Reviewerly/exHarmony huggingface dataset:
(Retracted) Cross-spectral human behavior recognition based on deep convolutional networks for global temporal representation

The purpose of behavior recognition is to recognize the actions of the human body in action. It plays a great role in surveillance, video recommendation, and human-computer interaction with video. With the rise of neural networks, behavior recognition has also continued to develop and progress and has reached a relatively advanced level. However, behavior recognition is still insufficient in recognizing complex human movements and recognizing videos in different bands. To solve this problem, this paper establishes a convolutional neural network (CNN) cross-spectral human behavior recognition algorithm based on global time domain representation. It adopts the method of time-domain feature extraction, construction of optimized convolutional neural network layers, and global time-domain cross-spectrum construction. It also uses videos from the unified compliance framework (UCF)-sports and UCF-11 datasets for experiments. Experiments show that the algorithm achieves an average accuracy of 90% in the behavior recognition of UCF-sports. It still maintains an average accuracy rate of >90 % in the more complex behavior recognition of UCF-11, and the highest accuracy rate is 93%.
Rancang Bangun E-Learning Menggunakan Framework Bootstrap

An e-learning learning process is a technological development that can complement the current learning model, namely the face-to-face conventional model so that the existence of e-learning can help implement an efficient learning system. If e-learning is not working well or is still done manually, it is difficult to get fast and precise results in a short time. Web-based e-learning is an alternative learning media to support and complement conventional learning models. An e-learning built using the Bootstrap framework is an e-learning web development using a framework that will get very good and interesting results as well as web display results that are better and dynamic also makes the website more compatible with web browsers and various devices. The results of web e-learning using the Bootstrap framework can make it easier for users to interact and provide more efficient results in the learning process.
A privacy-preserving student status monitoring system

Abstract Timely feedback of students’ listening status is crucial for teaching work. However, it is often difficult for teachers to pay attention to all students at the same time. By leveraging surveillance cameras in the classroom, we are able to assist the teaching work. However, the existing methods either lack the protection of students’ privacy, or they have to reduce the accuracy of success, because they are concerned about the leakage of students’ privacy. We propose federated semi-supervised class assistance system to evaluate the listening status of students in the classroom. Rather than training the semi-supervised model in a centralized manner, we train a semi-supervised model in a federated manner among various monitors while preserving students’ privacy. We also formulate a new loss function according to the difference between the pre-trained initial model and the expected model to restrict the training process of the unlabeled data. By applying the pseudo-label assignment method on the unlabeled data, the class monitors are able to recognize the student class behavior. In addition, simulation and real-world experimental results demonstrate that the performance of the proposed system outperforms that of the baseline models.
MoFiM: A morphable fish modeling method for underwater binocular vision system

Abstract Fish morphology is an essential basis for fishery management, as it can reflect the growth status of fishes. Noncontact 3D reconstruction of underwater fish is a new way to obtain fish morphology. While it is difficult to reconstruct fish on account of the inadequate information caused by fish swimming and poor underwater imaging. This article introduces a morphable fish modeling method for the underwater binocular vision system. First, we define a fish representation based on selected landmarks. Then, we propose a chirality‐supervision incorporated hourglass network to estimate fish orientation and fish 2D landmarks simultaneously, and calculate fish 3D landmarks by triangulation. Next, we propose a fish modeling method which is based on 3D landmarks and introduce the optimization procedure of fish modeling. Finally, we obtain the complete 3D fish model corresponding to the input images. To train our network and build a parametric model, we constructed an underwater vision dataset and fish instance dataset respectively. We conducted experiments with grass carp as an example, and the experimental results show that our method can achieve effective fish modeling and is useful for noncontact measurement of underwater fish.
Type-2 Fuzzy Sets and Newton’s Fuzzy Potential in an Algorithm of Classification Objects of a Conceptual Space

Abstract This paper deals with Gärdenfors’ theory of conceptual spaces. Let $${\mathcal {S}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> </mml:math> be a conceptual space consisting of 2-type fuzzy sets equipped with several kinds of metrics. Let a finite set of prototypes $$\tilde{P}_1,\ldots ,\tilde{P}_n\in \mathcal {S}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:mo>…</mml:mo> <mml:mo>,</mml:mo> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mi>n</mml:mi> </mml:msub> <mml:mo>∈</mml:mo> <mml:mi>S</mml:mi> </mml:mrow> </mml:math> be given. Our main result is the construction of a classification algorithm. That is, given an element $${\tilde{A}}\in \mathcal {S},$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>A</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mo>∈</mml:mo> <mml:mi>S</mml:mi> <mml:mo>,</mml:mo> </mml:mrow> </mml:math> our algorithm classifies it into the conceptual field determined by one of the given prototypes $$\tilde{P}_i.$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mover> <mml:mi>P</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mi>i</mml:mi> </mml:msub> <mml:mo>.</mml:mo> </mml:mrow> </mml:math> The construction of our algorithm uses some physical analogies and the Newton potential plays a significant role here. Importantly, the resulting conceptual fields are not convex in the Euclidean sense, which we believe is a reasonable departure from the assumptions of Gardenfors’ original definition of the conceptual space. A partitioning algorithm of the space $$\mathcal {S}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>S</mml:mi> </mml:math> is also considered in the paper. In the application section, we test our classification algorithm on real data and obtain very satisfactory results. Moreover, the example we consider is another argument against requiring convexity of conceptual fields.
Position information attention networks for explosive mobile phone classification

There is a growing concern about public security, especially the discovery of explosives hidden in mobile phones during security checks. However, there is almost no public dataset of explosive mobile phones to study this topic. We contribute the first explosive mobile phone benchmark dataset for security screening, named explosive mobile phones x-ray image dataset, which will be publicly available. Note that explosive mobile phone classification is a typical class imbalance task. Although the number of explosive mobile phones is far smaller than the number of normal mobile phones, one missed phone can cause a huge loss of life and property. To accurately identify explosives hidden in mobile phones, we propose a module called position information attention module (PIAM). Benefiting from aggregating the position information encoded in networks along the channel and spatial domains, PIAM highlights informative features of explosives. In addition, PIAM combines with other networks effectively at a low cost, empowering them with the ability to identify important details. Furthermore, in the face of the class imbalance, we propose a sample-oriented coefficient called sample cost with an update rule. Extensive experimental results show that PIAM and sample cost significantly improve the performance of many excellent networks in explosive mobile phone classification.
Content Analysis on Homelessness Issues at Airports by News Media Mining

The number of homeless people at airports has increased in recent years. As airports are safe, transit-accessible, convenient, and climate-controlled facilities with food and amenities, these places are attractive to homeless people who need a safe and secure place to stay. The main struggle of airports in this regard is maintaining a balance between customers, who are mostly the traveling public, and dealing with homeless people delicately. Moreover, because of their poverty and insufficient or no access to healthcare, these people suffer from physical and mental issues. With the COVID-19 pandemic, this problem became more critical. Many news media outlets started to report on homelessness at airports. News-framing impacts have some contribution in the context of this issue. However, the impact of news coverage on “airport and homelessness” has not yet been studied. News-framing effects have been identified in the context of tourist destinations. Although many studies have explored homelessness and transit, this issue at airports has not been well studied. This study provides a brief overview of the issue of homelessness in the transportation domain, including transit and aviation. Additionally, this study collected news articles related to “airport and homelessness” (71 articles) both during the COVID-19 pandemic (March 1, 2020–July 21, 2021) and before the pandemic (before March 1, 2020). These news articles contain around 50,000 words. As the data is unsupervised in nature, a text network analysis was performed to determine the latent information from these textual contents. The findings of this study can shed some light on this scientifically unexplored but widely discussed issue.
A User Perspective on HTR Methods for the Automatic Transcription of Rare Scripts: The Case of <i>Codex Runicus</i>

Recent breakthroughs in Artificial Intelligence, Deep Learning, and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraining. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare, and analyse different transcription methods for rare scripts. We evaluate their performance in a real-use case of a medieval manuscript written in the runic script ( Codex Runicus ) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.
Modified block-matching algorithm for moving object tracking in video surveillance

Video surveillance has risen as one of the most promising methods for people who live alone in their dwellings. Few video surveillance innovations have recently been introduced. However, due to various changes in illumination, abrupt shifts in target appearance, identical non-target artifacts in the background, and occlusions, developing a reliable video surveillance algorithm remains a difficult challenge. This work attempts to introduce a new framework for moving object detection and tracking by following four major phases: “Video-to-Frame Conversion, Pre-Processing, Background Subtraction, Feature-Based Multi-object Detection, Multi-object Tracking by Filtering”. Initially, in the Video-to-Frame Conversion process, the recorded input video clips are transformed into distinct frames. During pre-processing, the noise is removed from the video frame using a filtering approach, and thereby the nature of the images will be enhanced. In the proposed work, a Weiner filter is used to remove noise and other undesirable features during the pre-processing. Then, to distinguish the frontal areas of objects, background subtraction is performed using the neutrosophic set in noiseless video frames (pre-processed frames). The objects in the background-subtracted frames are separated using Improved Region Growing (IRG) segmentation model in the Feature-Based Multi-object Detection phase. The objects in the frames are determined from this segmented image. The Modified Full Search Algorithm is being used to track the object (motion estimation) on the video frame after it has been identified in the segmented phase. The Modified full search block matching algorithm (MFSA) is introduced in this research work to find the appropriate mobility. Promising results have been obtained by the proposed work, and also the mathematical excellence of the new method is also proven over other state-of-the-art models.
Student’s performance evaluation according to the cognitive domain: fuzzy logic approach

Abstract Teaching learning process is one of the important aspects in academic performance evaluation of students. This gives an idea of how much a student has gained the knowledge and skill in a particular subject during the entire season. In this paper, a multi criteria-based decision support system based on six levels of revised Bloom’s cognitive domain has been presented to measure the cognitive knowledge and skill acquired by a student. The fuzzy set theoretic approach has been adopted to overcome the vagueness/fuzziness present in the evaluation process and the inexact causal relationship between input and output parameters has been addressed using the Mamdani fuzzy IF-THEN rules. Finally, a comparative study has been depicted between the marks obtain by a group of students in the exam with their cognitive knowledge score.
Towards Speech-Based Collaboration Detection in a Noisy Classroom

An Intelligent Orchestration System, such as our FACT [1], should act like an automated teaching assistant that helps teachers provide relevant, timely help. To do so, it needs to know what the students are doing and thus who needs help more than the others. This is especially important when students work in small groups and the teacher's ability to monitor every group frequently diminishes. This project is an attempt to investigate the feasibility and challenges of only using the students' speech to predict each group's collaboration status. We are using machine-learning techniques to build models that agree with our human annotator's collaboration status judgments.
Online Higher Education in the Wake of COVID-19: A Systems Thinking Perspective

Abstract COVID-19 has adversely affected almost all the spheres of life across the globe. As a result of subsequent lockdowns, Higher Education Institutes (HEIs) face the challenge of continuing educational services and overcoming related barriers. Developed countries have an upper edge in transitioning to remote learning models with systems in place for online HE. On the contrary, in several developing countries like Pakistan, HEIs had to step into the uncharted waters of online education. This paper adopts a systems thinking perspective and reviews the complex nature of online education by looking it from three levels i.e., micro, meso, and macro while discussing the response of HEIs in Pakistan during the COVID-19. It is imperative to understand the interactions of the various components and devise a tailored solution that works within the local context and aligns all three levels for online education solutions.
The Challenge of Willingness to Blockchain Traceability Adoption: An Empirical Investigation of the Main Drivers in China

With the rapid development of blockchain and of other new technologies, more and more traditional industries and new technologies have been integrated. This paper considers the practical application of blockchain technology in the field of modern agriculture and develops on the basis of technology-organization-environment (TOE) framework. Six variables were considered, including technical complexity (TC), expected revenue (PE), network diversity (ND), organizational innovation (OI), Degree of Government supervision (DG), and government support (GS), as the driving and inhibiting effects on the willingness to adopt blockchain traceability. The proposed research model was tested using structural equation model with data collected from 155 manufacturers in China. The results show that PE and GS have a positive impact on the willingness to use blockchain traceability and ND has a negative impact. The results will help government departments and relevant marketers to expand their understanding of the determinants affecting the adoption of blockchain traceability in agriculture, and provide valuable insights for the promotion of blockchain traceability in agriculture.
Arabic Speech Processing: State of the Art and Future Outlook

The aim of this article is to study the Arabic speech processing applications which have introduced the voice communication as a solution for specific situations (non-native speakers, speakers with voice disabilities, learners of Arabic vocabulary, speech recognition or speech synthesis). We present the principal applications of processing the Arabic spoken language accentuating the most challenges preventing obtaining better results. The current paper gives in detail the followed approaches and the applied techniques in the automatic processing applications of spoken Arabic, so it can be a reference study for researchers and developers who deal with this topic.
Design of Multiprocessor Architecture for Watermarking and Tracing Images Using QR Code

Several manipulations like copying, editing and diffusing multimedia tracks illegally through the internet and Peer to Peer networks do not represent any challenge even to simple customers but represent a dangerous phenomenon for the software industry. In this paper, the proposed technique consists in embedding a fingerprint, a QR code using an image watermarking and tracing approaches which are applied both on multiprocessor architecture. The proposal has respectively two novelties: using the QR-code as a tracing code provides many advantages as supporting a large amount of information in a compact format, damaging resiliency and addressing problems of computational costs and time of the existing tracing codes during the accusation step. The whole scheme is adapted to be supported by an embedded architecture. The experimental assessments show that the proposed choice has responded to the different requirements of the application.
The Black-Box Syndrome: Embracing Randomness in Machine Learning Models

Acknowledging the 'curse' of dimensionality, the educational sector has reasonably turned to automated (in some cases autonomous) solutions, in the process of extracting and communicating patterns in data, to promote innovative teaching and learning experiences. As a result, Learning Analytics (LA) and Educational Data Mining (EDM) have both been relying on various Machine Learning (ML) techniques to project novel and meaningful predictions. This inclusion has led to the need for developing new professional skills in the teaching community, that go beyond digital competence and data literacy. This paper seeks to address the issue of ML adoption in educational settings by using an interactive Exploratory Learning Environment (ELE) to test the potential impact of randomness on explainability. The goal is to investigate how misconceptions about stochasticity can lead to distorted projections or expectations, and potentially expose any lack of transparency (to teachers), indirectly affecting the overall trust in artificially intelligent tools.
Distributional Estimation of Personalized Second-Language Vocabulary Sizes with Wordlists that the Learner is Likely to Know

Vocabulary is essential for second language learners to read documents. Prior studies on vocabulary size estimation, especially in applied linguistics, have been based on the naive assumption that language learners acquire words based on the frequency of balanced corpora. As this assumption is often invalid, vocabulary size estimates have been somewhat inaccurate for educational use. Therefore, this study proposes a novel method for the distributional and more informative estimation of second language vocabulary size from vocabulary test results. To this end, our method makes personalized binary estimations regarding whether a learner knows a word, followed by aggregating the estimation results for many words for a learner. Experimental results using large vocabulary tests showed that our methods provided distributional and more informative estimations compared with baseline methods.
BERT-POS: Sentiment Analysis of MOOC Reviews Based on BERT with Part-of-Speech Information

Sentiment analysis and part-of-speech tagging are two main tasks in the field of Natural Language Processing (NLP). Bidirectional Encoder Representation from Transformers (BERT) model is a famous NLP model proposed by Google. However, the accuracy of sentiment analysis of the BERT model is not significantly improved compared with traditional machine learning. In order to improve the accuracy of sentiment analysis of the BERT model, we propose Bidirectional Encoder Representation from Transformers with Part-of-Speech Information (BERT-POS). It is a sentiment analysis model combined with part-of-speech tagging for iCourse (launched in 2014, one of the largest MOOC platforms in China). Specifically, this model consists of the part-of-speech information on the embedding layer of the BERT model. To evaluate this model, we construct large data sets which are derived from iCourse. We compare the BERT-POS with several classical NLP models on the constructed data sets. The experimental results show that the accuracy of sentiment analysis of the BERT-POS is better than other baselines.
Decorrelation of a Sequence of Color Images Through Hierarchical Adaptive Color KLT

In this work is presented new algorithm for Hierarchical Adaptive Color Karhunen–Loève transform (HACKLT) aimed at the decorrelation of sequences of color RGB images. To increase the decorrelation, on each triad of components of same color, obtained from three consecutive images, is applied Color KLT (CKLT). To achieve maximum decorrelation, for each three sequential color images is detected the optimum orientation of the 3-component vectors used for the calculation of their covariance matrix of size 3 × 3, in one of the directions x, y or z. The so chosen orientation enhances the diagonalization of the covariance matrix. The hierarchical organization of the algorithm permits the calculations to stop in the level, in which the covariance matrix is diagonalized. Besides, by using HACKLT is achieved very high power concentration in the first eigen images of the decomposition. Compared to the “classic” KLT, used for the vectors which represent a sequence of images and are oriented along the direction “z” (for example, time), the computational complexity of the new algorithm is twice lower. The high efficiency of HACKLT in respect of the decorrelation defines its most suitable application areas: video compression, computer vision, extraction of features for objects recognition, objects tracking in video sequences, etc.