Reviewerly/exHarmony huggingface dataset:
Threshold-Based Belief Change

In this paper we study changes of beliefs in a ranking-theoretic setting using non-extremal implausibility thresholds for belief. We represent implausibilities as ranks and introduce natural rank changes subject to a minimal change criterion. We show that many of the traditional AGM postulates for revision and contraction are preserved, except for the postulate of Preservation which is invalid. The diagnosis for belief contraction is similar, but not exactly the same. We demonstrate that the one-shot versions of both revision and contraction can be represented as revisions based on semiorders, but in two subtly different ways. We provide sets of postulates that are sound and complete in the sense that they allow us to prove representation theorems. We show that, and explain why, the classical duality between revision and contraction, as exhibited by the Levi and Harper identities, is partly broken by threshold-based belief changes. We also study the logic of iterated threshold-based revision and contraction. The traditional Darwiche-Pearl postulates for iterated revision continue to hold, as well as two additional postulates that characterize ranking-based revision as a restricted `improvement' operator. We investigate the dual notion of iterated threshold-based belief contraction and provide a new set of postulates for it, characterizing contraction as a restricted 'degrading' operator.
Multi-label image classification model based on multi-scale semantic attention and graph attention network

Current research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images. However, in the existing methods, the label correlation is calculated based on the statistical information of the data. This label correlation is global and depends on the data set, and is not suitable for all samples. In the process of extracting image features, the The characteristic information of small objects is easily lost, resulting in low classification accuracy of small objects. For this reason, this paper innovatively proposes a multi-label image classification model based on multi-scale semantic attention and graph attention network. vector, followed by feature fusion to enhance the feature information of small objects, and then use the self-attention mechanism in the graph attention module to adaptively mine the correlation between categories in the image, and propose an attention regularization loss. The mAP of the model on the two public datasets of VOC 2007 and MS-COCO 2014 reached 95.5% and 83.4%, respectively, and most of the indicators are better than the existing state-of-the-art methods.
Research on wind farm intrusion detection algorithm based on network time protocol message periodic feature

In response to the problems of undetectable intrusions caused by the absence of authentication mechanism in the NTP protocol used in wind power monitoring systems, an intrusion detection algorithm for wind farm NTP messages based on message periodicity and autoregressive model was proposed. The periodic feature of the wind farm NTP messages under normal and intruded conditions was analyzed. An autoregressive model based on the current network conditions was built, the model parameters based on the Yule-Walker method were estimated and the detection thresholds were determined within 95% confidence intervals. The proposed algorithm was validated based on real capture messages from wind farms. The results show that the proposed intrusion detection algorithm can effectively identify denial-of-service attacks, man-in-the-middle attacks and clock offset attacks.
A Novel Personalized Business Recommendation Analysis Method Based on Big Data Intelligence

With the rapid development of e-commerce technology, e-commerce platform has become the main place for enterprises to recommend goods and users to consume goods. Countless enterprises present tens of thousands of goods on e-commerce platforms for users to choose, but this brings inconvenience to users in selecting and purchasing goods, as well as problems such as information overload. To solve this problem, most e-commerce platforms have applied personalized recommendation technologies and algorithms, but in actual application process, personalized recommendation systems also have problems such as sparse matrix, cold start and user differences. In order to effectively improve the existing problems and improve the recommendation quality, based on the basic theory of big data and e-commerce personalized recommendation. This paper constructs an optimized collaborative filtering recommendation algorithm using the value of commodity attributes and the Paasche coefficient, and compares the recommendation effect of the constructed recommendation algorithm with four common recommendation algorithms, verifying the effectiveness of the constructed recommendation algorithm.
Transfer-Based Particle Swarm Optimization for Large-Scale Dynamic Optimization With Changing Variable Interactions

Cooperative coevolutionary algorithms are popular to solve large-scale dynamic optimization problems via divide-and-conquer mechanisms. Their performance depends on how decision variables are grouped and how changing optima are tracked. However, existing decomposition methods are computationally expensive, resulting in limitations under dynamic variable interactions. Quick online decomposition is still a challenging issue, along with solution reconstruction for new subproblems. This paper proposes transfer-based particle swarm optimization, which adopts a dynamic differential grouping for online decomposition and a solution transfer strategy in response to environmental changes. Particularly, once an environmental change occurs, the dynamic differential grouping readjusts historical groupings based on the change severity of variable interactions. In addition, according to the similarity between subproblems in successive environments, the solution transfer strategy constructs new solutions from historical ones through dimension mapping. Multiple swarms are created to explore subareas of subproblems. Experimental results show that the proposed algorithm outperforms state-of-the-art algorithms on problem instances up to 1000-D in terms of solution optimality. The dynamic differential grouping obtains accurate groupings using less function evaluations.
Unsupervised Anomaly Detection &amp; Diagnosis: A Stein Variational Gradient Descent Approach

Detecting and diagnosing anomalies in observational data plays a crucial role in various real-world applications, such as e-commerce applet maintenance. Unsupervised machine learning techniques are typically employed for anomaly detection and diagnosis due to their convenience and independence from labeled data. Density estimation (DE), as one of the most widely used unsupervised machine learning techniques for anomaly detection, can be categorized into kernel density estimation (KDE)-based methods and normalizing flow (NF)-based methods. While KDE-based methods offer fast computation speed, they often ignore the complex manifold structure present in observational data. On the other hand, NF-based methods address the manifold issue but suffer from longer computation times. In this study, we propose a novel DE-based anomaly detection & diagnosis method using Stein Variational Gradient Descent (SVGD), aiming to leverage the strengths of KDE and NF approaches. Firstly, we rigorously derive the DE capability of SVGD through mathematical analysis. Subsequently, we demonstrate the ability of the SVGD method to perform anomaly diagnosis based on input feature attribution. Finally, to validate the effectiveness of our approach, we conduct experiments using synthetic, benchmark, and industrial datasets. The results demonstrate the superior performance and practical applicability of our proposed method.
Dual-Oriented Contrast for Recommendation with A Stop-Gradient Operation

Recently, contrastive loss is adopted as a main objective of recommender systems. InfoNCE-like losses penalize hard negative items more and control the strength of penalties with a temperature, called hardness-aware sensitivity. However, since they leverageuser->item patterns in a non-symmetric way, negative items are pushed away from anchor users and attract semantically-similar items to each other, focusing on the distribution of item embeddings. We point out that user embeddings also have inherent semantic structures that can be captured fromitem->user patterns. This paper presents Dual-oriented Contrast(DuCo), a novel symmetric learning objective for recommendation to learn more comprehensive representations fromusereftrightarrowitem patterns. DuCo controls user-/item-centric hardness-aware sensitivities and simultaneously optimizes the score distributions over sampled items (user-oriented contrast) and users (item-oriented contrast). This aims to explore ideal user and item distributions that are locally clustered and globally uniform. However, since user-/item-side temperatures are interdependent, naive control over temperatures may break the underlying semantic structures of the other side. To this end, we employ a stop-gradient operation to preserve the individual characteristics of user/item embedding distributions. Furthermore, we balance user-/item-oriented contrasts during learning to maintain consistent high-rank performance (e.g., recall@1). Empirical results show that DuCo contributes to the top-k user and item prediction simultaneously, and outperforms state-of-the-art learning objectives across different backbones from ID-based to neighbor-based encoders.
Digital Training in The Master's Programs in Public Universities of The Andean Community

The use of new technologies at the university level is essential for professional training in the XXI century, where the teacher is a key player. In this sense, the present research aims to analyze digital training in master's degree programs in public universities in the member countries of the Andean Community (CAN) in the areas of education, social sciences and humanities. For this purpose, master's degree programs were reviewed considering those with a published curriculum. The results show that 41.3% of the master's programs evaluated in the CAN offer training courses in digital skills, 15.4% offer training in specific programs and 43.2% do not offer training in digital competencies. It was concluded that the training in digital skills proposed by the master's programs in the CAN is still scarce; therefore, it is recommended to promote regional policies aimed at reducing the gaps in digital skills.
MultiPLe: Multilingual Prompt Learning for Relieving Semantic Confusions in Few-shot Event Detection

Event detection (ED) is a challenging task in the field of information extraction. Due to the monolingual text and rampant confusing triggers, traditional ED models suffer from semantic confusions in terms of polysemy and synonym, leading to severe detection mistakes. Such semantic confusions can be further exacerbated in a practical situation where scarce labeled data cannot provide sufficient semantic clues. To mitigate such bottleneck, we propose a multilingual prompt learning (MultiPLe) framework for few-shot event detection (FSED), including three components, i.e., a multilingual prompt, a hierarchical prototype and a quadruplet contrastive learning module. In detail, to ease the polysemy confusion, the multilingual prompt module develops the in-context semantics of triggers via the multilingual disambiguation and prior knowledge in pretrained language models. Then, the hierarchical prototype module is adopted to diminish the synonym confusion by connecting the captured inmost semantics of fuzzy triggers with labels at a fine granularity. Finally, we employ the quadruplet contrastive learning module to tackle the insufficient label representation and potential noise. Experiments on two public datasets show that MultiPLe outperforms the state-of-the-art baselines in weighted F1-score, presenting a maximum improvement of 13.63% for FSED.
YOLOv3 and YOLOv5-based automated facial mask detection and recognition systems to prevent COVID-19 outbreaks

Object detection system in light of deep learning have been monstrously effective in complex item identification task images and have shown likely in an extensive variety of genuine applications counting the Coronavirus pandemic. Ensuring and enforcing the proper use of face masks is one of the main obstacles in containing and reducing the spread of the infection among the population. This paper aims to find out how the urban population of a megacity uses facial masks correctly. Using YOLOv3 and YOLOv5, we trained and validated a brand-new dataset to identify images as "with mask", "without mask", and "mask not in position". In the YOLOv3 we carried out three pre-trained models which are: YOLOv3, YOLOv3-tiny, and SPP-YOLOv3. In addition, we utilized five pre-trained models in the YOLOv5: YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. The dataset is included 6550 pictures with three classes. On mAP, the dataset achieved a commendable 95% performance accuracy. This research can be used to monitor the proper use of face masks in various public spaces through automated scanning.
A Privacy-Preserving Evolutionary Computation Framework for Feature Selection

Feature selection is a crucial process in data science that involves selecting the most effective subset of features. Evolutionary computation (EC) is one of the most commonly-used feature selection techniques and has demonstrated good performance, which can help find the suitable feature subset based on training data and fitness information. However, in real-world scenarios, the exact fitness information and privacy-protected data cannot be directly accessed due to privacy and security issues, which leads to a great optimization challenge. To solve such privacy-preserving feature selection problems efficiently, this paper proposes a novel EC-based feature selection framework that balances data privacy and optimization efficiency, together with three contributions. First, based on the rank-based cryptographic function that returns the rank of solutions rather than the exact fitness information, this paper proposes a new fitness function to guide the EC algorithm to approach the global optimum without knowing the exact fitness information and the dataset, thereby preserving data privacy. Second, by integrating the proposed method and EC algorithms, this paper develops a new differential evolution and particle swarm optimization algorithms for efficient feature selection. Finally, experiments are conducted on public datasets, which demonstrate that the proposed method can maintain feature selection efficiency while preserving data privacy.
SML: Semantic Machine Learning Model Ontology

Artificial Intelligence is a set of technologies that simulate human-like cognition, using computer software and systems, to perform tasks associated with intelligent beings. One method of doing so, is Machine Learning (ML), which enhances system efficiency based on learning algorithms that create models from data and its underlying patterns. Nowadays, many ML models are being generated with different characteristics (e.g., type of the algorithm used, data set used to train it, resulting model performance), thus making the selection of a suitable model for a given use case a complex task, especially for non-expert users (with no or limited knowledge in ML). In this paper, we propose SML, an ontology-based model for Semantic Machine Learning description. SML allows, mainly, to describe and store ML models’ characteristics with their operational specifications, related data features, contextual usage, and evaluation metrics/scores to facilitate and improve ML model selection. The conducted experiments show promising results on both the efficiency and the performance levels.
REFLECTION OF THE IDEAS OF COLLECTIVE CREATIVE AFFAIRS IN THE PROCESS OF PREPARING A PROFESSIONAL STUDENTS’ TEAM

The article analyzes the relevance of the ideas of Academician Igor Petrovich Ivanov in modern education. Understanding of the need to use the method of collective creative affairs in the training of specialists in the conditions of team activities emphasized. The substantiation of the basic methodological foundation of the study based on the features of the federal state educational standards, consideration of the problems of the quality of education, taking into account the specifics of the team activity of students in the chosen profession is given. The algorithm of creation and functioning of a professional team of students, its stages are described. The educational and developmental effects achieved due to the results of the study are determined
DEVELOPMENT TRAINING FOR WEBSITE USER INTERFACE OF YAYASAN AL-JIHAD SURABAYA

This paper focused is Training in User Interface Development on the Al-Jihad Foundation Surabaya Website. The purpose of this research is to find out how to design a User Interface at the Al-Jihad Foundation Surabaya and develop a User Interface design related to the addition of features. The problem of this research is whether the user interface design used by the Al Jihad Surabaya Foundation is correct and correct. The method in this study uses a qualitative case study approach. Which is where the data collection technique comes from observation of research objects and direct interviews with staff of the Al-Jihad Foundation Surabaya. The conclusions drawn from this study are the user interface is used to increase comfort and convenience on the website of the Surabaya al-Jihad Foundation, in order to attract users' interest in accessing the Surabaya Al-Jihad Foundation website. Based on the results of the questionnaire, it shows 97% of respondents like the user interface on the new website.
VARIASI FORMULA PUMAKKAL TERHADAP KADAR N, P, K PUPUK CAIR LIMBAH AIR KOLAM LELE SEBAGAI BAHAN AJAR BIOLOGI BERUPA LEMBAR KEGIATAN PESERTA DIDIK

Catfish pond water waste has the potential value to produce fertilizer with innovations in its treatment, converting waste into liquid organic fertilizer (LOF) that can be used by the community as the fertilizer of plants. The aims of this study were 1) To find out which pumakkal formula variation affected the levels of N, P, K in catfish pond wastewater, 2) To find out which pumakkal formula had the best effect on N, P, K levels in catfish pond wastewater, 3) To determine to find out the results of research in the form of LKPD suitable for use as biology teaching materials. This study uses an experimental research design. This study used 1 control and 5 treatments and 3 replications. Treatment 1 used 3 isolates, treatment 2 used 6 isolates, treatment 3 used 9 isolates, treatment 4 used 12 isolates, and treatment 5 used 15 isolates. The results of the test for liquid fertilizer from catfish pond wastewater showed that P5 was the best degrading agent with yields of N (0.533%), P (3.176%), and K (3.994%). Based on the analysis of the validation of teaching materials, this research is suitable for learning biology in the form of student activity sheets with a value of 86% validation of material aspects and 86% of design aspects.
Towards a Model for the Detection of Firearms using Machine Learning Algorithms: A Case Study

Every day, we face an increase in cases of violence and criminality committed by organized crime groups. This generates public safety problems and deteriorates the quality of life of inhabitants. Consequently, local authorities require more specialized security equipment. One kind is surveillance devices, but the monitoring process is very costly and fallible, and it is inefficient for human beings to analyze and process large amounts of data in an instant. Objective: In this contribution, we propose a prototype for the detection of firearms using deep learning algorithms with the objective of automating and optimizing this process. Methodology: To develop the firearm detection software, two important steps were carried out: creating a dataset with the presence of firearms and training a neural network to detect them. Results: Tests performed on the neural network YOLOv5 object detection model revealed a mAP of 0.645 and an average accuracy with 0.5 IoU at 0.915. Conclusions: The YOLO detection system allowed us to provide the developed model with the relevant image segments for firearm detection. In the future, low light detection can be improved and a more advanced version of YOLOv6 can be used to achieve higher accuracy in both images and videos.
SafercITies. Intelligent System for the Analysis and Monitoring of Citizen Security

The rapid growth of the Internet and its widespread use have created new challenges in ensuring the safety of citizens in online spaces. This paper presents a project that aims to develop a comprehensive platform that addresses different dimensions of citizen security, covering both physical and psychological well-being. The platform serves as a bridge between public authorities and citizens, enabling individuals to report incidents, events or threats they encounter online, while providing preventive and protective measures. This paper outlines the motivation, objectives and key features of the platform, highlighting its potential to create a safer social environment.
Reuse of Ontological Knowledge in Open Science: Models, Sources, Repositories

Intelligent Web applications require regular updates of knowledge from the external open information space. Currently, a significant amount of knowledge oriented towards Web applications utilizes ontological representations that which facilitate interoperability and information reuse. Many existing ontologies are available in repositories as open information resources. While repositories of ontologies provide enhanced functionality for search and analysis, they often fail to meet user requirements. We propose to extend the definition of specific types of ontologies using a formal model of ontologies instead of relying solely on natural language definitions. By selecting these special cases based on the type of information resources used as ontology sources, we can identify ontologies with similar properties. Special cases of ontologies can be defined by applying various restrictions to the formal model elements, allowing simpler analysis methods. In this work, we propose definitions of taxonomy and catalog as an example of this approach. Furthermore, we perform a detailed analysis of the formal model of the Wiki ontology for both semantic and non-semantic sources.
ECOLOGICAL DEGRADATION AND OLIGARCHIC NETWORKS IN INDONESIAN NOVELS

The exploitation of nature has led to an ecological crisis that is not only a local threat but has expanded as a global catastrophe. Literature represents the degradation of nature as well as the power relations among the oligarchic networks operating around it to build ecological awareness for the community. This descriptive-qualitative research design aims to describe the form of ecological degradation and the role of oligarchic power networks that appear in Indonesian novels. The data sources were purposively selected from Indonesian novels with ecoliteracy insights, including Api Awan Asap by Korrie Layun Rampan (2015), Jemangilak Tak Pernah Menangis by Martin Aleida (2004), and Kelomang by Qizink La Aziva (2016). In addition, data sources related to nature and the environment depicted in the three novels were also used. The data were obtained using reading and note-taking techniques. Furthermore, it was analyzed qualitatively using the perspective of literary ecology through categorization, tabulation, and inference. The results of this study concluded that (1) the forms of ecological damage presented in Indonesian novels are (a) river sedimentation and pollution, (b) coastal abrasion, and (c) forest fires. (2) Forms of natural damage occur due to exploitation actions carried out by oligarchic networks supported by government regulations and licenses. The study of ecological literary works builds awareness of the process of ecological degradation and the perpetrators of destruction that form a systemic network.