Reviewerly/exHarmony huggingface dataset:
Exploring the Robustness of Human Parsers Toward Common Corruptions

Human parsing aims to segment each pixel of the human image with fine-grained semantic categories. However, current human parsers trained with clean data are easily confused by numerous image corruptions such as blur and noise. To improve the robustness of human parsers, in this paper, we construct three corruption robustness benchmarks, termed LIP-C, ATR-C, and Pascal-Person-Part-C, to assist us in evaluating the risk tolerance of human parsing models. Inspired by the data augmentation strategy, we propose a novel heterogeneous augmentation-enhanced mechanism to bolster robustness under commonly corrupted conditions. Specifically, two types of data augmentations from different views, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., image-aware augmentation and model-aware image-to-image transformation, are integrated in a sequential manner for adapting to unforeseen image corruptions. The image-aware augmentation can enrich the high diversity of training images with the help of common image operations. The model-aware augmentation strategy that improves the diversity of input data by considering the model’s randomness. The proposed method is model-agnostic, and it can plug and play into arbitrary state-of-the-art human parsing frameworks. The experimental results show that the proposed method demonstrates good universality which can improve the robustness of the human parsing models and even the semantic segmentation models when facing various image common corruptions. Meanwhile, it can still obtain approximate performance on clean data.
AdaVis: Adaptive and Explainable Visualization Recommendation for Tabular Data

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based approaches implicitly assume that there is only one appropriate visualization for a specific dataset, which is often not true for real applications. Also, they often work like a black box, and are difficult for users to understand the reasons for recommending specific visualizations. To fill the research gap, we propose AdaVis, an adaptive and explainable approach to recommend one or multiple appropriate visualizations for a tabular dataset. It leverages a box embedding-based knowledge graph to well model the possible one-to-many mapping relations among different entities (i.e., data features, dataset columns, datasets, and visualization choices). The embeddings of the entities and relations can be learned from dataset-visualization pairs. Also, AdaVis incorporates the attention mechanism into the inference framework. Attention can indicate the relative importance of data features for a dataset and provide fine-grained explainability. Our extensive evaluations through quantitative metric evaluations, case studies, and user interviews demonstrate the effectiveness of AdaVis.
A Consortium Blockchain-Enabled Evidence Sharing System for Public Interest Litigation

Procuratorates, as the prosecutor in public interest litigation (PIL), need to obtain evidence from other PIL stakeholders including citizens, companies, governmental agencies, IoT monitoring devices and so on. However, the evidence sharing is not smooth due to the lack of secure data sharing and privacy protection during case investigation and evidence collection. Therefore, the authors propose a consortium blockchain-based secure data sharing and privacy protection scheme named PILChain. The involved organizations are connected as peers in PILChain. The safety of uploaded evidence and user privacy can be guaranteed with a fine-grained access control and zero-knowledge identity proof. InterPlanetary File System is introduced to store large evidence files off-chain, further enhancing the data security and system scalability. The security of PILChain is analyzed in terms of access control, evidence confidentiality, evidence integrity, traceability, privacy, and scalability. Last, the authors evaluate the performance of the developed prototype system by implementing PILChain on Hyperledger Fabric.
Identification of actual energy policy and security research topics in Elsevier and MDPI publications for 2021–2023. Part 2. Clustering of publications. Automatic extractive referencing

This article focuses on identifying relevant research topics in Elsevier and MDPI publications 2021–2023 on energy policy and security based on publication clustering. It is shown that name groups extracted from titles and abstracts can be used to cluster publications to identify relevant research topics. The extraction of noun phrases was carried out using the Sifaka software package. A table of the 5,000 most frequent name groups was constructed, the terms of which were retained in the title and abstract texts, and the remaining terms were deleted. The prepared texts were used to cluster the bibliometric records using a demo version of Carrot2 and the Lingo3G algorithm. The annotation texts for each cluster were subjected to automatic extractive referencing using the sumy program with the Text-rank algorithm and the stopwords-en-ISO.txt list. The possibility of using extractive referencing technology to describe the dominant topic of individual publication clusters is demonstrated. A list of dominant themes in energy policy and security publications in Elsevier and MDPI publications for 2021–2023 is given.
Identification of actual energy policy and security research topics in Elsevier and MDPI publications for 2021–2023. Part 1. Term clustering and topic referencing with ChatGPT

This article aims to identify relevant research topics in Elsevier and MDPI publications for the years 2021–2023 on energy policy and security issues. The research was based on the identification of keywords/phrases from title texts and abstracts using PatternRank software based on KeyBERT technology and name group extraction. These keywords/phrases were considered as a possible alternative to the author keywords. The bibliometric analysis of these terms using VOSviewer identified eight clusters describing energy policy and security issues in detail. Textual descriptions of the topics in each cluster are provided using ChartGPT technology.
Efficient integration of big data with blockchain: Challenges, opportunity and future

&lt;p&gt;Big data has become more and more popular, piquing the interest of both researchers and technologists as well as business executives. Despite its advantages, big data has a number of problems that necessitate a one-stop shop. Blockchain technology has seen a considerable increase in usage, which has had a substantial impact on its applications and led to a variety of useful outcomes. In the areas of identity, trust, decentralization, data-driven decisions, data ownerships, etc., there are notable game-changers. As a result, Blockchain is frequently acknowledged as an effective fix for big data issues. Among the solutions it suggests are decentralized private data management and digital property resolution. Together, two these technologies can develop beneficial solutions.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
New Proxy Signature Scheme over Elliptic Curves using Chaotic Maps Applicable during Pandemic COVID-19

Abstract: Digitalization has attracted the world to collect increasing data. Background: Proxy signature is a digital alternative for signing documents in the absence of the original signer. Methods: In this paper, we have used the mathematical methods and concepts of Chaotic maps (CMs) and elliptic curve cryptography. Results: We have proposed a new proxy signature scheme (PSS). Security of our PSS relies on "elliptic curve discrete logarithm (ECDL) and integer factorization (FAC) problems". It requires only low-complexity computation, which increases efficiency. Conclusion: It is the first PSS in such a security setting and can also be assumed to be secure in the post-quantum cryptographic world. It can be highly used digitally during thePandemic conditions like COVID-19.
Artificial Intelligence Based MPPT Techniques of Photo Voltaic System

In this paper, Particle Swarm Optimisation (PSO) investigates the global optimal solution by taking advantage of the memory of the particle and the swarm. PSO has evolved into one of the most significant Swam Intelligence techniques and Evolutionary Computation algorithms due to its characteristics of low constraint on the continuity of goal function and joint of search space, and capacity to adapt to dynamic environments. The development of algorithms over the years is then discussed, along with applications in multi-objective optimisation, neural networks, electronics, etc. The remaining issues and potential prospects for PSO research are then examined. One of the concepts of swarm intelligence introduced in the field of computing and artificial intelligence is particle swarm optimisation (PSO). PSO is a novel collective and distributed intelligent paradigm for problem solving, primarily in the field of optimisation, without centralized control or the provision of a global model. In this work, the basic PSO, its improvements, its applications to various systems, including electric power systems, and its premature convergence as well as its combination with other intelligent algorithms to enhance search capacity and shorten the time required to exit local optimums are all thoroughly reviewed.
PEMBELAJARAN BAHASA INGGRIS MELALUI CONTEXTUAL TEACHING AND LEARNING DI KELURAHAN TALISE VALANGGUNI KECAMATAN MANTIKULORE KOTA PALU

For the people of Palu, English has yet to become an important part of communication that must be introduced to children early. English is only known and mastered by academics. Meanwhile, the general public (non-academics) uses Indonesian or regional languages in various daily communication activities. This community service activity aims to introduce English to elementary school-age children. The implementation took place in the Talise-Valangguni sub-district, Mantikulore District. The servants in this activity are lecturers and students from the Faculty of Teacher Training and Education, Muhammadiyah University, Palu. From this activity, 16 elementary school students were interested in participating in learning English. The small number of participants shows that interest and motivation in learning English still needs to be higher.&#x0D;
Analysis of the application effect of BLS in national education for students from Hong Kong and Macao

BLS(Broad Learning System) has been proved to be an effective learning system in various tests, and there are not many related studies on the verification of width-based learning in national education in China. This paper discusses the application of BLS in national condition education, and evaluate
Investigation of signature biometric evidence using deep learning for forecasting personality

A crucial field of study, personality assessment has applications in marketing, human resources, and psychology. Existing approaches, however, frequently rely on arbitrary self-report surveys, which might introduce biases and constraints. As a result, there is a need for trustworthy and impartial personality testing tools. Using signature biometric data, we suggest a Salp swarm-optimized Siamese neural network (SSO-SNN) in this research to forecast personality attributes. In order to gauge the effectiveness of the suggested strategy, we first collect information from the CIU handwritten database. A median filter is used during pre-processing to reduce noise in the gathered signature photos. The SSO-SNN uses the SSO algorithm's optimization capabilities to speed up the SNN's learning process. Numerous tests are run to determine how effective the suggested strategy is in terms of metrics like accuracy, precision, recall, and f1-score. Experimental outcomes demonstrate the better performance of the suggested SSO-SNN technique in forecasting personality traits when compared to existing approaches.
Next-generation attendance tracking: Automated face detection for lecture attendance

Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.
Rediscussion on Quantifiers &amp;quot;Du&amp;quot; and &amp;quot;Si&amp;quot; in the Inscriptions of Bronze Chime Bells

The quantifiers "Du" and "Si" in the inscriptions of bronze chime bells are important contents in the study of the ancient Chinese music suspension. There is no dispute that "Du" refers to eight bells, while there is more dispute that "Si" refers to several bells. Zheng Xuan takes 32 bells as the "Si", Du Yu takes 16 bells as the "Si", and Yang Bojun takes a set of bells that can play music in a complete tone and music scale as the "Si". Each view has its unique and correct part, but in terms of unearthed objects, it seems that these views are all insufficient. From the inscriptions of bronze chime bells, "Du" and "Si" should be interpreted as "many" instead of definite numbers, and the numerals used in front of them may reflect the ritual system to a certain extent.
An Arabic Probabilistic Parser Based on a Property Grammar

The specificities of Arabic parsing, such as agglutination, vocalization, and the relatively order-free words in Arabic sentences, remain major issues to consider. To promote its robustness, such parseing should define different types of constraints. Property Grammar (PG) formalism verifies the satisfiability of the constraints directly on the units of the structure, thanks to its properties (or relations). In this context, we propose to build a probabilistic parser with syntactic properties, using a PG, and we measure the production rules in terms of different implicit information and in particular the syntactic properties. We experimented with our parser on the treebank ATB, using the parsing algorithm CYK, and we obtained encouraging results. Our method is also automatic for implementation of most property types. Its generalization for other languages or corpus domains (using treebanks) could be a good perspective. Its combination with pre-trained models of BERT may also make our parser faster.
Pengembangan Media Pembelajaran Matematika Berbasis Android Menggunakan MIT App Inventor Pada Materi Statistika Siswa Kelas VIII SMP N 19 Kota Kupang

Inventor In Statistick Materials For Class VIII Students Of SMP N 19 Kota Kupang aims to produce products in the form of Android application-based learning media using MITT App Inventor In Statistick Materials For Class VIII Students Of SMP N 19 Kota Kupang which includes valid, practical, and effective aspects.&#x0D; The method used in this study is the Research and Development (R&amp;D) method and the model used in the development of this android application-based learning media is the ADDIE development model which consists of 5 stages, namely analysis, design, development, implementation and evaluation. Data collection techniques and instruments used were interviews, questionnaires and tests. Data analysis techniques used are validity analysis techniques, practicality analysis techniques and effectiveness analysis techniques.&#x0D; The results showed that the android based learning media that had been developed was feasible to be used in learning Statistick Materials For Class VIII Students Of SMP N 19 Kota Kupang. The results of the feasibility of the learning media were obtained from the material expert assessment with an average of 4.0 and the media expert assessment of 4.36 so it was categorized as "very valid", the subject teacher's response with an average response result of 4.43 so it was included in the "very practical" category ”, as well as the results of student tests and student responses using learning media, the percentage of student graduation obtained is 80.65% so that it is included in the "very effective" category, while the average student response for this learning media is 4.44 so that based on test results and student responses, the learning media developed is very effective.&#x0D; Keywords: Development, Android-based Learning Media, Statistics
Two-stage complex action recognition framework for real-time surveillance automatic violence detection

Abstract Violent action classification in community-based surveillance is a particularly challenging concept in itself. The ambiguity of violence as a complex action can lead to the misclassification of violence-related crimes in detection models and the increased complexity of intelligent surveillance systems leading to greater costs in operations or cost of lives. This paper demonstrates a novel approach to performing automatic violence detection by considering violence as complex actions mitigating oversimplification or overgeneralization of detection models. The proposed work supports the notion that violence is a complex action and is classifiable through decomposition into more identifiable actions that could be easily recognized by human action recognition algorithms. A two-stage framework was designed to detect simple actions which are sub-concepts of violence in a two-stream action recognition architecture. Using a basic logistic regression layer, simple actions were further classified as complex actions for violence detection. Varying configurations of the work were tested, such as applying action silhouettes, varying activation caching sizes, and different pooling methods for post-classification smoothing. The framework was evaluated considering accuracy, recall, and operational speed considering its implications in community deployment. The experimental results show that the developed framework reaches 21 FPS operation speeds for real-time operations and 11 FPS for non-real-time operations. Using the proposed variable caching algorithm, median pooling results in accuracy reaching 83.08% and 80.50% for non-real-time and real-time operations. In comparison, applying max pooling results to recalls reached 89.55% and 84.93% for non-real-time and real-time operations, respectively. This paper shows that complex action decomposition is deemed to be an appropriate method through the comparable performance with existing efforts that have not considered violence as complex actions implying a new perspective for automatic violence detection in intelligent surveillance systems.
Approach for Ranking in Recommender System Using Hinge Loss Function

In recent years, the use of the recommendation system has grown immensely due to the count of users using the web has increased. When plenty of products available online, recommendation system attempts to suggest products to the users associated with their interest. Recommender systems are designed to advise things to the user based on many different factors. These systems predict the most suitable items that the users are most probably to select and that are of users’ interest. There are three main techniques to gather the feedback from users. Feedback is categorized into explicit (such as scores), implicit (such as browser history and click logs), and hybrid. Recommender device is a technique that look for “rating,” “liking” a person could show to a thing. Ranking algorithms rely upon search question provided by persons, who recognizes what they are searching. Ranking algorithms normally put more relevant data closer to the top of the list. Many recommendation algorithms utilize ranking methods to provide a list of items ranked according to the user’s preferences. In this work, we will perform ranking and feedback for recommendation using matrix factorization technique called SVD and suggest top-N products to the person depend on person history also we calculate Loss using Hinge Loss Function and Bayesian Personalized Ranking Loss.
Framing of Quality Questions for Quality Code Snippets

Nowadays, developers or programmers depend on the reusability of code to implement software as it reduces time and effort. Therefore, most developers depend on code bases or on Question and Answers (Q&A) sites to reuse the existing code. They look for exact code snippets by simply giving questions to Q&A websites. But the problem is when programmers give code snippets with low-quality question titles leads to retrieving irrelevant answers. Hence, our approach helps users (who are providing code snippets to Q&A sites) in the forming of high-quality questions by automatically generating question titles for a code fragment using the token-based approach with LSTM deep neural network model with 96% accuracy which is a better than the existing model. Our model evaluated three programming languages (e.g., Java, Python, C#) datasets collected from GitHub.
Attract Contributors with 'help wanted' Issues

Maintaining a package can be a lonesome activity, which sometimes poses a problem if you prefer team work or if you encounter a very thorny-for-you problem.Beside belonging to a supportive community of maintainers (like rOpenSci 😉), for collaborative help and commiseration you can try to build a community of contributors around your package!In this post, we’ll explore one tool helping you towards that goal: “help wanted” issues, with which your
Interactive Efficient Multi-Task Network for RGB-D Semantic Segmentation

Semantic segmentation is significant for robotic indoor activities. However, relying solely on RGB modality often leads to poor results due to limited information. Introducing other modalities can improve performance but also increases complexity and cost, making it unsuitable for real-time robotic applications. To address the balance issue of performance and speed in robotic indoor scenarios, we propose an interactive efficient multitask RGB-D semantic segmentation network (IEMNet) that utilizes both RGB and depth modalities. On the premise of ensuring rapid inference speed, we introduce a cross-modal feature rectification module, which calibrates the noise of RGB and depth modalities and achieves comprehensive cross-modal feature interaction. Furthermore, we propose a coordinate attention fusion module to achieve more effective feature fusion. Finally, an instance segmentation task is added to the decoder to assist in enhancing the performance of semantic segmentation. Experiments on two indoor scene datasets, NYUv2 and SUNRGB-D, demonstrate the superior performance of the proposed method, especially on the NYUv2, achieving 54.5% mIoU and striking an excellent balance between performance and inference speed at 42 frames per second.
Manufacture and Method of Creating an Anatomical 3D Model of the Internal Structures of the Human Head

The article discusses the production of a 3D anatomical model using software designed to convert volumetric MRI data into surfaces. This software made it possible to highlight the surface of the deep structures of the human brain.