Reviewerly/exHarmony huggingface dataset:
Active stereo vision three-dimensional reconstruction by RGB dot pattern projection and ray intersection

Active stereo vision is important in reconstructing objects without obvious textures. However, it is still very challenging to extract and match the projected patterns from two camera views automatically and robustly. In this paper, we propose a new pattern extraction method and a new stereo vision matching method based on our novel structured light pattern. Instead of using the widely used 2D disparity to calculate the depths of the objects, we use the ray intersection to compute the 3D shapes directly. Experimental results showed that the proposed approach could reconstruct the 3D shape of the object significantly more robustly than state of the art methods that include the widely used disparity based active stereo vision method, the time of flight method and the structured light method. In addition, experimental results also showed that the proposed approach could reconstruct the 3D motions of the dynamic shapes robustly.
An analysis of the search mechanisms of the bees algorithm

The Bees Algorithm has been successfully applied for over a decade to a large number of optimisation problems. However, a mathematical analysis of its search capabilities, the effects of different parameters used, and various design choices has not been carried out. As a consequence, optimisation of the Bees Algorithm has so far relied on trial-and-error experimentation. This paper formalises the Bees Algorithm in a rigorous mathematical description, beyond the qualitative biological metaphor. A review of the literature is presented, highlighting the main variants of the Bees Algorithm, and its analogies and differences compared with other optimisation methods. The local search procedure of the Bees Algorithm is analysed, and the results experimentally checked. The analysis shows that the progress of local search is mainly influenced by the size of the neighbourhood and the stagnation limit in the site abandonment procedure, rather than the number of recruited foragers. In particular, the analysis underlines the trade-off between the step size of local search (a large neighbourhood size favours quick progress) and the likelihood of stagnation (a small neighbourhood size prevents premature site abandonment). For the first time, the implications of the choice of neighbourhood shape on the character of the local search are clarified. The paper reveals that, particularly in high-dimensional spaces, hyperspherical neighbourhoods allow greater search intensification than hypercubic neighbourhoods. The theoretical results obtained in this paper are in good agreement with the findings of several experimental studies. It is hoped that the new mathematical formalism here introduced will foster further understanding and analysis of the Bees Algorithm, and that the theoretical results obtained will provide useful parameterisation guidelines for applied studies.
Investigating the repurchase intention of Bitcoin: empirical evidence from China

Purpose Cryptocurrencies, such as Bitcoin, generate innovative and fast exchanges without any physical form and facilitate online payments; thus, they may bring about revolutions of the future economic system. Recent investigations reveal that China, the second largest Bitcoin market, accounts for a huge volume of Bitcoin trading and mining, which can cast distinct influences on future values of Bitcoin. Therefore, it would be of great significance to probe into the repurchase intention of Bitcoin of the Chinese individuals. Design/methodology/approach One hundred and forty-three questionnaires were collected from Chinese respondents. SPPS was employed for data analysis of the proposed hypotheses. Findings The results show that expectation has a positive impact on perceived enjoyment and perceived ease of use. A positive relationship between perceived ease of use and perceived enjoyment was confirmed. The findings also reveal that expectation, perceived enjoyment and perceived ease of use have significant impact on satisfaction. Moreover, it is found that perceived enjoyment, perceived ease of use and satisfaction significantly influence the repurchase intention of Bitcoin. Research limitations/implications This study encourages future comparative studies to be conducted. Besides, it is recommended to find out other possible antecedents of repurchase intention. Moreover, this study suggested negative effects of Bitcoin to be explored. Practical implications In a practical standpoint, this study provides valuable suggestions about cryptocurrencies use and regularization. For instance, education and learnability issues of novice users need to be considered. Further, the regularization/implementation of cryptocurrencies/blockchain technologies is also suggested. Originality/value This is the pioneer endeavor which investigates the repurchase intention of Bitcoin. The findings explore some of the possible antecedents which influence repurchase intention of Bitcoin. These findings provide valuable insights and enrich the existing body of literature in the domain of Bitcoin.
Quality Evaluation of Cyber Threat Intelligence Feeds

In order to mount an effective defense, information about likely adversaries, as well as their techniques, tactics and procedures is needed. This so-called cyber threat intelligence helps an organization to better understand its threat profile. Next to this understanding, specialized feeds of indicators about these threats downloaded into a firewall or intrusion detection system allow for a timely reaction to emerging threats. These feeds however only provide an actual benefit if they are of high quality. In other words, if they provide relevant, complete information in a timely manner. Incorrect and incomplete information may even cause harm, for example if it leads an organization to block legitimate clients or if the information is too unspecific and results in an excessive amount of collateral damage. In this paper, we evaluate the quality of 17 open source cyber threat intelligence feeds over a period of 14 months, and 7 additional feeds over 7 months. Our analysis shows that the majority of indicators are active for at least 20 days before they are listed. Additionally, we have found that many list have biases towards certain countries. Finally, we also show that blocking listed IP addresses can yield large amounts of collateral damage.
Multi-exposure image fusion based on linear embeddings and watershed masking

High dynamic range imaging (HDRI) is a challenging technology but yet demanding for modern imaging applications. Low-cost image sensors have limited dynamic range, and it is not always possible to capture and display natural scenes with high contrast and information loss in any exposure is inevitable. Three solutions for HDRI are using expensive high dynamic range (HDR) cameras with HDR-compatible displays, tone mapping operators for low dynamic range (LDR) screens, and capturing and fusing multiple exposures of the same LDR scene via image fusion algorithms. Companies that produce user grade devices prefer multi-exposure fusion (MEF) approaches to obtain HDR-like images for LDR screens due to its low cost. Hence, merging a stack of images containing different exposures of the same scene into a single informative image is an attractive research field. In this study, a novel, simple yet effective method is proposed for static image exposure fusion. The developed technique is based on weight map extraction via linear embeddings and watershed masking. The main advantage lies in watershed masking-based adjustment for obtaining accurate weights for image fusion. The comprehensive experimental comparisons demonstrate very strong visual and statistical results, and this approach should facilitate future MEF studies.
SMArT: Training Shallow Memory-aware Transformers for Robotic Explainability

The ability to generate natural language explanations conditioned on the visual perception is a crucial step towards autonomous agents which can explain themselves and communicate with humans. While the research efforts in image and video captioning are giving promising results, this is often done at the expense of the computational requirements of the approaches, limiting their applicability to real contexts. In this paper, we propose a fully-attentive captioning algorithm which can provide state-of-the-art performances on language generation while restricting its computational demands. Our model is inspired by the Transformer model and employs only two Transformer layers in the encoding and decoding stages. Further, it incorporates a novel memory-aware encoding of image regions. Experiments demonstrate that our approach achieves competitive results in terms of caption quality while featuring reduced computational demands. Further, to evaluate its applicability on autonomous agents, we conduct experiments on simulated scenes taken from the perspective of domestic robots.
IT Ticket Classification: The Simpler, the Better

Recently, automatic classification of IT tickets has gained notable attention due to the increasing complexity of IT services deployed in enterprises. There are multiple discussions and no general opinion in the research and practitioners' community on the design of IT ticket classification tasks, specifically the choice of ticket text representation techniques and classification algorithms. Our study aims to investigate the core design elements of a typical IT ticket text classification pipeline. In particular, we compare the performance of TF-IDF and linguistic features-based text representations designed for ticket complexity prediction. We apply various classifiers, including kNN, its enhanced versions, decision trees, naïve Bayes, logistic regression, support vector machines, as well as semi-supervised techniques to predict the ticket class label of low, medium, or high complexity. Finally, we discuss the evaluation results and their practical implications. As our study shows, linguistic representation not only proves to be highly explainable but also demonstrates a substantial prediction quality increase over TF-IDF. Furthermore, our experiments evidence the importance of feature selection. We indicate that even simple algorithms can deliver high-quality prediction when using appropriate linguistic features.
Fast Transformers with Clustered Attention

Transformers have been proven a successful model for a variety of tasks in sequence modeling. However, computing the attention matrix, which is their key component, has quadratic complexity with respect to the sequence length, thus making them prohibitively expensive for large sequences. To address this, we propose clustered attention, which instead of computing the attention for every query, groups queries into clusters and computes attention just for the centroids. To further improve this approximation, we use the computed clusters to identify the keys with the highest attention per query and compute the exact key/query dot products. This results in a model with linear complexity with respect to the sequence length for a fixed number of clusters. We evaluate our approach on two automatic speech recognition datasets and show that our model consistently outperforms vanilla transformers for a given computational budget. Finally, we demonstrate that our model can approximate arbitrarily complex attention distributions with a minimal number of clusters by approximating a pretrained BERT model on GLUE and SQuAD benchmarks with only 25 clusters and no loss in performance.
High-Fidelity Generative Image Compression

We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architectures, training strategies, as well as perceptual losses. In contrast to previous work, i) we obtain visually pleasing reconstructions that are perceptually similar to the input, ii) we operate in a broad range of bitrates, and iii) our approach can be applied to high-resolution images. We bridge the gap between rate-distortion-perception theory and practice by evaluating our approach both quantitatively with various perceptual metrics, and with a user study. The study shows that our method is preferred to previous approaches even if they use more than 2x the bitrate.
Caption-Supervised Face Recognition: Training a State-of-the-Art Face Model Without Manual Annotation

The advances over the past several years have pushed the performance of face recognition to an amazing level. This great success, to a large extent, is built on top of millions of annotated samples. However, as we endeavor to take the performance to the next level, the reliance on annotated data becomes a major obstacle. We desire to explore an alternative approach, namely using captioned images for training, as an attempt to mitigate this difficulty. Captioned images are widely available on the web, while the captions often contain the names of the subjects in the images. Hence, an effective method to leverage such data would significantly reduce the need of human annotations. However, an important challenge along this way needs to be tackled: the names in the captions are often noisy and ambiguous, especially when there are multiple names in the captions or multiple people in the photos. In this work, we propose a simple yet effective method, which trains a face recognition model by progressively expanding the labeled set via both selective propagation and caption-driven expansion. We build a large-scale dataset of captioned images, which contain 6.3M faces from 305K subjects. Our experiments show that using the proposed method, we can train a state-of-the-art face recognition model without manual annotation ( $$99.65\%$$ in LFW). This shows the great potential of caption-supervised face recognition.
Human activity recognition-based path planning for autonomous vehicles

Human activity recognition (HAR) is a wide research topic in a field of computer science. Improving HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine and in the field of autonomous vehicles. The system that is able to recognise human and its activity without any errors and anomalies would lead to safer and more empathetic autonomous systems. During this research work, multiple neural networks models, with different complexity, are being investigated. Each model is re-trained on the proposed unique data set, gathered on automated guided vehicle (AGV) with the latest and the modest sensors used commonly on autonomous vehicles. The best model is picked out based on the final accuracy for action recognition. Best models pipeline is fused with YOLOv3, to enhance the human detection. In addition to pipeline improvement, multiple action direction estimation methods are proposed.
CenterNet Heatmap Propagation for Real-Time Video Object Detection

The existing methods for video object detection mainly depend on two-stage image object detectors. The fact that two-stage detectors are generally slow makes it difficult to apply in real-time scenarios. Moreover, adapting directly existing methods to a one-stage detector is inefficient or infeasible. In this work, we introduce a method based on a one-stage detector called CenterNet. We propagate the previous reliable long-term detection in the form of heatmap to boost results of upcoming image. Our method achieves the online real-time performance on ImageNet VID dataset with 76.7% mAP at 37 FPS and the offline performance 78.4% mAP at 34 FPS.
The improved mayfly optimization algorithm

Abstract The mayfly optimization (MO) algorithm was proposed with a better hybridization of the particle swarm optimization (PSO) and the differential evolution (DE) algorithms. The velocity would be relevant to the Cartesian distance among the relevant individuals. In this paper, a reasonable revision for the velocity updating equations was proposed based on the idea of moving towards each other as capable as they can. Simulation results proved that the improved MO algorithm would perform better than the original one.
Dynamic and Static Context-Aware LSTM for Multi-agent Motion Prediction

Multi-agent motion prediction is challenging because it aims to foresee the future trajectories of multiple agents (e.g. pedestrians) simultaneously in a complicated scene. Existing work addressed this challenge by either learning social spatial interactions represented by the positions of a group of pedestrians, while ignoring their temporal coherence (i.e. dependencies between different long trajectories), or by understanding the complicated scene layout (e.g. scene segmentation) to ensure safe navigation. However, unlike previous work that isolated the spatial interaction, temporal coherence, and scene layout, this paper designs a new mechanism, i.e., Dynamic and Static Context-aware Motion Predictor (DSCMP), to integrates these rich information into the long-short-term-memory (LSTM). It has three appealing benefits. (1) DSCMP models the dynamic interactions between agents by learning both their spatial positions and temporal coherence, as well as understanding the contextual scene layout. (2) Different from previous LSTM models that predict motions by propagating hidden features frame by frame, limiting the capacity to learn correlations between long trajectories, we carefully design a differentiable queue mechanism in DSCMP, which is able to explicitly memorize and learn the correlations between long trajectories. (3) DSCMP captures the context of scene by inferring latent variable, which enables multimodal predictions with meaningful semantic scene layout. Extensive experiments show that DSCMP outperforms state-of-the-art methods by large margins, such as 9.05% and 7.62% relative improvements on the ETH-UCY and SDD datasets respectively.
Permissioned Blockchain-Based Anonymous and Traceable Aggregate Signature Scheme for Industrial Internet of Things

For large-scale data transmission of the Industrial Internet of Things (IIoT), aggregate signature is an effective approach. It can compress the signatures of different senders to save bandwidth. In order to maintain the autonomous management of IIoT, massive sensing data are sent to the data center for intelligent analysis. The reliability of data is an important guarantee of the autonomous management of IIoT. Tracing abnormal senders is a challenge when hiding their real identity. Therefore, we design the first permissioned blockchain-based anonymous and traceable aggregate signature (PBATAS) scheme for IIoT. Smart contracts are used to authenticate anonymous sources and share cryptographic materials among entities, providing reliable regulatory support for IIoT. The regulator can quickly trace the abnormal data sources recorded on the blockchain, which is practical for the anonymous IIoT environment. Through the formal security proof of conditional anonymity, unforgeability, traceability, and resistance to coalition attacks, the proposed PBATAS is provably secure. Performance analysis demonstrates that PBATAS is effective.
Breast Cancer Detection using Machine Learning

Cancer is the 2nd source of death in the world. The main reason for this increased death rate is the delayed detection of cancerous tissue growth in a person. Nearly 60% patients with breast cancer are diagnosed in advanced stages. The main objective of our paper is to enhance an image processing algorithm for earlier finding of breast cancer. X-ray mammogram images which have been acquired are used as input Images. [1] The pre-processing of input images are carried out by applying Gaussian Filter and Edge detection techniques to enhance image quality. Wavelet Transform is useful to identified first order features and GLCM based second order features are extracted from the Pre-processed images. The statistical parameters are then used for classification using DNN a Multilayer supervised classifier. Dataset images are created from the training phase. In testing Phase the acquired image from a patient is given as input to the classifier after completing the image processing steps such as Pre-processing and feature extraction. The output of the classifier consists of two classes, normal and abnormal respectively. [2] The entire algorithm is developed in Python language. The Processing time for testing and conformation of Positive cases is very minimum. Using deep learning neural network classifier an accuracy rate of 92% is reached.
Application of AI technology in modern digital marketing environment

Purpose The purpose of this paper is to explore the various application of artificial intelligence (AI) to social media and digital advertising professionals and agencies to specialize to an advanced degree and maintain collaboration and creativity to bring a better return on investment. Design/methodology/approach Digital marketers are still oblivious to the importance of AI application, while some others simply do not know how to implement it. AI is currently acting as a significant disruption in digital and social media marketing worldwide. Findings Based on the literature review, the paper identifies the various AI applications in the field of digital media marketing. Originality/value This paper can serve as a useful guide for social media marketers to implement AI applications to impact digital marketing strategies better.
Physical ZKP for Connected Spanning Subgraph: Applications to Bridges Puzzle and Other Problems

An undirected graph G is known to both the prover P and the verifier V, but only P knows a subgraph H of G. Without revealing any information about H, P wants to convince V that H is a connected spanning subgraph of G, i.e. H is connected and contains all vertices of G. In this paper, we propose an unconventional zero-knowledge proof protocol using a physical deck of cards, which enables P to physically show that H satisfies the condition without revealing it. We also show applications of this protocol to verify solutions of three well-known NP-complete problems: the Hamiltonian cycle problem, the maximum leaf spanning tree problem, and a popular logic puzzle called Bridges.
Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scale to high concurrencies than convolutional or fully-connected neural networks. We introduce a family of parallel algorithms for training GNNs and show that they can asymptotically reduce communication compared to previous parallel GNN training methods. We implement these algorithms, which are based on 1D, 1. 5D, 2D, and 3D sparse-dense matrix multiplication, using torch.distributed on GPU-equipped clusters. Our algorithms optimize communication across the full GNN training pipeline. We train GNNs on over a hundred GPUs on multiple datasets, including a protein network with over a billion edges.
Deep learning-based video stream reconstruction in mass-production diffractive optical systems

Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision systems. However, additional causes of quality loss have been encountered in the development of such systems. This study investigates the influence on the reconstructed image quality of such factors as limitations of mass production technology for diffractive optics, lossy video stream compression artifacts, and specificities of a neural network approach to image reconstruction. The paper offers an end-to-end deep learning-based image reconstruction framework to compensate for the additional factors of quality losing. It provides the image reconstruction quality sufficient for applied vision systems.