Reviewerly/exHarmony huggingface dataset:
Investigating Students' Conceptual Understanding of Socio-Environmental Problems

The aim of this study was to investigate how mobile outdoor learning supports the development of students' conceptual understanding about local socio-environmental problems and students' attitudes towards the learning activity. To explore these aspects, a study following a design-based research model was designed and conducted with 68 eighth-ninth grade students. The learning design of the activity incorporated a number of mobile learning tools: environmental data collecting devices, tablets and students' smartphones with outdoor learning apps to facilitate the learning activity, which was mainly conducted outdoors. Research data was collected with semi structured pre- and post-questionnaires. The results showed that the students' conceptual understanding about the study topic developed significantly, and they were positively minded about their experience in working on a socio-environmental issue. Based on the results, it can be concluded that mobile outdoor learning activities focusing on timely socio-environmental problems have the potential to increase students' conceptual understanding.
Medical image contrast enhancement using an improved fuzzy algorithm approach

Medical imaging systems are susceptible to noise indicated from nodes and different levels of radiation exposure, which causes degradation in the contrast of the medical image. Within this paper, we attempt to improve the contrast of the medical-image via a digital image processing technique. We enhanced the fuzzy algorithm approach established by the Pas. S. K algorithm. Our proposed approach avoided the loss of information when the planar-space of the initial image is altered towards the fuzzy-space. We presented the effective outcome of employing this approach on the computed tomographic image of a human chest cavity. We found that processing the image using the improved algorithm offered additional details in comparison to the original algorithm. However the percentage of gray pixels having zero values in the image utilizing the standard fuzzy algorithm is considerably more (approximately 14.2%) than the percent obtained from processing the enhanced algorithm (7.4%). The results signified that there is an information loss resulting from compelling some image elements to zero. These results were analyzed using MATLAB. Consequently, we concluded that the enhanced algorithm is noticeably more distinct than the conventional approach of the algorithm.
Parallel Calligraphy Robot: Framework and System Implementation

Robot calligraphy is a combination of Chinese calligraphy art and modern machine intelligence. Aiming at the problem of insufficient flexibility and accuracy of robot calligraphy, this paper proposes a parallel calligraphy robot framework based on parallel robotics. To evaluate, a prototype parallel calligraphy robot system is built, including an execution subsystem, a software-defined subsystem and an acquisition subsystem. A four-step paradigm including description, prediction, imitation and prescription processes is proposed to perform closed-loop learning for robot calligraphy. Experimental results show that the system can realize accurate expert calligraphy collection, realistic calligraphy simulation and fine calligraphy writing results.
Cattle Breeding Management using Smart System: A Systematic Literature Review

This paper uses a smart system to present a methodical literature review on cattle breeding management. Indonesia is a large country with a veritably large population. Indonesian people like to eat beef; thus the demand for beef in Indonesia always increases. Therefore, Indonesia needs a system to manage livestock data nationally. Sekolah Peternakan Rakyat (SPR) is one of the Bogor Agricultural Institute's programs to strengthen the people's livestock business. In addressing this issue, experimenters proposed using an intelligence system for cattle breeding management in SPR. The method used in this research is a systematic literature review (SLR). The approach used is PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Hence, this paper analyses the literature to fill the gap by conducting a scoping study to answer three pre-defined research questions. First, search for papers using publish or perish tools with a database of papers used for the last five years (2017 - 2022). Grounded on the search results, the total composition data for all data from the composition database used was 5513 papers, with an aggregate of 3010 papers in the form of journals. In comparison, the number of proceedings amounted to 239 papers. It is known that the content of cattle breeding management using information technology has been extensively carried out. Inquiries are generally carried out outside Indonesia. Although several papers discuss cattle breeding in Indonesia, the discussion is more towards livestock, not information technology.
A Scalable Adaptive Sampling Based Approach for Big Data Classification

Big Data is dealing with two major issues witch are the adaptation and scaling of data analysis techniques at the Big Data level by one side, such as the implementation of distribute Machine Learning algorithms. In another side, the reduction of data sets so that a processing can be performed using the existing Machine Learning techniques. It consists to determine a smallest and sufficient training set size that obtains the same accuracy as the entire available dataset. The proposed approach deals on the selection of instances number needed to be presented for data mining algorithms. GDAS is one of the adaptive sampling algorithms that can scale down the data. In this paper, an improved GDAS algorithm based on BLB technique and ScaSRS algorithm is presented, which substantially allows a better scalability and performances in terms of time needed. As validated by experiments on various datasets, our approach can achieve very prominent improvement in efficiency and also the resulting accuracy over previous works.
SEN: Stack Ensemble Shallow Convolution Neural Network for Signature-based Writer Identification

Signature-based writer identification (SWI) is an automated segmentation-free holistic approach where a person is identified based on their handwritten signature. Earlier research attempts mainly featured learning-based approaches where writing patterns were detected and fed to machine learning models for determining the writer. Nowadays, a deep learning-based approach is becoming very popular and several works are reported in the literature using such models. In this paper, we propose a two-stage convolution neural network (CNN) architecture that has two properties: (i) at first, two state-of-the-art CNN models namely VGG-19 and EfficientNet-B0 were truncated making them lightweight; (ii) Secondly, a stack ensemble network (SEN) was proposed where the truncated architectures were stacked along with a shallow base CNN model. The proposed system experimented on a newly built multi-script offline signature dataset where three popular Indic scripts namely: Bangla, Roman and Devanagari were considered. The proposed SEN outperforms individual CNN architectures in terms of recognition rate. In addition, the system converges considerably fast as the SEN architecture is shallower compared to heavier traditional networks. Overall, we obtained the highest writer identification accuracy of 99.44%, 99.04%, and 98.61% for Bangla, Roman, and Devanagari, respectively, by the proposed SEN architecture. Furthermore, the dataset used in this paper will be available freely for research purposes from the link mentioned in Section III.
Semantic Segmentation of High-Resolution Remote Sensing Images Based on Sparse Self-Attention

Semantic segmentation of high-resolution optical remote sensing images is an important but challenging task. To solve the problem that many semantic segmentation networks fail to efficiently utilize global and local context information to improve the segmentation performance, this paper proposes a semantic segmentation network based on sparse self-attention (SDANet) to model the global context dependencies. Specifically, the feature maps are first divided into four regions in spatial and channel dimensions, respectively, and the divided feature maps are rearranged to form new regions. Second, the position and channel self-attention operations are performed on the rearranged regions. Third, the feature maps are restored to the original combination and the position together with channel self-attention operations are performed again to obtain the output feature maps. Finally, semantic segmentation is completed based on the output feature maps. Extensive experiments conducted on the ISPRS Vaihingen dataset demonstrate that the proposed method is superior to self-attention-based DANet, CCNet, and other general semantic segmentation networks, such as FCN, Deeplabv3+, HRNet, etc.
An SoC-Oriented Coprocessor for Bilinear Pairing in Affine Coordinates

Bilinear pairing is the fundamental component of practical advanced cryptography. Cost-saving and security-conscious paring coprocessors are required in resource-constrained IoT (Internet-of-Things) devices. Security and cost have basically trade-off relationship. This paper proposes a cost -security-balanced pairing coprocessor for SoC (System on a Chip)-scale devices. Our proposed coprocessor's key point is that the coprocessor computes pairing in affine coordinates instead of projective coordinates that are used in many related studies. The evaluation results show that our proposed coprocessor is approximately 12 percent efficient in area-cycle product (ACP) compared to the current mainstream pairing computation in projective coordinates.
APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing

Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students’ dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question, but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student’s higher-order cognitive states of skills, which is used to predict the student’s future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.
End to End Deep Neural Network: An approach to clean noisy documents

Digitalization is required for the industry's evolution in each sub-sector. While the rate of digitalization has had an influence, traditional methods of document storage have been supplanted by digital techniques such as databases, softcopies, and so on. However, certain industries continue to use the conventional technique of storing data, such as hard copies and scanned digital documents. There would be a certainty of the presence of numerous factors, such as noise, background noise, obscured and blurred text, and watermarks, while collecting digital photos from these hard copies and scanned documents. The quality of the acquired image degrades as a result of these factors. Because of the variables listed above, the digital version, i.e., photographs of the physical document, has declined in quality. This article proposes a solution to this problem. The proposed model loosely follows the Encoder-Decoder structure. The model is trained on 2000 images and managed to achieve 66.33% accuracy on the validation set.
Text Summarization towards Scientific Information Extraction

Despite the exponential growth in scientific textual content, publications remain the primary means of disseminating vital research to experts within their respective fields. These texts are predominantly written for human consumption, resulting in two fundamental challenges; experts cannot efficiently remain well-informed to leverage the latest discoveries, and applications which rely on valuable insights buried in these texts cannot effectively build upon published results. Consequently, scientific progress stalls. Automatic Text Summarization (ATS) and Information Extraction (IE) are two essential fields which address this problem. While the two research topics are often studied independently, this work proposes to look at ATS in the context of IE, specifically as it relates to Scientific IE. However, Scientific Information Extraction faces several challenges; chiefly, the scarcity of relevant entities and insufficient training data. In this paper, we focus on extractive ATS, which identifies the most valuable sentences from textual content for the purpose of ultimately extracting scientific relations. We account for the associated challenges by means of an ensemble method through the integration of three weakly supervised learning models, one for each entity of the target relation. Notably, while the relation is well defined, we do not require previously annotated data for the entities composing the relation. The central objective is to generate balanced training data, which many advanced natural language processing models require. We apply this idea in the domain of materials science, extracting the polymer-glass transition temperature relation and achieve 94.7% recall (i.e., sentences which contain relations annotated by humans), while reducing the text by 99.3% of the original document.
Region-based Cross-modal Retrieval

Cross-modal retrieval aims to identify relevant information from different modalities, such as image and text. Existing works build a coarse relationship between whole image and whole text. However, the retrieval in fine-grained elements is more necessary in research and has many practical applications, such as explainable and interactive retrieval. In this paper, we propose a region-based cross-modal retrieval task that focuses on finding the semantic match between fine-grained parts of text and image, e.g., sentence in paragraph and region in image. The challenge of this task is to enhance fine-grained representation with contextual knowledge. To this end, we propose a Context-Aware Region Retrieval (CARR) model. It utilizes two pre-trained models as backbones, Faster RCNN for image and BERT for text, and transformer-based encoders to obtain the contextual aware representations of two modalities. We conduct experiments on Visual Genome and Localized Narratives datasets, and the experimental results demonstrate that the proposed model outperforms the baseline methods.
MG-DAN-SNR: Research on Improved POI Recommendation Algorithm Based on Gating and Mixture of Experts Model

With the development of LBSN, more and more attention has been paid to constructing interest point recommendation based on historical data of interest points, and there are many algorithms to predict and analyze interest points. However, in the traditional POI recommendation system, the user's information selection is not focused, which leads to the poor utilization of user data. To solve this problem, this paper improves the existing POI recommendation algorithm DAN-SNR, and constructs a new POI recommendation algorithm model MG-DAN-SNR based on deep neural network. The simulation experiment on Foursquare data set shows that the improved model has a significant improvement in the evaluation index, and can better recommend for users.
Comparative Analysis of Machine Learning Classifiers for Text Classification: Characterization of Features Probability Distribution

The proliferation of social networks, IoT, cloud computing platforms has enabled the collection and storage of massive amount of data feasible. A large portion of the gathered data is available in text form leading to requirement of efficient text mining techniques. One of the basic tasks in text mining is text classification. The paper presents a detailed comparative performance analysis of popular machine learning based classifiers for text classification. Even though support vector machine classifier outperforms others in many cases, none of the classifier can classify all datasets. In order to gain more insights, the probability distributions of features across datasets are examined and the emergence of power law probability distributions is confirmed in many. This finding provides a rationale for the effectiveness of maximum Tsallis entropy classifier recently proposed in the literature. These results will help practitioners in choosing appropriate text classification method.
CSAFNet: Channel Spatial Attention Fusion Network for RGB-T Semantic Segmentation

Segmenting urban scenes in poor lighting conditions using complementary information from RGB and thermal images is a challenge, and RGB-Thermal (RGB-T) semantic segmentation has been the subject of interesting research. However, most existing approaches use primitive fusion strategies, such as concatenation and element summation to achieve fusion of features from different modalities. Unfortunately, such strategies ignore the differences between RGB and thermal imaging mechanisms and do not effectively perform feature fusion, reducing the use of complementary information between them. In order to perform better feature fusion, we design CSAFNet specifically for the RGB-T urban scene parsing. Specifically, we propose CSAF module at each stage of RGB and thermal image feature fusion, through which multi-level useful feature information is mined and enhanced from different encoded streams. In addition, we evaluated the effectiveness of each module through ablation studies. The results show that the proposed CSAFNet exhibits excellent performance on the benchmark urban scene dataset.
Measuring Reproduciblity of Machine Learning Methods for Medical Diagnosis

The National Academy of Sciences, Engineering, and Medicine (NASEM) defines reproducibility as “obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis,” and replicability as “obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data” [1]. Due to an increasing number of applications of artificial intelligence and machine learning (AI/ML) to fields such as healthcare and digital medicine, there is a growing need for verifiable AI/ML results, and therefore reproducible research and replicable experiments. This paper establishes examples of irreproducible AI/ML applications to medical sciences and quantifies the variance of common AI/ML models (Artificial Neural Network, Naive Bayes classifier, and Random Forest classifiers) for tasks on medical data sets.
Correlation-Oriented Graph Learning for OCM

In this chapter, we study how to automatically evaluate the compatibility of a given outfit that involves a variable number of items with the graph learning technique. Existing graph learning-based methods focus on exploring the visual modality of fashion items, and seldom investigate an item’s textual aspect, i.e., the textual description. In fact, textual descriptions of fashion items usually contain key features, which benefit item representation learning. Notably, although some studies have attempted to incorporate the textual modality, they simply adopt early/late fusion or consistency regularization to boost performance. Nevertheless, the correlations among multimodalities are complex and sophisticated and are not yet clearly separated and explicitly modeled.
A Sentimental Analysis System Using Zero-Shot Machine Learning Technique

The internet has turned our lives upside down and has become a global means of communication. As the world is rapidly advancing, many new and challenging calls for humankind are associated. One of those challenges is analyzing the sentiments, i.e. opinions or feelings of the person or any user such as customers, while choosing and buying any product. For cases and situations like this, analysis of sentiments or opinion mining plays a significant role. Sentiment Analysis is vital because the customers can get an overview and understanding of reviews of the customers who have already purchased that particular product. Also, it helps them make decisions about their purchase and hence proceed forward accordingly. In comparison to the existing work, the proposed work considers all the sentiments throughout any conversation or review, whether they are good or bad, and hence classifies them further as positive and negative with their extent i.e. percentages of positivity and negativity in the statement. It also finds out the label of the review or any other conversation so that the users can get an idea about the domain of the conversation. Even though related research work has already been done, there is still a need to improve the accuracy and understandability of sentiment analysis. This work is mainly done by using the zero-shot learning technique. After classifying the reviews and predicting labels, the spaCy model is used with it to get essential keywords and phrases for the conversation. In the proposed work, this is done by discarding greetings with a score greater than 80%.
Individualization of teaching computer science to students of a technical university

The effectiveness of individualization of teaching computer science to students of a technical university based on adaptive management technology is determined by the implementation of the following principles: visibility; propaedeutic conditionality of studying computer science; contextuality of learning in a broad and narrow sense; individualization of learning; independence; informativeness; orientation of the learning process to the development of personality, taking into account its individual characteristics; flexibility and variability of the organization of training; the presence and consideration of semantic connections within the educational material; the formation of the student's subjective position; the relationship of arbitrariness and self-regulation; positive perspectives and self-management; self- development and self-realization, and will contribute to the creation of a methodological system for teaching computer science, focused on the formation of skills to carry out educational activities in the field of independent solution of professionally directed tasks.