Reviewerly/exHarmony huggingface dataset:
A novel residual block: replace Conv1× 1 with Conv3×3 and stack more convolutions

The residual structure has an important influence on the design of the neural network model. The neural network model based on residual structure has excellent performance in computer vision tasks. However, the performance of classical residual networks is restricted by the size of receptive fields, channel information, spatial information and other factors. In this article, a novel residual structure is proposed. We modify the identity mapping and down-sampling block to get greater effective receptive field, and its excellent performance in channel information fusion and spatial feature extraction is verified by ablation studies. In order to further verify its feature extraction capability, a non-deep convolutional neural network (CNN) was designed and tested on Cifar10 and Cifar100 benchmark platforms using a naive training method. Our network model achieves better performance than other mainstream networks under the same training parameters, the accuracy we achieved is 3.08 percentage point higher than ResNet50 and 1.38 percentage points higher than ResNeXt50. Compared with SeResNet152, it is 0.29 percentage point higher in the case of 50 epochs less training.
Fuzzy Evaluation of Inland Ship Lock Service Condition Based on Combination Weighting and Matter-Element Extension Cloud Model

Ship lock as a typical hydraulic structure has become an important node in waterway transportation. Due to the long operating life and high demand of throughput, many locks are under the overloaded operation situation. However, the service condition assessment of ship locks has rarely been directly studied, and there is a lack of an efficient and standardized method owing to the complex structure of the ship lock system. In this paper, a multi-level hierarchical system including 36 indexes was constructed based on the engineering breakdown structure theory. The synthetic weights of indexes were determined by the order relation method and entropy weight method combining subjectivity and objectivity. The extension cloud model combining the extension theory and cloud model was put forward, aiming to deal with the uncertainty of fuzziness and randomness in the evaluation process. Then, two typical locks were investigated, and the numerical scores indicated that their states belong to Level III and Level IV, respectively. The proposed method reveals the structural condition and provides theoretical reference for the maintenance of ship locks, which can be applied with generalizability and operability.
Double‐state chi‐square test based sparse grid quadrature filtering algorithm and its application in integrated navigation

Abstract Aiming to reduce the impact that the measurement uncertainty of nonlinear system brings to filtering accuracy, a novel sparse grid quadrature filter (SGQF) algorithm based on the double‐state chi‐square test is proposed. And the strap‐down inertial navigation system (SINS)/global navigation satellite system (GNSS) integrated navigation system is used to verify the algorithm. A discrete‐time system model with uncertain measurement is constructed, then the system state through the SGQF algorithm is estimated. To determine the uncertainty of the measurement, the double‐state chi‐square test is introduced. By comparing the results of the state recursor, the chi‐square test value is obtained and it is used to update the measurement noise. Further, the system state and error covariance are re‐estimated. So far a robust sparse grid filter algorithm with statistical adjustment of the measurement noise is obtained. The proposed algorithm can overcome the influence of error statistical characteristic amplification on system filtering accuracy. Taking the SINS/GNSS integrated navigation system as the application object, off‐line simulation experiments and online flight tests verify the robustness of the proposed algorithm.
An anonymous and fair auction system based on blockchain

Consumer approval of the online auction paradigm is demonstrated by rapidly expanding online auction transaction volumes. Bidders can access a wider variety of products, and sellers can reach a larger audience through online auction platforms. However, privacy concerns have reduced users' trust in auction platforms, and complicated disputes have led consumers to lose faith in online auctions, negatively impacting market growth. The emergence of blockchain technology has brought a new approach to solving these problems. Blockchain is a new generation of information technology that is decentralized, transparent, traceable, anti-tampering, and unforgeable, following big data, cloud computing, and artificial intelligence. In this paper, we present a blockchain-based online auction solution as well as an auction protocol that uses ring signatures to guarantee anonymity. Three features summarize this essay: (1) Using blockchain technology to ensure that the auction transaction process is transparent, traceable, tamper-proof, and unforgeable. (2) The ring signature-based anonymous auction protocol allows for concealing bidders' public keys in a group of public keys. Because malicious individuals are prevented from evaluating data by tracking numerous transactions of a single public key, this significantly protects bidders' privacy. (3) The proposed scheme effectively resolves disputes that may arise during the auction.
Efficient text-based evolution algorithm to hard-label adversarial attacks on text

Deep neural networks that play a pivotal role in fields such as images, text, and audio are vulnerable to adversarial attacks. In current textual adversarial attacks, the vast majority are configured with a black-box soft-label which is achieved by the gradient information or confidence of the model. Therefore, it becomes challenging and realistic to implement adversarial attacks using only the predicted top labels of the hard-label model. Existing methods to implement hard-label adversarial attacks use population-based genetic optimization algorithms. However, this approach requires significant query consumption, which is a considerable shortcoming. To solve this problem, we propose a new textual black-box hard-label adversarial attack algorithm based on the idea of differential evolution of populations, called the text-based differential evolution (TDE) algorithm. First, the method will judge the importance of the words of the initial rough adversarial examples, according to which only the keywords in the text sentence will be operated, and the rest of the words will be gradually replaced with the original words so as to reduce the words in the sentence in which the replacement occurs. Our method judges the quality of semantic similarity of the adversarial examples in the replacement process and deposits high-quality adversarial example individuals into the population. Secondly, the optimization process of adversarial examples is combined and optimized according to the word importance. Compared with existing methods based on genetic algorithm guidance, our method avoids a large number of meaningless repetitive queries and significantly improves the overall attack efficiency of the algorithm and the semantic quality of the generated adversarial examples. We experimented with multiple datasets on three text tasks of sentiment classification, natural language inference, and toxic comment, and also perform experimental comparisons on models and APIs in realistic scenarios. For example, in the Google Cloud commercial API adversarial attack experiment, compared to the existing hard-label method, our method reduces the average number of queries required for the attack from 6986 to 176, and increases semantic similarity from 0.844 to 0.876. It is shown through extensive experimental data that our approach not only significantly reduces the number of queries, but also significantly outperforms existing methods in terms of the quality of adversarial examples.
Research and application of downhole drilling depth based on computer vision technique

Gas drainage and water detection and release by drilling are essential to prevent gas and water disasters in China. The accurate drilling depth substantially affects gas drainage, water exploration, and safety problems. Consequently, it is essential to precisely monitor drilling depth by counting drill pipe to assure drilling efficacy. Based on increasingly sophisticated subterranean video surveillance technology, this paper leverages computer vision technology to implement precise drill pipe counting and calculates the corresponding drilling depth determined by the number of drill pipes. First, the operation of the drilling rig was examined, and the movement trajectory of the impact power head on the drilling rig feeding mechanism was estimated to quantify the number of drilling pipes. The optimized Retinex mine image enhancement algorithm is used to preprocess the initial underground image with issues including an unreliable light source, considerable light and shadow fluctuation, and blurred lens. Furthermore, the challenges of fixed scale and easy loss of target tracking in the Kernel Correlation Filter Algorithm are addressed by employing an adaptive scale algorithm and a re-detection approach to boost target location data accuracy. Eventually, the amount of drill pipes is calculated by the effective peak count of the displacement, the precise drilling depth is achieved, and the result is consistent with the actual drilling construction condition by evaluating the on-site drilling video. This study adopted computer vision tracking technology to tackle the challenge of calculating drilling depth and building progress accurately, ensuring the safe and efficient output of the mine.
A comprehensive review of the latest path planning developments for multi-robot formation systems

Abstract There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques.
The Impact of Data Quality on Software Testing Effort Prediction

Background: This paper investigates the impact of data quality on the performance of models predicting effort on software testing. Data quality was reflected by training data filtering strategies (data variants) covering combinations of Data Quality Rating, UFP Rating, and a threshold of valid cases. Methods: The experiment used the ISBSG dataset and 16 machine learning models. A process of three-fold cross-validation repeated 20 times was used to train and evaluate each model with each data variant. Model performance was assessed using absolute errors of prediction. A ‘win–tie–loss’ procedure, based on the Wilcoxon signed-rank test, was applied to identify the best models and data variants. Results: Most models, especially the most accurate, performed the best on a complete dataset, even though it contained cases with low data ratings. The detailed results include the rankings of the following: (1) models for particular data variants, (2) data variants for particular models, and (3) the best-performing combinations of models and data variants. Conclusions: Arbitrary and restrictive data selection to only projects with Data Quality Rating and UFP Rating of ‘A’ or ‘B’, commonly used in the literature, does not seem justified. It is recommended not to exclude cases with low data ratings to achieve better accuracy of most predictive models for testing effort prediction.
Flu-Net: two-stream deep heterogeneous network to detect flu like symptoms from videos using grey wolf optimization algorithm

The outbreak of COVID-19 (also known as Coronavirus) has put the entire world at risk. The disease first appears in Wuhan, China, and later spread to other countries, taking a form of a pandemic. In this paper, we try to build an artificial intelligence (AI) powered framework called Flu-Net to identify flu-like symptoms (which is also an important symptom of Covid-19) in people, and limit the spread of infection. Our approach is based on the application of human action recognition in surveillance systems, where videos captured by closed-circuit television (CCTV) cameras are processed through state-of-the-art deep learning techniques to recognize different activities like coughing, sneezing, etc. The proposed framework has three major steps. First, to suppress irrelevant background details in an input video, a frame difference operation is performed to extract foreground motion information. Second, a two-stream heterogeneous network based on 2D and 3D Convolutional Neural Networks (ConvNets) is trained using the RGB frame differences. And third, the features extracted from both the streams are combined using Grey Wolf Optimization (GWO) based feature selection technique. The experiments conducted on BII Sneeze-Cough (BIISC) video dataset show that our framework can 70% accuracy, outperforming the baseline results by more than 8%.
Blockchain-Enabled Smart Contract Architecture in Supply Chain Design

Emerging information technologies are making a powerful impact by challenging traditional business methods. Companies are adopting these technologies to make their supply chains more efficient and effective while making their business more profitable. Blockchain is no exception when it comes to the technological revolution helping businesses to be more transparent and better connected whilst enhancing the safety and security of operations. The world came to know Blockchain as a decentralised and distributed ledger system for financial transactions that became the foundation for Cryptocurrencies. However, its applications are not only limited to payment management. Researchers have also been exploring Blockchain’s potential for disrupting supply chains to make them resilient and agile, improve visibility and transparency, mitigate bullwhips, and improve supply chain coordination between suppliers and manufacturers (e.g., increased efficiency and speed). In this book chapter, we review the current literature and reports from international peer-reviewed journals and independent consulting firms and articulate a comprehensive standing of blockchain technology in contractual processes. Furthermore, we highlight the impact of Blockchain-enabled smart contracts in supply chain design and outline opportunities and certain caveats by presenting a SWOT analysis. Finally, we map the architecture of contractual processes within a supply chain to highlight the critical differences between traditional and Blockchain-enabled business progressions. This chapter can help businesses in understanding how Blockchain can improve their contractual processes and make procurement and payments a swift transaction.
Self-supervised learning and uncertainty estimation for surgical margin detection

Up to 35% of breast-conserving surgeries fail to resect all the tumors completely. Ideally, machine learning methods using the iKnife data, which uses Rapid Evaporative Ionization Mass Spectrometry (REIMS), can be utilized to predict tissue type in real-time during surgery, resulting in better tumor resections. As REIMS data is heterogeneous and weakly labeled, and datasets are often small, model performance and reliability can be adversely affected. Self-supervised training and uncertainty estimation of the prediction can be used to mitigate these challenges by learning the signatures of input data without their label as well as including predictive confidence in output reporting. We first design an autoencoder model using a reconstruction pretext task as a self-supervised pretraining step without considering tissue type. Next, we construct our uncertainty-aware classifier using the encoder part of the model with Masksembles layers to estimate the uncertainty associated with its predictions. The pretext task was trained on 190 burns collected from 34 patients from Basal Cell Carcinoma iKnife data. The model was further trained on breast cancer data comprising of 200 burns collected from 15 patients. Our proposed model shows improvement in sensitivity and uncertainty metrics of 10% and 15.7% over the baseline, respectively. The proposed strategies lead to improvements in uncertainty calibration and overall performance, toward reducing the likelihood of incomplete resection, supporting removal of minimal non-neoplastic tissue, and improved model reliability during surgery. Future work will focus on further testing the model on intraoperative data and additional exvivo data following collection of more breast samples.
A disparity refinement framework for learning-based stereo matching methods in cross-domain setting for laparoscopic images

Stereo matching methods that enable depth estimation are crucial for visualization enhancement applications in computer-assisted surgery (CAS). Learning-based stereo matching methods are promising to predict accurate results for applications involving video images. However, they require a large amount of training data, and their performance may be degraded due to domain shifts. Maintaining robustness and improving performance of learning-based methods are still open problems. To overcome the limitations of learning-based methods, we propose a disparity refinement framework consisting of a local disparity refinement method and a global disparity refinement method to improve the results of learning-based stereo matching methods in a cross-domain setting. Those learning-based stereo matching methods are pre-trained on a large public dataset of natural images and are tested on a dataset of laparoscopic images. Results from the SERV-CT dataset showed that our proposed framework can effectively refine disparity maps on an unseen dataset even when they are corrupted by noise, and without compromising correct prediction, provided the network can generalize well on unseen datasets. As such, our proposed disparity refinement framework has the potential to work with learning-based methods to achieve robust and accurate disparity prediction. Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, it will be beneficial to incorporate the proposed disparity refinement framework into existing networks for more accurate and robust depth estimation.
Open source video-based hand-eye calibration

Augmented reality is becoming prevalent in modern video-based surgical navigation systems. Augmented reality in forms of image-fusion between the virtual objects (i.e. virtual representation of the anatomy derived from pre-operative imaging modalities) and the real objects (i.e. anatomy imaged by a spatially-tracked surgical camera) facilitate the visualization and perception of the surgical scene. However, this requires spatial calibration between the external tracking system and the optical axis of the surgical camera, known as hand-eye calibration. With the standard implementation of the most common hand-eye calibration techniques being static-photo-based, the time required for data collection may inhibit the thoroughness and robustness to achieve an accurate calibration. To address these translational issues, we introduce a video-based hand-eye calibration technique with open-source implementation that is accurate and robust. Based on the point-to-line Procrustean registration, a short video of a tracked and pivot-calibrated ball-tip stylus was recorded where, in each frame of the tracked video, the 3D position of the ball-tip (point) and its projection onto the video (line) serve as a calibration data point. We further devise a data sampling mechanism designed to optimize the spatial configuration of the calibration fiducials, leading to consistently high quality hand-eye calibrations. To demonstrate the efficacy of our work, a Monte Carlo simulation was performed to obtain the mean target projection error as a function of the number of calibration data points. The results obtained, exemplified using a Logitech C920 Pro HD Webcam with an image resolution of 640 × 480, show that the mean projection error decreased as more data points were used per calibration, and the majority of mean projection errors fell below four pixels. An open-source implementation, in the form of a 3D Slicer module, is available on GitHub.
Can My WiFi Handle the Metaverse? A Performance Evaluation Of Meta's Flagship Virtual Reality Hardware

Extending human societies into virtual space through the construction of a metaverse has been a long-term challenge in both industry and academia. Achieving this challenge is now closer than ever due to advances in computer systems, facilitating large-scale online platforms such as Minecraft and Roblox that fulfill an increasing number of societal needs, and extended reality (XR) hardware, which provides users with state-of-the-art immersive experiences. For a metaverse to succeed, we argue that all involved systems must provide consistently good performance. However, there is a lack of knowledge on the performance characteristics of extended reality devices. In this paper, we address this gap and focus on extended- and virtual-reality hardware. We synthesize a user-centered system model that models common deployments of XR hardware and their trade-offs. Based on this model, we design and conduct real-world experiments with Meta's flagship virtual reality device, the Quest Pro. We highlight two surprising results from our findings which show that (i) under our workload, the battery drains 15% faster when using wireless offloading compared to local execution, and (ii) the outdated 2.4 GHz WiFi4 gives surprisingly good performance, with 99% of samples achieving a frame rate of at least 65 Hz, compared to the 72 Hz performance target. Our experimental setup and data are available at https://github.com/atlarge-research/measuring-the-metaverse.