Reviewerly/exHarmony huggingface dataset:
Clustering interval-valued data with adaptive Euclidean and City-Block distances

In several applications, data information is obtained in the form of intervals, such as the monthly temperature in a meteorological station or daily pollution levels in different locations. This paper proposes partitioning clustering algorithms for interval-valued data based on adaptive Euclidean and City-Block distances. Since some boundary variables may be more relevant for the clustering process, the proposals consider the joint weights of the relevance of the lower and upper boundaries of the interval-valued variables. Consequently, clusters of different shapes and sizes in some subspaces of the variables, even in specific boundaries of the interval-valued data, can be recognized. In addition, robust dissimilarity functions were introduced to reduce the influence of outliers in the data. The adaptive distances change at each iteration of the algorithms and can be different from one cluster to another. The methods optimize an objective function by alternating three steps for obtaining the representatives of each group, the cluster partition, and the relevance weights for the interval-valued variables. Experiments on synthetic and real data sets corroborate the robustness and usefulness of the proposed adaptive clustering methods.
Stroke Treatment Prediction Using Features Selection Methods and Machine Learning Classifiers

• Find the most suitable couple: Feature Selection method-Machine Learning method. • Select the most relevant primitives from a large set of attributes. • Predict the appropriate treatment for a patient suffering of stroke disease. Feature selection in data sets is an important task allowing to alleviate various machine learning and data mining issues. The main objectives of a feature selection method consist on building simpler and more understandable classifier models in order to improve the data mining and processing performances. Therefore, a comparative evaluation of the Chi-square method, recursive feature elimination method, and tree-based method (using Random Forest) used on the three common machine learning methods (K-Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed to select the most relevant primitives from a large set of attributes. Furthermore, determining the most suitable couple (i.e., feature selection method-machine learning method) that provides the best performance is performed. In this paper, an overview of the most common feature selection techniques is first provided: the Chi-Square method, the Recursive Feature Elimination method (RFE) and the tree-based method (using Random Forest). A comparative evaluation of the improvement (brought by such feature selection methods) to the three common machine learning methods (K- Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed. For evaluation purposes, the following measures: micro-F1, accuracy and root mean square error are used on the stroke disease data set. The obtained results show that the proposed approach (i.e., Tree Based Method using Random Forest, TBM-RF, decision tree classifier, DTC) provides accuracy higher than 85%, F1-score higher than 88%, thus, better than the KNN and NB using the Chi-Square, RFE and TBM-RF methods. This study shows that the couple - Tree Based Method using Random Forest (TBM-RF) decision tree classifier successfully and efficiently contributes to find the most relevant features and to predict and classify patient suffering of stroke disease.”
Joint compressing and partitioning of CNNs for fast edge-cloud collaborative intelligence for IoT

The artificial intelligence (AI) empowered advanced technologies have been widely applied to process in real-time the vast amount of data in the internet of things (IoT) for a fast response. However, traditional approaches to deploying AI models impose overwhelming computation and communication overheads. In this paper, we propose a novel edge-cloud collaborative intelligence scheme that jointly compresses and partitions Convolutional Neural Network (CNN) models for fast response in IoT applications. The proposed approach first accelerates a CNN by using an acceleration technique to generate new layers that can serve as candidate partitioning since their outputs are smaller than the unaccelerated layers. It then designs fine-grained prediction models to accurately estimate the execution latency for each layer in the CNN model, and finds an optimal partitioning. The proposed approach splits the compressed CNN model into two parts according to the optimal partitioning. The obtained two parts are deployed at the edge device and in the cloud, respectively, which collaboratively minimize the overall latency without compromising the accuracy of the deep CNN model. To the best of our knowledge, this is the first work that jointly compresses and partitions CNN models for fast edge-cloud collaborative intelligence considering both execution latency and communication latency. Experimental results show that the proposed technique can reduce the latency by up to 73.14% compared to five benchmarking methods.
Taxonomy of Factors Involved in Decision-Making to Sustain Organization Members’ Creativity

Creativity is highly valued in all areas of life, and it must be supported in the academic environment for the future prosperity of all organizations. This is the primary source of creativity for practice. The research presented is based on answering the following questions: Are an organization’s members willing to increase their motivation if the organization’s motivational efforts improve? What elements in decision-making are used to support the creativity and motivation of the organization’s members? The paper proposes a novel taxonomy of decision-making factors influencing organizations’ sustainable creativity based on linking the findings from the authors’ surveys. Its application will lead to an improvement in the organizations’ processes, especially in the process of education and knowledge generation. The sociological inquiry was used as the main data collection method. Other methods applied included content analysis, practical cases analysis, and comparison. Methods of mathematical-statistical analysis and deductive-inductive approach were used in the evaluation. This resulted in the confirmation of the impact of creative decision-making approaches by employees and managers on sustainable motivation and creativity in the organization. The taxonomy of decision-making factors for the support of sustainable creativity reflects the results of this original research combined with the findings summarized in the discussion.
Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set.
Fostering vocabulary learning: mind mapping app enhances performances of EFL learners

A number of Mobile-Assisted-Language-Learning (MALL) apps have been developed to enlarge students' vocabulary. However, few studies have discussed the potential to learn words deeply and productively. Therefore, this study evaluated the effectiveness of a mind mapping MALL app as an aid to assist northern Taiwan EFL participants in learning vocabulary. The research questions aimed to examine changes in vocabulary learning outcomes, and the perspectives the participants held regarding the app. Data were collected through tests (pretest, posttest and delayed posttest), visualized vocabulary knowledge mind mappings, and surveys. The posttest results showed the use of the mind mapping MALL app enhanced EFL vocabulary learning (t = 3.68, p<.05; Cohen's d = 0.81, r = 0.38). In addition, based on the questionnaire responses, experimental participants agreed that not only did the MALL app provide them with a feasible way to move from receptive ways of learning to productive ways of learning, but it also bolstered word consciousness and word retention. Analytical ANCOVA results also verified that learners benefited more from the mind mapping app with respect to learning performance in their delayed posttest (t = 3.45, p<.05; Cohen's d = 0.77, r = 0.36). The findings may be construed as an effective method towards learning foreign words and further exploring the potential of vocabulary learning and MALL.
Generalized Knowledge Distillation via Relationship Matching

The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and integrates it with the target model (a.k.a. the "student"), which expands the student's knowledge and improves its learning efficacy. Instead of enforcing the teacher to work on the same task as the student, we borrow the knowledge from a teacher trained from a general label space - in this "Generalized Knowledge Distillation (GKD)," the classes of the teacher and the student may be the same, completely different, or partially overlapped. We claim that the comparison ability between instances acts as an essential factor threading knowledge across tasks, and propose the RElationship FacIlitated Local cLassifiEr Distillation (stance-label confidence between models, ReFilled requires the teacher to reweight the hard tuples pushed forward by the student and then matches the similarity comparison levels between instances. An embedding-induced classifier based on the teacher model supervises the student's classification confidence and adaptively emphasizes the most related supervision from the teacher. ReFilled demonstrates strong discriminative ability when the classes of the teacher vary from the same to a fully non-overlapped set w.r.t. the student. It also achieves state-of-the-art performance on standard knowledge distillation, one-step incremental learning, and few-shot learning tasks.
Analyzing legal education mobile learner's behavior using deep learning under social media

Purpose Under emerging social media technology, mobile learners' behavior analysis and legality education have important practical significance. The research aims to detect the mobile learning (M-learning) learners' behavior in legality education under the background of the Internet era and improve the learning and teaching effect of online legality education and law popularization. Design/methodology/approach This paper proposes a model based on deep learning (DL) fuzzy clustering analysis (FCA), and bidirectional encoder and decoder (ENDEC) of converter model to detect the mobile learners' behaviors in online legality education under the current social media. Then, the effectiveness of the proposed model is tested. The proposed model expects to be applied to multimedia teaching and law popularization activities and provides some theoretical reference and practical value for improving the effectiveness of online teaching. Findings The experimental results show that in the learner behavior detection process of M-learning-oriented online legality education, the model's accuracy can reach 99.8%. The response time is shorter than other algorithms. Overall, the application effect of the proposed model and algorithm is good and can be applied in practice. Research limitations/implications The research results may lack universality due to the selected research methods. Therefore, researchers are encouraged to test the proposed methods further. In the future, it is necessary to expand the type and scale of text data to improve the accuracy of data detection. Practical implications The research results provide a specific theoretical reference and practical significance for improving the learning effect of online M-learning-oriented legality education. Originality/value This paper meets the needs of mobile learner behavior analysis based on social media.
Autonomous Mobile Robot Implemented in LEGO EV3 Integrated with Raspberry Pi to Use Android-Based Vision Control Algorithms for Human-Machine Interaction

Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. A prototype of a robot assembled using mechanical parts of a LEGO MINDSTORMS Robotic Kit EV3 and a Raspberry Pi controlled through servo algorithms of 2D and 2D1/2 vision approaches was implemented to tackle this challenge. This design is supported by simulations based on image, position, and a hybrid scheme for visual servo controllers. Practical implementation is operated using navigation guided by running up image-based visual servo control algorithms embedded in a Raspberry Pi that uses a control criterion based on error evolution to compute the difference between a target and sensed image. Images are collected by a camera installed on a mobile robotic platform manually and automatically operated and controlled using the Raspberry Pi. An Android application to watch the images by video streaming is shown here, using a smartphone and a video related to the implemented robot’s operation. This kind of robot might be used to complete field reactive tasks in the settings mentioned above, since the detection and control approaches allow self-contained guidance.
An improved coverage-oriented retrieval algorithm for large-area remote sensing data

With the rapid development of satellite technology, the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing. Hence, it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications. However, with the improvements in temporal and spatial resolution, remote sensing data have become fragmented, which brings challenges to data retrieval. At present, most data service platforms rely on the query engines to retrieve data. Retrieval results still have a large amount of data with a high degree of overlap, which must be manually selected for further processing. This process is very labour-intensive and time-consuming. This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area. The retrieval efficiency of this algorithm was analysed by applying different implementation practices: Arcpy, PyQGIS, and GeoPandas. The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.
Innovative Human Motion Sensing With Earbuds

Earbuds, ear-worn wearables, have attracted growing attention from both industry and academia. This trend has witnessed manufacturers embedding multiple sensors on earbuds to enrich their functionalities. For example, Apple AirPods, Sony WF-1000XM3, and Bose QuietControl 30, have been equipped with accelerometers for tapping interaction or multiple microphones for noise cancellation. On the other hand, the research community regards earbuds as a powerful personal-scale human sensing and computing platform. By integrating sensors like PPG, barometer, and ultrasonic sensors, researchers have been devising a plethora of earable sensing applications, such as blood pressure monitoring [1], facial expression recognition [2], and authentication [3].
Deep recurrent neural network‐based Aquila optimization‐based online shaming emotion analysis

Abstract Thanks to the emergence of social media and internet platforms, people currently have a plethora of possibilities for openly expressing their thoughts. As a result, it provides millions of ways to create intelligent systems; yet, some people take advantage of these platforms by insulting, harassing, or abusing others. As a result, emotional analysis of online shaming is necessary for avoiding the attackers' negative consequences. In this field, several studies are conducted to classify shaming and non‐shaming comments in order to identify shamers. The optimum classification, on the other hand, has yet to be established. As a result, we introduced a novel deep learning approach known as deep recurrent neural network based classifier, which categorizes the comments. Based on the category. To improve classification accuracy, we used the Aquila optimization algorithm, which improves shaming classification based on category. The experimental analysis is carried out by determining some performance indicators and comparing them to other existing works. The performance analysis reveals that the proposed method outperforms all other approaches.
A comparative analysis of two immersive virtual reality systems in the integration and visualization of natural hand interaction

Abstract It is generally accepted that the use of natural interaction provides a positive impact in Virtual Reality (VR) applications. Therefore, it is important to understand what is the best way to integrate and visualize this feature in VR. For this reason, this paper presents a comparative study of the integration of natural hand interaction in two immersive VR systems: a Cave Audio Visual Experience (CAVE) system –where users’ real hands are visible– and a non-see-through Head-Mounted Display (HMD) system –where only a virtual representation of the hands is possible–. In order to test the suitability of using this type of interaction in a CAVE and compare it to an HMD, we raise six research questions related to task performance, usability and perception differences regarding natural hand interaction with these two systems. To answer these questions, we designed an experiment where users have to complete a pick-and-place task with virtual balls and a text-typing task with virtual keyboards. In both systems, the same tracking technology, based on a Leap Motion device, was used. To the best of our knowledge this is the first academic work addressing a comparison of this type. Objective and subjective data were collected during the experiments. The results show that the HMD has a performance, preference and usability advantage over the CAVE with respect to the integration of natural hand interaction. Nevertheless, the results also show that the CAVE system can be, as well, successfully used in combination with an optical hand tracking device.
BlockQuery: Toward forensically sound cryptocurrency investigation

Cryptocurrency transaction forensic examinations need to guarantee completeness, confidentiality, and information integrity. Our work presents BlockQuery as a proof of concept blockchain query system for Bitcoin. BlockQuery is capable of detecting transactions generated by Hierarchical Deterministic (HD) wallets that many publicly available tools cannot find due to failures in their address derivation methods. Moreover, BlockQuery does not use third party servers as data providers and operates on a local copy of the blockchain to prevent information disclosure. Compared to other Bitcoin query tools, BlockQuery was designed from a forensic standpoint and meets all four of the defined querying criteria of being open source, confidential, automatically converting key representations, and allowing the manual adjustment of derivation depth.
Generative Adversarial Networks: Solution for Handling Imbalanced Datasets in Computer Vision

Many computer-vision based applications suffer from classification problems due to imbalanced datasets. The applications like image-based disease analysis, accident analysis, intrusion detection, disaster prediction analysis etc., have highly imbalanced datasets due to inadequate number of images for different classes. Traditional deep learning algorithms cannot perform well in such situations. Over a period of time Generative Adversarial Networks (GANs) became popular among the researchers due to its potential to generate synthetic images as well as to discriminate between original data and noise. GANs are more effective in case of unsupervised learning or semi supervised learning with absence of labeled data or presence of minimal labeled data. Firstly, this paper aims to discuss about the imbalanced dataset problems and GAN as its solution. Then it discusses the basic GAN architecture with its working principle. It provides a comparative analysis between variants of GANs and discusses different application domains where GANs can outperform than any other traditional deep learning algorithms. The paper also discusses about the future research directions using GANs. So, overall, this paper provides a detail study on GANs and its requirement in different application domains which will motivate the researchers to select an appropriate network algorithm for a specific application domain.
A Multitask Learning Framework for Abuse Detection and Emotion Classification

The rapid development of online social media makes abuse detection a hot topic in the field of emotional computing. However, most natural language processing (NLP) methods only focus on linguistic features of posts and ignore the influence of users’ emotions. To tackle the problem, we propose a multitask framework combining abuse detection and emotion classification (MFAE) to expand the representation capability of the algorithm on the basis of the existing pretrained language model. Specifically, we use bidirectional encoder representation from transformers (BERT) as the encoder to generate sentence representation. Then, we used two different decoders for emotion classification and abuse detection, respectively. To further strengthen the influence of the emotion classification task on abuse detection, we propose a cross-attention (CA) component in the decoder, which further improves the learning effect of our multitask learning framework. Experimental results on five public datasets show that our method is superior to other state-of-the-art methods.