Reviewerly/exHarmony huggingface dataset:
Gamifying Children's Linguistic Intelligence With the Duolingo App

This chapter discusses the use of Duolingo App to enhance children's linguistic intelligence. Linguistic intelligence is expertise in applying vocabularies effectively and efficiently. Duolingo is an application based on the Android platform which helps children mastering other languages that they can practice speaking, reading, listening, and writing through a play. Children in early childhood are in the period of ‘playing' spontaneously. They will do activities of playing without any instruction from others. The activity of playing naturally will stimulate the aspects of linguistic and symbolic that this development is closely related to learning to speak by involving the ability to vocalize. The use of this application will implement toward gamification method. Gamification uses game-based mechanics, aesthetics, and game thinking to engage people, motivate action, promote learning, and solve problems. This chapter provides an illustration that gamification can be used specifically in stimulating linguistic intelligence based on a case study in Indonesia.
BenSW: A Standard Dataset for Bengali Stop Word Detection

In the Bangla Language Processing system, stop word is the least contributed and resourceful domain than any other language. Stop word is used for sentence formation purposes without holding any significant value in expressing the semantic meaning of a sentence. The detection and removal of Bengali stop words is an essential preprocessing task in some fields such as sentiment analysis, text mining, information retrieval, text categorization, search index and retrieval system, text summarization. But there is no standard Bengali stop word list available, also no standard dataset is publicly available with the solution of stop word-related special case problem where sometimes stop word act as a content word contextwise and viceversa. So, in this paper, a standard dataset consists of 20k sentences that can solve the stop word-related special case problem, are introduced and made publicly available on GitHub that can motivate the researchers to contribute in this field and enrich the resource of the Bangla Language Processing system. This is the first publicly available Bengali stop word standard dataset, also the dataset is experimented with by applying different approaches and machine learning classifier algorithms, and the achieved accuracy is commendable.
Development of laboratory devices for real-time measurement of object 3D position using stereo cameras

Abstract Measuring the 3D position at any time of a given object in real-time automatically and well documented to understand a physical phenomenon is essential. Exploring a stereo camera in developing 3D images is very intriguing since a 3D image perception generated by a stereo image may be reprojected back to generate a 3D object position at a specific time. This research aimed to develop a device and measure the 3D object position in real-time using a stereo camera. The device was constructed from a stereo camera, tripod, and a mini-PC. Calibration was carried out for position measurement in X, Y , and Z directions based on the disparity in the two images. Then, a simple 3D position measurement was carried out based on the calibration results. Also, whether the measurement was in real-time was justified. By applying template matching and triangulation algorithms on those two images, the object position in the 3D coordinate was calculated and recorded automatically. The disparity resolution characteristic of the stereo camera was obtained varied from 132 pixels to 58 pixels for an object distance to the camera from 30 cm to 70 cm. This setup could measure the 3D object position in real-time with an average delay time of less than 50 ms, using a notebook and a mini-PC. The 3D position measurement can be performed in real-time along with automatic documentation. Upon the stereo camera specifications used in this experiment, the maximum accuracy of the measurement in X, Y , and Z directions are Δ X = 0.6 cm, Δ Y = 0.2 cm, and Δ Z = 0.8 cm at the measurement range of 30 cm–60 cm. This research is expected to provide new insights in the development of laboratory tools for learning physics, especially mechanics in schools and colleges.
Gesture Recognition for Autistic Children using Person Pose Estimation and Supervised Learning

Autism Spectrum Disorder is a neural disorder which affects the cognitive, emotional, physical and social health of the individual. Identifying the gesture of autistic children performs a vital role to prevent the meltdown and self-injury. In this research, we present a new approach to identify the gesture by identifying the poses through person pose estimation technique and use the features from person pose estimation to build a gesture classification model using supervised learning techniques. Proposed model achieved the highest accuracy for Random Forest technique with evaluation metrics of 83% for precision and 71% for recall.
Enhanced Capsule Networks With Improved Residual Modules

Capsule Network (CapsNet) has gained special attention recently in image classification since it has a great potential in recognizing multi-objects in images with overlapping and guaranteeing the robustness of image representation in the face of adversarial images. However, most CapsNets can only work well on simple datasets like MNIST while performing poorly on complex datasets due mainly to its weak feature extraction. To cope with this issue, a novel CapsNet, named Residual Module-Incorporated Enhanced CapsNet (Res-EnCapsNet), is provided. The main idea of Res-EnCapsNet is three-fold as follows: 1) three capsule layers are embedded to adequately extract advanced features from primary capsules, meanwhile a convolutional capsule layer is adopted in the last layer mentioned above to replace the traditional fully connected layer to reduce model parameters; 2) the residual modules are favorably applied into the convolutional capsule layers which are added after the primary capsule layer such that the vanishing gradients resulting from the deep networks are directly tackled; and 3) an improved residual module with cross-wise connection is put forward to take the information of early layers into consideration on back layers in an appropriate manner, which is evaluated through the comparison experiments with the traditional residual module with the orderly connection. Finally, experiments on four datasets including CIFAR10, SVHN, MNIST, and Fashion MNIST show that our proposed Res-EnCapsNet can outperform the state-of-the-art CapsNets on both simple and complex datasets.
Left Ventricle Segmentation based on a Segmentation-Adversarial Network with Complementary Labels

Accurate segmentation of the endocardium and epicardium of the left ventricle in some cardiac images, such as tMRIs, is a facet of clinical quantitative analysis. The complex structure of the cardiac system makes accurate heart segmentation a challenging task. Generative adversarial networks offer great advantages in learning data modeling, and adversarial training can effectively promote network parameters updatings. Segmentation-adversarial networks are a new popular means for segment image objects. However, this model requires abundant segmentation labels to complete model training, and it is difficult to achieve good medical image segmentation results. This paper proposes that complementary labels can be used to double the feature channels in the segmenting-adversarial network. In this way, more features can be supplied from the current training set, which can improve the training performance of the segmentation-adversarial network. In addition, the approach can reduce the impact of insufficient training data on model training. Additionally, the application of group normalization was found to further improve left ventricle segmentation accuracy. The validity of the proposed approach is illustrated through experiments.
An Effective Radar Signal Recognition Method Using Neural Architecture Search

Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.
USING SFG TO ANALYZE AUTHORIAL EMPHASIZERS AND PROPOSITIONAL INTENSIFIERS: A COMPARISON OF ALBANIAN AND ITALIAN ACADEMIC WRITING

Modality expresses high probability and total degree through boosters (Halliday 1985,Holmes 1990). Through them, writers reinforce statements with the assurance of reliableknowledge (cf. Hyland 1998b). This study compares the usage of boosting emphasizers (e.g. certainly) and intensifiers (e.g. completely) (cf. Quirk et al. 1985) and their orientation and manifestation (cf. Halliday &amp; Matthiessen 2014) in Albanian and Italian student academic writings in L1 and English as an L2. I compiled an Italian and an Italian English corpus (around 3 million words each) as well as an Albanian corpus (around 2.2 million words) and Albanian English one (around 700,000 words). The corpora are comparable in terms of genre, disciplinary domain, gender and the division of ‘hard’ and ‘soft’ sciences. Since very little research has been conducted on academic writing in Albania (Toska 2015), it is essential to initiate research in this field. The results showed that boosters were significantly favored in Italian and Italian English and less favored in Albanian. Conclusively, Italians show more commitment than Albanians.
Internal control within the conditions of performance budgeting of universities

Subject. This article discusses the issues of improving the financing of universities, the development of performance-based budgeting to support their activities. Objectives. The article aims to assess the problematic aspects of financial support for the activities of higher education institutions, including using the performance-based budgeting, and identify the features of its implementation that affect the system of internal control at universities. Methods. For the study, we used the methods of analysis, grouping, comparison, and generalization. Results. The article summarizes the problematic provisions of financing the activities of universities, including the use of the performance-based budgeting method, and justifies the need to transform the internal control of universities. Conclusions. The article concludes that the development of the system of financing the activities of universities generally corresponds to foreign practice, although there are some shortcomings in the financing of higher education institutions. Internal control of universities should be modernized taking into account the strategic nature of the project financing process and the possibilities of digitalization of control activities.
Machine Secret Key Recognition in a Homogeneous Environment

The most important characteristics of cryptographic systems is the management of a secret key and its resistance to brute force - attack with all possible variants of the key. The proposed article presents a scheme for recognizing secret keys using machine learning tools. Proposes a method for achieving encrypted communication without stipulating the encryption algorithm used. The experimental staging has achieved good results, and with secret keys with significant differences, the recognition is about 100%.
A Novel Maximum Mean Discrepancy-Based Semi-Supervised Learning Algorithm

To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier using highly confident unlabeled samples. The MMD-SSL algorithm performs three main steps. First, a multilayer perceptron (MLP) is trained based on the labeled samples and is then used to assign labels to unlabeled samples. Second, the unlabeled samples are divided into multiple groups with the k-means clustering algorithm. Third, the maximum mean discrepancy (MMD) criterion is used to measure the distribution consistency between k-means-clustered samples and MLP-classified samples. The samples having a consistent distribution are labeled as highly confident samples and used to retrain the MLP. The MMD-SSL algorithm performs an iterative training until all unlabeled samples are consistently labeled. We conducted extensive experiments on 29 benchmark data sets to validate the rationality and effectiveness of the MMD-SSL algorithm. Experimental results show that the generalization capability of the MLP algorithm can gradually improve with the increase of labeled samples and the statistical analysis demonstrates that the MMD-SSL algorithm can provide better testing accuracy and kappa values than 10 other self-training and co-training SSL algorithms.
Research on object detection algorithm based on deep learning for mobile Terminal

Abstract In order to improve object detection ability of robot, this study introduces an object detection algorithm which is based on deep learning. Firstly, a neural network that contains Convolution Layer, Pooling Layer and Fullly Connection Layer is designed to recognize whether the target object in image. Secondly, the Faster-RCNN, as a typical object detection algorithm, is used to detect the position of target object in image. Finally, the algorithm is transplanted on Raspberry Pi for deployment and testing. Results show that this algorithm can work well on Raspberry PI, and the mAP (mean average precision) and FPS (frames per second) reach 97% and 3. Meanwhile, the position of target object is accurately marked in image. Therefore, this study implies that the object detection algorithm based on deep learning can be implemented on mobile terminal and is helpful to improve object detection ability of mobile terminal.
Application of the Fine-Grained Soil Prospecting Method in Typical Covered Terrains of Northern China

In recent years, mineral resources near the surface are becoming scarce, causing focused mineral exploration on concealed deposits in covered terrains. In northern China, covered terrains are widespread and conceal bedrock sequences and mineralization. These represent geochemical challenges for mineral exploration in China. As a deep-penetrating geochemical technology that can reflect the information of deep anomalies, the fine-grained soil prospecting method has achieved ideal test results in arid Gobi Desert covered terrain, semi-arid grassland covered terrain, and alluvium soil covered terrain of northern China. The anomaly range indicated by the fine-grained soil prospecting method is very good with the known ore body location. The corresponding relationship can effectively indicate deep ore bodies and delineate anomalies in unknown areas. Overall, the fine-grained soil prospecting method can be applied to geochemical prospecting and exploration in covered terrains.
Lightweight Pedestrian Detection through Guided Filtering and Deep Learning

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time.
Maintaining AUC and H-measure over time

Abstract Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline setting, for example, when the classifier is being developed by a researcher. However, the running time becomes more crucial if our goal is to monitor the performance of a classifier over time. In this paper we study three algorithms for maintaining two measures. The first algorithm maintains area under the ROC curve (AUC) under addition and deletion of data points in $$\mathcal {O} \mathopen {}\left( \log n\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mfenced> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> time. This is done by maintaining the data points sorted in a self-balanced search tree. In addition, we augment the search tree that allows us to query the ROC coordinates of a data point in $$\mathcal {O} \mathopen {}\left( \log n\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mfenced> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> time. In doing so we are able to maintain AUC in $$\mathcal {O} \mathopen {}\left( \log n\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mfenced> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> time. Our next two algorithms involve in maintaining H -measure, an alternative measure based on the ROC curve. Computing the measure is a two-step process: first we need to compute a convex hull of the ROC curve, followed by a sum over the convex hull. We demonstrate that we can maintain the convex hull using a minor modification of the classic convex hull maintenance algorithm. We then show that under certain conditions, we can compute the H -measure exactly in $$\mathcal {O} \mathopen {}\left( \log ^2 n\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mfenced> <mml:msup> <mml:mo>log</mml:mo> <mml:mn>2</mml:mn> </mml:msup> <mml:mi>n</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> time, and if the conditions are not met, then we can estimate the H -measure in $$\mathcal {O} \mathopen {}\left( (\log n + \epsilon ^{-1})\log n\right)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow /> <mml:mfenced> <mml:mo>(</mml:mo> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>+</mml:mo> <mml:msup> <mml:mi>ϵ</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> </mml:mfenced> </mml:mrow> </mml:math> time. We show empirically that our methods are significantly faster than the baselines.
Students' Satisfaction of Learning Management System: A Study of Dipa Makassar University, Indonesia

Transition to online learning is the only option for the precarious situation because of the sudden need for physical distancing during the COVID-19 outbreak. In online learning, lecturers need a Learning Management System (LMS) as a platform for teaching and learning activities and strengthen the learning process through an online classroom environment. Dipa Virtual Learning (Divlearning) was introduced at Dipa Makassar University to support online learning activities. Since Divlearning has been implemented, no research has ever been conducted to determine the level of student satisfaction with the quality of the Divlearning system. The success of LMS has generally been determined by student satisfaction. This study was conducted in order to determine the level of student satisfaction towards Divlearning as LMS developed by Dipa Makassar University by using End-User Computing Satisfaction (EUCS) model. The result shows that students are satisfied with the Dipa Virtual Learning (Divlearning) system. The content variable has the highest average score, followed by other variables. The average value of the five variables ranged from 2.74 to 3.22.
Visual analysis of ecological hydrological intelligent process based on the water control of the Internet of Things

The WSN system in the ongoing Heihe River Basin Ecology-Hydrological Process Integrated Remote Sensing Observation Joint Experiment has continuously produced a huge amount and a wide variety of automatic observation data. In order to effectively manage and apply these data, an online visualization Web system has been designed and implemented. With the development of the Internet of Things technology, this paper studies the visualization of the ecological hydrological intelligent process of the river basin under the condition of water control, and analyzes the intelligent control of the ecological hydrology of different river basins.
An Efficient Enhanced Algorithm for Clustering Large Datasets

Clustering in data mining is a powerful tool for gaining knowledge. There is tremendous knowledge in the field of clustering as it has recently appeared in several different applications. The density-based method is a successful technique for large datasets (big data). Algorithms such as the well-known DBSCAN algorithm can find clusters of random shapes defined as high- and low-density regions. This paper presents an enhanced algorithm (called eDBSCAN) that works on large data sets as it can handle large sets of data with high accuracy. This is to obtain the set number of groups of clusters (K) on the experimental approach in the traditional DBSCAN algorithm. We used the classified data set to avoid the main problem of the partitioned technique that clarifies the number of K, i.e., clusters. An experimental study was conducted to prove the effectiveness of the proposed algorithm in finding the value of Epsilon parameters (Eps), where the algorithm was applied to a synthetic data set and it was proven that the eDBSCAN algorithm achieved scalability with large data sets and a high degree of purity.
The topic model of voice interactive dialogue based on the localized command and control system

The core problem of the human-machine dialogue system in the localized command and control system is to understand the information of the dialogue, and then give a reasonable answer that is satisfactory to the command system commander or assist the commander in completing a certain task. Facing huge interactive dialogue and other short text dialogue data, it is necessary to understand it efficiently and accurately. Daily interactive dialogues are carried out following certain themes, and mining the hidden subject information is a good way to grasp the inherent characteristics of the dialogue. The length of the dialogue text in the daily dialogue of the commander is small, and it has a high degree of randomness. The oral language is serious. Its themes are intertwined and the organizational structure is more chaotic than the news and other types of texts, which makes it difficult for the traditional topic model to capture the hidden words. The co-occurrence law of the contained words and documents cannot be directly applied to this type of text. Therefore, this article constructs a "pseudo-long document" method for short text problems. The topic information in a group of dialogues is relatively similar, and the word co-occurrence in the dialogue is more representative. Using this method to construct the training corpus of the LDA model can improve the topic model's capture of the document-word co-occurrence law to a certain extent. At the same time, this paper proposes an index based on the combination of perplexity and topic similarity to determine the optimal number of topics. Based on these two aspects, an interactive dialogue topic model is constructed.