Reviewerly/exHarmony huggingface dataset:
Carving Sustainability into Architecture Knowledge Practice

In the daily work of a software architect, knowledge is ubiquitous. In addition to technical expertise, architecture knowledge (AK) also requires practical experience in the representation, communication, and management of architectural decisions. However, there is a pressing need to also incorporate sustainability aspects, i.e., capturing decisions towards software systems that are environmentally, economically, and socially balanced in the long term. With this study, we aim to provide a review of AK concepts and their representation and communication from a practical point of view. Having this understanding, we explore where sustainability can be applied in daily practice and how we can address sustainability in architecture processes in the future. The paper presents an empirical study conducted in an industrial context encompassing a questionnaire survey with 32 participants and semi-structured interviews with 15 practitioners; both groups are from a major bank in the Netherlands. Based on the insights gained from combining our findings, we (i) provide a map of applied concepts for communicating and representing AK in a large enterprise, and (ii) discuss potential avenues for carving sustainability into current software architecture practice.
Multi-task ordinal regression with labeled and unlabeled data

Ordinal regression (OR) aims to construct the classifier from data with ordered class labels. At present, most of the OR methods consider the OR problem as a single learning task and assume that all samples in the learning task are labeled. Nevertheless, in practice, labeling a large number of samples may be costly. If there are not enough labeled samples available to train the classifier, their classification accuracy may be restricted. To deal with this problem, in this paper, we propose a novel multi-task semi-supervised ordinal regression (MTSSOR) method. Our method is able to incorporate the additional information from the related tasks and unlabeled samples into improving the performance of OR classifiers, when the labeled samples are insufficient. To the best of our knowledge, this is the first attempt on multi-task semi-supervised OR. In the experiments, we compare MTSSOR with the single-task supervised OR methods, single-task semi-supervised OR methods and multi-task supervised OR method on real-world multi-task OR datasets. The mean-zero error (MZE) and mean-absolute error (MAE) are used as evaluation metrics. The experimental results show that compared with these OR methods, MTSSOR can achieve a minimum of 0.015 and up to 0.152 improvements in term of MZE, and a minimum of 0.02 and up to 0.272 improvements in term of MAE.
Future Interactions with Virtual Reality Technology (VR)

Every major milestone in the history of science, technology and interaction design has stemmed from the collision of technology and human nature (Katona, 2021). From the original paper tape punching, it has evolved to keyboard input, and now to touch operation, voice control, voice recognition, and advanced gesture recognition, motion capture, eye recognition technologies, and so on. In the future, brain-computer interfaces and mind recognition will also be implemented. Each technological innovation and product upgrade will bring about changes in the way human-machine interaction takes place. In terms of development trends and general trends, the core and most important thing is to be human-centred, to conform to the natural movements of the human body and the laws of the body, and to allow people to access information in the most comfortable way. Virtual reality is a new revolution in the way people interact with each other, and the field of VR is still in the development stage. This essay focuses on virtual reality technology, with a focus on how virtual reality technology and human interaction will look like in the near future. The paper is divided into seven sections: introduction, definition and current status of virtual reality technology, analysis of how virtual reality interacts with people and future development, analysis of the future application and interaction of virtual reality technology in multiple fields, advantages of future virtual reality technology, possible problems and solutions of future virtual reality technology and summary.
A Study on Higher Education Students’ Attitude Toward Artificial Intelligence in Achieving Sustainable Social Empowerment in Assam

The persistent development of a nation depends prodigiously upon the competency and the efficiency of its citizens. The competency among the citizens can be cultivated by empowering them socially. The development of a country or a society in this twenty-first century depends upon how much the citizens are empowering themselves sustainably. In the present social context technology especially, artificial intelligence is catalyzing everything within the realm of possibility including achievement and perpetration of sustainable development goals (SDGS2030). The Government of India has taken various initiatives for the citizenry for being able to utilize the facilities of artificial intelligence to empower them socially and thence achieve the goals of sustainable development. Amalgamation of social empowerment and sustainable development leads to sustainable social empowerment. The present chapter is an endeavor to assay how artificial intelligence is a benefaction for social empowerment of the autochthon of India. It is also an attempt to spell out the current status of sustainable social empowerment in the state Assam in terms of SDG index of India developed by NITI Ayog and also to find out the attitude of the students in higher education toward the role of artificial intelligence in achieving sustainable social empowerment.
The Enlightenment of Aesthetics to Middle School Poetry Education

The focus of aesthetic thinking is "the same truth, goodness and beauty", which is embodied in the practice-based historical materialism which is established to meet the needs of The Times. Aesthetics believes that the truth, good and beautiful origin lies in the material production practice of human society, which grasp the basic contradiction between the subject and the object, and will be separated in human activities in the natural world and the human world re-integration, in order to effectively make the subject into the reality of freedom, so as to win a happy and wonderful life. The aesthetics of winning a happy and wonderful life is mainly manifested in the early "freedom to know the world", the middle "freedom to transform the world" and the late "freedom to feel the world". These are undoubtedly very special inspirations for the middle school poetry education to shape creative talents and "sound personality" who "seek truth", "be good" and "worship beauty" and realize the new realm of "human development and integrity construction".
Analisis Teknik Entity-Relationship Diagram Dalam Perancangan Database

The purpose of this research is to find out how to develop the data entity relationship model in the data based data management system. Where the entity relationship is a system that is important for the company or as a liaison to explain the relationship between data and database systems. There are many techniques used in database design. One of them is Entity Relationship Diagram (ERD). ERD is one of the main diagrams representing the conceptual data model that reflects the user data requirements in the database system. ERD is the first stage in database design. There are several things that must be considered in making ERD for database design. Each database must have entities that are interconnected and each entity must have attributes consisting of a primary key as the entity's characteristics and descriptive attributes. Many users fail to create databases due to errors in ERD creation. Conceptual errors, procedural errors, or technical errors. This article was created using the SLR method, namely reviewing various kinds of references that discuss database design, then drawing conclusions to make it easier for users to overcome problems that occur when making ERD and implementing them.
Temporal Difference Enhancement for High-Resolution Video Frame Interpolation

Video frame interpolation techniques provide a smoother visual experience by enhancing the temporal resolution of videos. To generate intermediate frames, numerous techniques estimate various parameters, such as optical flow and occlusion masks, directly on the original resolution images. As a result, processing high-resolution images requires more computing power and inference time. This paper proposes a lightweight network for high-resolution video frame interpolation that performs a complete interpolation workflow on low-resolution images to provide accurate low-resolution optical flow and occlusion masks. To effectively restore the optical flow and mask of the original resolution image, we propose an extremely lightweight temporal difference enhancement module that makes use of the hidden motion information in the temporal difference to aid in the restoration of optical flow and mask. The proposed network has comparable performance and faster inference speed for high-resolution video interpolation compared to the current mainstream network. The ablation experiment demonstrates the importance of the temporal difference module.
The dynamic contribution of implicit meaning to the context: Variability in real usage

The investigation of the complex relationship between implicitly conveyed meaning and context is central when it comes to defining, describing and understanding both implicit linguistic strategies and context. Despite being core notions in pragmatics, due to the complexity of the task, few fixed points have been reached so far in both directions. In this editorial, we provide some brief insights on the most prominent issues in the study of both notions and on their mutual influence. We then describe the multifarious picture of the path taken up to date in the understanding of the dynamic contribution of implicit meaning to the context, also discussing further possible theoretical and descriptive lines of investigation. Finally, we introduce the six research papers which make up this virtual special issue; we provide a short summary of their perspectives, methodologies and main findings, whilst also clarifying their contribution to the topic of the special issue.
A Pipeline and a Graph-Based Model for Paragraph Extraction: Take the Insurance Document as an Example

Paragraph extraction is the basic of some downstream tasks, and it's indispensable in some circumstances. For example, when we need process the insurance clause document with the identifying title, we extract the paragraph information to get the elements. In this paper, we will discuss the paragraph extraction method with the example of insurance clause document. We proposed a pipeline combining the rule-based and model-based method, and PEG, a graph-based model of text classification to solve the problem of paragraph extraction. We have verified the validity of the method with the insurance clause documents. Particularly we make the results from rule-based method as the input of the model and calculate the positional relation of the elements with rules to build edges, with the information used by R-GCN network, we can improve the accuracy of text classification. We also make the extraction for the special circumstance, such as the recognition of conclusion phrases. And the classification accuracy has been also improved with our model.
Super-Resolution Infrared Imaging via Degraded Information Distillation Network

Infrared imaging technology is widely used in civil and military applications. However, the cost of making high-resolution infrared detectors is expensive. For this reason, we propose an unsupervised super-resolution infrared imaging method for degraded information distillation network. We design a network model for progressive extraction of degradation information to learn more degradation information with discriminative features. We use dual-attention convolution to achieve feature adaption in both channel and space. We use sub-pixel convolution to implement the reconstruction of infrared images. We train our model using infrared images and evaluate the proposed method systematically. The experimental results show that our proposed method has good performance compared to other state-of-the-art methods.
"What's wrong with this product?"

With the widespread use of e-commerce, proper oversight and regulatory compliance become increasingly difficult, if not impossible, resulting in a heightened risk of harm to consumers from unsafe products. In this paper, we explore how online consumer reviews can be utilized to identify hazardous products that have previously been flagged in the European Union Safety Gate reports. Our research presents a general framework that can be beneficial for regulatory authorities, as well as a specific application to consumer electronics. We contribute a dataset of 3000 reviews of electronic products, 755 of which reference hazardous products, and conduct classification baselines, achieving an AUC of up to 80% with room for improvement. Furthermore, we discuss the legal basis for annotation and potential issues that may arise. Our proposed methodology and dataset are valuable resources for regulatory authorities in the European Union and provide evidence of the effectiveness of digital surveillance in protecting consumers.
A voice spoofing detection framework for IoT systems with feature pyramid and online knowledge distillation

Voice anti-spoofing is an important step for secure speaker verification in voice-enabled Internet of Things (IoT) systems. Most voice spoofing detection methods require significant computational costs and lack good generalization capability, making them unsuitable for deployment in IoT systems. To alleviate these problems, this work proposes a voice spoofing detection framework, which contains four parts, namely the compact model, the feature pyramid module (FPM), the feature aggregation module (FAM) and online knowledge distillation. FPM leverages multi-scale features from different layers of the compact model and then produces four embeddings with different resolutions. Instead of using frequently-used aggregation operations, FAM adaptively assigns weights for the four embeddings above and thus gains the high-quality soft embedding that can optimize the compact model in reverse. Moreover, the Kullback–Leibler (KL) divergence is employed to minimize the difference between the embedding predicted by the compact model and the soft embedding, which enables the compact model to learn dark knowledge in an online manner. We carry out a series of experiments on LA and PA corpora of ASVspoof 2019 dataset. These comprehensive experiments show that the proposed framework gives better performance than other methods without sacrificing inference complexity, demonstrating the superiority of our framework.
No Labels?

Labeling textual corpora in their entirety is infeasible in most practical situations, yet it is a very common need today in public and private organizations. In contexts with large unlabeled datasets, active learning methods may reduce the manual labeling effort by selecting samples deemed more informative for the learning process. The paper elaborates on a method for multi-class classification based on state-of-the-art NLP active learning techniques, performing various experiments in low-resource and imbalanced settings. In particular, we refer to a dataset of Dutch legal documents constructed with two levels of imbalance; we study the performance of task-adapting a pre-trained Dutch language model, BERTje, and of using active learning to fine-tune the model to the task, testing several selection strategies. We find that, on the constructed datasets, an entropy-based strategy slightly improves the F1, precision, and recall convergence rates; and that the improvements are most pronounced in the severely imbalanced dataset. These results show promise for active learning in low-resource imbalanced domains but also leave space for further improvement.
Interpretable Reinforcement Learning of Behavior Trees

The interpretability of reinforcement learning (RL) algorithms has become one of the significant challenges for artificial intelligence (AI) researchers. Behavior Trees (BTs) have enabled developers to design AI policies visually and comprehend the agent’s behaviors in robotics and computer games. Combining their strengths, researchers have proposed to utilize the RL algorithm to generate BTs to present learned policies automatically. Existing methods are devoted to the incremental generation or modification of pre-designed BTs. These efforts necessitate specialized knowledge and the manual design of initial BTs. In this paper, we present intelligent generation methods that directly represent the policies generated by Q-learning and its derived algorithms in the form of BTs to enhance the interpretability of RL. We investigate the tradeoff between the size and performance of BTs while attaining interpretability, intending to obtain balanced policies that are easy to comprehend and good in performance. Evaluations in several classic OpenAI Gym environments validate the effectiveness of our methods.
A database of detrital zircon geochronology ages of Cambrian to Paleogene deposits in South China

Abstract Complications of detrital zircons databases provide a means for statistically analysing a variety of significant geological problems. In this work, we tried to collect a database about the South China Phanerozoic detrital zircon geochronology data. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, the database contains a total of 699 samples with 55,532 U–Pb ages and 3,770 effective Hf isotope data, from 130 papers. Abundant information including reference title, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages, ε Hf ( t ) values, etc., have been involved in our database, and all data can be downloaded from DDE Data Publish &amp; Repository website, https://repository.deep‐time.org/ . Through the integrated data, we can improve the previous studies and avoid the waste of resources caused by a large number of repeated studies.
Intent with knowledge-aware multiview contrastive learning for recommendation

Abstract User–item interactions on e-commerce platforms involve various intents, such as browsing and purchasing, which require fine-grained intent recognition. Existing recommendation methods incorporate latent intent into user–item interactions; however, they overlook important considerations. First, they fail to integrate intents with semantic information in knowledge graphs, neglecting intent interpretability. Second, they do not exploit the structural information from multiple views of latent intents in user–item interactions. This study established the intent with knowledge-aware multiview contrastive learning (IKMCL) model for explanation in recommendation systems. The proposed IKMCL model converts latent intent into fine-grained intent, calculates intent weights, mines latent semantic information, and learns the representation of user–item interactions through multiview intent contrastive learning. In particular, we combined fine-grained intents with a knowledge graph to calculate intent weights and capture intent semantics. The IKMCL model performs multiview intent contrastive learning at both coarse-grained and fine-grained levels to extract semantic relationships in user–item interactions and provide intent recommendations in structural and semantic views. In addition, an intent-relational path was designed based on multiview contrastive learning, enabling the capture of semantic information from latent intents and personalized item recommendations with interpretability. Experimental results using large benchmark datasets indicated that the proposed model outperformed other advanced methods, significantly improving recommendation performance.
Data-driven Learning Meets Generative AI: Introducing the Framework of Metacognitive Resource Use

This paper explores the intersection of data-driven learning (DDL) and generative AI (GenAI), represented by technologies like ChatGPT, in the realm of language learning and teaching. It presents two complementary perspectives on how to integrate these approaches. The first viewpoint advocates for a blended methodology that synergizes DDL and GenAI, capitalizing on their complementary strengths while offsetting their individual limitations. The second introduces the Metacognitive Resource Use (MRU) framework, a novel paradigm that positions DDL within an expansive ecosystem of language resources, which also includes GenAI tools. Anchored in the foundational principles of metacognition, the MRU framework centers on two pivotal dimensions: metacognitive knowledge and metacognitive regulation. The paper proposes pedagogical recommendations designed to enable learners to strategically utilize a wide range of language resources, from corpora to GenAI technologies, guided by their self-awareness, the specifics of the task, and relevant strategies. The paper concludes by highlighting promising avenues for future research, notably the empirical assessment of both the integrated DDL-GenAI approach and the MRU framework.
Preserving model privacy for Federated Reinforcement Learning in Complementary Environments

Federated reinforcement learning (FRL) uses data from multiple partners interacting with the environment to train a global decision model while maintaining data privacy. In specific situations, it is necessary to protect not only data privacy but also model privacy. That is, our objective is to collaboratively train a global model that can be used privately and independently by the initiator. We refer to this need for model privacy preservation as model monopolization. It allows the task initiator to ensure the privacy of the model during the training process and to own the trained model alone. To the best of our knowledge, model monopolization protection in Federated Reinforcement Learning has not been studied. We propose monopolized federated reinforcement learning in complementary environments via function transformation. We also provide contribution metrics to ensure the sustainability of monopolized federated reinforcement learning and to provide a quantitative basis for task initiators to give back to task participants. The experimental results show that our method achieved model monopolization by training agents in the Grid-world.
Classification of Non-Civil Servant Performance Appraisal Using Naïve Bayes Classifier Algorithm

Employee performance assessment is a way to measure the level of employee productivity. In the process of assessing the performance of Non-Civil Servants (non-PNS) employees at the Regional Technical Implementation Unit of Education and Training of Cooperatives and Entrepreneurs (UPTD P3W) at this time, it is required to classify data based on several factors to find out whether the employee fits into the eligible category or not as the best employee to become a civil servant (PNS) candidate. The purpose of this research is to make it easier to determine the classification of the performance assessment of non-PNS employees at UPTD P3W using the Naïve Bayes Classifier Algorithm and to determine the level of accuracy in the classification of the performance assessment. In this study, the authors used 498 data as training data and 105 data as testing data for manual testing in Excel and for testing using RapidMiner tools. Based on the analysis in the study, the result of the predictions determines the best employees to become candidates for civil servants quickly and accurately, while from the tests performed by comparing training data and with data testing using RapidMiner tools, the accuracy rate is 84.76%.