the user can select the results obtained from a previous examination of a patient to compare them with the actual results. This option allows the doctor to have a view of the evolution of the cancer throughout the treatment period so that he/she can see if the patient's condition has improved or worsened. Considering that the most common primary tumors, which metastasize to the brain, are lung cancer, breast cancer, and melanoma, we also present use of the application for images related to lung cancer.
Immersive Analytics Application in Smart Agriculture and Animal Behavior

Immersive Analytics (IA) is potentially the most impactful new technology to be used in the recent years. This technology can integrate data analytics techniques, multisensory input, and output along the reality-virtuality continuum into immersive environments that allow the operator to have more interaction with complex data. IA is a disruptive mechanism for behavior research and treatment in controlled environments. Animal's behavioral interactions with its own environment, and other organisms, discloses significant information about the ecosystem. The aim of this paper is to provide a survey on the application of immersive analytics in two folds: i) the impact of immersive analytics in the smart farming and how this technology could help farmers to analyze and monitor their herd using more intelligent and efficient approaches, and ii) behavioral analysis of animals using different simulated environments and investigate their natural response to the immersive continuum.
Profile of teachers’ integrated science curricula that support by intelligent tutoring systems

Abstract Learning innovation by involving technology has helped the quality of education according to the concept of the Internet of Things (IoT). Intelligent tutoring systems are form of that support for science learning through science teacher competency improvement programs. This study aims to describe the types of science integration supported by ITS. A total of 29 science teachers joined this program which was implemented in blended learning. The science teacher lesson plan products were analyzed descriptively. The results show that the development of integrated, immersed and networked type lesson plans is in accordance with innovative learning, because of the scope of integration, learning themes, and appropriate learning activities. These results also demonstrate the effectiveness of using ITS to assist teachers in providing educational suggestions for developing integrated science learning.
Development of mobile learning: Basis of ethnopedagogy of Baduy Community, Banten Province

Abstract Educational development in the Industrial Revolution 4.0 and the law of the Republic of Indonesia No. 11 of 2019 (National System of Science and Technology) can affect to digital learning. In regard to this, it is necessary to develop an instructional media mobile that is interactive towards the development of technology based on the traditional and cultural knowledge. This media development was systematically designed phases such as preliminary phase analysis of material needs and characters of the students; and phase of developmental design using flash devices based on the traditional and cultural knowledge of Baduy Community. The next phase was validation and material review by the experts: 3 Chemical Engineering lecturers, 2 Chemical Teachers and 3 Communication Science lecturers. Should the media claimed to be valid, trial was conducted by researchers. Media trial and revision on a small group comprised of 10 students and 2 teachers. Then, it was followed by a larger group comprised of 70 students and 3 teachers. The result analysis of the media validation test is based on 26 Likert scale questions using SPSS tools and two open-ended questions. The result is valid with a significance &lt;0.05 with a Cronbach’s Alpha value of 0.877&gt; 0.05 (Reliable).
Learning Latent Factors from Diversified Projections and its Applications to Over-Estimated and Weak Factors

Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weak serial dependence, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases it is difficult to analyze the eigenvectors of the data matrix. To address this issue, we propose simple estimators of the latent factors using cross-sectional projections of the panel data, by weighted averages with pre-determined weights. These weights are chosen to diversify away the idiosyncratic components, resulting in ``diversified factors". Because the projections are conducted cross-sectionally, they are robust to serial conditions, easy to analyze and work even for finite length of time series. We formally prove that this procedure is robust to over-estimating the number of factors, and illustrate it in several applications, including post-selection inference, big data forecasts, large covariance estimation and factor specification tests. We also recommend several choices for the diversified weights.
Probabilistic truthlikeness, content elements, and meta-inductive probability optimization

Abstract The paper starts with the distinction between conjunction-of-parts accounts and disjunction-of-possibilities accounts to truthlikeness (Sects. 1, 2). In Sect. 3, three distinctions between kinds of truthlikeness measures (t-measures) are introduced: (i) comparative versus numeric t-measures, (ii) t-measures for qualitative versus quantitative theories, and (iii) t-measures for deterministic versus probabilistic truth. These three kinds of truthlikeness are explicated and developed within a version of conjunctive part accounts based on content elements (Sects. 4, 5). The focus lies on measures of probabilistic truthlikeness, that are divided into t-measures for statistical probabilities and single case probabilities (Sect. 4). The logical notion of probabilistic truthlikeness (evaluated relative to true probabilistic laws) can be treated as a subcase of deterministic truthlikeness for quantitative theories (Sects. 4–6). In contrast, the epistemic notion of probabilistic truthlikeness (evaluated relative to given empirical evidence) creates genuinely new problems, especially for hypotheses about single case probabilities that are evaluated not by comparison to observed frequencies (as statistical probabilities), but by comparison to the truth values of single event statements (Sect. 6). By the method of meta-induction, competing theories about single case probabilities can be aggregated into a combined theory with optimal predictive success and epistemic truthlikeness (Sect. 7).
Using Artificial Intelligence In Enhancing Banking Services

Artificial intelligence and machine learning are transforming the business nowadays as they have a wide range of uses in all aspects of any organization. This paper tackles how machine learning can help in the marketing and sales department in any business or bank as will be discussed in this paper. Machine learning can predict upcoming sales, predict customer preference, identify new clients, and reduce the cost and time of the sales agents. In the near future, the organization that will not go through machine learning will not be able to survive in the upcoming era of artificial intelligence technology. This paper will test various models of machine learning to find the best method possible that will be able to accurately predict sales.
Deep Learning for Robot Vision

Deep learning comes under a class of machine learning where we use it for extremely high-level output, like recognition of images, etc. It has been used in pattern recognition over a vast area such as handmade crafts to extract the data from learning procedures. At present, it has gained a great significance in robot vision. In this paper, we show how neural networks play a vital role in robot vision. Image segmentation, which is the initial step, is used to preprocess the images and videos. The multilayered artificial neural networks have a lot more applications. It can be applied in drug detection, military bases, and many more. The main objective of this paper is to review how deep learning algorithms and deep nets can be used in various areas of robot vision. There are some predefined deep learning algorithms that are available in the market, which are used here to perform this comparative study. These will help us to have a clear insight while building vision systems using deep learning.
Evaluation of NoSQL Databases Features and Capabilities for Smart City Data Lake Management

A data lake means there’s an immense data resource or repository. Data lake stores enormous data and uses advanced analytics to pair data from various sources with different types of structured, semi-structured and un-structured information. The lifeblood of a smart city is data. Effective data management is not limited to data collection and storage, but must also involve shared and combined data so that it can be accessed, analyzed and used across agencies, within organizations, and even across the society. NoSQL is a form of database that is becoming increasingly common among web firms. NoSQL databases are non-tabular and store data rather than relational tables in a different way. NoSQL databases come in a variety of forms, mainly document, key-value, wide-column, and graph based on their data model. NoSQL offers easier scalability, better performance compared to conventional relational databases, and consists of many data types, such as document, key-value, wide-column, and graph. This work studies NoSQL database features and capabilities by considering four indicators, namely performance, scalability, accuracy and complexity, in order to measure the compatibility of NoSQL databases with multiple data types. The result of the experiment reveals that when accommodating massive data volumes, MongoDB is the most stable NoSQL database.
A New Enhanced Arabic Light Stemmer for IR in Medical Documents

This paper introduces a new enhanced Arabic stemming algorithm for solving the information retrieval problem, especially in medical documents. Our proposed algorithm is a light stemming algorithm for extracting stems and root... | Find, read and cite all the research you need on Tech Science Press
Analysis of Writing Errors in Business English Writing for Business-related Majors: —Taking Dalian Neusoft University of Information as an Example

Business English writing is a kind of specialized English writing that applies English writing to the business field. It aims to cultivate students' ability and skills to use English written language for business communication to achieve various business purposes. This research takes Dalian Neusoft University of Information (DLNUI) as an example to make a detailed analysis on the frequency distribution of error types in business writings for non-English majors or non-business English majors of the School of Information and Business Management of DLNUI. By employing S. Pit Corder and R. Ellis's Error Analysis theory and the identification of different error types, the paper aims to analyze the causes of errors incurred in students' business English writing and propose corresponding teaching strategies.