Reviewerly/exHarmony huggingface dataset:
Industrial Internet Architecture Design for Intelligent Construction and Operation of Public Buildings Based on BP Algorithm

The feasible way to promote the transformation and upgrading of the construction industry is to vigorously develop the construction industry Internet to reduce capacity and cost. Smart life has brought a lot of convenience to people, and the construction of smart city needs more and more technical and skilled management talents who master the installation, debugging, operation and maintenance of modern intelligent equipment. With the maturity and gradual application of cloud computing, Internet of Things and GIS technologies, building intelligent operation and maintenance based on data link and big data analysis will usher in huge development opportunities. The energy consumption prediction model of large public buildings is analyzed by using GA algorithm to optimize the weights and thresholds of BPNN, and the validity of the model is verified by an example. Therefore, it is a new development trend in the field of architecture to use data mining technology to mine relevant information and provide reference for the prediction of electricity consumption of large public buildings. In order to ensure the efficiency and quality of the system, more and more basic equipment and information systems of all kinds of production and operation need to be strengthened daily. Therefore, more and more operation and maintenance management software and platform construction are emerging in the market.
Capabilities for knowledge management in virtual collaboration: A systematic review

Virtual collaboration has become an integrated part of knowledge-intensive work, especially after the pandemic. Researchers claim that working partly in the physical workplace and partly working virtually will persist in the future among knowledge workers. As knowledge sharing is a challenge when working across distances, it highlights the need for knowledge-intensive organizations to develop capabilities for knowledge management (KM). Capabilities will be investigated as organizational factors that create, extend, or modify behavior, aiming for effective KM across distances. This systematic literature review explores how knowledge creation, retention, and transfer are managed in virtual collaboration and the required capabilities for this process. Based on 33 articles, the study found that organizational structures facilitating frequent interactions and transactive memory, supported by technology, enable capabilities for KM. Hence, retention and transfer of tacit knowledge are challenged and might be a hindering factor&#x0D; .
Invariant Graph Neural Network for Out-of-Distribution Nodes

GNNs are effective for semi-supervised learning tasks on graphs, but they can suffer from bias due to distribution shifts between training and testing node distributions. In this paper, we propose the Invariant Graph Neural Network (IGNN) to address the issue of bias in GNNs. Specifically, IGNN learns the correlation of invariant features in different environments, where the spurious correlation changes in different environments. IGNN contains two components: the invariant graph partition component learns different graph environments and the invariant graph learning component regularizes the graph neural network to learn invariant graph representation in these environments. Extensive experiments have shown that the IGNN outperforms other methods for out-of-distribution nodes on several benchmark datasets.
Novel Method for Sentiment Analysis in Social Media Data Using Hybrid Deep Learning Model

It is common practise to employ a contextual mining approach called sentiment analysis (SA) to glean subjective but potentially helpful information from textual data. In order to recognise, analyse, and extract answers, states, or emotions from the data, it employs Natural Language Processing (NLP), language understanding, forensics, and cognitive science. Significant progress towards a better SA model may be made using the features analysis method. In recent years, feature extractions have made extensive use of GloVe and Word2vec embedding models. They require a huge corpora of text data for training and creating accurate vectors, but they ignore emotional and contextual information in the text. Out-of-Vocabulary (OOV) words are not considered while creating these vectors, hence some information may be lost if these methods are used. The limited availability of annotated data is another obstacle to sentiment categorization. Misclassification may occur when there is a discrepancy between the review and the label. In this research, we offer a hybrid SA model capable of overcoming the challenges posed by missing ratings and reviews due to noise, out-of-context phrases, and context. To investigate and conduct sentiment and appropriate analysis, this study proposes an Autoencoder Bi-directional Recurrent Neural Network (ABRNN) based on Bi-directional Encoding from Transformers (BET). The reviews are initially categorised by their polarity ratings using the zero-shot categorization. Then, the data is fed into a pre-trained BET system to extract embeddings based on the semantics and context of sentences. The neural network, made up of expanded and hybrid LSTM, was then fed the acquired contextual embedded vectors. For extracting both global and local spatial contextual characteristics from the embedded data, this approach employs expanded approach in place of traditional approach. The complete phrase sequencing is performed with the help of Hybrid Long Short-Term Memory (HLSTM). Measures of accuracy, precision, recall, f1-score, and area under the curve (AUC) are used to assess the ABRNN model on four separate text datasets from different domains. Because of this, ABRNN may be utilised well for SA processes on social media evaluations, with no loss of data.
Knowledge and ignorance in Belnap–Dunn logic

Abstract In this paper, we argue that the usual approach to modelling knowledge and belief with the necessity modality $\Box $ does not produce intuitive outcomes in the framework of the Belnap–Dunn logic ($\textsf{BD}$, alias $\textbf{FDE}$—first-degree entailment). We then motivate and introduce a nonstandard modality $\blacksquare $ that formalizes knowledge and belief in $\textsf{BD}$ and use $\blacksquare $ to define $\bullet $ and $\blacktriangledown $ that formalize the unknown truth and ignorance as not knowing whether, respectively. Moreover, we introduce another modality $\textbf{I}$ that stands for factive ignorance and show its connection with $\blacksquare $. We equip these modalities with Kripke-frame-based semantics and construct a sound and complete analytic cut system for $\textsf{BD}^{\blacksquare }$ and $\textsf{BD}^{\textbf{I}}$—the expansions of $\textsf{BD}$ with $\blacksquare $ and $\textbf{I}$. In addition, we show that $\Box $ as it is customarily defined in $\textsf{BD}$ cannot define any of the introduced modalities, nor, conversely, neither $\blacksquare $ nor $\textbf{I}$ can define $\Box $. We also demonstrate that $\blacksquare $ and $\textbf{I}$ are not interdefinable and establish the definability of several important classes of frames using $\blacksquare $.
The Positive Impact of Internet-Based Resources to Encourage Students’ Vocabulary Aspect

Internet-Based Resources is one of the solutions for increasing an aspect of English proficiency. This study aimed at knowing the Internet-Based Resources in their learning and to encourage English students’ vocabulary. The method used in this research is experimental research. Sample of this study consisted of English students including 26 students as the experimental group and 26 students as the control group. Each class had five sessions that must be completed, those were treatments for three times, one post-test, and one pre-test. The result found that IBR is effective for students’ vocabulary in writing an interesting, exciting to use and in combining their ideas with new words or vocabulary easier. The mean of the post-test in the control group was 73.53, on the other hand, in the experimental group was 83.12; the degrees of freedom totaled 25, the sig. (2-tailed) was 0.000, and the standard deviation was 8.500. The computation shows that the t-test has a higher score. Thus, it can be concluded that there is a positive impact of this method to encourage students’ vocabulary in English Language Program. The vocabulary mostly achieved for pre-test and post- test in the control group was 412 to 370. However, in the experimental group, the pre-test and post-test ranged around 355 to 470. The increasing ratio of the experimental groups in acquiring vocabularies over Internet-Based Resources was 13.90%.
A Study on the Application of Brainstorming Method in the Teaching of Intermediate Oral Chinese

Brainstorming, as a method of concentrating group thinking to stimulate creative thinking, was mainly used in the field of advertising design at the beginning of its emergence, and was gradually introduced into language teaching as a teaching method. The four basic principles of the brainstorming method are Welcome wild ideas，Withhold criticism，Go for quantity，Combine and improve ideas principles, and these four principles are also applicable to the teaching of intermediate oral Chinese. This study used teaching practice, and explored the feasibility of applying brainstorming to intermediate oral Chinese teaching. This study applies brainstorming to oral Chinese teaching in the third grade of the Chinese Department of H University. The classroom teaching practice shows that the application of brainstorming not only can stimulate students’ creativity of oral Chinese expression, but also can improve the enthusiasm and participation of students in oral Chinese class, and enhance the self-confidence of students’ oral expression. Generally speaking, the use of brainstorming method in the teaching of intermediate oral Chinese is very helpful for improving students’ oral Chinese learning.
Adversarial image generation by spatial transformation in perceptual colorspaces

Deep neural networks are known to be vulnerable to adversarial perturbations. The amount of these perturbations are generally quantified using $L_p$ metrics, such as $L_0$, $L_2$ and $L_\infty$. However, even when the measured perturbations are small, they tend to be noticeable by human observers since $L_p$ distance metrics are not representative of human perception. On the other hand, humans are less sensitive to changes in colorspace. In addition, pixel shifts in a constrained neighborhood are hard to notice. Motivated by these observations, we propose a method that creates adversarial examples by applying spatial transformations, which creates adversarial examples by changing the pixel locations independently to chrominance channels of perceptual colorspaces such as $YC_{b}C_{r}$ and $CIELAB$, instead of making an additive perturbation or manipulating pixel values directly. In a targeted white-box attack setting, the proposed method is able to obtain competitive fooling rates with very high confidence. The experimental evaluations show that the proposed method has favorable results in terms of approximate perceptual distance between benign and adversarially generated images. The source code is publicly available at https://github.com/ayberkydn/stadv-torch
Automatic Generation of Vocabulary Lists with Multiword Expressions

The importance of multiword expressions (MWEs) for language learning is well established. While MWE research has been evaluated on various downstream tasks such as syntactic parsing and machine translation, its applications in computer-assisted language learning has been less explored. This paper investigates the selection of MWEs for graded vocabulary lists. Widely used by language teachers and students, these lists recommend a language acquisition sequence to optimize learning efficiency. We automatically generate these lists using difficulty-graded corpora and MWEs extracted based on semantic compositionality. We evaluate these lists on their ability to facilitate text comprehension for learners. Experimental results show that our proposed method generates higher-quality lists than baselines using collocation measures.
Distillation of encoder-decoder transformers for sequence labelling

Driven by encouraging results on a wide range of tasks, the field of NLP is experiencing an accelerated race to develop bigger language models. This race for bigger models has also underscored the need to continue the pursuit of practical distillation approaches that can leverage the knowledge acquired by these big models in a compute-efficient manner. Having this goal in mind, we build on recent work to propose a hallucination-free framework for sequence tagging that is especially suited for distillation. We show empirical results of new state-of-the-art performance across multiple sequence labelling datasets and validate the usefulness of this framework for distilling a large model in a few-shot learning scenario.
Conclusion-based Counter-Argument Generation

In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument’s conclusion and to ensure that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.
A Systematic Search for Compound Semantics in Pretrained BERT Architectures

To date, transformer-based models such as BERT have been less successful in predicting compositionality of noun compounds than static word embeddings. This is likely related to a suboptimal use of the encoded information, reflecting an incomplete grasp of how the models represent the meanings of complex linguistic structures. This paper investigates variants of semantic knowledge derived from pretrained BERT when predicting the degrees of compositionality for 280 English noun compounds associated with human compositionality ratings. Our performance strongly improves on earlier unsupervised implementations of pretrained BERT and highlights beneficial decisions in data preprocessing, embedding computation, and compositionality estimation. The distinct linguistic roles of heads and modifiers are reflected by differences in BERT-derived representations, with empirical properties such as frequency, productivity, and ambiguity affecting model performance. The most relevant representational information is concentrated in the initial layers of the model architecture.
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding

Cross-task knowledge transfer via multi-task learning has recently made remarkable progress in general NLP tasks. However, entity tracking on the procedural text has not benefited from such knowledge transfer because of its distinct formulation, i.e., tracking the event flow while following structural constraints. State-of-the-art entity tracking approaches either design complicated model architectures or rely on task-specific pre-training to achieve good results. To this end, we propose MeeT, a Multi-task learning-enabled entity Tracking approach, which utilizes knowledge gained from general domain tasks to improve entity tracking. Specifically, MeeT first fine-tunes T5, a pre-trained multi-task learning model, with entity tracking-specialized QA formats, and then employs our customized decoding strategy to satisfy the structural constraints. MeeT achieves state-of-the-art performances on two popular entity tracking datasets, even though it does not require any task-specific architecture design or pre-training.
PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers

Building natural language inference (NLI) benchmarks that are both challenging for modern techniques, and free from shortcut biases is difficult. Chief among these biases is “single sentence label leakage,” where annotator-introduced spurious correlations yield datasets where the logical relation between (premise, hypothesis) pairs can be accurately predicted from only a single sentence, something that should in principle be impossible. We demonstrate that despite efforts to reduce this leakage, it persists in modern datasets that have been introduced since its 2018 discovery. To enable future amelioration efforts, introduce a novel model-driven technique, the progressive evaluation of cluster outliers (PECO) which enables both the objective measurement of leakage, and the automated detection of subpopulations in the data which maximally exhibit it.
Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments

The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.
Role of Foreign Remittances, Foreign Aid and Financial Development in Determining Human Development of Selected Asian Countries

Foreign Remittances play a key role in improving growth and development in economies. The inflow of global remittance in developing economies has been observed as increasing extremely since 1990. This study analyzed the impact of remittances, foreign aid, and financial development on human development in selected Asian countries. We have used a random effect technique in this research. Our study results highlight that increased remittances and foreign aid have enhanced living standards and human development in the concerned Asian economies. Moreover, the role of financial development is to contribute towards human development along with increased urban population. Considering the study results, it is recommended that there should be much focus on remittances inflow in Asian economies. These countries must attract more aid by improving their growth and development policies. There should be more access to easy credit facilities for high investments and employment opportunities for the masses in these countries.
Causal multi-label learning for image classification

In this paper, we investigate the problem of causal image classification with multi-label learning. As multi-label learning involves a diversity of supervision signals, it is considered a challenging issue to solve. Previous approaches have attempted to improve performance by identifying label-related image areas or exploiting the co-occurrence of labels. However, these methods are often characterized by complicated procedures, tedious computations, and a lack of intuitive interpretations. To overcome these limitations, we propose a novel approach that incorporates the concept of causal inference, which has been shown to be beneficial in other computer vision problems. Our method, called causal multi-label learning (CMLL), enables the selection of multiple objects from the original image through a multi-class attention module. These objects are then subjected to causal intervention to learn the causal relationships between different labels. Our proposed approach is both elegant and effective, with low computational cost and few parameters required for the multi-class causal intervention approach. Extensive tests and ablation studies demonstrate that the proposed method significantly improves prediction performance without a significant increase in training and inference times.
Model-Based Generation of Attack-Fault Trees

Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and Attack Trees and can be used to model and model-check a holistic view on both safety and security. Manually creating a complete AFT for the whole system is, however, a daunting task. It needs to span multiple abstraction layers, e.g., abstract application architecture and data flow as well as system and library dependencies that are affected by various vulnerabilities. We present an AFT generation tool-chain that facilitates this task using partial Fault and Attack Trees that are either manually created or mined from vulnerability databases. We semi-automatically create two system models that provide the necessary information to automatically combine these partial Fault and Attack Trees into complete AFTs using graph transformation rules.
Deriving Abstract Interpreters from Skeletal Semantics

This paper describes a methodology for defining an executable abstract interpreter from a formal description of the semantics of a programming language. Our approach is based on Skeletal Semantics and an abstract interpretation of its semantic meta-language. The correctness of the derived abstract interpretation can be established by compositionality provided that correctness properties of the core language-specific constructs are established. We illustrate the genericness of our method by defining a Value Analysis for a small imperative language based on its skeletal semantics.
Research on Privacy-Preserving for Cloud Computing Based on Blockchain

A great security challenge to cloud computing is data security and privacy-preserving. In this paper, a method for cloud computing security is proposed based on blockchain and homomorphic encryption algorithm. In the paper, cloud clients can be authenticated by any other client according to blockchain methods based on ECC signature algorithm and hash function. At the same time, data privacy of cloud clients can be protected well and data of cloud clients can’t be forged or repudiated. ECC homomorphic encryption algorithm is also used for cipher-processing in cloud-store and privacy-preserving data aggregation in cloud computing. Better privacy-preserving performance can be achieved, compared to current methods.
Fibonet: A Light-weight and Efficient Neural Network for Image Segmentation

In recent years, accurate models for image segmentation have become larger and more complex. However, it is hard to apply them into embedded devices which usually have limited space for data, energy and computing. Meanwhile, since many embedded devices do not support modifications of computing units, simple models with such requirements cannot be adapted to these devices. To achieve high accuracy for image segmentation in embedded devices, we propose a light-weight and efficient neural network, named Fibonet. Fibonet is constructed by cascading two Fiboblocks, with the Fibonacci structure which adjusts the combination of basic computing units through skip connections and feature reusing so that it is less computationally intensive and dataset-demanding. The experiments demonstrate that Fibonet can be embedded to the mobile terminal for real-time segmentation and can effectively balance accuracy and computing resources. Compared with Resnet18, Fibonet achieves 10.8% higher accuracy performance (mIoU: 0.533 vs. 0.481) with 84.4% fewer parameters (0.441M vs. 2.82M) using similar model width and depth.