Reviewerly/exHarmony huggingface dataset:
Virtual Occlusions Through Implicit Depth

For augmented reality (AR), it is important that virtual assets appear to ‘sit among’ real world objects. The virtual element should variously occlude and be occluded by real matter, based on a plausible depth ordering. This occlusion should be consistent over time as the viewer's camera moves. Unfortunately, small mistakes in the estimated scene depth can ruin the downstream occlusion mask, and thereby the AR illusion. Especially in real-time settings, depths inferred near boundaries or across time can be inconsistent. In this paper, we challenge the need for depth-regression as an intermediate step. We instead propose an implicit model for depth and use that to predict the occlusion mask directly. The inputs to our network are one or more color images, plus the known depths of any virtual geometry. We show how our occlusion predictions are more accurate and more temporally stable than predictions derived from traditional depth-estimation models. We obtain state-of-the-art occlusion results on the challenging ScanNetv2 dataset and superior qualitative results on real scenes.
Network-Free, Unsupervised Semantic Segmentation with Synthetic Images

We derive a method that yields highly accurate semantic segmentation maps without the use of any additional neural network, layers, manually annotated training data, or supervised training. Our method is based on the observation that the correlation of a set of pixels belonging to the same semantic segment do not change when generating synthetic variants of an image using the style mixing approach in GANs. We show how we can use GAN inversion to accurately semantically segment synthetic and real photos as well as generate large training image-semantic segmentation mask pairs for downstream tasks.
Research on Data Fusion Algorithm in New Energy Replenishment Layout Based on RSA Algorithm

In the energy supplement layout of new energy vehicles, if the vehicle end and pile end data are integrated and applied, the layout accuracy will be greatly improved. However, the key fields of data fusion are often private data, such as personal phone number, ID number, VIN, etc. Due to the impact of relevant laws and regulations of the data security law, these data cannot be transmitted, and the integration of data is blocked, affecting the accurate layout based on automobile ecological data in the energy supplement layout. This paper constructs a data fusion algorithm for the application of the new energy supplement scenario in the automobile industry, which eliminates the need for a third-party platform and ensures that key data are operated in an encrypted state. Through experiments, the RSA algorithm can effectively encrypt the original data, and can achieve the data fusion between the two sides under encryption. Therefore, this algorithm can meet the needs of data circulation and application between enterprises, and help improve the accuracy of the energy supplement layout.
Linguistic Characteristics for English Graded Level Texts Discrimination

Despite the efforts, an open problem in the state-of-the-art is locating appropriate texts at the correct grade to be used for the learning process. This difficulty, in part, is associated with the dependency of the language on the subject. Training with one domain and testing in another is only sometimes feasible. In search of domain independence, this work presents classification experiments by linguistic characteristics on English documents corresponding to twelve scholar grades. Results include grouping documents into three, four, six, and twelve classes. As expected, by increasing the number of classes, the classification accuracy decreases from 91% with three classes to 50% using all twelve classes. It is interesting to observe that the top-ranked discriminant attributes are the same when increasing the number of classes.
Modifier Embedding for Temporal Knowledge Graph Embedding

As a major research direction of temporal knowledge graph, the embedding and completion of temporal knowledge graph is a bridge for the application of temporal knowledge graph to various applications. Therefore, this research direction has a high position in the research of temporal knowledge graph.temporal knowledge graph embedding models are also receiving increasing attention. In this paper, we propose a modifier embedding model, and use this model to transform the quaternion of the temporal knowledge graph into a triplet. After that, we fused it with the DistMult model to obtain a new temporal knowledge graph embedding model, and conducted experiments on a dataset. Compared with existing models, our model has a good effect. After adjusting the embedding coefficients of the modified embedded model, we found that the embedding coefficients also have a certain impact on the effect of the model.
Object Detection in Harsh Underwater Environment Based on YOLOv5s-CCAA

In the current process of underwater object detection, due to the serious weakness of underwater light, the complex environment of underwater object organisms and the existence of most of them in the form of small capital, making the existing technology unable to accurately detect underwater object organisms. In order to solve the problems and difficulties of object detection in harsh underwater environment, In this paper, an improved underwater object detection algorithm YOLOv5s-CCAA based on YOLOv5s is proposed. The CLAHE(Contrast Limited Adaptive Histogram Equalization) algorithm was used in the image preprocessing part to solve the problems such as image blur and distortion caused by underwater light weakening. A new C2f convolution module is introduced to replace the original C3 convolution module of YOLOv5, so as to strengthen the characteristics of the detection object in advance and solve the problems caused by the multi-pose and environment of the detection object. By introducing the CA attention neural mechanism, the model can better improve the classification and accuracy of the object detection. The accuracy of underwater small object detection is improved by integrating ASFF (adaptive spatial feature fusion). The experimental results show that the improved underwater object detection algorithm YOLOv5s-CCAA can better solve the problems caused by weak underwater light and environment, and effectively improve the accuracy of underwater object detection.
A Sequential Recommendation Model Based on Social Behavior

Traditional social recommendation mainly combines the user’s social relationship and rating information, which can solve the problem of data sparsity in the recommendation algorithm, but ignores the fact that the user’s interests are constantly changing, and the user’s social relationship not only has a trust relationship, but also has a distrust relationship that will affect the recommendation results. Therefore, this paper proposes a sequence recommendation model based on social behavior (SBRec). First, it uses the attention mechanism to build a user dynamic interest feature model, and then according to the user’s social network, it filters out the social list that users trust and distrust, and obtains the positive and negative social impact of users through the historical behavior of friends, The user’s dynamic interest characteristics are combined with the user’s positive and negative social impact to get the recommendation results. The accuracy of the SBRec method is verified by the experiments in the actual data set Epings, and the hit rate was improved by about 6%.
Adaptive Mixup-Based Domain Adaptation Method for Intelligent Fault Diagnosis

Recent years have witnessed the successful application of domain adaptive methods to tackle intelligent fault diagnosis of rotating machinery under variable working conditions. However, existing work always ignores the learning of feature discriminability when developing transferable models based on domain-invariant representation learning strategies. In addition, they have difficulty handling the knowledge transfer between domains with significant differences. To address these problems, an adaptive mixup-based adversarial network (AMAN) is proposed in this paper. It develops an inter-domain mixup method based on the sample adaptive screening strategy to generate high-quality virtual samples to guide domain adaptation while improving the learned feature representations’ discriminability. The comprehensive results of numerous DA diagnosis tasks built on the gearbox dataset validate AMAN’s effectiveness and application prospect.
Evaluation of Off-Grid Solar Power Plant Implementation on Biofloc Fishery System

We have implemented a 1500Wp off-grid solar power plant (SPP) in biofloc fishery system. Through this paper, we try to provide an overview of the SPP’s effectiveness based on data collected from April 2022 to September 2022. The analysis is carried out to obtain the average daily, monthly and predicted annual energy yield. The analysis is continued to the average daily and monthly irradiation as well as the performance ratio. From an economic aspect using net present value (NPV), we present the calculation results which show that the off-grid SPP system is not a profitable investment. It can be profitable if there is at least an 8% increase in electricity tariff per year.
Improved YOLOv5 Algorithm for Power Insulator Defect Detection

Intelligent inspection of transmission lines by UAVs has become the mainstream of the industry, and insulator defect detection is a key part of the intelligent inspection operation. To address the problem of low accuracy of insulator defect detection in complex environments, this paper proposes an improved YOLOv5s-based insulator defect detection algorithm. First, the K-means algorithm is used to cluster the data set to obtain the best anchor frame size, which effectively improves the generalization ability and localization accuracy of the model; second, the Backbone part of YOLOv5s is embedded with the Coordinate Attention module to improve the feature extraction ability of the network to solve the influence of invalid features on the recognition accuracy; finally, the EIOU-Loss is used to improve the accuracy of insulator defect detection. Finally, the performance of the model is optimized using the EIOU-Loss function, and ablation experiments are set up to validate the proposed method. The experimental results show that the Precious and mAP of the improved YOLOv5s model are improved by2.S% and 1.6%, respectively, compared with the original YOLOv5s network.
Unconventional Monolithic Electronics in a Conventional Silicon Photonics Platform

To enrich state-of-the-art optical chips with electronic functionalities, we present the integration of unconventional transistors and circuits in a standard silicon photonics platform, with no modifications to the conventional fabrication process employed by photonic foundries. This approach allows us to include on-chip electronic building blocks to support the optical functionality at zero additional cost while maintaining optical excellence and foundry interoperability. To showcase the benefits of monolithic electronics, we integrated a transconductance amplifier and an analog multiplexer for sequential readout of 16 photodetectors, distributed in a large-scale architecture of Mach–Zehnder interferometers (MZIs). The circuits, successfully validated in optical experiments, allow the reduction of electrical input-output lines of a photonic chip without any optical penalty, thus targeting one of the main bottlenecks that limit further scaling of photonic circuits.
Learning to Rank: Performance and Practical Barriers to Deployment in Enterprise Search

In the field of Web Search (WS), Learning to Rank has become the machine learning method of choice for researchers interested in ranking. In addition to WS, ranking is also the primary challenge for engineers when deploying a new Enterprise Search (ES) service within an organization. This paper compares the implementation and performance of traditional, relatively simple methodologies such as BM25 with Learning to Rank and examines the common assumption that whatever method is best for WS must also be best for ES. We experiment on a small manually annotated dataset derived from a ‘real world’ ES service of a large organization. Results indicate that the nDCG@5 performance of one of the traditional methods (BM25-pf) falls just 6.1% short of the Learning to Rank method, with nDGC scores of 0.93 versus 0.99 respectively. Subsequently, we discuss implementation trade-offs between traditional and LTR methods and highlight the fact that traditional methods require far fewer deployment resources, as well as not requiring feature engineering, data preprocessing, or relevance judgements. A further advantage of the BM25 approach is that it does not entail an open-ended commitment for periodic ranking model retraining.
Development of an intelligent controller for sports training system based on FPGA

Abstract Training, sports equipment, and facilities are the main aspects of sports advancement. Countries are investing heavily in the training of athletes, especially in table tennis. Athletes require basic equipment for exercises, but most athletes cannot afford the high cost; hence, the necessity for developing a low-cost automated system has increased. To enhance the quality of the athletes’ training, the proposed research focuses on using the enormous developments in artificial intelligence by developing an automated training system that can maintain the training duration and intensity whenever necessary. In this research, an intelligent controller has been designed to simulate training patterns of table tennis. The intelligent controller will control the system that sends the table tennis balls’ intensity, speed, and duration. The system will detect the hand sign that has been previously assigned to different speeds using an image detection method and will work accordingly by accelerating the speed using pulse width modulation techniques. Simply showing the athletes’ hand sign to the system will trigger the artificial intelligent camera to identify it, sending the tennis ball at the assigned speed. The artificial intelligence of the proposed device showed promising results in detecting hand signs with minimum errors in training sessions and intensity. The image detection accuracy collected from the intelligent controller during training was 90.05%. Furthermore, the proposed system has a minimal material cost and can be easily installed and used.
Nonsalient Object Detection Algorithm Based on Infrared Image

In view of the current situation of low visibility at night and increasing poaching activities in wildlife reserves, the use of unmanned aerial vehicle (UAV) monitoring equipped with thermal infrared (TIR) cameras has become a trend. In order to overcome the difficulties of small objects and low resolution in infrared images, this letter proposes a non-salient object detection algorithm in infrared images based on deep learning. Based on Grid R-CNN, we designed a feature extraction network, improved the region proposal network by guided anchoring (GA-RPN), and introduced the slice inference mechanism. Our method makes the network selectively fuse multi-scale features to generate high-quality proposals and improve detection accuracy. Experimental results show our method is superior to other object detection algorithms for non-salient elephant images on the BIRDSAI dataset.
Theory-Driven Design in Metaverse Virtual Reality Learning Environments: Two Illustrative Cases

The metaverse entails modes of interactivity that hold enormous potential for education in various contexts and domains. The metaverse represents a convergence of technologies, such as immersive virtual reality (VR) and augmented reality (AR), that allow for multimodal engagements with digital objects, virtual environments, and people. In this article, we focus specifically on the ways that VR interactions with the metaverse can enhance learning and education. In our view, two primary issues have so far hindered the development of successful metaverse educational applications: first, a lack of theory-driven designs that use technologies such as VR in ways that are consistent with what we know about how people learn, and second, insufficient methods of evaluating metaverse technologies that go beyond usability and instead capture their efficacy for improving learning outcomes. To address these issues, this paper aims to explore how three learning theories–experiential learning theory, distributed cognition theory, and embodied learning theory–can be applied to the design of educational VR, and how these theories can be leveraged to better support educational applications of the metaverse. We also introduce two science education VR environments developed in our lab that employ these theories to demonstrate how the design and development of the educational metaverse can be guided by research and be evaluated on its ability to generate new learning.
A Transformer-Based Educational Virtual Assistant Using Diacriticized Latin Script

A virtual assistant or smart chatbot should be able to understand user questions and respond correctly and usefully, even if the questions are posed ungrammatically with misspellings and other errors. This paper describes the design and construction of a text-to-text virtual assistant in Vietnamese, a language that uses the Latin script with a liberal use of diacritics, for supporting students at a large university with over forty thousand students. The flexible virtual assistant consists of two integrated chatbots, both using Transformers: a) a closed-domain chatbot, trained on over thirty-five thousand factual question-answer pairs to engage in university-related conversation, and b) a second open-domain chatbot, trained on a large movie dialog dataset to engage in general conversation. The integrated virtual assistant classifies a question as either factual or general, and engages the appropriate chatbot to respond in a flexible, appropriate and natural manner. Although Vietnamese uses diacritics copiously, even educated users have a propensity to forgo the use of diacritics, and as a result, to facilitate smooth text-based communication, our design includes extensive pre-processing that uses learned Transformers to restore missing diacritics and correct misspellings. Our Transformer models outperform existing approaches for diacritic restoration and are better than several other methods for spelling correction in Vietnamese. In addition, the closed-domain chatbot performs better than other generative chatbots that have been developed to assist students in a university environment, irrespective of language and location.
On Hardware-Aware Design and Optimization of Edge Intelligence

Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learning models and heterogeneity of edge devices make the design of edge intelligence systems a challenging task. Hardware-agnostic methods face some limitations when implementing edge systems. Thus, hardware-aware methods are attracting more attention recently. In this paper, we present our recent endeavors in hardware-aware design and optimization for edge intelligence. We delve into techniques such as model compression and neural architecture search to achieve efficient and effective system designs. We also discuss some challenges in hardware-aware paradigm.
A comparative analysis of machine learning algorithms for hate speech detection in social media

A&lt;b&gt; &lt;/b&gt;detecting and mitigating hate speech in social media, particularly on platforms like Twitter, is a crucial task with significant societal impact. This research study presents a comprehensive comparative analysis of machine learning algorithms for hate speech detection, with the primary goal of identifying an optimal algorithmic combination that is simple, easy to implement, efficient, and yields high detection performance. Through meticulous pre-processing and rigorous evaluation, the study explores various algorithms to determine their suitability for hate speech detection. The focus is finding a combination that balances simplicity, ease of implementation, computational efficiency, and strong performance metrics. The findings reveal that the combination of naïve Bayes and decision tree algorithms achieves a high accuracy of 0.887 and an F1-score of 0.885, demonstrating its effectiveness in hate speech detection. This research contributes to identifying a reliable algorithmic combination that meets the criteria of simplicity, ease of implementation, quick processing, and strong performance, providing valuable guidance for researchers and practitioners in hate speech detection in social media. By elucidating the strengths and limitations of various algorithmic combinations, this research enhances the understanding of hate speech detection. It paves the way for developing robust solutions, creating a safer, more inclusive digital environment.
Teachers’ digital competences in higher education in Portugal and Spain

The potential of DTIC has brought new and emerging challenges to teachers, making it essential to acquire digital competences, especially in virtual learning environments and online technologies. In this sense, based on the &lt;i&gt;DigCompEdu CheckIn&lt;/i&gt; self-assessment questionnaire, validated for the Portuguese population by earlier studies, this research aims to identify the most fragile and robust areas of digital competences of Portuguese and Spanish university teachers. The quantitative methodological approach emphasizes teachers’ perception of their digital competences in three dimensions: teachers’ professional competences, teachers’ pedagogical competences and students’ competences and involved 347 teachers from Portugal and Spain. The results show that teachers of the two institutions have an overall moderate level of digital proficiency–level B1 and B2–and that the differences encountered between Portuguese and Spanish teachers (for example, Portuguese teachers have level A2 in area 4–assessment–, while Spanish teachers are already at level B2) highlight the need to invest in specific training that may address specific frailties, and therefore allow for the promotion of their digital competences. Results relating to teaching experience and age show that there is no direct relation between either factor or being digitally competent. Regarding gender, results reveal that men had slightly better results than women. Overall, what the results show is the need for teachers to increase the level of digital competence through specific training, prepared according to the specificities of each institution, and the importance of developing public policies that prepare teachers for a more digital education.