Reviewerly/exHarmony huggingface dataset:
The Effects of the Communicative Approach and the Use of Information Technology on Students’ Motivation and Achievement in Indonesian Language Learning

This study aimed to explain the structural influence of the implementation of the communicative approach and the use of information technology in Indonesian language learning on students’ motivation and learning achievement. This research was conducted in Blitar, Indonesia, and 186 students were chosen as the samples. An explanatory research design was used in this research. The data collection techniques used questionnaires and documentation, while data analysis techniques used descriptive statistics and structural equation modeling. The results of the analysis showed that there was a direct effect of the communication approach and the use of information technology on students’ learning motivation. There was a significant direct effect of students’ learning motivation on students’ achievement in the Indonesian language class. The communicative approach had an indirect effect on students’ achievement and showed a stronger coefficient of the effect on learning motivation compared to the use of information technology. These findings were discussed in depth based on the theory and results of previous studies.
Projected feature assisted coarse to fine point cloud registration method for large-size 3D measurement

Fringe projection profilometry has gained significant interest due to its high precision, enhanced resolution, and simplified design. Typically, the spatial and perspective measurement capability is restricted by the lenses of the camera and projector in accordance with the principles of geometric optics. Therefore, large-size object measurement requires data acquisition from multiple perspectives, followed by point cloud splicing. Current point cloud registration methods usually rely on 2D feature textures, 3D structural elements, or supplementary tools, which will increase costs or limit the scope of the application. To address large-size 3D measurement more efficiently, we propose a low-cost and feasible method that combines active projection textures, color channel multiplexing, image feature matching and coarse-to-fine point registration strategies. Using a composite structured light with red speckle patterns for larger areas and blue sinusoidal fringe patterns for smaller ones, projected onto the surface, which allows us to accomplish simultaneous 3D reconstruction and point cloud registration. Experimental results demonstrate that the proposed method is effective for the 3D measurement of large-size and weak-textured objects.
The diachronic change of research article abstract difficulty across disciplines: a cognitive information-theoretic approach

Abstract Text difficulty refers to the ease with which a text can be read and understood, and the difficulty of research article abstracts has long been a hot topic. Previous studies have found that research article abstracts are difficult to read in general and that abstracts have gradually become more and more difficult. However, the widely used measurements, such as FRE and SMOG, have long been criticized in that they use only simplistic and surface-level indicators as proxies for complex cognitive processes of reading, and the sophisticated cognitive theory and Natural Language Processing/machine learning-based methods seem not that easy to use and interpret. A theoretically sound and methodologically neat measurement of text difficulty should be called for. Besides, the diachronic changes of abstract difficulty across disciplines have been under-researched. To address these issues, this study adopted a cognitive information-theoretic approach to investigate the diachronic change of text difficulty of research article abstracts across the areas of natural sciences, social sciences, and humanities. 1890 abstracts were sampled over a period of 21 years, and two indexes, i.e. entropy from information theory and mean dependency distance from cognitive science, were employed for the calculation of cognitive encoding/decoding difficulty. The results show that in general, the cognitive encoding difficulty of abstracts has been increasing in the past two decades, while the cognitive decoding difficulty of abstracts has been decreasing. Regarding the disciplinary variations, the humanities show no significant diachronic change in encoding difficulty, and the social sciences show no significant diachronic change in decoding difficulty. These phenomena can be attributed to the traits of abstracts, the nature of academic knowledge, the cognitive mechanism in human languages and the features of different disciplines. This study has implications for the innovations in theories and methods of measurement of text difficulty, as well as an in-depth understanding of the disciplinary variations in academic writing and the essence of research article abstracts for research article writers, readers, the scientific community, and academic publishers.
A systematic literature review of Arabic dialect sentiment analysis

Sentiment analysis is the process of using natural language processing, computational linguistics, and other text analysis techniques to identify and extract subjective information in order to generate a judgment about the attitude or emotional state behind the text. It has been applied to many fields, including marketing, politics, and psychology. This paper presents a systematic literature review (SLR) of sentiment analysis for dialectical Arabic (DA). The variation among these dialects is primarily based on differences in grammar, vocabulary, and syntax, which makes it hard for researchers to perform polarity classification for DA. This is where our SLR comes in, assessing multiple aspects of sentiment analysis for DA as well as smoothing the advancement of researchers' works for related studies. We have identified all the steps that have a crucial influence on the machine learning model applied for dialect sentiment analysis, including text annotation, text preprocessing, feature extraction, and the approaches adopted. We have also determined the challenges and open issues of sentiment analysis for Arabic dialect (SAAD), where research efforts should be focused.
Age Range Classification Through Facial Recognition Using Keras Model

This study focuses on the development of an age range classification through facial recognition and Keras Model using a Raspberry Pi Camera. Keras Model and a convolutional neural network will be implemented to help the age range classification. Raspberry Pi 4 will be incorporated as the microprocessor that the device will use and process the deep learning through this study. The Raspberry Pi Camera v2 is used as the camera sensor that will take faces as input to classify the age range of a subject. Both the Raspberry Pi 4 and camera module will be used to detect a person’s face in real-time and predict the age range of the subject to which the researchers were able to build an age range classification through facial recognition using the Keras Model. The researchers were able to acquire an accuracy of 84.38% upon using Keras Model and convolutional neural network.
YOLOv7 for Face Mask Identification Based on Deep Learning

The World Health Organization (WHO) has publicized a global public health emergency due to the COVID-19 coronavirus pandemic. Wearing a mask in public can provide protection against the spread of disease. Tremendous progress has been made in object detection in recent times, thanks in large part to deep learning models, which have shown encouraging results when it comes to recognizing objects in images. Recent technological developments have made this progress possible. Wearing a mask in public is one way to prevent the transmission of COVID-19 from others. Our study employs You Only Look Once (YOLO) v7 to determine whether a subject is wearing a mask, and then divides them into three groups depending on the degree to which they are wearing a mask correctly (none, bad, and good). In this study, we merged two datasets, the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD), to conduct our experiment. These models' evaluations and ratings include crucial criteria. According to our data, YOLOv7 achieves the highest mAP (98.5%) in the "Good" class.
A Systematic Analysis on Task Scheduling Algorithms for Resource Allocation of Virtual Machines on Cloud Computing Environments

In the emerging field of technology and science, cloud computing is an effective IT service model providing computing services like database storage, network, analytics and software to the consumer and user over the internet. The Cloud computing archetype offers various benefits over the traditional self-hosting computing solution, though it has the critical challenge of assigning the task to the computing resource. Task scheduling is a prominent issue for all cloud providers and having an efficient task scheduling algorithm is a must for processing. Plentiful task scheduling algorithms are implemented with different techniques and defined by various performance factors like increasing throughput, minimizing time and makespan. This paper outlines a survey on existing task scheduling algorithms in the cloud platform. A set of contexts and scenarios are created to examine and contrast how different task scheduling algorithm work. Task scheduling algorithms, namely FCFS, Short Job First (SJF), Round Robin, Priority algorithm, Modified Short Job First, Enhanced Round Robin and Genetic algorithm, are investigated in this paper based on performance metrics like makespan, turnaround time, No. of Virtual Machines and cloudlets.
Breast Cancer Prediction using Feature Selection and Classification with XGBoost

As a result of high population growth, medical research and early sickness identification has become a responsibility. As the inhabitants grows, the likelihood of succumbing to breast cancer rises. A illness detection system that's automated reduces the danger of mortality, provides dependable, efficient, and rapid reaction, and assists doctors in disease diagnosis. to reinforce the predictive efficacy of current models and To estimate the risk of breast cancer, proposed model used Extreme Gradient Boosting (XGBoost) and compared it to Logistic Regression (LR), The Random Forest (RF), and Support Vector Machines (SVM). In this work, target picking the most weighted features using the XGBoost method and training the model with those features was performed. This approach yielded the identical accuracy as training the model with all features, however training the model with all features takes longer. The suggested approach has produced findings that are comparable and may help radiologists provide opinions
Human Voice Analysis to Determine Age and Gender

Age estimation from a speaker's voice is a research area that has garnered significant interest in recent years. The most popular method to extract voice features presently involves using MFCC coefficients which are the statistical features acquired from a kind of Spectral depiction of the audio clip (a nonlinear “spectrum-of-a-spectrum”). In the Mel scale, the divisions of the frequency band closely mimic the human auditory systems response. Post extraction, the needed features are extracted and the relevant classification or regression models/operations are applied. Numerous deep learning algorithms have been used so far in the studies for age and gender estimation. In this paper, the attempt is the same. A new labeled dataset is built by collecting the audio samples of people belonging to a variety of age groups and two genders (male and female) from the web. To this dataset the MFCC feature extraction program was applied; after extraction, different regression models were used to compute the age of the speakers and classification models to identify their genders.
A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown that we only need to label less than one-tenth of the data for model training. Furthermore, the agricultural domain ontology is constructed by BILSTM-CRF named entity recognition model and relationship extraction model. Moreover, there are a total of 13,983 entities and 26,498 relationships built in the neo4j graph database.
Text Sentiment Analysis Based on Multi-Layer Bi-Directional LSTM with a Trapezoidal Structure

Sentiment analysis, commonly called opinion mining or emotion artificial intelligence (AI), employs biometrics, computational linguistics, natural language processing, and text analysis to systematically identify, extract, measure, and investigate affective states and subjective data. Sentiment analysis algorithms include emotion lexicon, traditional machine learning, and deep learning. In the text sentiment analysis algorithm based on a neural network, multi-layer Bi-directional long short-term memory (LSTM) is widely used, but the parameter amount of this model is too huge. Hence, this paper proposes a Bi-directional LSTM with a trapezoidal structure model. The design of the trapezoidal structure is derived from classic neural networks, such as LeNet-5 and AlexNet. These classic models have trapezoidal-like structures, and these structures have achieved success in the field of deep learning. There are two benefits to using the Bi-directional LSTM with a trapezoidal structure. One is that compared with the single-layer configuration, using the of the multi-layer structure can better extract the high-dimensional features of the text. Another is that using the trapezoidal structure can reduce the model's parameters. This paper introduces the Bi-directional LSTM with a trapezoidal structure model in detail and uses Stanford sentiment treebank 2 (STS-2) for experiments. It can be seen from the experimental results that the trapezoidal structure model and the normal structure model have similar performances. However, the trapezoidal structure model parameters are 35.75% less than the normal structure model.
Robot Time Optimal Trajectory Planning Based on Improved Simplified Particle Swarm Optimization Algorithm

In order to tackle the robot trajectory planning problem with the short running time as the optimization goal, a time-optimal trajectory planning algorithm was presented based on improved simplified particle swarm optimization (ISPSO). The robot’s trajectory was constructed by 3-5-3 polynomial interpolation in the joint space of the robot. Under the condition of satisfying the velocity constraint, the objective function was constructed by the sum of the time intervals between each node. ISPSO was used to optimize the objective function. The algorithm was improved by optimizing the inertia weight updating method and introducing a golden sine segmentation algorithm as an optimization operator. Compared with other particle swarm optimization algorithms, ISPSO had higher search velocity and accuracy. The effectiveness of the proposed algorithm was demonstrated through simulations using the PUMA 560 industrial robot, which resulted in a 19% reduction in time compared to the simplified particle swarm algorithm. The simulation results show that ISPSO achieved time optimization under the condition of velocity constraint, which proved its superiority in trajectory planning.
Graph Relational Reinforcement Learning for Mobile Robot Navigation in Large-Scale Crowded Environments

Mobile robot autonomous navigation in large-scale environments with crowded dynamic objects and static obstacles is still an essential yet challenging task. Recent works have demonstrated the potential of using deep reinforcement learning to enable autonomous navigation in crowds. However, only considering the human-robot interactions results in short-sighted and unsafe behaviors, and they typically use hand-crafted features and assume the global observation range, leading to large performance declines in large-scale crowded environments. Recent advances have shown the power of graph neural networks to learn local interactions among surrounding objects. In this paper, we consider autonomous navigation task in large-scale environments with crowded static and dynamic objects (such as humans). Particularly, local interactions among dynamic objects are learned for better-understanding their moving tendency and relational graph learning is introduced for aggregating both the object-object interactions and object-robot interactions. In addition, local observations are transformed into graphical inputs to achieve the scalability to various number of surrounding dynamic objects and various static obstacle patterns, and the globally guided reinforcement learning strategy is introduced to achieve the fixed-sized learning model even in large-scale complex environments. Simulation results validate our generalizability to various environments and advanced performance compared with existing works in large-scale crowded environments. In particular, our method with only local observations performs better than the benchmarks with global complete observability. Finally, physical robotic experiments demonstrate our effectiveness and practical applicability in real scenarios.
Adversarial Attacks for Black-Box Recommender Systems via Copying Transferable Cross-Domain User Profiles

As widely used in data-driven decision-making, recommender systems have been recognized for their capabilities to provide users with personalized services in many user-oriented online services, such as E-commerce (e.g., Amazon, Taobao, etc.) and Social Media sites (e.g., Facebook and Twitter). Recent works have shown that deep neural networks-based recommender systems are highly vulnerable to adversarial attacks, where adversaries can inject carefully crafted fake user profiles (i.e., a set of items that fake users have interacted with) into a target recommender system to promote or demote a set of target items. Instead of generating users with fake profiles from scratch, in this article, we introduce a novel strategy to obtain “fake” user profiles via copying cross-domain user profiles, where a reinforcement learning based black-box attacking framework (CopyAttack+) is developed to effectively and efficiently select cross-domain user profiles from the source domain to attack the target system. Moreover, we propose to train a local surrogate system for mimicking adversarial black-box attacks in the source domain, so as to provide transferable signals with the purpose of enhancing the attacking strategy in the target black-box recommender system. Comprehensive experiments on three real-world datasets are conducted to demonstrate the effectiveness of the proposed attacking framework.
Jaccard Index in Ensemble Image Segmentation: An Approach

Many methods have been applied to image segmentation, including unsupervised, supervised, and even deep learning-based models. Semantic and instance segmentation are the two most widely researched forms of segmentation. It is of value to use multiple methods to segment an image. In this paper, we present an image segmentation ensemble methodology. Multiple image segmentation methods are applied to an image and merged to create one segmentation using the proposed method. The technique uses the Jaccard index algorithm, sometimes called the Jaccard similarity coefficient and commonly known as Intersection over Union (IoU). This resulted in better segmentation results than the respective individual segmentation methods. This experiment was applied to mathematical expression recognition (MER), with the expressions taken from blackboards with varying degrees of noise, and lighting conditions, from different classroom environments. A summary of empirical results from the segmentation of multiple images is presented in the paper.
Matching Knowledge Graphs in Entity Embedding Spaces: An Experimental Study

Entity alignment (EA) identifies equivalent entities that locate in different knowledge graphs (KGs), and has attracted growing research interests over the last few years with the advancement of KG embedding techniques. Although a pile of embedding-based EA frameworks have been developed, they mainly focus on improving the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entity representation learning</i> , while largely overlook the subsequent stage that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matches KGs in entity embedding spaces</i> . Nevertheless, accurately matching entities based on learned entity representations is crucial to the overall alignment performance, as it coordinates individual alignment decisions and determines the global matching result. Hence, it is essential to understand how well existing solutions for matching KGs in entity embedding spaces perform on present benchmarks, as well as their strengths and weaknesses. To this end, in this article we provide a comprehensive survey and evaluation of matching algorithms for KGs in entity embedding spaces in terms of effectiveness and efficiency on both classic settings and new scenarios that better mirror real-life challenges. Based on in-depth analysis, we provide useful insights into the design trade-offs and good paradigms of existing works, and suggest promising directions for future development.
Capsule neural tensor networks with multi-aspect information for Few-shot Knowledge Graph Completion

Few-shot Knowledge Graph Completion (FKGC) has recently attracted significant research interest due to its ability to expand few-shot relation coverage in Knowledge Graphs. Prevailing FKGC approaches focus on exploiting the one-hop neighbor information of entities to enhance few-shot relation embedding. However, these methods select one-hop neighbors randomly and neglect the rich multi-aspect information of entities. Although some methods have attempted to leverage Long Short-Term Memory (LSTM) to learn few-shot relation embedding, they are sensitive to the input order. To address these limitations, we propose the Capsule Neural Tensor Networks with Multi-Aspect Information approach (short for InforMix-FKGC). InforMix-FKGC employs a one-hop neighbor selection strategy based on how valuable they are and encodes multi-aspect information of entities, including one-hop neighbors, attributes and literal description. Then, a capsule network is responsible for integrating the support set and deriving few-shot relation embedding. Moreover, a neural tensor network is used to match the query set with the support set. In this way, InforMix-FKGC can learn few-shot relation embedding more precisely so as to enhance the accuracy of FKGC. Extensive experiments on the NELL-One and Wiki-One datasets demonstrate that InforMix-FKGC significantly outperforms ten state-of-the-art methods in terms of Mean Reciprocal Rank and [email protected].