Reviewerly/exHarmony huggingface dataset:
Incrementally Verifiable Computation via Rate-1 Batch Arguments

Non-interactive delegation schemes enable producing succinct proofs (that can be efficiently verified) that a machine M transitions from c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> to c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> in a certain number of deterministic steps. We here consider the problem of efficiently merging such proofs: given a proof Π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> that M transitions from c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> to c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , and a proof Π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> that M transitions from c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> to c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> , can these proofs be efficiently merged into a single short proof (of roughly the same size as the original proofs) that M transitions from c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> to c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> ? To date, the only known constructions of such a mergeable delegation scheme rely on strong non-falsifiable “knowledge extraction” assumptions. In this work, we present a provably secure construction based on the standard LWE assumption. As an application of mergeable delegation, we obtain a construction of incrementally verifiable computation (IVC) (with polylogarithmic length proofs) for any (unbounded) polynomial number of steps based on LWE; as far as we know, this is the first such construction based on any falsifiable (as opposed to knowledge-extraction) assumption. The central building block that we rely on, and construct based on LWE, is a rate-l batch argument (BARG): this is a non-interactive argument for NP that enables proving k NP statements $x_{1},\ldots, x_{k}$ with communication/verifier complexity m + o(m), where m is the length of one witness. rate-1 BARGs are particularly useful as they can be recursively composed a super-constant number of times.
Object detection based on cortex hierarchical activation in border sensitive mechanism and classification-GIou joint representation

By imitating the brain neurons for object perception, the deep networks enable a comprehensive feature characterization in the task of object detection. Considering such a perceptual ability is usually bounded in a box area for feature extraction, the balance of dimension reduction and feature information retaining has been taken into account in more recent studies, especially for the information preservation in the border areas. Motivated by the mechanism of neuron cortex activation, in this work, a novel function based on cortex hierarchical activation is proposed to achieve more effective border sensitive mechanism by joint pooling in backbone networks. In order to avoid the parameter solidification, this strategy is also capable to benefit the feature extraction on the border without unnecessary model re-training. Furthermore, by replacing the square kernel with a designed band shape kernel, more adequate feature description can be obtained on the border via the combination of the strip hierarchical pooling and strip max pooling. With an extension of the proposed activation function on classification-GIoU joint representation, the overall detection accuracy has been further improved. Experimental evaluations on the COCO benchmark datasets have shown that the proposed work has a superior performance in comparison to other state-of-the-art detection approaches.
DTTrans: PV Power Forecasting Using Delaunay Triangulation and TransGRU

In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7-15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41-60% improvement when PV sites are aggregated and achieves as low as 3-4% of forecasting error on average.
A Lightweight Deep Learning Approach for Liver Segmentation

Liver segmentation is a prerequisite for various hepatic interventions and is a time-consuming manual task performed by radiology experts. Recently, various computationally expensive deep learning architectures tackled this aspect without considering the resource limitations of a real-life clinical setup. In this paper, we investigated the capabilities of a lightweight model, UNeXt, in comparison with the U-Net model. Moreover, we conduct a broad analysis at the micro and macro levels of these architectures by using two training loss functions: soft dice loss and unified focal loss, and by substituting the commonly used ReLU activation function, with the novel Funnel activation function. An automatic post-processing step that increases the overall performance of the models is also proposed. Model training and evaluation were performed on a public database—LiTS. The results show that the UNeXt model (Funnel activation, soft dice loss, post-processing step) achieved a 0.9902 dice similarity coefficient on the whole CT volumes in the test set, with 15× fewer parameters in nearly 4× less inference time, compared to its counterpart, U-Net. Thus, lightweight models can become the new standard in medical segmentation, and when implemented thoroughly can alleviate the computational burden while preserving the capabilities of a parameter-heavy architecture.
An Enhanced Deep Learning-Based DeepFake Video Detection and Classification System

The privacy of individuals and entire countries is currently threatened by the widespread use of face-swapping DeepFake models, which result in a sizable number of fake videos that seem extraordinarily genuine. Because DeepFake production tools have advanced so much and since so many researchers and businesses are interested in testing their limits, fake media is spreading like wildfire over the internet. Therefore, this study proposes five-layered convolutional neural networks (CNNs) for a DeepFake detection and classification model. The CNN enhanced with ReLU is used to extract features from these faces once the model has extracted the face region from video frames. To guarantee model accuracy while maintaining a suitable weight, a CNN enabled with ReLU model was used for the DeepFake-detection-influenced video. The performance evaluation of the proposed model was tested using Face2Face, and first-order motion DeepFake datasets. Experimental results revealed that the proposed model has an average prediction rate of 98% for DeepFake videos and 95% for Face2Face videos under actual network diffusion circumstances. When compared with systems such as Meso4, MesoInception4, Xception, EfficientNet-B0, and VGG16 which utilizes the convolutional neural network, the suggested model produced the best results with an accuracy rate of 86%.
MedDeblur: Medical Image Deblurring with Residual Dense Spatial-Asymmetric Attention

Medical image acquisition devices are susceptible to producing blurry images due to respiratory and patient movement. Despite having a notable impact on such blind-motion deblurring, medical image deblurring is still underexposed. This study proposes an end-to-end scale-recurrent deep network to learn the deblurring from multi-modal medical images. The proposed network comprises a novel residual dense block with spatial-asymmetric attention to recover salient information while learning medical image deblurring. The performance of the proposed methods has been densely evaluated and compared with the existing deblurring methods. The experimental results demonstrate that the proposed method can remove blur from medical images without illustrating visually disturbing artifacts. Furthermore, it outperforms the deep deblurring methods in qualitative and quantitative evaluation by a noticeable margin. The applicability of the proposed method has also been verified by incorporating it into various medical image analysis tasks such as segmentation and detection. The proposed deblurring method helps accelerate the performance of such medical image analysis tasks by removing blur from blurry medical inputs.
Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

Abstract Automatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.
Pervasive Augmented Reality (AR) for Assistive Production: Comparing the use of a Head-Mounted Display (HMD) versus a Hand-Held Device (HHD)

Pervasive Augmented Reality (AR) has been explored to provide experiences that are continuous in space and time, while incorporating layers of contextual information. While most research efforts have been devoted to explore serious games, museums and smart environments, we argue that another interesting area of application is assistive production, in particular training novel workers by displaying instructions throughout the shop floor on what to do. This work describes a user study with 12 participants, aimed at comparing the use of three distinct conditions: C1 - Head-Mounted Display (HMD), C2 - Handheld Device (HHD) and C3 - Paper manual during a set of realistic training tasks, selected based on a collaboration with partners from the industry sector. Results suggest condition C1 was preferred by the majority of participants, being considered more useful to support the worker's task as it is seems more efficient, hands-free and has a better tracking technology.
Augmented Reality Technology: Current Applications, Challenges and its Future

The term augmented reality (AR) refers to a technology that unites virtual things with the actual environment and communicate directly with one another. Nowadays, augmented reality is receiving a lot of study attention. It is one of the few ideas that, though formerly deemed impractical and unattainable and can today be used quite successfully. Research and development on the AR are still in their early stages at many colleges and high-tech enterprises. Like our smart phones and laptops, many researchers predict that it will be one of the most popular technologies in the future. This paper provides a comprehensive study of AR including its working, applications, current challenges and future trends.Although augmented reality applications are used in many areas, the most important areas are focused in this paper. In the light of all these, this study is a compilation study and in the context attention is drawn to usage of augmented reality in current life scenario.
A New Density Peak Clustering Algorithm With Adaptive Clustering Center Based on Differential Privacy

A new density peak clustering (DPC) algorithm with adaptive clustering center based on differential privacy was proposed to solve the problems of poor adaptability of high-dimensional data, inability to automatically determine clustering centers, and privacy problems in clustering analysis. First, to solve the problem of poor adaptability of high-dimensional data, cosine distance was used to measure the similarity between high-dimensional datasets. Then, aiming at the subjective problem of clustering center selection, from the perspective of ranking graph, the weight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(i-1)/i$ </tex-math></inline-formula> was introduced creatively, the slope trend of ranking graph was redefined to realize the adaptive clustering center. Finally, aiming at the privacy problem, the Laplacian noise of appropriate privacy budget was added to the core statistic (local density) of the algorithm to achieve the balance between privacy protection and algorithm effectiveness. Experimental results on both the synthetic and UCI datasets show that this algorithm can not only realize the automatic selection of clustering center, but also solve the privacy problem in clustering analysis, and improve the clustering evaluation index greatly, which proves the effectiveness of the algorithm.
Investigation of virtual &amp; augmented reality classroom learning environments in university STEM education

ABSTRACTABSTRACTThis study investigates the use of virtual and augmented reality (VAR) in higher education on students' self-efficacy in science and engineering, and academic performance. An innovative VAR classroom is set up as an immersive and interactive learning environment at Nanyang Technological University for curriculum-based learning. This classroom is equipped with 6 individual VR displays and headsets, where high-end computers are connected to each display providing the computational power required for VAR technology. 46 undergraduate engineering students experience the VAR lessons over the course of 5 weeks. The self-efficacy and academic performance of the students are measured for quantitative analysis with surveys and regular (pre-/post-) tests as they participated in the lessons. Post-lesson focus-group discussions and interviews are conducted for qualitative analysis. The data analysis shows a significant increase in students' self-efficacy levels and academic performance after 5 weeks of VAR lessons. It is found that the collaborative, interactive and immersive nature of the VAR learning environment enriches students' learning experiences and thus contributes to their learning effectiveness. The study provides insights into how the VAR learning environment affects undergraduate students' learning, and how the VAR system can be deployed for effective STEM educationKEYWORDS: Virtual realityaugmented realitytechnology enhanced learningself-efficacySTEM education AcknowledgementThe authors would like to thank Song Jun Hong and Karthigan Chris Ganesan for many helpful discussions.Disclosure statementNo potential conflict of interest was reported by the author(s).Ethic approval statementThis research was approved by NTU-IRB for the period from 30 June 2020–28 February 2024. The NTU-IRB reference number for this study is IRB-2020-01-032.Additional informationFundingThis research is supported by the Ministry of Education, Singapore, under its Tertiary Research Fund (Project MOE2018-TRF-006).
Modeling multiple latent information graph structures via graph convolutional network for aspect-based sentiment analysis

Abstract Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of aspects in a sentence. Recently, graph convolution network (GCN) model combined with attention mechanism has been used for ABSA task over graph structures, achieving promising results. However, these methods of modeling over graph structure fail to consider multiple latent information in the text, i.e., syntax, semantics, context, and so on. In addition, the attention mechanism is vulnerable to noise in sentences. To tackle these problems, in this paper, we construct an efficient text graph and propose a matrix fusion-based graph convolution network (MFLGCN) for ABSA. First, the graph structure is constructed by combining statistics, semantics, and part of speech. Then, we use the sequence model combined with the multi-head self-attention mechanism to obtain the feature representation of the context. Subsequently, the text graph structure and the feature representation of context are fed into GCN to aggregate the information around aspect nodes. The attention matrix is obtained by combining sequence model, GCN and the attention mechanism. Besides, we design a filter layer to alleviate the noise problem in the sentence introduced by the attention mechanism. Finally, in order to make the context representation more effective, attention and filtering matrices are integrated into the model. Experimental results on four public datasets show that our model is more effective than the previous models, demonstrating that using our text graph and matrix fusion can significantly empower ABSA models.
Neural Networks based Object Detection Techniques in Computer Vision

Computer vision is a subdomain of artificial intelligence that permits computers to extract useful info from digital images and videos. Artificial Intelligence enables to think, but in Computer Vision enable to see, observe and understand Computer Vision includes many techniques to solve real time problems. This research work focuses on one among these techniques viz. object detection. A computer vision technology called object detection helps locate and identify objects in images. In this paper mainly focusing on Neural network-based object detection techniques and how these techniques will impact on real time applications.
ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

The process of assigning bugs to particular programmers is called bug assignment in software engineering. The programmer can fix the bugs by applying their knowledge. This research article presents an Ant colony optimization-based programmer recommendation model (ProRE) to manage software bugs precisely. The proposed ProRE model performs four operations: data pre-processing, i.e., data Pre-processing, extraction, feature selection, and programmer recommendation process. The feature selection stage utilized the Ant colony optimization (ACO) method to determine the appropriate subsets of features from all features. In the programmer recommendation stages, three programmer metrics, i.e., functionality ranking, bug occurrence, and mean Bug fixing time, are utilized for the recommendation assignment. The effectiveness of the proposed programmer recommendation system is assessed using datasets from Mozilla, Eclipse, Firefox, JBoss, and OpenFOAM. It is noted that the proposed model offers a better recommendation strategy over the other available systems. The simulation findings of the proposed ProRE model are also analyzed with well-known available ML methods, i.e., SVM, NB, and C4.5. It is observed that the recommendation results have improved by an average of 4%, 10%, and 12% compared to SVM, C4.5, and NB-based models. Programmer recommendation software is implemented for allocating the bugs to accurate programmers. It has been found that the proposed ProRE model generates more optimistic outcomes than existing ones.
Virtual reality and machine learning for predicting visual attention in a daylit exhibition space: A proof of concept

Lighting features act as driving forces that control visual attention and perception. VR can analyse the relationship between human behavior and environment stimuli, while machine learning (ML) can exploit these data to develop a predictive model. This paper aims to develop an approach for predicting visual attention using VR and ML algorithms. VR was applied to experiment 4 virtual 360 scenes of a generic daylit exhibition space, and 41 participants were recruited. Measurements of head tracking were investigated to capture the areas of interest (AoI) within the space under different daylight conditions. Features were extracted to train the ML models through luminance, spatial contrast, and center bias. Results showed the potential of ML for predicting the visual behaviour. When comparing predicted with measured data, the accuracy reached 71 % using ensemble bagged trees algorithm. This study acts as a proof-of-concept for predicting visual attention using ML algorithms, highlighting the potentials of ML and VR for adopting human-centric design. Thus, it allows architects and lighting designers to promote more interesting and interacting visual experience.
Site classification using deep‐learning‐based image recognition techniques

Abstract Classification of local soil conditions is important for the interpretation of structural seismic damage, which also plays a vital role in site‐specific seismic hazard analyses. In this study, we propose to classify sites as an image recognition task using a deep convolutional neural network (DCNN)‐based technique. We design the input image as a combination of the topographic slope and the mean horizontal‐to‐vertical spectral ratio (HVSR) of earthquake recordings. A DCNN model with five convolutional layers is trained using 1649 sites in Japan. The recall rates for site classes C, D, and E using our DCNN classifier for Japanese sites are 82%, 70%, and 60%, respectively. When compared with existing site classification schemes relying on predefined standard HVSR curves, our proposed method achieves the highest total accuracy rate (between 73% and 75%). The generality and applicability of our trained classifier are further validated using sites in Europe with a total accuracy between 64% and 66%. The proposed data‐driven approach could be extended to other types of site amplification functions in the future.