Reviewerly/exHarmony huggingface dataset:
A Detailed Analysis of a Systematic Review About Requirements Engineering Processes for Multi-agent Systems

Requirements Engineering is a crucial area of Software Engineering, as it is through it that what a system should actually do is established, what are its functionalities and what is the expected behavior for the software. It is also through the requirements definition that the basic criteria to determine the software quality are established. All this is equally true for multiagent systems, systems composed of autonomous agents that can work together to solve complex problems. Nevertheless, requirements engineering for this kind of system presents its own challenges, as this type of system has specific requirements that are not normally found in other types of software. Taking this into account, we first performed a systematic literature review in order to determine the current state of the development processes that support requirements engineering for multiagent systems, classifying their coverage regarding the requirements engineering subareas and their support to the BDI model - a widely used model for the cognitive agents development. However, due to the lack of space, in the original publication we were only able to discuss the retrieved studies in a general way. Therefore, in this new work, we discuss the studies of the first review in more depth, emphasizing mainly the methodologies that support the BDI model and the gaps found after analysing them. Furthermore, we highlight the possibilities for further research based on the study of the recovered works.
Hardware and Software Co-design for Soft Switch in ViT Variants Processing Unit

As the application of pure Transformer in CV field, ViT shows the generality of Transformer model. However, it requires costly training on large datasets. Recently, some researchers trying to improve the training efficiency of ViT by combining ViT and CNN together which will use the inductive bias of CNN. In these models, the MHSA layer carries other modules on its side, but existing architectures cannot take advantage of this feature to customize designs to improve computational efficiency and resource utilization. The use of FPGA to customize specialized computing units can meet this need, but the existing hardware computing units can’t adapt to the combination of different types of layers, and switching between different models will result in expensive re-production costs. In this paper, we use hardware and software co-operation to design the FPGA computing unit and divide the layers according to their functions. Convolution and Transformer are classified into one category. Under the coordination deployment of software, it mix the outputs of the same type of layers through soft switches, so as to adapt to those flexible models. Compared with the performance of the original model on CPU, it achieves the acceleration performance of 26 $$\times $$ under the condition that the accuracy is only decreased by 0.9%. And the structure of common data block reduces the size of hardware resource unit by 91.7%.
Fuzzy Information Measures Feature Selection Using Descriptive Statistics Data

Feature selection (FS) has proven its importance as a preprocessing for improving classification performance. The success of FS methods depends on extracting all the possible relations among features to estimate their informative amount well. Fuzzy information measures are powerful solutions that extract the different feature relations without information loss. However, estimating fuzzy information measures consumes high resources such as space and time. To reduce the high cost of these resources, this paper proposes a novel method to generate FS based on fuzzy information measures using descriptive statistics data (DS) instead of the original data (OD). The main assumption behind this is that the descriptive statistics of features can hold the same relations as the original features. Over 15 benchmark datasets, the effectiveness of using DS has been evaluated on five FS methods according to the classification performance and feature selection cost.
Low-Quality $$\textit{DanMu}$$ Detection via Eye-Tracking Patterns

With the development of online video platforms, a comment visualization system that inserts dynamic and contextualized comments on a video has become popular in Japan and China, known as $$\textit{DanMu}$$ , which provides a feeling of “virtual liveness”. However, at the same time, it also brings some bad influences such as goal impediment and information overload, distraction problems, impolite and irrelevant comments. To solve this problem, there are several studies utilizing textual content for low-quality $$\textit{DanMu}$$ detection. However, they leave out the visual context and do not consider users’ watching behavior. To this end, in this paper, we propose an end-to-end multimodal classification framework for low-quality $$\textit{DanMu}$$ detection. Specifically, we first design a lab-based user study to investigate users’ watching patterns. Based on the discovered fixation patterns, we propose a new fusion method to fuse them with textual context. Moreover, visual content is also considered with a further fusion mechanism. Our model outperforms other baselines in almost all classification metrics in the real-world dataset.
$$\varepsilon -$$MC Nets: A Compact Representation Scheme for Large Cooperative Game Settings

In this paper we put forward $$\varepsilon $$ -MC nets, a novel succinct rule-based representation scheme for large cooperative games. First, we provide a polynomial algorithm that reaches the proposed representation by exploiting the agents’ estimates over marginal contributions, along with their acceptable information loss, $$\varepsilon $$ , regarding these estimates. Then we introduce the notion of equivalence classes of agents, and exploit it to (i) obtain an even more compact representation; and (ii) derive new, previously unheld, beliefs over the value of unobserved agent collaboration patterns. Moreover, we present theoretical and empirical results on the information loss arising from this “representational compression”, and on the degree of succinctness achieved. Notably, we show that an arbitrary number of merges to reach the compressed representation, exhibits an information loss that does not exceed $$\varepsilon $$ . Finally, we provide theoretical guarantees for the coalitional relative error and the Shapley value in the $$\varepsilon $$ -MC net with respect to the initial representation.
From OWL to Graphol: Importing Ontologies into Eddy the Editor

In this demo we showcase the most recent enhancement to Eddy, the visual editor for ontologies in the graphical language Graphol, which is the capability of fully importing ontologies expressed in the W3C standard OWL 2 language. We will illustrate and motivate the innovative choices we have adopted for achieving this goal, which are geared towards a more incremental approach to the importing process, designed to mitigate the commonly recurring challenges of providing a visually and conceptually rational layout of the ontology in graphical form.
Cognitive Architecture for Naval Mission Management

Information analysis related to a merchant or Navy ship and its environment is required in order to make the appropriate decisions during naval missions. However, human capabilities are no longer sufficient to reliably and rapidly apply methodologies at their disposal in an uncertain and time-constrained environment. Decision-makers face with uncertain situations where they lack time and resources. To deal with this situation, the design and the development of a decision support system would make it possible, thanks to the combination of cognitive sciences and the computing power of modern IT resources, to reduce the time required (1) to analyse the situation and (2) to select an appropriate action for the situation. This work is based on a decision-making method from Natural Decision Making (NDM) called Recognition Primed Decision (RPD), used for emergency decision-making. The proposal is therefore to formalize a decision support system and more specifically a cognitive architecture based on RPD associated to the adaptive capacities of the deep reinforcement learning, and capable of reacting in a dynamic environment with novel situations.Information analysis related to a merchant or Navy ship and its environment is required in order to make the appropriate decisions during naval missions. However, human capabilities are no longer sufficient to reliably and rapidly apply methodologies at their disposal in an uncertain and time-constrained environment. Decision-makers face with uncertain situations where they lack time and resources. To deal with this situation, the design and the development of a decision support system would make it possible, thanks to the combination of cognitive sciences and the computing power of modern IT resources, to reduce the time required (1) to analyse the situation and (2) to select an appropriate action for the situation. This work is based on a decision-making method from Natural Decision Making (NDM) called Recognition Primed Decision (RPD), used for emergency decision-making. The proposal is therefore to formalize a decision support system and more specifically a cognitive architecture based on RPD associated to the adaptive capacities of the deep reinforcement learning, and capable of reacting in a dynamic environment with novel situations.
Multi-view Heterogeneous Network Embedding

In the real world, the complex and diverse relations among different objects can be described in the form of networks. At the same time, with the emergence and development of network embedding, it has become an effective tool for processing networked data. However, most existing network embedding methods are designed for single-view networks, which have certain limitations in describing and characterizing the network semantics. Therefore, it motivates us to study the problem of multi-view network embedding. In this paper, we propose a Multi-View Embedding method for Heterogeneous Networks, called MVHNE. It mainly focuses on the preservation of the network structure and the semantics, and we do not process them separately, but consider their mutual dependence instead. Specifically, to simplify heterogeneous networks, a semantics-based multi-view generation approach was explored. Then, based on the generated semantic views, our model has two concerns, namely the preservation of single-view semantics and the enhanced view collaboration. With extensive experiments on three real-world datasets, we confirm the validity of considering the interactions between structure and semantics for multi-view network embedding. Experiments further demonstrate that our proposed method outperforms the existing state-of-the-art methods.
Proposing System for Classifying Voucher Images Using Siamese Model

Voucher image classification is a complex process that can Please confirm if the corresponding author is correctly identified. Amend if necessary. be influenced by many factors. Currently, people use their hands to classify voucher images. However, there are many problems such as processing time, efficiency, and accuracy of classification. When the data is small and their distributions are not uniform, the machine learning techniques used to classify them are not high accuracy. Therefore, the Siamese model is proposed to classify images with little data without equal between classes in this paper. The results show that the model achieves an accuracy of 99.57% for both forward and backward images when the number of epochs for training is only 100. This proves that the proposed method has a promising application in practice.
Software and Hardware Fusion Multi-Head Attention

Recently, Transformer has achieved state-of-the-arts results in several research areas such as Natural Language Processing and Computer Vision. Due to Transformer has a very large number of parameters and its core module Multi-Head Attention has a complex structure, the optimization of Multi-Head Attention for Transformer is now the research hotspots. However, most of the current work focused on software model optimization or hardware accelerator design, but unilateral optimization from algorithms or hardware is difficult to give full play to comprehensive performance of Multi-Head Attention, which is not well adapted to its characteristics. To solve the above problem, we propose a Software and Hardware Fusion Multi-Head Attention structure, which has less inference latency with tiny accuracy loss than the existing software optimization methods and hardware accelerators. We implement this design on Xilinx ZCU102 and validate this model accuracy and inference time using CIFAR-10 dataset, and obtained accuracy within 1% loss with respect to the baseline, and inference time 15.19 times of the baseline.
ECCKG: An Eventuality-Centric Commonsense Knowledge Graph

Eventuality-centric knowledge graphs are essential resources for many downstream applications. However, current knowledge graphs mainly focus on knowledge about entities while ignoring the real-world eventualities (including events and states). To fill this gap, we propose to build ECCKG, a high-quality eventuality-centric commonsense knowledge graph. We argue that rule-based methods are of great value for knowledge graph construction, but must be used in conjunction with other techniques such as crowdsourcing. We thus create ECCKG by combining rule-based reasoning with crowdsourcing. We first acquire seed ECCKG by manually filtering out the incorrect and duplicate eventuality-related commonsense assertions in ConceptNet 5.5. Then we enrich the seed ECCKG with a set of logical rules iteratively. Finally, we generate new commonsense assertions by instantiating the existing eventualities. The resulting ECCKG contains more than 1.3 million eventuality-centric commonsense knowledge tuples which is about 15 times larger than ConceptNet 5.5. A manual evaluation shows that ECCKG outperforms other eventuality-centric commonsense knowledge graphs in terms of both quality and quantity. We also demonstrate the usefulness of ECCKG by the extrinsic use case of commonsense knowledge acquisition. ECCKG is available at https://zenodo.org/record/6084081 .
Sparse Dense Transformer Network for Video Action Recognition

The action recognition backbone has continued to advance. The two-stream method based on Convolutional Neural Networks (CNNs) usually pays more attention to the video’s local features and ignores global information because of the limitation of Convolution kernels. Transformer based on attention mechanism is adopted to capture global information, which is inferior to CNNs in extracting local features. More features can improve video representations. Therefore, a novel two-stream Transformer model is proposed, Sparse Dense Transformer Network(SDTN), which involves (i) a Sparse pathway, operating at low frame rate, to capture spatial semantics and local features; and (ii) a Dense pathway, running at high frame rate, to abstract motion information. A new patch-based cropping approach is presented to make the model focus on the patches in the center of the frame. Furthermore, frame alignment, a method that compares the input frames of the two pathways, reduces the computational cost. Experiments show that SDTN extracts deeper spatiotemporal features through input policy of various temporal resolutions, and reaches 82.4% accuracy on Kinetics-400, outperforming the previous method by more than 1.9% accuracy.
Human-Machine Collaboration Based Named Entity Recognition

Named Entity Recognition (NER) is an important task in Natural Language Processing (NLP), and its main goal is to extract the required entities from a given text and label the entity type. Not only is an important research topic, but its identification quality and efficiency also have an important impact on follow-up tasks (such as machine translation, intelligent question answering system, etc.). Current research methods are primarily based on Deep Learning (DL) models. In practical applications, such models often rely on a large number of labeled data, which will show a certain limitation in applications in specific domains. At the same time, due to the high uncertainty of human language and the openness involving problems, the DL model does not reach the point of complete replacement of humanity. Human-machine collaboration refers to that in the changing environment, human and machines perform tasks alternately, so that the task can achieve the best performance with a small amount of human participation. In this paper, we propose a Human-Machine Collaboration based Named Entity Recognition (HMCNER) model by utilizing the complex cognitive reasoning ability of human beings and combining with existing DL model. The extensive experimental results show that our model can efficiently complete the NER task based on the existing research results, and have practical application.
Recommendation via Collaborative Diffusion Generative Model

Despite the success of classical collaborative filtering (CF) methods in the recommendation systems domain, we point out two issues that essentially limit this class of models. Firstly, most classical CF models predominantly yield weak collaborative signals, which makes them deliver suboptimal recommendation performance. Secondly, most classical CF models produce unsatisfactory latent representations resulting in poor model generalization and performance. To address these limitations, this paper presents the Collaborative Diffusion Generative Model (CODIGEM), the first-ever denoising diffusion probabilistic model (DDPM)-based CF model. CODIGEM effectively models user-item interactions data by obtaining the intricate and non-linear patterns to generate strong collaborative signals and robust latent representations for improving the model’s generalizability and recommendation performance. Empirically, we demonstrate that CODIGEM is a very efficient generative CF model, and it outperforms several classical CF models on several real-world datasets. Moreover, we illustrate through experimental validation the settings that make CODIGEM provide the most significant recommendation performance, highlighting the importance of using the DDPM in recommendation systems.
Student Behavior Analysis and Performance Prediction Based on Blended Learning Data

Blended teaching has the characteristics of small scale, strong controllability, definite learning tasks and consideration of both online and offline teaching. The quantitative evaluation indicators of learners' blended learning behavior enthusiasm and stability are proposed, and then used for learning behavior analysis and performance prediction. It analyzes the distribution, correlation, consistency and effectiveness of online and offline learning behavior indicators, and it is found that there is a high correlation between learning behavior indicators and the final grade. The prediction is carried on the data set composed of learning behavior indicators, students' basic information, online and offline learning data. The improved forest optimization algorithm is applied to select features. The naive Bayes, decision tree and random forest classifier are used to predict the final performance. The experiments show that the learning behavior indicators can effectively reduce the scale of feature set and improve the performance prediction effect.
On the Relationship with Toulmin Method to Logic-Based Argumentation

Toulmin presents a model of argumentation, in which claims can be justified in response to challenges. The model replaces the traditional concepts of 'claim' and 'premise' with new concepts of 'claim', 'data', 'warrant', 'qualifier', 'rebuttal', and 'backing'. Due to the significance of Toulmin's argumentation, this work investigates its relationship to our recently introduced logic-based argumentation [14]. We show that Toulmin's idea does not only give a visual interpretation of the logic-based argumentation, but also yields a human-understandable form. Finally, the paper wraps up the investigation's result and formalizes a novel 2-Tier argumentation framework, that combines the advantages of both Toulmin's model and the logic-based argumentation system.
Learning Advisor-Advisee Relationship from Multiplex Network Structure

The analysis of the advisor-advisor relationship can provide rich information for understanding the interactions among entities in academic network. Most of the existing methods mine the relationship in collaboration network. They focus on extracting deep relationships between attributes, but ignore the structure of nodes. Compared with the multiplex network, the collaboration network can only provide a single view, which describes whether the two nodes have co-authored publications. To tackle these problems, we propose an Attention-based Multiplex Network Structure Fusion (AMNSF) method for mining the advisor-advisee relationship. AMNSF takes the multiplex network as input composed of the acknowledgment network and the collaboration network where the acknowledgment network provides the social relationship between entities. We make full use of the network’s structure information and design a novel network fusion mechanism to integrate the structure information from each layer. Finally, we conduct extensive experiments on a variety of datasets. The experimental results show that the proposed model outperforms the state-of-the-art methods.
Multirelational Tensor Graph Attention Networks for Knowledge Fusion in Smart Enterprise Systems

Augmented Intelligence of Things empowered by knowledge graph drives cognitive intelligence for smart enterprise management systems (EMS). Knowledge fusion technology can effectively integrate knowledge from different sources, thereby improving the accuracy and richness of the knowledge graph, which is of great significance to the sustainable development of smart EMS. Traditional machine learning methods on graphs face challenges in the fusion of complex and multirelational enterprise knowledge graphs due to inherent defects in relation semantic and local structure information capturing. In order to break through these limitations and improve EMS knowledge graphs, we propose tensor-based graph attention networks for multirelational graph representation learning (MR-GAT), and apply it to the critical tasks in knowledge fusion: Entity and relation alignment. Specifically, we innovatively adopt tensor operations to adequately model the interactions between entities and relations in EMS knowledge graph to learn more accurate representations. Additionally, we propose a relation attention mechanism, which focuses on assigning weights in the process of aggregating local semantic information for relation learning in an EMS knowledge graph. Furthermore, we develop a joint entity and relation alignment framework by utilizing the proposed multirelational graph attention networks to improve the accuracy of knowledge fusion. Experimental evaluations on three datasets present that the proposed approach outperforms the baseline models by about 1.4% on average in terms of the mean reciprocal rank metric, which demonstrates the superior ability of the proposed MR-GAT in representation learning for knowledge fusion in smart EMS.