Reviewerly/exHarmony huggingface dataset:
Self Regulated Development Learning Model Based on Local Culture to Improve Elementary School Students’ Explanatory Writing Skills

This study aims to develop a learning model to improve elementary school students' explanatory writing skills. This development research used the Plomp model (preliminary research, prototyping phase, and assessment). Data collection techniques were carried out by interview, observation, and documentation study. The data collection tool employed was a written test sheet for students’ explanatory writing skills. After conducting a needs analysis, it was found that the explanatory writing material is new in elementary school. Thus, the students' explanatory writing skills in this study were very low. The teacher then needed a model focusing on the students' development in explanatory writing skills. This research, therefore, produced a learning model of self-regulated strategy development based on local culture to improve the explanatory writing skills of elementary school students. This model can motivate and increase students’ confidence in writing by choosing several strategies provided and measuring, analyzing, and activating early abilities, thereby increasing students’ content knowledge. The results also revealed an increase in students’ explanatory writing skills. Moreover, this special model pays attention to how to write explanatory in elementary school. Further, this model cannot only be used in elementary schools but can also be used at higher school levels. Therefore, future researchers are advised to conduct research by testing the effectiveness of learning models with similar themes at higher school levels.
A novel single robot image shadow detection method based on convolutional block attention module and unsupervised learning network

Shadow detection plays a very important role in image processing. Although many algorithms have been proposed in different environments, it is still a challenging task to detect shadows in natural scenes. In this paper, we propose a convolutional block attention module (CBAM) and unsupervised domain adaptation adversarial learning network for single image shadow detection. The new method mainly contains three steps. Firstly, in order to reduce the data deviation between the domains, the hierarchical domain adaptation strategy is adopted to calibrate the feature distribution from low level to high level between the source domain and the target domain. Secondly, in order to enhance the soft shadow detection ability of the model, the boundary adversarial branch is proposed to obtain structured shadow boundary. Meanwhile, a CBAM is added in the model to reduce the correlation between different semantic information. Thirdly, the entropy adversarial branch is combined to further suppress the high uncertainty at the boundary of the prediction results, and it obtains the smooth and accurate shadow boundary. Finally, we conduct abundant experiments on public datasets, the RMSE has the lowest values with 9.6 and BER with 6.6 on ISTD dataset, the results show that the proposed shadow detection method has better edge structure compared with the existing deep learning detection methods.
Combining OCL and natural language

The growing popularity and availability of pretrained natural language models opens the door to many interesting applications combining natural language (NL) with software artefacts. A couple of examples are the generation of code excerpts from NL instructions or the verbalization of programs in NL to facilitate their comprehension.
Understanding and Mitigating Remote Code Execution Vulnerabilities in Cross-platform Ecosystem

JavaScript cross-platform frameworks are becoming increasingly popular. They help developers easily and conveniently build cross-platform applications while just needing only one JavaScript codebase. Recent security reports showed several high-profile cross-platform applications (e.g., Slack, Microsoft Teams, and Github Atom) suffered injection issues, which were often introduced by Cross-site Scripting (XSS) or embedded untrusted remote content like ads. These injections open security holes for remote web attackers, and cause serious security risks, such as allowing injected malicious code to run arbitrary local executables in victim devices (referred to as XRCE attacks). However, until now, XRCE vectors and behaviors and the root cause of XRCE were rarely studied and understood. Although the cross-platform framework developers and community responded quickly by offering multiple security features and suggestions, these mitigations were empirically proposed with unknown effectiveness.
A Survey on Application of LSTM as a Deep Learning Approach in Traffic Classification for SDN

SDN is a three-layered architecture with a centralized controller. Due to the increased usage of the Internet, there is a rapid generation of data traffic. Identifying and classifying traffic for effective network management is of prime importance today. SDN with deep learning (DL) model is a key area that has achieved high recognition. Moreover, in order to identify and classify network data traffic, the classification model needs to achieve high accuracy. This will ease network management and resource allocation. There exist many DL models for traffic classification but in this research, long short-term memory (LSTM) is studied, and insights are highlighted. Survey concludes that LSTM model for traffic classification is being used in the major area of data security and intrusion detection. Delay in classifying network traffic needs a special game theory approach which could not be handled by many LSTM models.
Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles

Background Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Objective Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. Methods We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Results Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Conclusion Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.
Matching Logic for Concurrent Programs Based on Rely/Guarantee and Abstract Patterns

This paper combines rely/guarantee, abstract patterns and matching logic to reason about concurrent programs in a modular and compositional manner. According to the separation property, the state can be divided into two disjoint parts, the local state and the shared state. We use matching logic to deal with the local state, and use rely/guarantee and abstract patterns to deal with the shared state. The power of rely/guarantee is to describe interference between concurrent programs. The advantage of abstract patterns is supporting fictional separation, which indicates that we logically consider abstract patterns to represent disjoint elements, although these elements are not disjoint under a certain implementation. By combining the advantages of rely/guarantee, abstract patterns and matching logic, our approach realize that clients of the module can be verified completely according to the specification of the module, regardless of the implementation of the module. In addition, we use several examples to illustrate our approach, define our logic judgments, and prove the soundness of our logic.
ERSDMM: A Standard Digitalization Modeling Method for Emergency Response Based on Knowledge Graph

Standard digitalization is a crucial step in social and economic development and the transformation of digital technology. Standard digitalization is of great significance in the promotion of sustainable economic and social development. This paper proposes a standard digitalization modeling method for emergency response (ERSDMM) based on knowledge graph (KG). Firstly, this paper analyzes the knowledge structure of emergency response standards (ERS) and constructs a “seven-dimensional” model of ERS based on the public safety triangle theory. An ontology model of the emergency response domain is then created. Secondly, ERS and emergency scenario fine-grained knowledge are extracted. Thirdly, a standard reorganization model is constructed to meet the needs of the scenario response. Finally, the ERSDMM is applied to the GB 21734-2008, which proves that the ERSDMM is available. Taking RES as an example, this paper explores the path and practice of standard digitalization. ERSDMM solves standards-related problems, such as overlapping content, coarse knowledge granularity, incomplete coverage of elements, and difficulty in acquiring knowledge.
An Efficient Approach to Retrieve Information for Desktop Search Engine

The Internet may not be the only source of information. Despite not remembering the relevant terms, individuals understand the need to access documents. Entity Disambiguation is a technique for deciphering the text in processed documents and queries. In this work, an outline of a desktop search engine using entity disambiguation at its foundation has been presented. In this article, disambiguate terms/entities using a Naive Bayes probabilistic model created to comprehend the keyword depending on the set it is part of, which is inspired by the probabilistic taxonomy Probase. There are three parts to our implementation: Text extraction, the conceptualization of obtained items, and index updating/matching. Experimental results obtained by implementation of the methodology yield truthfulness of the approach.
Design of Automatic Correction System for UAV’s Smoke Trajectory Angle Based on KNN Algorithm

Unmanned aerial vehicles (UAVs) have evolved with the progress of science and technology in recent years. They combine high-tech, such as information and communications technology, mechanical power, remote control, and electric power storage. In the past, drones could be flown only via remote control, and the mounted cameras captured images from the air. Now, UAVs integrate new technologies such as 5G, AI, and IoT in Taiwan. They have a great application value in a high-altitude data acquisition, entertainment performances (such as night light shows and UAV shows with smoke), agriculture, and 3D modeling. UAVs are susceptible to the natural wind when spraying smoke into the air, which leads to a smoke track offset. This study developed an autocorrect system for UAV smoke tracing. An AI model was used to calculate smoke tube angle corrections so that smoke tube angles could be immediately corrected when smoke is sprayed. This led to smoke tracks being consistent with flight tracks.
Submission Template for ACM Papers

A major development in academic libraries has been their recognition of the need to support research data services (RDS) to advance knowledge and science, augment novel solutions to social and economic impediments, and amplify immense potentials for competitiveness, productivity, and livability. This study investigated research data service maturity in academic libraries of developed countries with the intent to provide valuable insights and implications for other academic libraries. The maturity model informed the study. The study used in-depth web analysis and literature review to examine research data services in top-rated 100 university libraries of developed countries. Study findings showed a clear and well-developed landscape of research data services in these academic libraries; 81 (94%) of the investigated universities offered a wide array of both informational and technical services. The most common services consisted research data curation and storage service 81 (94%), research data management and training service 81 (94%), research data management reference service 80 (93%), research data introduction service 80 (93%) and data management plan and guidance 79 (92%) respectively. The least common service was the resource recommendation service 64 (74%). Study findings, therefore, suggest that the current services are extensive, robust and mature, signifying an overall maturity of the RDS landscape in academic libraries of developed countries.
Contrast Enhancement Method Based on Multi-Scale Retinex and Adaptive Gamma Correction

The most common methods to improve the quality of images with insufficient visibility are retinex-based and gamma correction methods. The fundamental assumption of retinex theory is that the color of an object can be represented as the multiplication of its illumination and reflectance. The retinex-based method improves the quality of the insufficiently visible image by repairing its illumination. The multi-scale retinex (MSR) is a classic retinex-based method. Though MSR better enhances the details of the image, it sometimes reverses its lightness value. The method based on adaptive gamma correction with weighting distribution (AGCWD) is to modify the visibility of images by gamma function. However, AGCWD provides a good enhancement effect on low-contrast areas, it also enhances the high-light region making it too bright. In this paper, a method that combines the advantages of MSR and AGCWD is proposed. Firstly, the advatages of MSR and AGCWD are preserved into detailed image through the weight that considers illumination. Then, the image constructed by combining the detailed and original images could maintain the contrast of the high-light region and enhance details of the low-light region. The validity of the proposed method is shown by experiments using several images.
High-speed 3D shape measurement using efficient moiré-assisted three-frequency heterodyne phase unwrapping algorithm

The conventional three-frequency heterodyne phase unwrapping (CTHPU) algorithm works very well in measuring isolated objects or discontinuous surfaces. However, it is unsuitable for high-speed applications due to many exposures to reconstruct one 3D profile. To this end, we propose a moiré-assisted three-frequency heterodyne phase unwrapping (MTHPU) algorithm, in which only four fringe patterns are needed to be projected to update one 3D result in dynamic scenes. In the proposed method, the wrapped phase distributions of the highest-frequency fringe patterns are calculated by three-step phase-shifting technique to ensure accuracy. And, the heterodyne phase maps, which are used to fulfill the absolute phase unwrapping, are extracted by a novel complex computational moiré algorithm from the other two fringe patterns and the highest-frequency wrapped phase. Moreover, the number of exposures can be further reduced to four to produce a 3D measurement by multiplexing the projecting sequence. Therefore, the proposed MTHPU can achieve high accuracy and high-efficiency measurement in dynamic scenes. Experiments demonstrate that our method can achieve a high 3D reconstruction frame rate of 2381 Hz.
Analysis on the Recent Trends in Machine Translation

Machine translation is to translate one language into another language, which has undergone a great evolution. The model of machine translation has been continuously improved, aiming to make the translation effect closer to the artificial translation. This article briefly summarizes the development history of machine translation, and introduces the main models of each stage of development. The initial machine translation mode is the Rule Based Machine Translation (RBMT) and Statistical Machine Translation (SMT). Recent mainstream translation approach enables Neural Machine Translation (NMT). It includes the input and the output, attention mechanism, and BLEU evaluation method. On this basis, there are also many expansion and innovation models, such as GPKD and other models to improve the evaluation effect. In general, machine translation can replace a part of human translation. However, it cannot completely replace human beings, because of the different human thinking and machine logic. People and machines have to cooperate with each other to improve the common efficiency.
Sentiment Classification of Code-Switched Text using Pre-Trained Multilingual Embeddings and Segmentation

With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-switched sentiment analysis, we propose a multi-step natural language processing algorithm utilizing points of code-switching in mixed text and conduct sentiment analysis around those identified points. The proposed sentiment analysis algorithm uses semantic similarity derived from large pre-trained multilingual models with a handcrafted set of positive and negative words to determine the polarity of code-switched text. The proposed approach outperforms a comparable baseline model by 11.2% for accuracy and 11.64% for F1-score on a Spanish-English dataset. Theoretically, the proposed algorithm can be expanded for sentiment analysis of multiple languages with limited human expertise.
Open access books: building blocks and policies

Watch the VIDEO. The complex realm of open science and scholarly communication consists of various increasingly interconnected parts. One of these building blocks is open access books. When taking a closer look at this particular building block, we see an intricate landscape of existing and emerging players, infrastructure, funding and policies. All these players and aspects play a role in a transition to open access for books as part of their larger open science policies. By focusing on the current state of affairs for this particular building block, we can describe the different stakeholders – commercial or community led – plus the roles they play and what infrastructure, metadata and identifiers are in place or still need to be developed. Furthermore, this session will take into account funding and policy developments related to open access books and its relation to the building block and open science as a whole. Based on this overview of the open access books landscape we invite the participants to discuss the implications for the larger goal of connecting the building blocks of Open Science. Enabling open science in this relatively small part of the scholarly communications realm comes with challenges for funders, publishers and libraries. Assuming that these challenges also exist for other open science building blocks, what can be learned from our experiences?
Colorimetry Evaluation for Video Mapping Rendering

Perceptually accurate colour reproduction is a core requirement of video mapping applications, where objective evaluation of colour rendering chain taking into account human perception becomes greatly beneficial. In this article, we present a workflow for colorimetry evaluation of video mapping software rendering chain, that we implemented in open-source video mapping software Splash, with a set a common metrics for image quality assessment and tools for colour reproduction evaluation. We introduce an accompanying graphical visualization template to help accurate interpretation of the metrics used. We describe different use case examples that we performed with our tool, proving the workflow efficient for simple, understandable and reproducible colorimetry evaluation.
LivePose: Democratizing Pose Detection for Multimedia Arts and Telepresence Applications on Open Edge Devices

We present LivePose: an open-source (GPL license) tool that democratizes pose detection for multimedia arts and telepresence applications, optimized for and distributed on open edge devices. We designed the architecture of LivePose with a 5-stage pipeline (frame capture, pose estimation, dimension mapping, filtering, output) sharing streams of data flow, distributable on networked nodes. We distribute LivePose and dependencies packages and filesystem images optimized for edge devices (NVIDIA Jetson). We showcase multimedia arts and telepresence applications enabled by LivePose.
Improvement of DGA Long Tail Problem Based on Transfer Learning

As the number of classes increases in traditional multiple classification and recognition tasks, there is often the problem of a long tail: the sample data is mainly distributed in a few classes. In the detection of domain names generating malware (DGA - domain generation algorithm), due to the variability of malware, the number of classes of DGA is also increasing and shows a long tail nature. However, in previous DGA detection research focused on the classes of a large amount of data so they do not address the long tail characteristics. We propose an effective knowledge transfer DGA detection model that transfers the knowledge learned in the previous stage of training to the next stage, and optimizes the impact of the long tail problem on the detection model. In order to inherit the continuity of the model, we propose a data balance review method, which can alleviate the catastrophic forgetting problem of transfer learning and detect new classes without retraining the whole model. Finally, the macro average F1 score of our model is 76.6%, 8.74% higher than ATT_BiLSTM and 6.34% higher than ATT_CNN_BiLSTM. So our model optimizes the long tail problem and better predicts all classes.