Reviewerly/exHarmony huggingface dataset:
Solving floating pollution with deep learning: A novel SSD for floating objects based on continual unsupervised domain adaptation

Intelligent identification and monitoring of floating materials is extremely crucial in water environment management as floating pollution severely damages water ecosystems. However, it remains a challenging problem in practical applications due to high scene complexity, small-scale targets, and model domain shifting. Aimed at this problem, this paper proposes a novel object detector based on a continual unsupervised domain adaptation method, which improves the detection accuracy and generalization capability of the object detector for small floating targets. Specifically, this paper proposes an improved object detection algorithm-single shot multi-Box detector for floating targets to adapt to the detection task of small floating targets. Then, a new paradigm of domain adaptation method-continual unsupervised domain adaptation is proposed to solve the issue of converting data between the source and target domains. Simultaneously, this paper integrates the detector and domain adaptation method to improve the model’s detection accuracy for small floating targets in a new target domain. The proposed method is trained and compared with the state-of-the-art methods based on multiple scenarios. As a result, the detection accuracy of the proposed method using an input image size of 300 × 300 reaches 82.2%, the detection speed can reach 68.5 frames per second under the graphics processing unit, the computation amount of floating-point numbers is reduced to 3.3 billion, and the size of the model is compressed into 25.3 MB. This paper extends the application of floating object detection in water surface vision, which is crucial for the control of water pollution.
Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods

Augmented (AR) and Mixed Reality (MR) technologies are enablers of the Industry 4.0 paradigm and are spreading at high speed in production. Main applications include design, training, and assembly guidance. The latter is a pressing concern, because assembly is the process that accounts for the biggest portion of total cost within production. Teaching and guiding operators to assemble with minimal effort and error rates is pivotal. This work presents the development of a comprehensive MR application for guiding novice operators in following simple assembly instructions. The app follows innovative programming logic and component tracking in a dynamic environment, providing an immersive experience that includes different guidance aids. The application was tested by experienced and novice users, data were drawn from the performed experiments, and a questionnaire was submitted to collect the users’ perception. Results indicate that the MR application was easy to follow and even gave confidence to inexperienced subjects. The guidance support was perceived as useful by the users, though at times invasive in the field of view. Further development effort is required to draw from this work a complete and usable architecture for MR application in assembly, but this research forms the basis to achieve better, more consistent instructions for assembly guidance based on component tracking.
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems

Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from language models pre-trained on general-domain texts. Traditional event detection approaches primarily focus on the general domain and ignore these two problems in the power system domain. To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED , leveraging a semantic channel and a topological channel to enrich information interaction from short texts. Concretely, the semantic channel refines textual representations with semantic similarity, building the semantic information interaction among potential event-related words. The topological channel generates a relation-type-aware graph modeling word dependencies, and a word-type-aware graph integrating part-of-speech tags. To further reduce errors worsened by professional terminologies in type analysis, a type learning mechanism is designed for updating the representations of both the word type and relation type in the topological channel. In this way, the information sparsity and professional term occurrence problems can be alleviated by enabling interaction between topological and semantic information. Furthermore, to address the lack of labeled data in power systems, we built a Chinese event detection dataset based on electrical Power Event texts, named PoE . In experiments, our model achieves compelling results not only on the PoE dataset, but on general-domain event detection datasets including ACE 2005 and MAVEN.
PlantStereo: A High Quality Stereo Matching Dataset for Plant Reconstruction

Stereo matching is a depth perception method for plant phenotyping with high throughput. In recent years, the accuracy and real-time performance of the stereo matching models have been greatly improved. While the training process relies on specialized large-scale datasets, in this research, we aim to address the issue in building stereo matching datasets. A semi-automatic method was proposed to acquire the ground truth, including camera calibration, image registration, and disparity image generation. On the basis of this method, spinach, tomato, pepper, and pumpkin were considered for experiment, and a dataset named PlantStereo was built for reconstruction. Taking data size, disparity accuracy, disparity density, and data type into consideration, PlantStereo outperforms other representative stereo matching datasets. Experimental results showed that, compared with the disparity accuracy at pixel level, the disparity accuracy at sub-pixel level can remarkably improve the matching accuracy. More specifically, for PSMNet, the EPE and bad−3 error decreased 0.30 pixels and 2.13%, respectively. For GwcNet, the EPE and bad−3 error decreased 0.08 pixels and 0.42%, respectively. In addition, the proposed workflow based on stereo matching can achieve competitive results compared with other depth perception methods, such as Time-of-Flight (ToF) and structured light, when considering depth error (2.5 mm at 0.7 m), real-time performance (50 fps at 1046 × 606), and cost. The proposed method can be adopted to build stereo matching datasets, and the workflow can be used for depth perception in plant phenotyping.
Convergence of Blockchain to Artificial Intelligence Applications

In the era of modern technology, blockchain is out of harm's way in making proceedings ample of its reliability trademark, and chiefly for its immutability, and it doesn't hang on any mediator network while any transaction happens. The use of this technology helped solve many problems in society by detecting the problem which happens in the industry sphere, similarly trust, unambiguously, security, and reliability of statistics processing. The objective of the article is to review the blockchain technologies with the focus of reviewing blockchain in artificial intelligence (AI) applications. With the exception of cryptocurrency, blockchain technology can also avail oneself of financial and social services and healthcare facilities, which is risk-free.
The Impact of Artificial Intelligence on Search Engine

In an aim to improve search engine results, AI interferes to boost it. Thus, this chapter investigates the impact of artificial intelligence on search engine. It includes the most advanced techniques of artificial intelligence that improve search engine optimization (SEO) rankings. In order not to fall in the hassle of having no ranking for your website, artificial intelligence can uplift a website position in the search engine. It is important to state that the growth of SEO has an integral role in digital marketing through AI. Moreover, the obtained results in research studies approve that the integration of artificial intelligence is vital for the progress of search engines.
NFTS: WHAT OPPORTUNITIES AND CHALLENGES IN TOURISM?

Purpose – In previous works we discussed the contribution of digital tourism not only to the improvement of the reputation of a Country (i.e. Sentiment Analysis) but also of the possibility to create opportunities for a new kind of online tourism. In recent times, with the development of the so-called Metaverse, also influenced by the pandemic emergency, Countries’ digital efforts in creating tourism opportunities gave new perspectives related to the possibility to capitalize on digital content, transforming them – jointly with physical tourism experiences – into possible further incomes and means to increase a Countries’ reputation. Specifically, in this context, we considered the enabling technology represented by Non-Fungible Tokens (NFTs), an innovative application of blockchains creating a unique opportunity in several sectors, including tourism. This work aims to explore possible applications of NFTs in tourism, analyzing the experiences currently in place. Methodology – Due to the innovative topic and the very few experiences in NFTs applications in tourism, this paper represents a preliminary review of current projects implementing the use of NFTs, so that the applied methodology consists mainly in qualitative analysis and discussion about the possible development of this technology in tourism. To this purpose, we introduced some applications currently in place, such as those of travel agencies like Travala, of some airlines (Air Europa, Emirates), Cruises companies (Norwegian Cruise Line), of the Economic Development Ministry of Slovenia (project “I Feel Nft”, presented at Expo Dubai 2020, representing the first of this kind in the world). The paper explores possible implications and further development of these initiatives, designing possible metrics to measure their success, paving the way for future quantitative analysis once data about the introduced case-studies is available. Findings – Through the discussion of the cases found, it appears that the main benefits of digitisation through NFTs consist in promoting and presenting major tourist destinations, exhibitions, works of art and companies’ products in the form of tokens, digital content and souvenirs for visitors. However, some of the analysed cases also suggest possible applications for actual commerce in the tourism sector, including ticketing services and hotel bookings. Other possible applications can be hypothesised with regard to the certified training of staff working in the hospitality sector. Originality of the research – The paper is highly innovative and currently unique in that the adoption of NFTs in tourism is recent and still open to proposals for implementation. The contribution is therefore very important, both in terms of operational proposals (applications, guidelines and good practices) and in terms of defining metrics for measuring the effectiveness of NFTs in the tourism economy.
Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE

Over the years, multi-agent systems (MAS) technologies have shown their usefulness in creating distributed applications focused on autonomous intelligent processes. For this purpose, many frameworks for supporting multi-agent systems have been developed, normally oriented towards a particular type of agent architecture (e.g., reactive or deliberative agents). It is common, for example, for a multi-agent platform supporting the BDI (Belief, Desire, Intention) model to provide this agent model exclusively. In most of the existing agent platforms, it is possible to develop either behavior-based agents or deliberative agents based on the BDI cycle, but not both. In this sense, there is a clear lack of flexibility when agents need to perform part of their decision-making process according to the BDI paradigm and, in parallel, require some other behaviors that do not need such a deliberation process. In this context, this paper proposes the introduction of an agent architecture called Flexible Agent Architecture (FAA) that supports the development of multi-agent systems, where each agent can define its actions in terms of different computational models (BDI, procedural, neural networks, etc.) as behaviors, and combine these behaviors as necessary in order to achieve its goals. The FAA architecture has been integrated into a real agent platform, SPADE, thus extending its original capabilities in order to develop applications featuring reactive, deliberative, and hybrid agents. The integration has also adapted the existing facilities of SPADE to all types of behaviors inside agents, for example, the coordination of agents by using a presence notification mechanism, which is a unique feature of SPADE. The resulting SPADE middleware has been used to implement a case study in a simulated robotics scenario, also shown in the paper.
Lightweight Video Super-Resolution for Compressed Video

Video compression technology for Ultra-High Definition (UHD) and 8K UHD video has been established and is being widely adopted by major broadcasting companies and video content providers, allowing them to produce high-quality videos that meet the demands of today’s consumers. However, high-resolution video content broadcasting is not an easy problem to be resolved in the near future due to limited resources in network bandwidth and data storage. An alternative solution to overcome the challenges of broadcasting high-resolution video content is to downsample UHD or 8K video at the transmission side using existing infrastructure, and then utilizing Video Super-Resolution (VSR) technology at the receiving end to recover the original quality of the video content. Current deep learning-based methods for Video Super-Resolution (VSR) fail to consider the fact that the delivered video to viewers goes through a compression and decompression process, which can introduce additional distortion and loss of information. Therefore, it is crucial to develop VSR methods that are specifically designed to work with the compression–decompression pipeline. In general, various information in the compressed video is not utilized enough to realize the VSR model. This research proposes a highly efficient VSR network making use of data from decompressed video such as frame type, Group of Pictures (GOP), macroblock type and motion vector. The proposed Convolutional Neural Network (CNN)-based lightweight VSR model is suitable for real-time video services. The performance of the model is extensively evaluated through a series of experiments, demonstrating its effectiveness and applicability in practical scenarios.
Effectiveness of Rectangular Cartogram for Conveying Quantitative Information: An Eye Tracking-Based Evaluation

A rectangular cartogram is a kind of graphical representation that uses a rectangular area to represent the attributes of geographical objects and is widely used in the representation of statistical data such as population, economic statistics, and epidemic distribution. However, there is currently little research on the effectiveness of rectangular cartograms for conveying quantitative data. In this paper, the rectangular cartogram and unclassed choropleth map are compared to evaluate the rectangular cartogram’s effectiveness in quantity representation through task-driven eye-tracking cognitive experiments. In addition, the accuracy and efficiency of information communication and the differences in the reading processes of map readers when using the two types of maps for different tasks have been analyzed to provide references for more effective use of rectangular cartograms in the future. The main contributions of this paper are: (1) it has been proven by experiments that when completing the four tasks of locate, compare, find extreme, and estimate, the participants have higher accuracy rates with the rectangular cartogram; (2) the reading process shown by the eye tracking experiment shows that the more complex the task is, the more patient the participants would be with the rectangular cartogram, and the more pronounced the advantages of the rectangular cartogram are.
A Multi-Attention Approach Using BERT and Stacked Bidirectional LSTM for Improved Dialogue State Tracking

The modern digital world and associated innovative and state-of-the-art applications that characterize its presence, render the current digital age a captivating era for many worldwide. These innovations include dialogue systems, such as Apple’s Siri, Google Now, and Microsoft’s Cortana, that stay on the personal devices of users and assist them in their daily activities. These systems track the intentions of users by analyzing their speech, context by looking at their previous turns, and several other external details, and respond or act in the form of speech output. For these systems to work efficiently, a dialogue state tracking (DST) module is required to infer the current state of the dialogue in a conversation by processing previous states up to the current state. However, developing a DST module that tracks and exploit dialogue states effectively and accurately is challenging. The notable challenges that warrant immediate attention include scalability, handling the unseen slot-value pairs during training, and retraining the model with changes in the domain ontology. In this article, we present a new end-to-end framework by combining BERT, Stacked Bidirectional LSTM (BiLSTM), and a multiple attention mechanism to formalize DST as a classification problem and address the aforementioned issues. The BERT-based module encodes the user’s and system’s utterances. The Stacked BiLSTM extracts the contextual features and multiple attention mechanisms to calculate the attention between its hidden states and the utterance embeddings. We experimentally evaluated our method against the current approaches over a variety of datasets. The results indicate a significant overall improvement. The proposed model is scalable in terms of sharing the parameters and it considers the unseen instances during training.
Table Structure Recognition Method Based on Lightweight Network and Channel Attention

The table recognition model rows and columns aggregated network (RCANet) uses a semantic segmentation approach to recognize table structure, and achieves better performance in table row and column segmentation. However, this model uses ResNet18 as the backbone network, and the model has 11.35 million parameters and a volume of 45.5 M, which is inconvenient to deploy to lightweight servers or mobile terminals. Therefore, from the perspective of model compression, this paper proposes the lightweight rows and columns attention aggregated network (LRCAANet), which uses the lightweight network ShuffleNetv2 to replace the original RCANet backbone network ResNet18 to simplify the model size. Considering that the lightweight network reduces the number of feature channels, it has a certain impact on the performance of the model. In order to strengthen the learning between feature channels, the rows attention aggregated (RAA) module and the columns attention aggregated (CAA) module are proposed. The RAA module and the CAA module add the squeeze and excitation (SE) module to the original row and column aggregated modules, respectively. Adding the SE module means the model can learn the correlation between channels and improve the prediction effect of the lightweight model. The experimental results show that our method greatly reduces the model parameters and model volume while ensuring low-performance loss. In the end, the average F1 score of our model is only 1.77% lower than the original model, the parameters are only 0.17 million, and the volume is only 0.8 M. Compared with the original model, the parameter amount and volume are reduced by more than 95%.
Global Solar Radiation Forecasting Based on Hybrid Model with Combinations of Meteorological Parameters: Morocco Case Study

The adequate modeling and estimation of solar radiation plays a vital role in designing solar energy applications. In fact, unnecessary environmental changes result in several problems with the components of solar photovoltaic and affects the energy generation network. Various computational algorithms have been developed over the past decades to improve the efficiency of predicting solar radiation with various input characteristics. This research provides five approaches for forecasting daily global solar radiation (GSR) in two Moroccan cities, Tetouan and Tangier. In this regard, autoregressive integrated moving average (ARIMA), autoregressive moving average (ARMA), feed forward back propagation neural networks (FFBP), hybrid ARIMA-FFBP, and hybrid ARMA-FFBP were selected to compare and forecast the daily global solar radiation with different combinations of meteorological parameters. In addition, the performance in three approaches has been calculated in terms of the statistical metric correlation coefficient (R2), root means square error (RMSE), stand deviation (σ), the slope of best fit (SBF), legate’s coefficient of efficiency (LCE), and Wilmott’s index of agreement (WIA). The best model is selected by using the computed statistical metric, which is present, and the optimal value. The R2 of the forecasted ARIMA, ARMA, FFBP, hybrid ARIMA-FFBP, and ARMA-FFBP models is varying between 0.9472% and 0.9931%. The range value of SPE is varying between 0.8435 and 0.9296. The range value of LCE is 0.8954 and 0.9696 and the range value of WIA is 0.9491 and 0.9945. The outcomes show that the hybrid ARIMA–FFBP and hybrid ARMA–FFBP techniques are more effective than other approaches due to the improved correlation coefficient (R2).
Practitioners’ Perspectives towards Requirements Engineering: A Survey

In this paper, we discuss the results of our survey among 84 practitioners in order to understand practitioners’ perspectives towards requirements engineering. We asked 28 questions to learn the practitioners’ motivations, the techniques and technologies used for different activities, practitioners’ experiences with customer involvement, and any challenges encountered. Some important results are as follows: the practitioners’ top motivations are the precise communication of requirements and analyzing the requirements to detect issues. Most practitioners (i) insist on using natural languages, (ii) specify requirements as the use case and scenario descriptions, (iii) neglect using/transforming requirements for making high-level decisions and reasoning about requirements, (iv) neglect the specifications of quality requirements and their reasoning while considering quality requirements important, and (v) neglect any technologies for facilitating requirements engineering (e.g., meta-modeling technologies, formal verification tools, and advanced tools). Practitioners are challenged by the cost and effort spent in specifying requirements, the omissions of errors, misinterpretations of requirements and their incorrect (manual) transformations, and customers’ lack of technical knowledge. With the survey results, practitioners can gain an awareness on the general perspectives, academics can trigger new research addressing the observed issues, and tool vendors can improve their tools with regard to the weaknesses determined.