Reviewerly/exHarmony huggingface dataset:
Heterogeneous Network Representation Learning Based on Adaptive Multi-channel Graph Convolution

Network representation learning (NRL) is an important technique for network analysis. Heterogeneous information networks (HINs) contain multiple types of nodes and edges, which describe the personalized information of nodes and complex relationships between nodes. In this paper, we propose a Heterogeneous Adaptive Multi-Channel Graph Convolutional Networks (HAM-GCN) for HIN representation learning. In HAM-GCN, we design three channels to extract the specific and common embeddings with respect to each meta-path from node features, topological structures, and their combinations simultaneously. In addition, we design both channel-level attention and semantic-level attention to fuse the low-dimensional representations obtained from different channels and different meta-paths for learning the final representation. A large number of experiments on three benchmark data sets show that HAM-GCN extracts the most relevant information from topological structure and node features, which significantly improves the classification accuracy than other baseline algorithms.
Student Digital Literacy in Singkawang School Through 5M Activities for Independent Learning

The Digital era is a period or Era where almost all fields in the order of life have been assisted by digital technology. In the context of education, digital competence can be interpreted as the use of technology in a convincing, appropriate, and safe way to achieve learning and educational goals. The research objective is to reveal the digital literacy skills of Singkawang City Junior High School students through 5M activities to support independent learning. The research method uses a quantitative approach. Researchers directly collect this study's source of data from the original subject, namely junior high school (SMP) students. This research was conducted in a junior high school in Singkawang City. The population of this study was as straight as junior high school students from grades VII-VIII from the age of 12 to 15 years, totaling 125. The sampling technique is probability sampling, namely area (cluster) sampling. The data collection technique used in this study was a test question. The instrument used to measure the variable of students' digital literacy ability was previously tested for validity and reliability. Data analysis technique to test research questions with ANOVA test with sig.0.05. The study results showed that the significant value data for each school was greater than 0.05, which means that the data is normally distributed. The five variants of the five schools that represent the population are the same (homogeneous). Digital literacy skills using 5M activities positively impact the learning process to support independent learning. There is a difference in the average digital literacy ability of junior high school students in Singkawang City after being given planting using 5M activities with a sig value of 0.00 &lt; 0.05. But the results obtained by each school is different, but the results are still in the very good category
Defining, Analyzing, Querying and Assuring Product Line Models

Throughout its development and maintenance cycle, software is subject to many activities including design, analysis, evolution, testing, safety certification, querying, debugging, repair. Since industry commonly adopts software product lines to manage the development of complex families of software systems, there is a clear need to support all of these activities for product lines. In this talk, I will describe a number of approaches based on "lifting" – a reinterpretation of the meaning of product-level activities for the entire product line. I will also describe design decisions for the underlying tool support and results of applying these tools in the automotive domain.
Academic Freedom, Deemed Exports and International Students, Part 3

This column addresses: 1. Smallsats; 2. Smallsats and Software Engineering; 3. Smallsats and Export restrictions
Using Theia Trace Viewer to Visualize JPF Traces

Java Pathfinder (JPF) is currently the most feature-rich platform for an in-depth analysis of programs that run on the Java Virtual Machine (JVM). However, using JPF and interpreting its results is a challenge. Dependencies on specific Java versions inhibit the adoption of JPF, and its textual output makes it hard to understand its analysis results. To this end, we present the Theia Trace Viewer (TTV) for JPF traces, which features a web-based graphical user interface. It does not only make it easier to get an overview of JPF traces, but it can visualize traces without requiring an installation on the client side, thus making JPF more accessible at the technical level as well. We evaluated TTV with several groups of users, showing the potential of our approach.
Graph-Based Recommendation Engine for Stock Investment Decisions

Real-time recommender systems face the challenge of fast-changing data and the necessity of providing the answers in almost no time. In this paper, we discuss the case of a recommendation system for the stock market that uses knowledge about investors similar to the target user and combines it with a technical analysis of the stocks.
Polar Question

This squib investigates the semantic value, discourse context, syntactic structure and prosody of a polar question with a focus (F-polQ) in Korean, e.g., JONF-i oassni? ‘Did JOHNF come?’, which has not been paid much attention to. First we observe that in generating an alternative set, an F-polQ goes with a focused declarative sentence (F-Decl) on the one hand, and in that its ordinary semantic value is the set of positive and negative propositions, {p, ￢p}, it goes with an ordinary polar question on the other. Based on this, this study shows that i) the alternative set of propositions generated by an F-polQ refers to the presupposed wh-question, just like an F-Decl does, and that ii) whereas the wh-Question that provides a proper context for an occurrence of a F-Decl originates from the hearer part, forming a question-answer dialogue, the wh-Question generated by a F-polQ, indicated by the prosody of L- phrase accent, originates from the speaker part. Additionally, it is suggested that the rising boundary tone H% indicates the source of the wh-question presupposed by the F-polQ is the speaker herself.
Development of a Data Curatorship and Management module at a CODeL higher education institution

As a developing subdiscipline within the context of archives and records management, data curatorship and management focuses on maintaining, preserving and adding value to digital research data throughout its lifecycle. Though data curatorship and management have been implemented within archives and records management programs in industrialized countries, the training of archivists and records managers in the principles and application of data curation in Africa has not received the same attention. To rectify this, the International Council on Archives (ICA) proposed a generic curriculum for modules with the focus on data curatorship. Linked to these guidelines, this article addresses the development of a data curatorship and management module within a Comprehensive Open Distance eLearning (CODeL) University.The research relied on the views and experiences that the authors obtained during the design phase, contextualized within an interpretivism paradigm. Policies, existing literature and guidelines proposed by the ICA informed the scope and context of the information included within the module. Aligned to the principles of the bricolage design, the ADDIE model and responsive curriculum theory was applied to provide a framework for the development of the data curatorship and management module at the CODeL institution. Findings revealed that developing a data curatorship and management module for the African context entails the consideration of many factors to ensure that students are prepared for applying new knowledge within changing workplace environments.
A Novel DST-SBPMRM-Based Compressed Video Steganography Using Transform Coefficients of Motion Region

This research study presents a new way to compress video steganography utilizing transform coefficients. Using a confidential stego key, a hidden cover video frame is chosen to disguise a secret message. An exhaustive search block matching algorithm approach was utilized as a cover object to extract a motion region from the secret frame. It was then translated into the frequency domain using the discrete sine transform, with its random least significant bits carrying the secret message bits known as a stego frame. The stego video is composed by encoding the stego frames using Advanced Video Coding/H.264 compression technology and communication over an unsecured network. Quality assessment parameter imperceptibility in the form of average mean squared error obtained between 0.0000 and 0.0076 and average peak signal-to-noise ratio obtained between 69.304 and Inf (infinity) dB are used to evaluate the effectiveness of the proposed video steganography method. The robustness of the system is evaluated by the parameters of similarity and bit error rate achieved at values closer to 1 and 0.0004%-0.0821%, respectively, while the embedding capacity is determined using the hiding ratio acquired at values in the range of 0.58353% to 2.6959%. Furthermore, the experimental findings of the proposed method are compared with those of existing techniques in order to demonstrate that there is significant improvement in the video steganography quality in all of the aforementioned categories.
Image Inpainting Using Deep Learning

Inpainting is the process of restoration of the damaged or missing part of an image. Its original application was the recovery of damaged artistic pieces by filling in the missing parts. The task of image inpainting is the reconstruction of the damaged parts in such a way that the recovered cannot be detected by a common observer. The introduction of computer technology has allowed this process to take a ‘computational’ approach towards reconstruction of images. Images can be digitized resulting in the expansion of inpainting applications to include reconstruction of patches due to loss in transmission; reversing of scratches, lines or overlays in digitized images; to removal or introduction of texts and objects on scene. Traditional approaches are good for reconstruction of small patches and noise removal. However, they fail in case of large patches and complex images due to lack of sufficient data. This chapter introduces the use of deep learning in image inpainting. Techniques such as Shift Net, Deep Fill and Context Encoder will be discussed. Further, advanced techniques such as globally and locally consistent image completion, and patch-based image inpainting with GANs shall be introduced.
Incorporating Machine Learning into Block Mining Towards Data Integrity in Blockchain Based Iot Solutions

The development of many blockchain-based applications has been done by integrating IoT devices for information collection and sharing. However, faced with the challenges of the heterogeneity of IoT devices and their resource constraints, the security of their use in the majority of blockchain solutions relies heavily on the authentication of these devices. In addition to this, there is a lack of consideration for anomaly detection in IoTs, which can have direct consequences on the integrity of the information contained in the blockchain. In this paper, we propose a fully decentralized security protocol for blockchain-based IoT solutions that focuses on the integrity of the data provided by the collector nodes before it is written to the blockchain.The protocol introduces the detection of possible anomalies in the nodes using machine learning algorithms fed by the nodes’ operation traces in the extraction process itself. Anomaly detection thus becomes an integral and mandatory part of the mining process. The protocol has extremely low coupling and is therefore completely independent of any machine learning model. The performance of the designed and developed system was tested using three well-known machine learning models: decision tree (DTs), support vector machine (SVM) and multilayer perceptron (MLP). The simulation results show that in terms of accuracy, the DTs outperforms the MLP and the SVM, while the SVM is slightly more accurate than the MLP. As for sensitivity, the SVM is slightly superior to the decision tree. Regarding the processing time, we notice that the SVM takes more time followed by the MLP and finally the DTs. Finally, the results obtained allow us to conclude that the protocol is realistic and feasible and shows its ability to improve the mining process.
Android Implementation of Traditional Indonesia Fashion Application

Indonesia has been gifted with a wide range of diverse cultures from Sabang to Merauke, making Indonesia one of the biggest country with multi ethnic groups, races and cultures. Meanwhile, a lack of information that can be accessed by the students causing a big issue that needs to be addressed. The information of Indonesian traditional clothing is very limited, which has been represented as a characteristic of tribe and culture in each region and also used in the traditional ceremony. Therefore, this study aims to develop an Android-based educational game of traditional Indonesian clothing. The method used in this study was Research and Development approach. The results of this study showed that developing an educational game "Introduction to Traditional Clothing in Indonesia" on the Android platform,that provided information to users about any traditional musical instruments in Indonesia, helped students to recognize the particular culture through its traditional clothing as it could give an insight to them. From the results of testing carried out with the black box method, it could be concluded that this application enabled to function well and provide the beneficial information to user about the traditional clothing. &#x0D;
Intelligent computational techniques of machine learning models for demand analysis and prediction

In the proposed model, a novel approach is introduced to discover an optimal machine learning model for food demand prediction. To create an exemplary model, we used twelve different machine learning models to analyse and interpret the historical data. Feature engineering techniques have been deployed to yield better performance. All methods were evaluated using RMSE evaluation metrics to determine the optimal model. Our methodology is one of its kind to reduce the error rate to a marginal level. The novelty of our research is that the root mean square error (RMSE) value for the demand prediction was reduced to 2.61e-16 using linear regression, thus achieving a better performance. The random forest, decision tree, and extreme gradient boosting regression also performed well, producing an RMSE value of 1.42e-9, 1.93e-15, and 4.87e-18 respectively. The predictive power of the system was 100% for R-squared metrics.
Online label distribution learning based on kernel extreme learning machine

Label distribution learning(LDL) can reflect the significance of related labels in the sample compared to traditional label learning such as single-label learning (SLL) and multi-label learning (MLL). Label distribution learning problems are far more complex than SLL and MLL, as both the related labels and the significance of each class label described to each of the input samples are to be identified. Presently, more and more corresponding researches on label distribution learning are gradually emerging. However, almost all existing studies only consider batch learning when training the data without considering how to deal with streaming data. For this reason, a novel label distribution method is proposed in this paper to process online stream label distribution data, which is based on the idea of online learning and combined with an effective kernel extreme learning machine model. The proposed method can effectively deal with the stream data based on the designed sequential learning models. Finally, the experimental comparisons with the five commonly used label distribution learning models are conducted on 10 real label distribution benchmarks datasets, which further demonstrate the validity, feasibility, and stability of the method proposed in this paper.
Sarcasm Detection on Text for Political Domain— An Explainable Approach

In the era of social media, a large volume of data is generated by applications such as the industrial internet of things, IoT, Facebook, Twitter, and individual usage. Artificial intelligence and big data tools plays an important role in devising mechanisms for handling this vast volume of data as per the required usage of data to form important information from this unstructured data. When the data is publicly available on the internet and social media, it is imperative to treat the data carefully to respect the sentiments of the individuals. In this paper, the authors have attempted to solve three problems for treating the data using AI and data science tools, weighted statistical methods, and explainability of sarcastic comments. The first objective of this research study is sarcasm detection, and the next objective is to apply it to a domain-specific political Reddit dataset. Moreover, the last is to predict sarcastic words using counterfactual explainability. The textare extracted from the self-annotated Reddit corpus dataset containing 533 million comments written in English language, where 1.3 million comments are sarcastic. The sarcasm detection based model uses a weighted average approach and deep learning models to extract information and provide the required output in terms of content classification. Identifying sarcasm from a sentence is very challenging when the sentence has content that flips the polarity of positive sentiment into negative sentiment. This cumbersome task can be achieved with artificial intelligenceand machine learningalgorithms that train the machine and assist in classifying the required content from the sentences to keep the social media posts acceptable to society. There should be a mechanism to determine the extent to which the model's prediction could be relied upon. Therefore, the explination of the prediction is essential. We studied the methods and developed a model for detecting sarcasm and explaining the prediction. Therefore, the sarcasm detection model with explainability assists in identifying the sarcasmfrom the reddit post and its sentiment score to classify given textcorrectly. The F1-score of 75.75% for sarcasm and 80% for the explainability model proves the robustness of the proposed model.
Image Enhancement in Foggy Images using Dark Channel Prior and Guided Filter

Haze is very apparent in images shot during periods of bad weather (fog). The image's clarity and readability are both diminished as a result. As part of this work, we suggest a method for improving the quality of the hazy image and for identifying any objects hidden inside it. To address this, we use the picture enhancement techniques of Dark Channel Prior and Guided Filter. The Saliency map is then used to segment the improved image and identify passing vehicles. Lastly, we describe our method for calculating the actual distance in units from a camera-equipped vehicle of an item (another vehicle).Our proposed solution can warn the driver based on the distance to help them prevent an accident. Our suggested technology improves images and accurately detects vehicles nearly 100% of the time.
Spatio-Temporal Human Action Recognition Model using Deep Learning Techniques

Two-stream human recognition achieved great success in the development of video action recognition using deep learning. Recently many studies have shown that two-stream action recognition is a powerful feature extractor. The main contribution in this work is to develop a two-stream model based on spatial and temporal networks using convolutional neural networks with a convolution long-short term memory. The two-stream model with ImageNet pre-trained weights is used to retrieve spatial and temporal features. Output feature maps of the two-stream model are fused using sum fusion and fed as input to convolutional long-short-term memory. SoftMax function is used to get the final classification score. To avoid overfitting, we have adopted the data augmentation techniques. Finally, we demonstrated that the proposed model performs well in comparison to state-of-the-art two-stream models with an accuracy of 96.1% on UCF 101 dataset and 70.9% accuracy on the HMDB dataset.
Design of Laboratory Identification Equipment Based on Intelligent Identification Technology

The design of an identification equipment for user of academic computer laboratory was discussed. It used fingerprint identification technique to identify the user based on present using of academic computer lab. It introduced the detailed design of fingerprint identification system, including hardware components, software functions and communication protocol developing, and how to save and handle mass data by single-chip microcomputer as well. Experimental results show that it is helpful digital campus construction.
Law applicable to proprietary issues of crypto-assets

Crypto-assets (tokens on a distributed ledger network) can be handled much in the same way as tangible assets as they may be held without the involvement of intermediaries and traded on a peer-to-peer basis by virtue of the blockchain technology. Consequently, crypto-assets give rise to proprietary issues in the virtual world, as do tangible assets in the real world. This article will consider how the law applicable to the proprietary issues of crypto-assets should be determined. It will first examine some of the cases where restitution was sought of crypto-asset units and consider what issues arising in such contexts may be characterised as proprietary for the purpose of conflict of laws. Finding that the conventional connecting factors for proprietary issues are not suitable for crypto-assets, this article will consider whether party autonomy, generally rejected for proprietary issues, should be embraced as well as what the objective connecting factors should be.